1. Brief Bioinform. 2016 Jul;17(4):696-712. doi: 10.1093/bib/bbv066. Epub 2015 Aug 17. Drug-target interaction prediction: databases, web servers and computational models. Chen X, Yan CC, Zhang X, Zhang X, Dai F, Yin J, Zhang Y. Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com. DOI: 10.1093/bib/bbv066 PMID: 26283676 [Indexed for MEDLINE] 2. Nucleic Acids Res. 2018 Jan 4;46(D1):D930-D936. doi: 10.1093/nar/gkx1024. ECOdrug: a database connecting drugs and conservation of their targets across species. Verbruggen B(1), Gunnarsson L(1), Kristiansson E(2), Österlund T(2), Owen SF(3), Snape JR(3)(4), Tyler CR(1). Author information: (1)Biosciences, College of Life & Environmental Sciences, University of Exeter, Exeter EX4 4QD, UK. (2)Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg SE-416 12, Sweden. (3)Global Environment, AstraZeneca, Cheshire SK10 4TF, UK. (4)School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK. Pharmaceuticals are designed to interact with specific molecular targets in humans and these targets generally have orthologs in other species. This provides opportunities for the drug discovery community to use alternative model species for drug development. It also means, however, there is potential for mode of action related effects in non-target wildlife species as many pharmaceuticals reach the environment through patient use and manufacturing wastes. Acquiring insight in drug target ortholog predictions across species and taxonomic groups has proven difficult because of the lack of an optimal strategy and because necessary information is spread across multiple and diverse sources and platforms. We introduce a new research platform tool, ECOdrug, that reliably connects drugs to their protein targets across divergent species. It harmonizes ortholog predictions from multiple sources via a simple user interface underpinning critical applications for a wide range of studies in pharmacology, ecotoxicology and comparative evolutionary biology. ECOdrug can be used to identify species with drug targets and identify drugs that interact with those targets. As such, it can be applied to support intelligent targeted drug safety testing by ensuring appropriate and relevant species are selected in ecological risk assessments. ECOdrug is freely accessible and available at: http://www.ecodrug.org. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gkx1024 PMCID: PMC5753218 PMID: 29140522 3. Nucleic Acids Res. 2016 Jan 4;44(D1):D1069-74. doi: 10.1093/nar/gkv1230. Epub 2015 Nov 17. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Yang H(1), Qin C(2), Li YH(3), Tao L(2), Zhou J(3), Yu CY(3), Xu F(4), Chen Z(5), Zhu F(6), Chen YZ(7). Author information: (1)Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543, Singapore Innovative Drug Research Centre and College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, P. R. China. (2)Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543, Singapore. (3)Innovative Drug Research Centre and College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, P. R. China. (4)College of Pharmacy, State Key Laboratory of Medicinal Chemical Biology and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, P. R. China. (5)Zhejiang Key Laboratory of Gastro-intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang Chinese Medical University, No. 54 Youdian Road, Hangzhou 310006, China. (6)Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543, Singapore Innovative Drug Research Centre and College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, P. R. China zhufeng@cqu.edu.cn. (7)Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543, Singapore csccyz@nus.edu.sg. Extensive drug discovery efforts have yielded many approved and candidate drugs targeting various targets in different biological pathways. Several freely accessible databases provide the drug, target and drug-targeted pathway information for facilitating drug discovery efforts, but there is an insufficient coverage of the clinical trial drugs and the drug-targeted pathways. Here, we describe an update of the Therapeutic Target Database (TTD) previously featured in NAR. The updated contents include: (i) significantly increased coverage of the clinical trial targets and drugs (1.6 and 2.3 times of the previous release, respectively), (ii) cross-links of most TTD target and drug entries to the corresponding pathway entries of KEGG, MetaCyc/BioCyc, NetPath, PANTHER pathway, Pathway Interaction Database (PID), PathWhiz, Reactome and WikiPathways, (iii) the convenient access of the multiple targets and drugs cross-linked to each of these pathway entries and (iv) the recently emerged approved and investigative drugs. This update makes TTD a more useful resource to complement other databases for facilitating the drug discovery efforts. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gkv1230 PMCID: PMC4702870 PMID: 26578601 [Indexed for MEDLINE] 4. Curr Protein Pept Sci. 2018;19(6):537-561. doi: 10.2174/1389203718666161108091609. Drug-Target Interactions: Prediction Methods and Applications. Anusuya S(1), Kesherwani M(2), Priya KV(1), Vimala A(1), Shanmugam G(3), Velmurugan D(2)(4), Gromiha MM(1). Author information: (1)Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai - 600036, Tamil Nadu, India. (2)Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy campus, Chennai 600 025, Tamil Nadu, India. (3)Department of Biotechnology, Mahendra Arts and Science College, Kalippatti, Tamil Nadu, India. (4)Bioinformatics Infrastructure Facility (BIF), University of Madras, Guindy Campus, Chennai 600 025, India. Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1389203718666161108091609 PMID: 27829350 5. Nucleic Acids Res. 2017 Jul 3;45(W1):W356-W360. doi: 10.1093/nar/gkx374. PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Wang X(1), Shen Y(2), Wang S(2), Li S(1), Zhang W(2), Liu X(1), Lai L(2), Pei J(2), Li H(1). Author information: (1)State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China. (2)Center for Quantitative Biology, AAIS and BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China. The PharmMapper online tool is a web server for potential drug target identification by reversed pharmacophore matching the query compound against an in-house pharmacophore model database. The original version of PharmMapper includes more than 7000 target pharmacophores derived from complex crystal structures with corresponding protein target annotations. In this article, we present a new version of the PharmMapper web server, of which the backend pharmacophore database is six times larger than the earlier one, with a total of 23 236 proteins covering 16 159 druggable pharmacophore models and 51 431 ligandable pharmacophore models. The expanded target data cover 450 indications and 4800 molecular functions compared to 110 indications and 349 molecular functions in our last update. In addition, the new web server is united with the statistically meaningful ranking of the identified drug targets, which is achieved through the use of standard scores. It also features an improved user interface. The proposed web server is freely available at http://lilab.ecust.edu.cn/pharmmapper/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gkx374 PMCID: PMC5793840 PMID: 28472422 6. Annu Rev Pharmacol Toxicol. 2014;54:9-26. doi: 10.1146/annurev-pharmtox-011613-135943. Epub 2013 Aug 30. The druggable genome: Evaluation of drug targets in clinical trials suggests major shifts in molecular class and indication. Rask-Andersen M(1), Masuram S, Schiöth HB. Author information: (1)Department of Neuroscience and Uppsala Biomedical Center, Uppsala University, 75124 Uppsala, Sweden; email: helgi.schioth@neuro.uu.se. The largest innovations within pharmaceutical development come through new compounds that have unique and novel modes of action. These innovations commonly involve expanding the protein space targeted by pharmaceutical agents. At present, information about drugs and drug targets is available online via public databases such as DrugBank and the Therapeutic Targets Database. However, this information is biased, understandably so, toward established drugs and drug-target interactions. To gain a better overview of the drug-targeted portion of the human proteome and the directions of current drug development, we developed a data set of clinical trial drug-target interactions based on CenterWatch's Drugs in Clinical Trials Database, one of the largest databases of its kind. Our curation identified 475 potentially novel clinical trial drug targets. This review aims to identify trends in drug development based on the potentially novel targets currently being explored in clinical trials. DOI: 10.1146/annurev-pharmtox-011613-135943 PMID: 24016212 [Indexed for MEDLINE] 7. IEEE J Biomed Health Inform. 2017 Mar;21(2):561-572. doi: 10.1109/JBHI.2015.2513200. Epub 2015 Dec 30. Predicting Drug-Target Interactions With Multi-Information Fusion. Peng L, Liao B, Zhu W, Li Z, Li K. Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets. DOI: 10.1109/JBHI.2015.2513200 PMID: 26731781 [Indexed for MEDLINE] 8. Sci Rep. 2017 Aug 14;7(1):8087. doi: 10.1038/s41598-017-08079-7. Screening drug-target interactions with positive-unlabeled learning. Peng L(1)(2), Zhu W(1), Liao B(3), Duan Y(4), Chen M(1), Chen Y(5), Yang J(6). Author information: (1)Key Laboratory for Embedded and Network Computing of Hunan Province, College of Information Science and Engineering, Hunan University, Changsha Hunan, 410082, China. (2)College of Information Engineering, Changsha Medical University, Changsha Hunan, 410219, China. (3)Key Laboratory for Embedded and Network Computing of Hunan Province, College of Information Science and Engineering, Hunan University, Changsha Hunan, 410082, China. dragonbw@163.com. (4)Hunan University of Commerce, Changsha Hunan, 410205, China. (5)College of Drug, Changsha Medical University, Changsha Hunan, 410219, China. (6)Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, NY, 10029, USA. Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning. However, usually only positive DTIs are deposited in known databases, which challenges computational methods to predict novel DTIs due to the lack of negative samples. To overcome this dilemma, researchers usually randomly select negative samples from unlabeled drug-target pairs, which introduces a lot of false-positives. In this study, a negative sample extraction method named NDTISE is first developed to screen strong negative DTI examples based on positive-unlabeled learning. A novel DTI screening framework, PUDTI, is then designed to infer new drug repositioning candidates by integrating NDTISE, probabilities that remaining ambiguous samples belong to the positive and negative classes, and an SVM-based optimization model. We investigated the effectiveness of NDTISE on a DTI data provided by NCPIS. NDTISE is much better than random selection and slightly outperforms NCPIS. We then compared PUDTI with 6 state-of-the-art methods on 4 classes of DTI datasets from human enzymes, ion channels, GPCRs and nuclear receptors. PUDTI achieved the highest AUC among the 7 methods on all 4 datasets. Finally, we validated a few top predicted DTIs through mining independent drug databases and literatures. In conclusion, PUDTI provides an effective pre-filtering method for new drug design. DOI: 10.1038/s41598-017-08079-7 PMCID: PMC5556112 PMID: 28808275 [Indexed for MEDLINE] 9. AAPS J. 2017 Sep;19(5):1264-1275. doi: 10.1208/s12248-017-0092-6. Epub 2017 Jun 2. Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review. Cheng T(1), Hao M(1), Takeda T(1), Bryant SH(1), Wang Y(2). Author information: (1)National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA. (2)National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA. ywang@ncbi.nlm.nih.gov. The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We also summarize popular public data resources and online tools for DTI prediction. It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data of drug molecules across multiple biological targets, and drug-induced gene expressions. More often, the heterogeneous data were integrated to offer better performance. However, challenges remain such as handling data imbalance, incorporating negative samples and quantitative bioactivity data, as well as maintaining cross-links among different data sources, which are essential for large-scale and automated information integration. DOI: 10.1208/s12248-017-0092-6 PMID: 28577120 [Indexed for MEDLINE] 10. J Chem Inf Model. 2017 Oct 23;57(10):2395-2400. doi: 10.1021/acs.jcim.7b00175. Epub 2017 Oct 12. PhID: An Open-Access Integrated Pharmacology Interactions Database for Drugs, Targets, Diseases, Genes, Side-Effects, and Pathways. Deng Z(1), Tu W(1), Deng Z(1), Hu QN(1)(2). Author information: (1)Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences , Wuhan, 430071, China. (2)Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences , 300308, Tianjin, China. The current network pharmacology study encountered a bottleneck with a lot of public data scattered in different databases. There is a lack of an open-access and consolidated platform that integrates this information for systemic research. To address this issue, we have developed PhID, an integrated pharmacology database which integrates >400 000 pharmacology elements (drug, target, disease, gene, side-effect, and pathway) and >200 000 element interactions in branches of public databases. PhID has three major applications: (1) assisting scientists searching through the overwhelming amount of pharmacology element interaction data by names, public IDs, molecule structures, or molecular substructures; (2) helping visualizing pharmacology elements and their interactions with a web-based network graph; and (3) providing prediction of drug-target interactions through two modules: PreDPI-ki and FIM, by which users can predict drug-target interactions of PhID entities or some drug-target pairs of their own interest. To get a systems-level understanding of drug action and disease complexity, PhID as a network pharmacology tool was established from the perspective of data layer, visualization layer, and prediction model layer to present information untapped by current databases. DOI: 10.1021/acs.jcim.7b00175 PMID: 28906116 [Indexed for MEDLINE] 11. BMC Bioinformatics. 2017 May 31;18(Suppl 7):248. doi: 10.1186/s12859-017-1639-3. In silico re-identification of properties of drug target proteins. Kim B(1), Jo J(2), Han J(1), Park C(3), Lee H(4). Author information: (1)Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro,Buk-gu, Gwangju, 61005, Republic of Korea. (2)Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, 24105, Republic of Korea. (3)Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, 24105, Republic of Korea. chungoo@jnu.ac.kr. (4)Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro,Buk-gu, Gwangju, 61005, Republic of Korea. hyunjulee@gist.ac.kr. BACKGROUND: Computational approaches in the identification of drug targets are expected to reduce time and effort in drug development. Advances in genomics and proteomics provide the opportunity to uncover properties of druggable genomes. Although several studies have been conducted for distinguishing drug targets from non-drug targets, they mainly focus on the sequences and functional roles of proteins. Many other properties of proteins have not been fully investigated. METHODS: Using the DrugBank (version 3.0) database containing nearly 6,816 drug entries including 760 FDA-approved drugs and 1822 of their targets and human UniProt/Swiss-Prot databases, we defined 1578 non-redundant drug target and 17,575 non-drug target proteins. To select these non-redundant protein datasets, we built four datasets (A, B, C, and D) by considering clustering of paralogous proteins. RESULTS: We first reassessed the widely used properties of drug target proteins. We confirmed and extended that drug target proteins (1) are likely to have more hydrophobic, less polar, less PEST sequences, and more signal peptide sequences higher and (2) are more involved in enzyme catalysis, oxidation and reduction in cellular respiration, and operational genes. In this study, we proposed new properties (essentiality, expression pattern, PTMs, and solvent accessibility) for effectively identifying drug target proteins. We found that (1) drug targetability and protein essentiality are decoupled, (2) druggability of proteins has high expression level and tissue specificity, and (3) functional post-translational modification residues are enriched in drug target proteins. In addition, to predict the drug targetability of proteins, we exploited two machine learning methods (Support Vector Machine and Random Forest). When we predicted drug targets by combining previously known protein properties and proposed new properties, an F-score of 0.8307 was obtained. CONCLUSIONS: When the newly proposed properties are integrated, the prediction performance is improved and these properties are related to drug targets. We believe that our study will provide a new aspect in inferring drug-target interactions. DOI: 10.1186/s12859-017-1639-3 PMCID: PMC5471946 PMID: 28617227 [Indexed for MEDLINE] 12. Database (Oxford). 2015 Mar 27;2015. pii: bav015. doi: 10.1093/database/bav015. Print 2015. Colorectal cancer drug target prediction using ontology-based inference and network analysis. Tao C(1), Sun J(1), Zheng WJ(1), Chen J(1), Xu H(2). Author information: (1)Center for Computational Biomedicine, School of Biomedical informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA and Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. (2)Center for Computational Biomedicine, School of Biomedical informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA and Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA hua.xu@uth.tmc.edu. Identification of novel drug targets is a critical step in drug development. Many recent studies have produced multiple types of data, which provides an opportunity to mine the relationships among them to predict drug targets. In this study, we present a novel integrative approach that combines ontology reasoning with network-assisted gene ranking to predict new drug targets. We utilized colorectal cancer (CRC) as a proof-of-concept use case to illustrate the approach. Starting from FDA-approved CRC drugs and the relationships among disease, drug, gene, pathway, and SNP in an ontology representing PharmGKB data, we inferred 113 potential CRC drug targets. We further prioritized these genes based on their relationships with CRC disease genes in the context of human protein-protein interaction networks. Thus, among the 113 potential drug targets, 15 were selected as the promising drug targets, including some genes that are supported by previous studies. Among them, EGFR, TOP1 and VEGFA are known targets of FDA-approved drugs. Additionally, CCND1 (cyclin D1), and PTGS2 (prostaglandin-endoperoxide synthase 2) have reported to be relevant to CRC or as potential drug targets based on the literature search. These results indicate that our approach is promising for drug target prediction for CRC treatment, which might be useful for other cancer therapeutics. © The Author 2015. Published by Oxford University Press. DOI: 10.1093/database/bav015 PMCID: PMC4375358 PMID: 25818893 [Indexed for MEDLINE] 13. BMC Bioinformatics. 2017 Jan 17;18(1):39. doi: 10.1186/s12859-017-1460-z. Link prediction in drug-target interactions network using similarity indices. Lu Y(1), Guo Y(1), Korhonen A(2). Author information: (1)Computer Laboratory, University of Cambridge, JJ Thompson Avenue, Cambridge, UK. (2)Computer Laboratory, University of Cambridge, JJ Thompson Avenue, Cambridge, UK. alk23@cam.ac.uk. BACKGROUND: In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning - methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome sequence, binding types, causes of interactions, etc., and do not perform satisfactorily when such information is unavailable. We propose a new, alternative method for DTI prediction that makes use of only network topology information attempting to solve this problem. RESULTS: We compare our method for DTI prediction against the well-known RBM approach. We show that when applied to the MATADOR database, our approach based on node neighborhoods yield higher precision for high-ranking predictions than RBM when no information regarding DTI types is available. CONCLUSION: This demonstrates that approaches purely based on network topology provide a more suitable approach to DTI prediction in the many real-life situations where little or no prior knowledge is available about the characteristics of drugs, targets, or their interactions. DOI: 10.1186/s12859-017-1460-z PMCID: PMC5240398 PMID: 28095781 [Indexed for MEDLINE] 14. Brief Bioinform. 2016 Nov;17(6):1070-1080. Epub 2015 Oct 21. Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database. Barneh F, Jafari M, Mirzaie M. Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com. DOI: 10.1093/bib/bbv094 PMID: 26490381 [Indexed for MEDLINE] 15. J Biomed Semantics. 2017 Nov 9;8(1):50. doi: 10.1186/s13326-017-0161-x. Drug target ontology to classify and integrate drug discovery data. Lin Y(1), Mehta S(1)(2), Küçük-McGinty H(1)(3), Turner JP(4), Vidovic D(1)(4), Forlin M(1)(4), Koleti A(1), Nguyen DT(5), Jensen LJ(6), Guha R(5), Mathias SL(7), Ursu O(7), Stathias V(4), Duan J(1)(3), Nabizadeh N(1), Chung C(1), Mader C(1), Visser U(3), Yang JJ(7), Bologa CG(7), Oprea TI(8), Schürer SC(9)(10). Author information: (1)Center for Computational Science, University of Miami, Coral Gables, FL, USA. (2)Department of Applied Chemistry, Delhi Technological University, Delhi, India. (3)Department of Computer Science, University of Miami, Coral Gables, FL, USA. (4)Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA. (5)National Center for Advancing Translational Science, Rockville, MD, USA. (6)Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. (7)Department of Internal Medicine, Translational Informatics Division, University of New Mexico School of Medicine, Albuquerque, NM, USA. (8)Department of Internal Medicine, Translational Informatics Division, University of New Mexico School of Medicine, Albuquerque, NM, USA. toprea@salud.unm.edu. (9)Center for Computational Science, University of Miami, Coral Gables, FL, USA. sschurer@miami.edu. (10)Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA. sschurer@miami.edu. BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource. DOI: 10.1186/s13326-017-0161-x PMCID: PMC5679337 PMID: 29122012 [Indexed for MEDLINE] 16. Nat Rev Drug Discov. 2011 Aug 1;10(8):579-90. doi: 10.1038/nrd3478. Trends in the exploitation of novel drug targets. Rask-Andersen M(1), Almén MS, Schiöth HB. Author information: (1)Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala Biomedical Center, 75124 Uppsala, Sweden. The discovery and exploitation of new drug targets is a key focus for both the pharmaceutical industry and academic biomedical research. To provide an insight into trends in the exploitation of new drug targets, we have analysed the drugs that were approved by the US Food and Drug Administration during the past three decades and examined the interactions of these drugs with therapeutic targets that are encoded by the human genome, using the DrugBank database and extensive manual curation. We have identified 435 effect-mediating drug targets in the human genome, which are modulated by 989 unique drugs, through 2,242 drug-target interactions. We also analyse trends in the introduction of drugs that modulate previously unexploited targets, and discuss the network pharmacology of the drugs in our data set. DOI: 10.1038/nrd3478 PMID: 21804595 [Indexed for MEDLINE] 17. Curr Drug Targets. 2014;15(12):1089-93. PfalDB: an integrated drug target and chemical database for Plasmodium falciparum. Kumar A, Agarwal N, Pant L, Singh JP, Ghosh I, Subbarao N(1). Author information: (1)School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi - 110067, India. nsrao@mail.jnu.ac.in. Plasmodium falciparum is one of the deadliest protozoan parasite species among those that cause malaria. Uncontrolled use of antimalarial drugs has resulted in evolutionary selection pressure favoring high levels of resistance to antimalarials; currently P.falciparum shows resistance to all classes of antimalarials. Therefore it is essential to identify novel drug targets, and design selective anti-malarials which can overcome resistance. While many drug targets are freely available in various public domain resources, a single comprehensive source of data containing easily searchable and retrievable information is currently lacking. To facilitate the total integration and mining of data emerging from different drug consortia and also to prioritize drug targets for structure-based drug design, an open-access, inclusive comprehensive database for Plasmodium falciparum was established. Meta data of known/modeled structures along with binding site parameters of drug targets have been included in the database. Additionally, chemical compounds showing a positive inhibitory assay against Plasmodium falciparum or known drug targets have also been provided. The database is accessible at http://pfaldb.jnu.ac.in. The database provides diverse information regarding the structure, sequence, stage specific gene expression, pathway, action mechanism, essentiality and druggability for each drug target, and literature to assess the validation status of individual drug targets. It also includes information on individual anti-malarials with their activity and bioassay. PMID: 25198774 [Indexed for MEDLINE] 18. Methods. 2015 Jul 15;83:98-104. doi: 10.1016/j.ymeth.2015.04.036. Epub 2015 May 6. Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering. Shi JY(1), Yiu SM(2), Li Y(3), Leung HC(4), Chin FY(5). Author information: (1)School of Life Sciences, Northwestern Polytechnical University, No. 127, Youyi Road West, Xi'an, Shaanxi 710072, China. Electronic address: jianyushi@nwpu.edu.cn. (2)Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong. Electronic address: smyiu@cs.hku.hk. (3)Department of Psychiatry, The University of Hong Kong, Pokfulam Road, Hong Kong. Electronic address: liym1018@hku.hk. (4)Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong. Electronic address: cmleung2@cs.hku.hk. (5)Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong. Electronic address: chin@cs.hku.hk. Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved. DOI: 10.1016/j.ymeth.2015.04.036 PMID: 25957673 [Indexed for MEDLINE] 19. Curr Protein Pept Sci. 2018;19(5):468-478. doi: 10.2174/1389203718666161122103057. A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences. Huang YA(1), You ZH(2), Chen X(3). Author information: (1)Department of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong. (2)Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China. (3)School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China. BACKGROUND: Drug-Target Interactions (DTI) play a crucial role in discovering new drug candidates and finding new proteins to target for drug development. Although the number of detected DTI obtained by high-throughput techniques has been increasing, the number of known DTI is still limited. On the other hand, the experimental methods for detecting the interactions among drugs and proteins are costly and inefficient. OBJECTIVE: Therefore, computational approaches for predicting DTI are drawing increasing attention in recent years. In this paper, we report a novel computational model for predicting the DTI using extremely randomized trees model and protein amino acids information. METHOD: More specifically, the protein sequence is represented as a Pseudo Substitution Matrix Representation (Pseudo-SMR) descriptor in which the influence of biological evolutionary information is retained. For the representation of drug molecules, a novel fingerprint feature vector is utilized to describe its substructure information. Then the DTI pair is characterized by concatenating the two vector spaces of protein sequence and drug substructure. Finally, the proposed method is explored for predicting the DTI on four benchmark datasets: Enzyme, Ion Channel, GPCRs and Nuclear Receptor. RESULTS: The experimental results demonstrate that this method achieves promising prediction accuracies of 89.85%, 87.87%, 82.99% and 81.67%, respectively. For further evaluation, we compared the performance of Extremely Randomized Trees model with that of the state-of-the-art Support Vector Machine classifier. And we also compared the proposed model with existing computational models, and confirmed 15 potential drug-target interactions by looking for existing databases. CONCLUSION: The experiment results show that the proposed method is feasible and promising for predicting drug-target interactions for new drug candidate screening based on sizeable features. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1389203718666161122103057 PMID: 27875970 20. J Chem Inf Model. 2015 Sep 28;55(9):1804-23. doi: 10.1021/acs.jcim.5b00120. Epub 2015 Aug 26. GESSE: Predicting Drug Side Effects from Drug-Target Relationships. Pérez-Nueno VI(1), Souchet M(1), Karaboga AS(1), Ritchie DW(2). Author information: (1)Harmonic Pharma , Espace Transfert, 615 rue du Jardin Botanique, 54600 Villers-les-Nancy, France. (2)INRIA Nancy - Grand Est, Equipe Capsid, 615 rue du Jardin Botanique, 54600 Villers-les-Nancy, France. The in silico prediction of unwanted side effects (SEs) caused by the promiscuous behavior of drugs and their targets is highly relevant to the pharmaceutical industry. Considerable effort is now being put into computational and experimental screening of several suspected off-target proteins in the hope that SEs might be identified early, before the cost associated with developing a drug candidate rises steeply. Following this need, we present a new method called GESSE to predict potential SEs of drugs from their physicochemical properties (three-dimensional shape plus chemistry) and to target protein data extracted from predicted drug-target relationships. The GESSE approach uses a canonical correlation analysis of the full drug-target and drug-SE matrices, and it then calculates a probability that each drug in the resulting drug-target matrix will have a given SE using a Bayesian discriminant analysis (DA) technique. The performance of GESSE is quantified using retrospective (external database) analysis and literature examples by means of area under the ROC curve analysis, "top hit rates", misclassification rates, and a χ(2) independence test. Overall, the robust and very promising retrospective statistics obtained and the many SE predictions that have experimental corroboration demonstrate that GESSE can successfully predict potential drug-SE profiles of candidate drug compounds from their predicted drug-target relationships. DOI: 10.1021/acs.jcim.5b00120 PMID: 26251970 [Indexed for MEDLINE] 21. BMC Syst Biol. 2015;9 Suppl 4:S2. doi: 10.1186/1752-0509-9-S4-S2. Epub 2015 Jun 11. A weighted and integrated drug-target interactome: drug repurposing for schizophrenia as a use case. Huang LC, Soysal E, Zheng W, Zhao Z, Xu H, Sun J. BACKGROUND: Computational pharmacology can uniquely address some issues in the process of drug development by providing a macroscopic view and a deeper understanding of drug action. Specifically, network-assisted approach is promising for the inference of drug repurposing. However, the drug-target associations coming from different sources and various assays have much noise, leading to an inflation of the inference errors. To reduce the inference errors, it is necessary and critical to create a comprehensive and weighted data set of drug-target associations. RESULTS: In this study, we created a weighted and integrated drug-target interactome (WinDTome) to provide a comprehensive resource of drug-target associations for computational pharmacology. We first collected drug-target interactions from six commonly used drug-target centered data sources including DrugBank, KEGG, TTD, MATADOR, PDSP K(i) Database, and BindingDB. Then, we employed the record linkage method to normalize drugs and targets to the unique identifiers by utilizing the public data sources including PubChem, Entrez Gene, and UniProt. To assess the reliability of the drug-target associations, we assigned two scores (Score_S and Score_R) to each drug-target association based on their data sources and publication references. Consequently, the WinDTome contains 546,196 drug-target associations among 303,018 compounds and 4,113 genes. To assess the application of the WinDTome, we designed a network-based approach for drug repurposing using mental disorder schizophrenia (SCZ) as a case. Starting from 41 known SCZ drugs and their targets, we inferred a total of 264 potential SCZ drugs through the associations of drug-target with Score_S higher than two in WinDTome and human protein-protein interactions. Among the 264 SCZ-related drugs, 39 drugs have been investigated in clinical trials for SCZ treatment and 74 drugs for the treatment of other mental disorders, respectively. Compared with the results using other Score_S cutoff values, single data source, or the data from STITCH, the inference of 264 SCZ-related drugs had the highest performance. CONCLUSIONS: The WinDTome generated in this study contains comprehensive drug-target associations with confidence scores. Its application to the SCZ drug repurposing demonstrated that the WinDTome is promising to serve as a useful resource for drug repurposing. DOI: 10.1186/1752-0509-9-S4-S2 PMCID: PMC4474536 PMID: 26100720 [Indexed for MEDLINE] 22. Apoptosis. 2016 Jul;21(7):778-94. doi: 10.1007/s10495-016-1250-5. Current situation and future usage of anticancer drug databases. Wang H(1), Yin Y(2), Wang P(2), Xiong C(3)(4), Huang L(3)(4), Li S(2), Li X(2), Fu L(5). Author information: (1)College of Mathematics, Tonghua Normal University, Tonghua, 134002, China. whz-98@126.com. (2)State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China. (3)College of Life Sciences, Sichuan University, Chengdu, 610064, China. (4)State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center of Biotherapy, Chengdu, 610041, China. (5)State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center of Biotherapy, Chengdu, 610041, China. leilei_fu@163.com. Cancer is a deadly disease with increasing incidence and mortality rates and affects the life quality of millions of people per year. The past 15 years have witnessed the rapid development of targeted therapy for cancer treatment, with numerous anticancer drugs, drug targets and related gene mutations been identified. The demand for better anticancer drugs and the advances in database technologies have propelled the development of databases related to anticancer drugs. These databases provide systematic collections of integrative information either directly on anticancer drugs or on a specific type of anticancer drugs with their own emphases on different aspects, such as drug-target interactions, the relationship between mutations in drug targets and drug resistance/sensitivity, drug-drug interactions, natural products with anticancer activity, anticancer peptides, synthetic lethality pairs and histone deacetylase inhibitors. We focus on a holistic view of the current situation and future usage of databases related to anticancer drugs and further discuss their strengths and weaknesses, in the hope of facilitating the discovery of new anticancer drugs with better clinical outcomes. DOI: 10.1007/s10495-016-1250-5 PMID: 27193464 [Indexed for MEDLINE] 23. Database (Oxford). 2015 Jul 30;2015:bav069. doi: 10.1093/database/bav069. Print 2015. OCDB: a database collecting genes, miRNAs and drugs for obsessive-compulsive disorder. Privitera AP(1), Distefano R(2), Wefer HA(3), Ferro A(4), Pulvirenti A(4), Giugno R(5). Author information: (1)Department of Clinical and Experimental Medicine, University of Catania, Viale A. Doria 6, Catania, Italy, Istituto di Scienze Neurologiche, CNR, Via Paolo Gaifami, 18, 95125 Catania, Italy. (2)Department of Computer Science, University of Verona, Strada le Grazie 15, Verona, Italy and. (3)KarolinskaInstitutet, Department of Microbiology, Tumor and Cell Biology, Science for Life Laboratory, Stockholm, Sweden. (4)Department of Clinical and Experimental Medicine, University of Catania, Viale A. Doria 6, Catania, Italy. (5)Department of Clinical and Experimental Medicine, University of Catania, Viale A. Doria 6, Catania, Italy, giugno@dmi.unict.it. Obsessive-compulsive disorder (OCD) is a psychiatric condition characterized by intrusive and unwilling thoughts (obsessions) giving rise to anxiety. The patients feel obliged to perform a behavior (compulsions) induced by the obsessions. The World Health Organization ranks OCD as one of the 10 most disabling medical conditions. In the class of Anxiety Disorders, OCD is a pathology that shows an hereditary component. Consequently, an online resource collecting and integrating scientific discoveries and genetic evidence about OCD would be helpful to improve the current knowledge on this disorder. We have developed a manually curated database, OCD Database (OCDB), collecting the relations between candidate genes in OCD, microRNAs (miRNAs) involved in the pathophysiology of OCD and drugs used in its treatments. We have screened articles from PubMed and MEDLINE. For each gene, the bibliographic references with a brief description of the gene and the experimental conditions are shown. The database also lists the polymorphisms within genes and its chromosomal regions. OCDB data is enriched with both validated and predicted miRNA-target and drug-target information. The transcription factors regulations, which are also included, are taken from David and TransmiR. Moreover, a scoring function ranks the relevance of data in the OCDB context. The database is also integrated with the main online resources (PubMed, Entrez-gene, HGNC, dbSNP, DrugBank, miRBase, PubChem, Kegg, Disease-ontology and ChEBI). The web interface has been developed using phpMyAdmin and Bootstrap software. This allows (i) to browse data by category and (ii) to navigate in the database by searching genes, miRNAs, drugs, SNPs, regions, drug targets and articles. The data can be exported in textual format as well as the whole database in.sql or tabular format. OCDB is an essential resource to support genome-wide analysis, genetic and pharmacological studies. It also facilitates the evaluation of genetic data in OCD and the detection of alternative treatments. © The Author(s) 2015. Published by Oxford University Press. DOI: 10.1093/database/bav069 PMCID: PMC4519680 PMID: 26228432 [Indexed for MEDLINE] 24. Mol Biosyst. 2016 Feb;12(2):520-31. doi: 10.1039/c5mb00615e. Prediction of drug-target interaction by label propagation with mutual interaction information derived from heterogeneous network. Yan XY(1), Zhang SW(2), Zhang SY(2). Author information: (1)Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China. zhangsw@nwpu.edu.cn and College of Computer Science, Xi'an Shiyou University, Xi'an, 710065, China. (2)Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China. zhangsw@nwpu.edu.cn. The identification of potential drug-target interaction pairs is very important, which is useful not only for providing greater understanding of protein function, but also for enhancing drug research, especially for drug function repositioning. Recently, numerous machine learning-based algorithms (e.g. kernel-based, matrix factorization-based and network-based inference methods) have been developed for predicting drug-target interactions. All these methods implicitly utilize the assumption that similar drugs tend to target similar proteins and yield better results for predicting interactions between drugs and target proteins. To further improve the accuracy of prediction, a new method of network-based label propagation with mutual interaction information derived from heterogeneous networks, namely LPMIHN, is proposed to infer the potential drug-target interactions. LPMIHN separately performs label propagation on drug and target similarity networks, but the initial label information of the target (or drug) network comes from the drug (or target) label network and the known drug-target interaction bipartite network. The independent label propagation on each similarity network explores the cluster structure in its network, and the label information from the other network is used to capture mutual interactions (bicluster structures) between the nodes in each pair of the similarity networks. As compared to other recent state-of-the-art methods on the four popular benchmark datasets of binary drug-target interactions and two quantitative kinase bioactivity datasets, LPMIHN achieves the best results in terms of AUC and AUPR. In addition, many of the promising drug-target pairs predicted from LPMIHN are also confirmed on the latest publicly available drug-target databases such as ChEMBL, KEGG, SuperTarget and Drugbank. These results demonstrate the effectiveness of our LPMIHN method, indicating that LPMIHN has a great potential for predicting drug-target interactions. DOI: 10.1039/c5mb00615e PMID: 26675534 [Indexed for MEDLINE] 25. PLoS One. 2013 Nov 21;8(11):e80129. doi: 10.1371/journal.pone.0080129. eCollection 2013. Predicting drug-target interactions using drug-drug interactions. Kim S(1), Jin D, Lee H. Author information: (1)School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, South Korea. Computational methods for predicting drug-target interactions have become important in drug research because they can help to reduce the time, cost, and failure rates for developing new drugs. Recently, with the accumulation of drug-related data sets related to drug side effects and pharmacological data, it has became possible to predict potential drug-target interactions. In this study, we focus on drug-drug interactions (DDI), their adverse effects ([Formula: see text]) and pharmacological information ([Formula: see text]), and investigate the relationship among chemical structures, side effects, and DDIs from several data sources. In this study, [Formula: see text] data from the STITCH database, [Formula: see text] from drugs.com, and drug-target pairs from ChEMBL and SIDER were first collected. Then, by applying two machine learning approaches, a support vector machine (SVM) and a kernel-based L1-norm regularized logistic regression (KL1LR), we showed that DDI is a promising feature in predicting drug-target interactions. Next, the accuracies of predicting drug-target interactions using DDI were compared to those obtained using the chemical structure and side effects based on the SVM and KL1LR approaches, showing that DDI was the data source contributing the most for predicting drug-target interactions. DOI: 10.1371/journal.pone.0080129 PMCID: PMC3836969 PMID: 24278248 [Indexed for MEDLINE] 26. Database (Oxford). 2018 Jan 1;2018:1-13. doi: 10.1093/database/bay083. Drug Target Commons 2.0: a community platform for systematic analysis of drug-target interaction profiles. Tanoli Z(1), Alam Z(1), Vähä-Koskela M(1), Ravikumar B(1), Malyutina A(1), Jaiswal A(1), Tang J(1)(2), Wennerberg K(1)(3), Aittokallio T(1)(2). Author information: (1)Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. (2)Department of Mathematics and Statistics, University of Turku, Turku, Finland. (3)Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark. Drug Target Commons (DTC) is a web platform (database with user interface) for community-driven bioactivity data integration and standardization for comprehensive mapping, reuse and analysis of compound-target interaction profiles. End users can search, upload, edit, annotate and export expert-curated bioactivity data for further analysis, using an application programmable interface, database dump or tab-delimited text download options. To guide chemical biology and drug-repurposing applications, DTC version 2.0 includes updated clinical development information for the compounds and target gene-disease associations, as well as cancer-type indications for mutant protein targets, which are critical for precision oncology developments. DOI: 10.1093/database/bay083 PMCID: PMC6146131 PMID: 30219839 [Indexed for MEDLINE] 27. Med Chem. 2018;14(3):212-224. doi: 10.2174/1573406413666171103093235. Vitamins Based Novel Target Pathways/Molecules as Possible Emerging Drug Targets for the Management of Tuberculosis. Sharma A(1), Jain K(1), Flora SJS(1). Author information: (1)National Institute of Pharmaceutical Education and Research, Shree Bhawani Paper Mill Road, ITI Compound, Raebareli 229010, U.P., India. BACKGROUND: Tuberculosis (TB) is a deadly infectious disease caused by the pathogen Mycobacterium tuberculosis (Mtb). Approximately, 1.8 and 1.3 million people are infected and die, from TB each year as estimated by the World Health Organization. Due to increase in the incidence of drug-resistant strains of Mtb, there is an urgent need to accelerate research which focuses on the development of new drugs with novel mechanism of action that can treat both drugsensitive and resistant TB infections. OBJECTIVE: The purpose of this review study was to describe vitamins as drug target that can be explored to develop new anti tubercular drugs that can treat both drug-sensitive and resistant TB infections. METHOD: The methodological approaches include literature review which is performed in the databases like PubMed, Web of Science, Scopus, Springer and Science Direct, etc. On the basis of evaluation of literature sources, the review was complied. RESULTS: This review study demonstrated that vitamins biosynthesis pathway could be used in the development of novel drug targets. Further sequencing of the Mtb genome facilitated research in target identification and validation that make possible the discovery of novel anti-TB agent with new mechanisms of action. Several compounds were identified, which target vitamin biosynthesis pathway /enzymes. Some other new targets were also identified and can be explored for the identification of novel structural moiety. CONCLUSION: Further exploration of these compounds which have been identified to target these vitamins related novel target pathways /molecules could led to the development of antitubercular drug which can be used in the treatment of drug sensitive and resistant TB. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1573406413666171103093235 PMID: 29110620 [Indexed for MEDLINE] 28. BMC Bioinformatics. 2008 Feb 19;9:104. doi: 10.1186/1471-2105-9-104. PDTD: a web-accessible protein database for drug target identification. Gao Z(1), Li H, Zhang H, Liu X, Kang L, Luo X, Zhu W, Chen K, Wang X, Jiang H. Author information: (1)Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China. zhentg@mail.shcnc.ac.cn BACKGROUND: Target identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding proteins may be discovered by docking a small molecule to a repository of proteins with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Target Fishing Docking) http://www.dddc.ac.cn/tarfisdock, which has been used widely by others. Recently, we have constructed a protein target database, Potential Drug Target Database (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation. DESCRIPTION: PDTD is a web-accessible protein database for in silico target identification. It currently contains >1100 protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and several online databases such as TTD, DrugBank and Thomson Pharma. The database covers diverse information of >830 known or potential drug targets, including protein and active sites structures in both PDB and mol2 formats, related diseases, biological functions as well as associated regulating (signaling) pathways. Each target is categorized by both nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules. The results can be downloaded in the form of mol2 file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores. CONCLUSION: PDTD serves as a comprehensive and unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage) or an existing target (i.e. new pharmacological or toxic relevance), thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at http://www.dddc.ac.cn/pdtd/. DOI: 10.1186/1471-2105-9-104 PMCID: PMC2265675 PMID: 18282303 [Indexed for MEDLINE] 29. PLoS One. 2017 Oct 31;12(10):e0186364. doi: 10.1371/journal.pone.0186364. eCollection 2017. Target-similarity search using Plasmodium falciparum proteome identifies approved drugs with anti-malarial activity and their possible targets. Mogire RM(1), Akala HM(2), Macharia RW(3), Juma DW(2), Cheruiyot AC(2), Andagalu B(2), Brown ML(2), El-Shemy HA(4), Nyanjom SG(5). Author information: (1)Department of Molecular Biology and Biotechnology, Pan African University Institute of Science, Technology and Innovation, Nairobi, Kenya. (2)Department of Emerging Infectious Diseases (DEID), United States Army Medical Research Directorate-Kenya (USAMRD-K), Kenya Medical Research Institute (KEMRI)-Walter Reed Project, Kisumu, Kenya. (3)Centre for Biotechnology and Bioinformatics, University of Nairobi, Nairobi, Kenya. (4)Department of Biochemistry, Faculty of Agriculture, Cairo University, Giza, Egypt. (5)Department of Biochemistry, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya. Malaria causes about half a million deaths annually, with Plasmodium falciparum being responsible for 90% of all the cases. Recent reports on artemisinin resistance in Southeast Asia warrant urgent discovery of novel drugs for the treatment of malaria. However, most bioactive compounds fail to progress to treatments due to safety concerns. Drug repositioning offers an alternative strategy where drugs that have already been approved as safe for other diseases could be used to treat malaria. This study screened approved drugs for antimalarial activity using an in silico chemogenomics approach prior to in vitro verification. All the P. falciparum proteins sequences available in NCBI RefSeq were mined and used to perform a similarity search against DrugBank, TTD and STITCH databases to identify similar putative drug targets. Druggability indices of the potential P. falciparum drug targets were obtained from TDR targets database. Functional amino acid residues of the drug targets were determined using ConSurf server which was used to fine tune the similarity search. This study predicted 133 approved drugs that could target 34 P. falciparum proteins. A literature search done at PubMed and Google Scholar showed 105 out of the 133 drugs to have been previously tested against malaria, with most showing activity. For further validation, drug susceptibility assays using SYBR Green I method were done on a representative group of 10 predicted drugs, eight of which did show activity against P. falciparum 3D7 clone. Seven had IC50 values ranging from 1 μM to 50 μM. This study also suggests drug-target association and hence possible mechanisms of action of drugs that did show antiplasmodial activity. The study results validate the use of proteome-wide target similarity approach in identifying approved drugs with activity against P. falciparum and could be adapted for other pathogens. DOI: 10.1371/journal.pone.0186364 PMCID: PMC5663372 PMID: 29088219 [Indexed for MEDLINE] 30. Comb Chem High Throughput Screen. 2015;18(8):784-94. RepurposeVS: A Drug Repurposing-Focused Computational Method for Accurate Drug-Target Signature Predictions. Issa NT, Peters OJ, Byers SW, Dakshanamurthy S(1). Author information: (1)Department of oncology, Georgetown University, Washington D.C. 20057, USA. sd233@georgetown.edu. We describe here RepurposeVS for the reliable prediction of drug-target signatures using X-ray protein crystal structures. RepurposeVS is a virtual screening method that incorporates docking, drug-centric and protein-centric 2D/3D fingerprints with a rigorous mathematical normalization procedure to account for the variability in units and provide high-resolution contextual information for drug-target binding. Validity was confirmed by the following: (1) providing the greatest enrichment of known drug binders for multiple protein targets in virtual screening experiments, (2) determining that similarly shaped protein target pockets are predicted to bind drugs of similar 3D shapes when RepurposeVS is applied to 2,335 human protein targets, and (3) determining true biological associations in vitro for mebendazole (MBZ) across many predicted kinase targets for potential cancer repurposing. Since RepurposeVS is a drug repurposing-focused method, benchmarking was conducted on a set of 3,671 FDA approved and experimental drugs rather than the Database of Useful Decoys (DUDE) so as to streamline downstream repurposing experiments. We further apply RepurposeVS to explore the overall potential drug repurposing space for currently approved drugs. RepurposeVS is not computationally intensive and increases performance accuracy, thus serving as an efficient and powerful in silico tool to predict drug-target associations in drug repurposing. PMCID: PMC5848469 PMID: 26234515 [Indexed for MEDLINE] 31. J Biomed Semantics. 2016 Sep 27;7(1):59. A drug target slim: using gene ontology and gene ontology annotations to navigate protein-ligand target space in ChEMBL. Mutowo P(1), Bento AP(2), Dedman N(2), Gaulton A(2), Hersey A(2), Lomax J(3), Overington JP(2). Author information: (1)European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. prudence@ebi.ac.uk. (2)European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. (3)Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. BACKGROUND: The process of discovering new drugs is a lengthy, time-consuming and expensive process. Modern day drug discovery relies heavily on the rapid identification of novel 'targets', usually proteins that can be modulated by small molecule drugs to cure or minimise the effects of a disease. Of the 20,000 proteins currently reported as comprising the human proteome, just under a quarter of these can potentially be modulated by known small molecules Storing information in curated, actively maintained drug discovery databases can help researchers access current drug discovery information quickly. However with the increase in the amount of data generated from both experimental and in silico efforts, databases can become very large very quickly and information retrieval from them can become a challenge. The development of database tools that facilitate rapid information retrieval is important to keep up with the growth of databases. DESCRIPTION: We have developed a Gene Ontology-based navigation tool (Gene Ontology Tree) to help users retrieve biological information to single protein targets in the ChEMBL drug discovery database. 99 % of single protein targets in ChEMBL have at least one GO annotation associated with them. There are 12,500 GO terms associated to 6200 protein targets in the ChEMBL database resulting in a total of 140,000 annotations. The slim we have created, the 'ChEMBL protein target slim' allows broad categorisation of the biology of 90 % of the protein targets using just 300 high level, informative GO terms. We used the GO slim method of assigning fewer higher level GO groupings to numerous very specific lower level terms derived from the GOA to describe a set of GO terms relevant to proteins in ChEMBL. We then used the slim created to provide a web based tool that allows a quick and easy navigation of protein target space. Terms from the GO are used to capture information on protein molecular function, biological process and subcellular localisations. The ChEMBL database also provides compound information for small molecules that have been tested for their effects on these protein targets. The 'ChEMBL protein target slim' provides a means of firstly describing the biology of protein drug targets and secondly allows users to easily establish a connection between biological and chemical information regarding drugs and drug targets in ChEMBL. The 'ChEMBL protein target slim' is available as a browsable 'Gene Ontology Tree' on the ChEMBL site under the browse targets tab ( https://www.ebi.ac.uk/chembl/target/browser ). A ChEMBL protein target slim OBO file containing the GO slim terms pertinent to ChEMBL is available from the GOC website ( http://geneontology.org/page/go-slim-and-subset-guide ). CONCLUSIONS: We have created a protein target navigation tool based on the 'ChEMBL protein target slim'. The 'ChEMBL protein target slim' provides a way of browsing protein targets in ChEMBL using high level GO terms that describe the molecular functions, processes and subcellular localisations of protein drug targets in drug discovery. The tool also allows user to establish a link between ontological groupings representing protein target biology to relevant compound information in ChEMBL. We have demonstrated by the use of a simple example how the 'ChEMBL protein target slim' can be used to link biological processes with drug information based on the information in the ChEMBL database. The tool has potential to aid in areas of drug discovery such as drug repurposing studies or drug-disease-protein pathways. DOI: 10.1186/s13326-016-0102-0 PMCID: PMC5039825 PMID: 27678076 32. Nucleic Acids Res. 2008 Jan;36(Database issue):D901-6. Epub 2007 Nov 29. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Wishart DS(1), Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M. Author information: (1)Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8. david.wishart@ulberta.ca DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With approximately 4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more 'experimental' drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food-drug interactions, drug-drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca. DOI: 10.1093/nar/gkm958 PMCID: PMC2238889 PMID: 18048412 [Indexed for MEDLINE] 33. J Cheminform. 2014 Apr 16;6:13. doi: 10.1186/1758-2946-6-13. eCollection 2014. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. Ru J(1), Li P(1), Wang J(1), Zhou W(1), Li B(1), Huang C(1), Li P(1), Guo Z(1), Tao W(1), Yang Y(2), Xu X(1), Li Y(2), Wang Y(1), Yang L(3). Author information: (1)Center for Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi 712100, China. (2)School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China. (3)Laboratory of Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China. BACKGROUND: Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed. DESCRIPTION: The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was built based on the framework of systems pharmacology for herbal medicines. It consists of all the 499 Chinese herbs registered in the Chinese pharmacopoeia with 29,384 ingredients, 3,311 targets and 837 associated diseases. Twelve important ADME-related properties like human oral bioavailability, half-life, drug-likeness, Caco-2 permeability, blood-brain barrier and Lipinski's rule of five are provided for drug screening and evaluation. TCMSP also provides drug targets and diseases of each active compound, which can automatically establish the compound-target and target-disease networks that let users view and analyze the drug action mechanisms. It is designed to fuel the development of herbal medicines and to promote integration of modern medicine and traditional medicine for drug discovery and development. CONCLUSIONS: The particular strengths of TCMSP are the composition of the large number of herbal entries, and the ability to identify drug-target networks and drug-disease networks, which will help revealing the mechanisms of action of Chinese herbs, uncovering the nature of TCM theory and developing new herb-oriented drugs. TCMSP is freely available at http://sm.nwsuaf.edu.cn/lsp/tcmsp.php. DOI: 10.1186/1758-2946-6-13 PMCID: PMC4001360 PMID: 24735618 34. Molecules. 2018 Aug 31;23(9). pii: E2208. doi: 10.3390/molecules23092208. Machine Learning for Drug-Target Interaction Prediction. Chen R(1), Liu X(2), Jin S(3), Lin J(4), Liu J(5). Author information: (1)Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen 361005, China. chenruolan@stu.xmu.edu.cn. (2)Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen 361005, China. xrliu@xmu.edu.cn. (3)Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen 361005, China. stjin.xmu@gmail.com. (4)Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen 361005, China. 23020161153321@stu.xmu.edu.cn. (5)Department of Instrumental and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China. cecyliu@xmu.edu.cn. Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers. DOI: 10.3390/molecules23092208 PMCID: PMC6225477 PMID: 30200333 [Indexed for MEDLINE] 35. Nucleic Acids Res. 2016 Jan 4;44(D1):D932-7. doi: 10.1093/nar/gkv1283. Epub 2015 Nov 20. CancerResource--updated database of cancer-relevant proteins, mutations and interacting drugs. Gohlke BO(1), Nickel J(1), Otto R(2), Dunkel M(2), Preissner R(3). Author information: (1)Charité - University Medicine Berlin, Structural Bioinformatics Group, Institute of Physiology & Experimental Clinical Research Center, Berlin 13125, Germany German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany. (2)Charité - University Medicine Berlin, Structural Bioinformatics Group, Institute of Physiology & Experimental Clinical Research Center, Berlin 13125, Germany. (3)Charité - University Medicine Berlin, Structural Bioinformatics Group, Institute of Physiology & Experimental Clinical Research Center, Berlin 13125, Germany German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany robert.preissner@charite.de. Here, we present an updated version of CancerResource, freely available without registration at http://bioinformatics.charite.de/care. With upcoming information on target expression and mutations in patients' tumors, the need for systems supporting decisions on individual therapy is growing. This knowledge is based on numerous, experimentally validated drug-target interactions and supporting analyses such as measuring changes in gene expression using microarrays and HTS-efforts on cell lines. To enable a better overview about similar drug-target data and supporting information, a series of novel information connections are established and made available as described in the following. CancerResource contains about 91,000 drug-target relations, more than 2000 cancer cell lines and drug sensitivity data for about 50,000 drugs. CancerResource enables the capability of uploading external expression and mutation data and comparing them to the database's cell lines. Target genes and compounds are projected onto cancer-related pathways to get a better overview about how drug-target interactions benefit the treatment of cancer. Features like cellular fingerprints comprising of mutations, expression values and drug-sensitivity data can promote the understanding of genotype to drug sensitivity associations. Ultimately, these profiles can also be used to determine the most effective drug treatment for a cancer cell line most similar to a patient's tumor cells. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gkv1283 PMCID: PMC4702908 PMID: 26590406 [Indexed for MEDLINE] 36. Brief Bioinform. 2017 Mar 1;18(2):333-347. doi: 10.1093/bib/bbw012. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Wu Z(1), Cheng F(2), Li J(1), Li W(1), Liu G(2), Tang Y(1). Author information: (1)Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China. (2)Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China. Computational prediction of drug-target interactions (DTIs) and drug repositioning provides a low-cost and high-efficiency approach for drug discovery and development. The traditional social network-derived methods based on the naïve DTI topology information cannot predict potential targets for new chemical entities or failed drugs in clinical trials. There are currently millions of commercially available molecules with biologically relevant representations in chemical databases. It is urgent to develop novel computational approaches to predict targets for new chemical entities and failed drugs on a large scale. In this study, we developed a useful tool, namely substructure-drug-target network-based inference (SDTNBI), to prioritize potential targets for old drugs, failed drugs and new chemical entities. SDTNBI incorporates network and chemoinformatics to bridge the gap between new chemical entities and known DTI network. High performance was yielded in 10-fold and leave-one-out cross validations using four benchmark data sets, covering G protein-coupled receptors, kinases, ion channels and nuclear receptors. Furthermore, the highest areas under the receiver operating characteristic curve were 0.797 and 0.863 for two external validation sets, respectively. Finally, we identified thousands of new potential DTIs via implementing SDTNBI on a global network. As a proof-of-principle, we showcased the use of SDTNBI to identify novel anticancer indications for nonsteroidal anti-inflammatory drugs by inhibiting AKR1C3, CA9 or CA12. In summary, SDTNBI is a powerful network-based approach that predicts potential targets for new chemical entities on a large scale and will provide a new tool for DTI prediction and drug repositioning. The program and predicted DTIs are available on request. © The Author 2016. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com. DOI: 10.1093/bib/bbw012 PMID: 26944082 [Indexed for MEDLINE] 37. Bioinformatics. 2015 Aug 1;31(15):2523-9. doi: 10.1093/bioinformatics/btv181. Epub 2015 Mar 29. Global optimization-based inference of chemogenomic features from drug-target interactions. Zu S(1), Chen T(2), Li S(1). Author information: (1)MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and. (2)MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA. MOTIVATION: Gaining insight into chemogenomic drug-target interactions, such as those involving the substructures of synthetic drugs and protein domains, is important in fragment-based drug discovery and drug repositioning. Previous studies evaluated the interactions locally, thereby ignoring the competitive effects of different substructures or domains, but this could lead to high false-positive estimation, calling for a computational method that presents more predictive power. RESULTS: A statistical model, termed Global optimization-based InFerence of chemogenomic features from drug-Target interactions, or GIFT, is proposed herein to evaluate substructure-domain interactions globally such that all substructure-domain contributions to drug-target interaction are analyzed simultaneously. Combinations of different chemical substructures were included since they may function as one unit. When compared to previous methods, GIFT showed better interpretive performance, and performance for the recovery of drug-target interactions was good. Among 53 known drug-domain interactions, 81% were accurately predicted by GIFT. Eighteen of the top 100 predicted combined substructure-domain interactions had corresponding drug-target structures in the Protein Data Bank database, and 15 out of the 18 had been proved. GIFT was then implemented to predict substructure-domain interactions based on drug repositioning. For example, the anticancer activities of tazarotene, adapalene, acitretin and raloxifene were identified. In summary, GIFT is a global chemogenomic inference approach and offers fresh insight into drug-target interactions. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. DOI: 10.1093/bioinformatics/btv181 PMID: 25819672 [Indexed for MEDLINE] 38. Curr Drug Metab. 2018 Aug 20. doi: 10.2174/1389200219666180821094047. [Epub ahead of print] Recent advances in the machine learning-based drug-target interaction prediction. Zhang W(1), Lin W(1), Zhang D(1), Wang S(1), Shi J(2), Niu Y(3). Author information: (1)School of Computer, Wuhan University, Wuhan 430072. China. (2)School of Mathematics and Statistics, Wuhan University, Wuhan 430072. China. (3)School of Mathematics and Statistics, South-central University for Nationalities, Wuhan 430074. China. The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods. In the paper, we review the recent advances of machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learning-based drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods. This study provides the guide to the development of computational methods for the drug-target interaction prediction. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1389200219666180821094047 PMID: 30129407 39. Anal Chim Acta. 2015 Apr 29;871:18-27. doi: 10.1016/j.aca.2015.02.032. Epub 2015 Feb 12. Large-scale identification of potential drug targets based on the topological features of human protein-protein interaction network. Li ZC(1), Zhong WQ(2), Liu ZQ(2), Huang MH(2), Xie Y(2), Dai Z(3), Zou XY(4). Author information: (1)School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China. Electronic address: zhanchao8052@gmail.com. (2)School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China. (3)School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China. (4)School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China. Electronic address: ceszxy@mail.sysu.edu.cn. Identifying potential drug target proteins is a crucial step in the process of drug discovery and plays a key role in the study of the molecular mechanisms of disease. Based on the fact that the majority of proteins exert their functions through interacting with each other, we propose a method to recognize target proteins by using the human protein-protein interaction network and graph theory. In the network, vertexes and edges are weighted by using the confidence scores of interactions and descriptors of protein primary structure, respectively. The novel network topological features are defined and employed to characterize protein using existing databases. A widely used minimum redundancy maximum relevance and random forests algorithm are utilized to select the optimal feature subset and construct model for the identification of potential drug target proteins at the proteome scale. The accuracies of training set and test set are 89.55% and 85.23%. Using the constructed model, 2127 potential drug target proteins have been recognized and 156 drug target proteins have been validated in the database of drug target. In addition, some new drug target proteins can be considered as targets for treating diseases of mucopolysaccharidosis, non-arteritic anterior ischemic optic neuropathy, Bernard-Soulier syndrome and pseudo-von Willebrand, etc. It is anticipated that the proposed method may became a powerful high-throughput virtual screening tool of drug target. Copyright © 2015 Elsevier B.V. All rights reserved. DOI: 10.1016/j.aca.2015.02.032 PMID: 25847157 [Indexed for MEDLINE] 40. Artif Intell Med. 2010 Feb-Mar;48(2-3):161-6. doi: 10.1016/j.artmed.2009.11.002. Epub 2009 Dec 3. Analysis of adverse drug reactions using drug and drug target interactions and graph-based methods. Lin SF(1), Xiao KT, Huang YT, Chiu CC, Soo VW. Author information: (1)Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu 300, Taiwan. linfion07@gmail.com OBJECTIVE: The purpose of this study was to integrate knowledge about drugs, drug targets, and topological methods. The goals were to build a system facilitating the study of adverse drug events, to make it easier to find possible explanations, and to group similar drug-drug interaction cases in the adverse drug reaction reports from the US Food and Drug Administration (FDA). METHODS: We developed a system that analyses adverse drug reaction (ADR) cases reported by the FDA. The system contains four modules. First, we integrate drug and drug target databases that provide information related to adverse drug reactions. Second, we classify drug and drug targets according to anatomical therapeutic chemical classification (ATC) and drug target ontology (DTO). Third, we build drug target networks based on drug and drug target databases. Finally, we apply topological analysis to reveal drug interaction complexity for each ADR case reported by the FDA. RESULTS: We picked 1952 ADR cases from the years 2005-2006. Our dataset consisted of 1952 cases, of which 1471 cases involved ADR targets, 845 cases involved absorption, distribution, metabolism, and excretion (ADME) targets, and 507 cases involved some drugs acting on the same targets, namely, common targets (CTs). We then investigated the cases involving ADR targets, ADME targets, and CTs using the ATC system and DTO. In the cases that led to death, the average number of common targets (NCTs) was 0.879 and the average of average clustering coefficient (ACC) was 0.067. In cases that did not lead to death, the average NCTs was 0.551, and the average of ACC was 0.039. CONCLUSIONS: We implemented a system that can find possible explanations and cluster similar ADR cases reported by the FDA. We found that the average of ACC and the average NCTs in cases leading to death are higher than in cases not leading to death, suggesting that the interactions in cases leading to death are generally more complicated than in cases not leading to death. This indicates that our system can help not only in analysing ADRs in terms of drug-drug interactions but also by providing drug target assessments early in the drug discovery process. 2009 Elsevier B.V. All rights reserved. DOI: 10.1016/j.artmed.2009.11.002 PMID: 19962282 [Indexed for MEDLINE] 41. Sci Rep. 2013;3:1445. doi: 10.1038/srep01445. CancerDR: cancer drug resistance database. Kumar R(1), Chaudhary K, Gupta S, Singh H, Kumar S, Gautam A, Kapoor P, Raghava GP. Author information: (1)Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, India. Cancer therapies are limited by the development of drug resistance, and mutations in drug targets is one of the main reasons for developing acquired resistance. The adequate knowledge of these mutations in drug targets would help to design effective personalized therapies. Keeping this in mind, we have developed a database "CancerDR", which provides information of 148 anti-cancer drugs, and their pharmacological profiling across 952 cancer cell lines. CancerDR provides comprehensive information about each drug target that includes; (i) sequence of natural variants, (ii) mutations, (iii) tertiary structure, and (iv) alignment profile of mutants/variants. A number of web-based tools have been integrated in CancerDR. This database will be very useful for identification of genetic alterations in genes encoding drug targets, and in turn the residues responsible for drug resistance. CancerDR allows user to identify promiscuous drug molecules that can kill wide range of cancer cells. CancerDR is freely accessible at http://crdd.osdd.net/raghava/cancerdr/ DOI: 10.1038/srep01445 PMCID: PMC3595698 PMID: 23486013 [Indexed for MEDLINE] 42. Sci Rep. 2016 Jan 25;6:19842. doi: 10.1038/srep19842. Essential proteins and possible therapeutic targets of Wolbachia endosymbiont and development of FiloBase--a comprehensive drug target database for Lymphatic filariasis. Sharma OP(1), Kumar MS(1). Author information: (1)Centre for Bioinformatics, School of Life Science, Pondicherry University, Pondicherry-605014, India. Lymphatic filariasis (Lf) is one of the oldest and most debilitating tropical diseases. Millions of people are suffering from this prevalent disease. It is estimated to infect over 120 million people in at least 80 nations of the world through the tropical and subtropical regions. More than one billion people are in danger of getting affected with this life-threatening disease. Several studies were suggested its emerging limitations and resistance towards the available drugs and therapeutic targets for Lf. Therefore, better medicine and drug targets are in demand. We took an initiative to identify the essential proteins of Wolbachia endosymbiont of Brugia malayi, which are indispensable for their survival and non-homologous to human host proteins. In this current study, we have used proteome subtractive approach to screen the possible therapeutic targets for wBm. In addition, numerous literatures were mined in the hunt for potential drug targets, drugs, epitopes, crystal structures, and expressed sequence tag (EST) sequences for filarial causing nematodes. Data obtained from our study were presented in a user friendly database named FiloBase. We hope that information stored in this database may be used for further research and drug development process against filariasis. URL: http://filobase.bicpu.edu.in. DOI: 10.1038/srep19842 PMCID: PMC4726333 PMID: 26806463 [Indexed for MEDLINE] 43. Gigascience. 2018 Aug 1;7(8). doi: 10.1093/gigascience/giy091. eModel-BDB: a database of comparative structure models of drug-target interactions from the Binding Database. Naderi M(1), Govindaraj RG(1), Brylinski M(1)(2). Author information: (1)Department of Biological Sciences, Louisiana State University, 202 Life Sciences Bldg, Baton Rouge, LA 70803, USA. (2)Center for Computation & Technology, Louisiana State University, 2054 Digital Media Center, Baton Rouge, LA 70803, USA. Background: The structural information on proteins in their ligand-bound conformational state is invaluable for protein function studies and rational drug design. Compared to the number of available sequences, not only is the repertoire of the experimentally determined structures of holo-proteins limited, these structures do not always include pharmacologically relevant compounds at their binding sites. In addition, binding affinity databases provide vast quantities of information on interactions between drug-like molecules and their targets, however, often lacking structural data. On that account, there is a need for computational methods to complement existing repositories by constructing the atomic-level models of drug-protein assemblies that will not be determined experimentally in the near future. Results: We created eModel-BDB, a database of  200,005 comparative models of drug-bound proteins based on   1,391,403 interaction data obtained from the Binding Database and the PDB library of 31 January 2017. Complex models in eModel-BDB were generated with a collection of the state-of-the-art techniques, including protein meta-threading, template-based structure modeling, refinement and binding site detection, and ligand similarity-based docking. In addition to a rigorous quality control maintained during dataset generation, a subset of weakly homologous models was selected for the retrospective validation against experimental structural data recently deposited to the Protein Data Bank. Validation results indicate that eModel-BDB contains models that are accurate not only at the global protein structure level but also with respect to the atomic details of bound ligands. Conclusions: Freely available eModel-BDB can be used to support structure-based drug discovery and repositioning, drug target identification, and protein structure determination. DOI: 10.1093/gigascience/giy091 PMCID: PMC6131211 PMID: 30052959 [Indexed for MEDLINE] 44. PLoS Comput Biol. 2016 Nov 28;12(11):e1005219. doi: 10.1371/journal.pcbi.1005219. eCollection 2016 Nov. Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction. Coelho ED(1), Arrais JP(2), Oliveira JL(1). Author information: (1)Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal. (2)Department of Informatics Engineering (DEI), Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, Coimbra, Portugal. De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/. DOI: 10.1371/journal.pcbi.1005219 PMCID: PMC5125559 PMID: 27893735 [Indexed for MEDLINE] Conflict of interest statement: The authors have declared that no competing interests exist. 45. CNS Neurosci Ther. 2018 Dec;24(12):1253-1263. doi: 10.1111/cns.13051. Epub 2018 Aug 14. Identification of novel immune-relevant drug target genes for Alzheimer's Disease by combining ontology inference with network analysis. Han ZJ(1)(2), Xue WW(1), Tao L(3), Zhu F(1)(2). Author information: (1)Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China. (2)Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China. (3)Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China. AIMS: Alzheimer's disease (AD) is one of the leading causes of death in elderly people. Its pathogenesis is greatly associated with the abnormality of immune system. However, only a few immune-relevant AD drug target genes have been discovered up to now, and it is speculated that there are still many potential drug target genes of AD (at least immune-relevant genes) to be discovered. Thus, this study was designed to identify novel AD drug target genes and explore their biological properties. METHODS: A combinatorial approach was adopted for the first time to discover AD drug targets by collectively considering ontology inference and network analysis. Moreover, a novel strategy limiting the distance of reasoning and in turn reducing noise interference was further proposed to improve inference performance. Potential AD drug target genes were discovered by integrating information of multiple popular databases (TTD, DrugBank, PharmGKB, AlzGene, and BioGRID). Then, the enrichment analyses of the identified drug targets genes based on nine well-known pathway-related databases were conducted to explore the function of the identified potential drug target genes. RESULTS: Eighteen potential drug target genes were finally identified, and 13 of them had been reported to be closely associated with AD. Enrichment analyses of these identified drug target genes, based on nine pathway-related databases, revealed that the enriched terms were primarily focus on immune-relevant biological processes. Four of those identified drug target genes are involved in the classical complement pathway and process of antigen presenting. CONCLUSION: The well-reproducible results showed the good performance of the combinatorial approach, and the remaining five new targets could be a good starting point for our understanding of the pathogenesis and drug discovery of AD. Moreover, this study supported validity of the combinatorial approach integrating ontology inference with network analysis in the discovery of novel drug target for neurological diseases. © 2018 John Wiley & Sons Ltd. DOI: 10.1111/cns.13051 PMID: 30106219 46. Sci Rep. 2016 Nov 15;6:36969. doi: 10.1038/srep36969. Computational Drug Target Screening through Protein Interaction Profiles. Vilar S(1)(2), Quezada E(3), Uriarte E(2), Costanzi S(4), Borges F(3), Viña D(5), Hripcsak G(1). Author information: (1)Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA. (2)Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain. (3)CIQUP, Department of Chemistry &Biochemistry, Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal. (4)Department of Chemistry, American University, 20016 Washington, DC, USA. (5)Department of Pharmacology, CIMUS, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain. The development of computational methods to discover novel drug-target interactions on a large scale is of great interest. We propose a new method for virtual screening based on protein interaction profile similarity to discover new targets for molecules, including existing drugs. We calculated Target Interaction Profile Fingerprints (TIPFs) based on ChEMBL database to evaluate drug similarity and generated new putative compound-target candidates from the non-intersecting targets in each pair of compounds. A set of drugs was further studied in monoamine oxidase B (MAO-B) and cyclooxygenase-1 (COX-1) enzyme through molecular docking and experimental assays. The drug ethoxzolamide and the natural compound piperlongumine, present in Piper longum L, showed hMAO-B activity with IC50 values of 25 and 65 μM respectively. Five candidates, including lapatinib, SB-202190, RO-316233, GW786460X and indirubin-3'-monoxime were tested against human COX-1. Compounds SB-202190 and RO-316233 showed a IC50 in hCOX-1 of 24 and 25 μM respectively (similar range as potent inhibitors such as diclofenac and indomethacin in the same experimental conditions). Lapatinib and indirubin-3'-monoxime showed moderate hCOX-1 activity (19.5% and 28% of enzyme inhibition at 25 μM respectively). Our modeling constitutes a multi-target predictor for large scale virtual screening with potential in lead discovery, repositioning and drug safety. DOI: 10.1038/srep36969 PMCID: PMC5109486 PMID: 27845365 [Indexed for MEDLINE] 47. Chem Biol Drug Des. 2014 Feb;83(2):174-82. doi: 10.1111/cbdd.12209. Epub 2013 Oct 5. Screening of drug target proteins by 2D ligand matching approach. Feng J(1), Guo H, Wang J, Lu T. Author information: (1)College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China. Drugs interacting with off-target proteins would bring about side-effects. The identification of the proteins that a drug can bind is thus valuable for evaluating its side-effects. We established a system based on PDB database for screening for proteins a drug could bind. Firstly, all complexes in the PDB database were sorted by species; then, a ligand database was established by extracting ligands from the structure data files. Secondly, all proteins were clustered according to their sequence similarity with the protein originally bound with the ligand in PDB. To search the potential target proteins of a drug, the query drug structure is compared with all ligands in the database to obtain similar scores. Ligands with similar sores greater than a certain threshold were flagged. Protein clusters associating with these ligands would be considered as potential targets of the query drug. To test the reliability of this approach, three drugs from DrugBank were used to search for their binding proteins by our method. The results showed that all the corresponding target proteins were found. The method presented here was rapid, scalable and could be used for high efficient drug side-effects analysis. © 2013 John Wiley & Sons A/S. DOI: 10.1111/cbdd.12209 PMID: 24034065 [Indexed for MEDLINE] 48. Curr Drug Metab. 2018 Sep 24. doi: 10.2174/1389200219666180925091851. [Epub ahead of print] A Review of Recent Advances and Research on Drug Target Identification Methods. Hu Y(1), Zhao T(1), Zhang N(1), Zhang Y(2), Cheng L(3). Author information: (1)School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001. China. (2)Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin 150088. China. (3)College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081. China. From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and in studying the mechanisms of diseases. The development of methods to identify drug targets has become a popular issue in this field of research. Originally, scientists used biological experiments to identify drug targets. However, recently, an increasing number of scientists use computational methods to identify drug targets. Although thousands of drug targets are estimated to exist, only hundreds of these potential targets have been verified. Therefore, in this paper, we systematically review the recent work on identifying drug targets. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1389200219666180925091851 PMID: 30251599 49. Curr Drug Targets. 2018 Dec 4. doi: 10.2174/1389450120666181204164721. [Epub ahead of print] Understanding Membrane Protein Drug Targets in Computational Perspective. Gong J(1), Tan X(1), Chen Y(1), Sun P(1), He F(1), Zhang L(2), Li Y(1), Ma Z(1), Wang H(1). Author information: (1)School of Information Science and Technology, Northeast Normal University, Changchun. China. (2)School of Computer Science and Engineering, Changchun University of Technology, Changchun. China. Membrane proteins play a crucial physiological role in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in drug discovery due to a membrane protein can be regarded as a vital hub while most drug effect by drug-target interaction and the biological process can be supposed as a cycled network. In this review, typical membrane protein targets are described, including GPCRs, transporters, ion channels. In addition, network servers and databases referring to the drug and target information as well as drug discovery data are concluded. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for prediction of drug-target interaction containing network-based approach and machine learning based approach as well as current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in quantitative bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1389450120666181204164721 PMID: 30516106 50. Brief Bioinform. 2018 Dec 18. doi: 10.1093/bib/bby119. [Epub ahead of print] Interactive visual analysis of drug-target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing. Tanoli Z(1), Alam Z(1), Ianevski A(1)(2), Wennerberg K(3), Vähä-Koskela M(1), Aittokallio T(1)(2)(4). Author information: (1)Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland. (2)Helsinki Institute for Information Technology, Aalto University, Espoo, Finland. (3)University of Copenhagen, Biotech Research & Innovation Centre (BRIC). (4)Department of Mathematics and Statistics, University of Turku, Turku, Finland. Knowledge of the full target space of drugs (or drug-like compounds) provides important insights into the potential therapeutic use of the agents to modulate or avoid their various on- and off-targets in drug discovery and precision medicine. However, there is a lack of consolidated databases and associated data exploration tools that allow for systematic profiling of drug target-binding potencies of both approved and investigational agents using a network-centric approach. We recently initiated a community-driven platform, Drug Target Commons (DTC), which is an open-data crowdsourcing platform designed to improve the management, reproducibility and extended use of compound-target bioactivity data for drug discovery and repurposing, as well as target identification applications. In this work, we demonstrate an integrated use of the rich bioactivity data from DTC and related drug databases using Drug Target Profiler (DTP), an open-source software and web tool for interactive exploration of drug-target interaction networks. DTP was designed for network-centric modeling of mode-of-action of multi-targeting anticancer compounds, especially for precision oncology applications. DTP enables users to construct an interaction network based on integrated bioactivity data across selected chemical compounds and their protein targets, further customizable using various visualization and filtering options, as well as cross-links to several drug and protein databases to provide comprehensive information of the network nodes and interactions. We demonstrate here the operation of the DTP tool and its unique features by several use cases related to both drug discovery and drug repurposing applications, using examples of anticancer drugs with shared target profiles. DTP is freely accessible at http://drugtargetprofiler.fimm.fi/. DOI: 10.1093/bib/bby119 PMID: 30566623 51. Curr Top Med Chem. 2017;17(15):1709-1726. doi: 10.2174/1568026617666161116143440. Bioinformatics and Drug Discovery. Xia X(1). Author information: (1)Department of Biology, Faculty of Science, University of Ottawa, Ottawa, Ontario, Canada. Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanismbased drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules and metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data and software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1568026617666161116143440 PMCID: PMC5421137 PMID: 27848897 [Indexed for MEDLINE] 52. Comb Chem High Throughput Screen. 2016;19(2):121-8. Large-Scale Prediction of Drug Targets Based on Local and Global Consistency of Chemical-Chemical Networks. Huang G(1), Feng K, Li X, Peng Y. Author information: (1)Department of Mathematics, Shaoyang University, Shaoyang, Hunan 422000, China. guohuahhn@163.com. It is crucial to identify the molecular targets of a compound during the course of the new drug discovery and drug development. Due to the complexity of biological systems, finding drug targets by biological experiments is very tedious and expensive. In the paper, we used chemicalchemical interactions in the STITCH database to construct a network of drug-drug association. Based on the network, a learning method keeping local and global consistency was presented to infer drug targets. We achieved an accuracy of 57.75% in the first order prediction using leave-one-out cross validation, which was higher than the accuracy of 53.77% achieved by the local neighbor model. We manually validated 27 absent drug targets in the crossvalidation using drug-target interactions from other databases. Applying the presented method to large-scale prediction of unknown targets, we manually confirmed 14 pairs of drug-target interactions among the newly predicted drug targets. These results suggested that the presented method was a promising tool for large-scale identification of drug targets. PMID: 26552438 [Indexed for MEDLINE] 53. J Cheminform. 2015 Aug 19;7:40. doi: 10.1186/s13321-015-0089-z. eCollection 2015. Optimizing drug-target interaction prediction based on random walk on heterogeneous networks. Seal A(1), Ahn YY(1), Wild DJ(1). Author information: (1)Indiana University Bloomington, School of Informatics and computing, Bloomington, USA. BACKGROUND: Predicting novel drug-target associations is important not only for developing new drugs, but also for furthering biological knowledge by understanding how drugs work and their modes of action. As more data about drugs, targets, and their interactions becomes available, computational approaches have become an indispensible part of drug target association discovery. In this paper we apply random walk with restart (RWR) method to a heterogeneous network of drugs and targets compiled from DrugBank database and investigate the performance of the method under parameter variation and choice of chemical fingerprint methods. RESULTS: We show that choice of chemical fingerprint does not affect the performance of the method when the parameters are tuned to optimal values. We use a subset of the ChEMBL15 dataset that contains 2,763 associations between 544 drugs and 467 target proteins to evaluate our method, and we extracted datasets of bioactivity ≤1 and ≤10 μM activity cutoff. For 1 μM bioactivity cutoff, we find that our method can correctly predict nearly 47, 55, 60% of the given drug-target interactions in the test dataset having more than 0, 1, 2 drug target relations for ChEMBL 1 μM dataset in top 50 rank positions. For 10 μM bioactivity cutoff, we find that our method can correctly predict nearly 32.4, 34.8, 35.3% of the given drug-target interactions in the test dataset having more than 0, 1, 2 drug target relations for ChEMBL 1 μM dataset in top 50 rank positions. We further examine the associations between 110 popular top selling drugs in 2012 and 3,519 targets and find the top ten targets for each drug. CONCLUSIONS: We demonstrate the effectiveness and promise of the approach-RWR on heterogeneous networks using chemical features-for identifying novel drug target interactions and investigate the performance. DOI: 10.1186/s13321-015-0089-z PMCID: PMC4540752 PMID: 26300984 54. J Cheminform. 2018 Aug 20;10(1):41. doi: 10.1186/s13321-018-0297-4. Probing the chemical-biological relationship space with the Drug Target Explorer. Allaway RJ(1), La Rosa S(2), Guinney J(1), Gosline SJC(3). Author information: (1)Sage Bionetworks, 1100 Fairview Avenue N, Seattle, WA, 98109, USA. (2)Children's Tumor Foundation, New York, NY, 10005, USA. (3)Sage Bionetworks, 1100 Fairview Avenue N, Seattle, WA, 98109, USA. sara.gosline@sagebionetworks.org. Modern phenotypic high-throughput screens (HTS) present several challenges including identifying the target(s) that mediate the effect seen in the screen, characterizing 'hits' with a polypharmacologic target profile, and contextualizing screen data within the large space of drugs and screening models. To address these challenges, we developed the Drug-Target Explorer. This tool allows users to query molecules within a database of experimentally-derived and curated compound-target interactions to identify structurally similar molecules and their targets. It enables network-based visualizations of the compound-target interaction space, and incorporates comparisons to publicly-available in vitro HTS datasets. Furthermore, users can identify molecules using a query target or set of targets. The Drug Target Explorer is a multifunctional platform for exploring chemical space as it relates to biological targets, and may be useful at several steps along the drug development pipeline including target discovery, structure-activity relationship, and lead compound identification studies. DOI: 10.1186/s13321-018-0297-4 PMCID: PMC6102167 PMID: 30128806 55. Chin J Nat Med. 2015 Oct;13(10):751-9. doi: 10.1016/S1875-5364(15)30075-3. Drug-target networks for Tanshinone IIA identified by data mining. Chen SJ(1). Author information: (1)Department of Traditional Chinese Medicine, Zhejiang Pharmaceutical College, Ningbo 315100, China. Electronic address: chenshaojun@hotmail.com. Tanshinone IIA is a pharmacologically active compound isolated from Danshen (Salvia miltiorrhiza), a traditional Chinese herbal medicine for the management of cardiac diseases and other disorders. But its underlying molecular mechanisms of action are still unclear. The present investigation utilized a data mining approach based on network pharmacology to uncover the potential protein targets of Tanshinone IIA. Network pharmacology, an integrated multidisciplinary study, incorporates systems biology, network analysis, connectivity, redundancy, and pleiotropy, providing powerful new tools and insights into elucidating the fine details of drug-target interactions. In the present study, two separate drug-target networks for Tanshinone IIA were constructed using the Agilent Literature Search (ALS) and STITCH (search tool for interactions of chemicals) methods. Analysis of the ALS-constructed network revealed a target network with a scale-free topology and five top nodes (protein targets) corresponding to Fos, Jun, Src, phosphatidylinositol-4, 5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA), and mitogen-activated protein kinase kinase 1 (MAP2K1), whereas analysis of the STITCH-constructed network revealed three top nodes corresponding to cytochrome P450 3A4 (CYP3A4), cytochrome P450 A1 (CYP1A1), and nuclear factor kappa B1 (NFκB1). The discrepancies were probably due to the differences in the divergent computer mining tools and databases employed by the two methods. However, it is conceivable that all eight proteins mediate important biological functions of Tanshinone IIA, contributing to its overall drug-target network. In conclusion, the current results may assist in developing a comprehensive understanding of the molecular mechanisms and signaling pathways of in a simple, compact, and visual manner. Copyright © 2015 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved. DOI: 10.1016/S1875-5364(15)30075-3 PMID: 26481375 [Indexed for MEDLINE] 56. Nucleic Acids Res. 2010 Jul;38(Web Server issue):W609-14. doi: 10.1093/nar/gkq300. Epub 2010 Apr 29. PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Liu X(1), Ouyang S, Yu B, Liu Y, Huang K, Gong J, Zheng S, Li Z, Li H, Jiang H. Author information: (1)Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China. In silico drug target identification, which includes many distinct algorithms for finding disease genes and proteins, is the first step in the drug discovery pipeline. When the 3D structures of the targets are available, the problem of target identification is usually converted to finding the best interaction mode between the potential target candidates and small molecule probes. Pharmacophore, which is the spatial arrangement of features essential for a molecule to interact with a specific target receptor, is an alternative method for achieving this goal apart from molecular docking method. PharmMapper server is a freely accessed web server designed to identify potential target candidates for the given small molecules (drugs, natural products or other newly discovered compounds with unidentified binding targets) using pharmacophore mapping approach. PharmMapper hosts a large, in-house repertoire of pharmacophore database (namely PharmTargetDB) annotated from all the targets information in TargetBank, BindingDB, DrugBank and potential drug target database, including over 7000 receptor-based pharmacophore models (covering over 1500 drug targets information). PharmMapper automatically finds the best mapping poses of the query molecule against all the pharmacophore models in PharmTargetDB and lists the top N best-fitted hits with appropriate target annotations, as well as respective molecule's aligned poses are presented. Benefited from the highly efficient and robust triangle hashing mapping method, PharmMapper bears high throughput ability and only costs 1 h averagely to screen the whole PharmTargetDB. The protocol was successful in finding the proper targets among the top 300 pharmacophore candidates in the retrospective benchmarking test of tamoxifen. PharmMapper is available at http://59.78.96.61/pharmmapper. DOI: 10.1093/nar/gkq300 PMCID: PMC2896160 PMID: 20430828 [Indexed for MEDLINE] 57. Indian J Pharmacol. 2013 Sep-Oct;45(5):434-8. doi: 10.4103/0253-7613.117719. Sexually transmitted diseases putative drug target database: a comprehensive database of putative drug targets of pathogens identified by comparative genomics. Malipatil V(1), Madagi S, Bhattacharjee B. Author information: (1)Department of Bioinformatics, Karnataka State Women University, Bijapur, Karnataka, India. OBJECTIVE: Sexually transmitted diseases (STD) are the serious public health problems and also impose a financial burden on the economy. Sexually transmitted infections are cured with single or multiple antibiotics. However, in many cases the organism showed persistence even after treatment. In the current study, the set of druggable targets in STD pathogens have been identified by comparative genomics. MATERIALS AND METHODS: The subtractive genomics scheme exploits the properties of non-homology, essentiality, membrane localization and metabolic pathway uniqueness in identifying the drug targets. To achieve the effective use of data and to understand properties of drug target under single canopy, an integrated knowledge database of drug targets in STD bacteria was created. Data for each drug targets include biochemical pathway, function, cellular localization, essentiality score and structural details. RESULTS: The proteome of STD pathogens yielded 44 membrane associated proteins possessing unique metabolic pathways when subjected to the algorithm. The database can be accessed at http://biomedresearchasia.org/index.html. CONCLUSION: Diverse data merged in the common framework of this database is expected to be valuable not only for basic studies in clinical bioinformatics, but also for basic studies in immunological, biotechnological and clinical fields. DOI: 10.4103/0253-7613.117719 PMCID: PMC3793511 PMID: 24130375 [Indexed for MEDLINE] 58. Hum Hered. 2018;83(2):79-91. doi: 10.1159/000492574. Epub 2018 Oct 22. Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies. Prinz J(1), Koohi-Moghadam M(1)(2), Sun H(2), Kocher JA(1), Wang J(3)(4). Author information: (1)Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, Arizona, USA. (2)Department of Chemistry, The University of Hong Kong, Hong Kong, SAR, China. (3)Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, Arizona, USAWang.Junwen@mayo.edu. (4)Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, USAWang.Junwen@mayo.edu. AIMS: We propose a novel machine learning approach to expand the knowledge about drug-target interactions. Our method may help to develop effective, less harmful treatment strategies and to enable the detection of novel indications for existing drugs. METHODS: We developed a novel machine learning strategy to predict drug-target interactions based on drug side effects and traits from genome-wide association studies. We integrated data from the databases SIDER and GWASdb and utilized them in a unique way by a neural network approach. RESULTS: We validate our method using drug-target interactions from the STITCH database. In addition, we compare the chemical similarity of the predicted target to known targets of the drug under consideration and present literature-based evidence for predicted interactions. We find drug combination warnings for drugs we predict to target the same protein, hinting to synergistic effects aggravating harmful events. This substantiates the translational value of our approach, because we are able to detect drugs that should be taken together with care due to common mechanisms of action. CONCLUSION: Taken together, we conclude that our approach is able to generate a novel and clinically applicable insight into the molecular determinants of drug action. © 2018 S. Karger AG, Basel. DOI: 10.1159/000492574 PMID: 30347404 59. Bioinformatics. 2013 Aug 15;29(16):2071-2. doi: 10.1093/bioinformatics/btt345. Epub 2013 Jun 12. PiHelper: an open source framework for drug-target and antibody-target data. Aksoy BA(1), Gao J, Dresdner G, Wang W, Root A, Jing X, Cerami E, Sander C. Author information: (1)Computational Biology Center, Memorial Sloan-Kettering Cancer Center, NY 10065, USA. pihelper@cbio.mskcc.org MOTIVATION: The interaction between drugs and their targets, often proteins, and between antibodies and their targets, is important for planning and analyzing investigational and therapeutic interventions in many biological systems. Although drug-target and antibody-target datasets are available in separate databases, they are not publicly available in an integrated bioinformatics resource. As medical therapeutics, especially in cancer, increasingly uses targeted drugs and measures their effects on biomolecular profiles, there is an unmet need for a user-friendly toolset that allows researchers to comprehensively and conveniently access and query information about drugs, antibodies and their targets. SUMMARY: The PiHelper framework integrates human drug-target and antibody-target associations from publicly available resources to help meet the needs of researchers in systems pharmacology, perturbation biology and proteomics. PiHelper has utilities to (i) import drug- and antibody-target information; (ii) search the associations either programmatically or through a web user interface (UI); (iii) visualize the data interactively in a network; and (iv) export relationships for use in publications or other analysis tools. AVAILABILITY: PiHelper is a free software under the GNU Lesser General Public License (LGPL) v3.0. Source code and documentation are at http://bit.ly/pihelper. We plan to coordinate contributions from the community by managing future releases. DOI: 10.1093/bioinformatics/btt345 PMCID: PMC3722529 PMID: 23766416 [Indexed for MEDLINE] 60. Zhongguo Zhong Yao Za Zhi. 2016 Feb;41(3):377-382. doi: 10.4268/cjcmm20160303. [Application of drug-target prediction technology in network pharmacology of traditional Chinese medicine]. [Article in Chinese] Wu CW(1), Lu L(1), Liang SW(1), Chen C(1), Wang SM(1). Author information: (1)Research Center of Guangdong Province of Traditional Chinese Medicine Quality Engineering, Key Laboratory of State Administration of Traditional Chinese Medicine for Digitization of Chinese Medicine Evaluation, School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, China. In recent years, network pharmacology has been developed rapidly, and especially, the concept of ″network target″ has brought a new era in the field of traditional Chinese medicine (TCM). The integrity and systematicness emphasized in network pharmacology comply with the characteristics of holistic view and treatment in Chinese medicine. It can provide deeper insights into the underlying mechanisms of TCM theories, including the illustration on action mechanism of Chinese medicine, selection of pharmacodynamic materials and the combination principles of various Chinese herbs, etc. Therefore, this theory is more suitable for TCM academic characteristics and practical conditions. The key problem in network pharmacology is how to efficiently and quickly identify the interactions between large amounts of drugs and target proteins. As an efficient and high throughput way, drug-target prediction technology can reduce costs, quickly predict the component targets, and provide foundation for the application of TCM network pharmacology. In view of the large amount of compounds and target databases, different prediction methods and technologies have been developed, and used to predict the drug-target interactions. Many virtual screening technologies have been successfully applied to network pharmacology. Based on different prediction principles, drug-target prediction technology can be generally divided into four types： ligand-based prediction, receptor-based prediction, machine learning and combined prediction. In this paper, we are going to review the prediction methods of drug-target interactions and give acomprehensive elaboration of their application in network pharmacology of TCM, hoping to provide beneficial references for various Chinese medicine researchers. Copyright© by the Chinese Pharmaceutical Association. DOI: 10.4268/cjcmm20160303 PMID: 28868850 [Indexed for MEDLINE] Conflict of interest statement: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose. 61. Comb Chem High Throughput Screen. 2016;19(2):129-35. Analysis of A Drug Target-based Classification System using Molecular Descriptors. Lu J(1), Zhang P, Bi Y, Luo X. Author information: (1)School of Pharmacy, Yantai University, Yantai, Shandong 264005, China. lujing_ytu@126.com. Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a drug annotation and classification using a target-based classification system. In this study, we gave an investigation on five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V) Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods, maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their important roles in the identification of drug-target classes. PMID: 26552442 [Indexed for MEDLINE] 62. Genomics Inform. 2016 Dec;14(4):241-254. doi: 10.5808/GI.2016.14.4.241. Epub 2016 Dec 31. Drug Target Identification and Elucidation of Natural Inhibitors for Bordetella petrii: An In Silico Study. Rath SN(1), Ray M(1), Pattnaik A(1), Pradhan SK(1). Author information: (1)BIF Centre, Department of Bioinformatics, Orissa University of Agriculture and Technology, Bhubaneswar 751003, India. Environmental microbes like Bordetella petrii has been established as a causative agent for various infectious diseases in human. Again, development of drug resistance in B. petrii challenged to combat against the infection. Identification of potential drug target and proposing a novel lead compound against the pathogen has a great aid and value. In this study, bioinformatics tools and technology have been applied to suggest a potential drug target by screening the proteome information of B. petrii DSM 12804 (accession No. PRJNA28135) from genome database of National Centre for Biotechnology information. In this regards, the inhibitory effect of nine natural compounds like ajoene (Allium sativum), allicin (A. sativum), cinnamaldehyde (Cinnamomum cassia), curcumin (Curcuma longa), gallotannin (active component of green tea and red wine), isoorientin (Anthopterus wardii), isovitexin (A. wardii), neral (Melissa officinalis), and vitexin (A. wardii) have been acknowledged with anti-bacterial properties and hence tested against identified drug target of B. petrii by implicating computational approach. The in silico studies revealed the hypothesis that lpxD could be a potential drug target and with recommendation of a strong inhibitory effect of selected natural compounds against infection caused due to B. petrii, would be further validated through in vitro experiments. DOI: 10.5808/GI.2016.14.4.241 PMCID: PMC5287131 PMID: 28154518 63. Mol Biosyst. 2016 Mar;12(3):1006-14. doi: 10.1039/c5mb00650c. An improved approach for predicting drug-target interaction: proteochemometrics to molecular docking. Shaikh N(1), Sharma M(1), Garg P(1). Author information: (1)Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S. A. S. Nagar, Punjab 160062, India. prabhagarg@niper.ac.in gargprabha@yahoo.com. Proteochemometric (PCM) methods, which use descriptors of both the interacting species, i.e. drug and the target, are being successfully employed for the prediction of drug-target interactions (DTI). However, unavailability of non-interacting dataset and determining the applicability domain (AD) of model are a main concern in PCM modeling. In the present study, traditional PCM modeling was improved by devising novel methodologies for reliable negative dataset generation and fingerprint based AD analysis. In addition, various types of descriptors and classifiers were evaluated for their performance. The Random Forest and Support Vector Machine models outperformed the other classifiers (accuracies >98% and >89% for 10-fold cross validation and external validation, respectively). The type of protein descriptors had negligible effect on the developed models, encouraging the use of sequence-based descriptors over the structure-based descriptors. To establish the practical utility of built models, targets were predicted for approved anticancer drugs of natural origin. The molecular recognition interactions between the predicted drug-target pair were quantified with the help of a reverse molecular docking approach. The majority of predicted targets are known for anticancer therapy. These results thus correlate well with anticancer potential of the selected drugs. Interestingly, out of all predicted DTIs, thirty were found to be reported in the ChEMBL database, further validating the adopted methodology. The outcome of this study suggests that the proposed approach, involving use of the improved PCM methodology and molecular docking, can be successfully employed to elucidate the intricate mode of action for drug molecules as well as repositioning them for new therapeutic applications. DOI: 10.1039/c5mb00650c PMID: 26822863 [Indexed for MEDLINE] 64. PLoS One. 2015 May 7;10(5):e0126492. doi: 10.1371/journal.pone.0126492. eCollection 2015. Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System. Chen L(1), Chu C(2), Lu J(3), Kong X(4), Huang T(4), Cai YD(5). Author information: (1)College of Life Science, Shanghai University, Shanghai, People's Republic of China; College of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China. (2)Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China. (3)Department of Medicinal Chemistry, School of Pharmacy, Yantai University, Shandong, Yantai, People's Republic of China. (4)Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China. (5)College of Life Science, Shanghai University, Shanghai, People's Republic of China. Drug-target interaction (DTI) is a key aspect in pharmaceutical research. With the ever-increasing new drug data resources, computational approaches have emerged as powerful and labor-saving tools in predicting new DTIs. However, so far, most of these predictions have been based on structural similarities rather than biological relevance. In this study, we proposed for the first time a "GO and KEGG enrichment score" method to represent a certain category of drug molecules by further classification and interpretation of the DTI database. A benchmark dataset consisting of 2,015 drugs that are assigned to nine categories ((1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens) was constructed by collecting data from KEGG. We analyzed each category and each drug for its contribution in GO terms and KEGG pathways using the popular feature selection "minimum redundancy maximum relevance (mRMR)" method, and key GO terms and KEGG pathways were extracted. Our analysis revealed the top enriched GO terms and KEGG pathways of each drug category, which were highly enriched in the literature and clinical trials. Our results provide for the first time the biological relevance among drugs, targets and biological functions, which serves as a new basis for future DTI predictions. DOI: 10.1371/journal.pone.0126492 PMCID: PMC4423955 PMID: 25951454 [Indexed for MEDLINE] 65. Infect Disord Drug Targets. 2017;17(2):130-142. doi: 10.2174/1871526516666161230150219. In silico Analysis of Toxins of Staphylococcus aureus for Validating Putative Drug Targets. Mohana R(1), Venugopal S(1). Author information: (1)Department of Integrative Biology, VIT University, Vellore-632014, Tamil Nadu, India. Toxins are one among the numerous virulence factors produced by the bacteria. These are powerful poisonous substances enabling the bacteria to encounter the defense mechanism of human body. The pathogenic system of Staphylococcus aureus is evolved with various exotoxins that cause detrimental effects on human immune system. Four toxins namely enterotoxin A, exfoliative toxin A, TSST-1 and γ-hemolysin were downloaded from Uniprot database and were analyzed to understand the nature of the toxins and for drug target validation. The results inferred that the toxins were found to interact with many protein partners and no homologous sequences for human proteome were found, and based on similarity search in Drugbank, the targets were identified as novel drug targets. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1871526516666161230150219 PMID: 28034364 [Indexed for MEDLINE] 66. Acta Biochim Pol. 2018;65(2):209-218. doi: 10.18388/abp.2017_2327. Epub 2018 Jun 18. Prioritizing and modelling of putative drug target proteins of Candida albicans by systems biology approach. Ismail T(1), Fatima N(1), Muhammad SA(2), Zaidi SS(3), Rehman N(1), Hussain I(1), Tariq NUS(4), Amirzada I(1), Mannan A(1). Author information: (1)Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, 22060, Pakistan. (2)Institute of Molecular Biology and BioTechnology, Bahauddin Zakariya University, Multan, 46000, Pakistan. (3)Department of Pharmaceutics, University of Florida, Gainesville, 36090, USA. (4)Department of Pharmaceutics Margella Institute of Health Science, Rawalpindi, 44000 Pakistan. Candida albicans (Candida albicans) is one of the major sources of nosocomial infections in humans which may prove fatal in 30% of cases. The hospital acquired infection is very difficult to treat affectively due to the presence of drug resistant pathogenic strains, therefore there is a need to find alternative drug targets to cure this infection. In silico and computational level frame work was used to prioritize and establish antifungal drug targets of Candida albicans. The identification of putative drug targets was based on acquiring 5090 completely annotated genes of Candida albicans from available databases which were categorized into essential and non-essential genes. The result indicated that 9% of proteins were essential and could become potential candidates for intervention which might result in pathogen eradication. We studied cluster of orthologs and the subtractive genomic analysis of these essential proteins against human genome was made as a reference to minimize the side effects. It was seen that 14% of Candida albicans proteins were evolutionary related to the human proteins while 86% are non-human homologs. In the next step of compatible drug target selections, the non-human homologs were sequentially compared to the human microbiome data to minimize the potential effects against gut flora which accumulated to 38% of the essential genome. The sub-cellular localization of these candidate proteins in fungal cellular systems indicated that 80% of them are cytoplasmic, 10% are mitochondrial and the remaining 10% are associated with the cell wall. The role of these non-human and non-gut flora putative target proteins in Candida albicans biological pathways was studied. Due to their integrated and critical role in Candida albicans replication cycle, four proteins were selected for molecular modeling. For drug designing and development, four high quality and reliable protein models with more than 70% sequence identity were constructed. These proteins are used for the docking studies of the known and new ligands (unpublished data). Our study will be an effective framework for drug target identifications of pathogenic microbial strains and development of new therapies against the infections they cause. DOI: 10.18388/abp.2017_2327 PMID: 29913479 [Indexed for MEDLINE] 67. Gene. 2016 Jan 1;575(1):132-43. doi: 10.1016/j.gene.2015.08.044. Epub 2015 Aug 28. Identification of putative drug targets in Vancomycin-resistant Staphylococcus aureus (VRSA) using computer aided protein data analysis. Hasan MA(1), Khan MA(2), Sharmin T(2), Hasan Mazumder MH(3), Chowdhury AS(3). Author information: (1)Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong-4331, Bangladesh. Electronic address: anayet_johny@yahoo.com. (2)Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, Bangladesh. (3)Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong-4331, Bangladesh. Vancomycin-resistant Staphylococcus aureus (VRSA) is a Gram-positive, facultative aerobic bacterium which is evolved from the extensive exposure of Vancomycin to Methicillin resistant S. aureus (MRSA) that had become the most common cause of hospital and community-acquired infections. Due to the emergence of different antibiotic resistance strains, there is an exigency to develop novel drug targets to address the provocation of multidrug-resistant bacteria. In this study, in-silico genome subtraction methodology was used to design potential and pathogen specific drug targets against VRSA. Our study divulged 1987 proteins from the proteome of 34,549 proteins, which have no homologues in human genome after sequential analysis through CD-HIT and BLASTp. The high stringency analysis of the remaining proteins against database of essential genes (DEG) resulted in 169 proteins which are essential for S. aureus. Metabolic pathway analysis of human host and pathogen by KAAS at the KEGG server sorted out 19 proteins involved in unique metabolic pathways. 26 human non-homologous membrane-bound essential proteins including 4 which were also involved in unique metabolic pathway were deduced through PSORTb, CELLO v.2.5, ngLOC. Functional classification of uncharacterized proteins through SVMprot derived 7 human non-homologous membrane-bound hypothetical essential proteins. Study of potential drug target against Drug Bank revealed pbpA-penicillin-binding protein 1 and hypothetical protein MQW_01796 as the best drug target candidate. 2D structure was predicted by PRED-TMBB, 3D structure and functional analysis was also performed. Protein-protein interaction network of potential drug target proteins was analyzed by using STRING. The identified drug targets are expected to have great potential for designing novel drugs against VRSA infections and further screening of the compounds against these new targets may result in the discovery of novel therapeutic compounds that can be effective against Vancomycin resistant S. aureus. Copyright © 2015 Elsevier B.V. All rights reserved. DOI: 10.1016/j.gene.2015.08.044 PMID: 26319513 [Indexed for MEDLINE] 68. Bioinformatics. 2013 Jul 15;29(14):1821-2. doi: 10.1093/bioinformatics/btt289. Epub 2013 May 21. TiPs: a database of therapeutic targets in pathogens and associated tools. Lepore R(1), Tramontano A, Via A. Author information: (1)Department of Physics, Sapienza University, 00185 Rome, Italy. MOTIVATION: The need for new drugs and new targets is particularly compelling in an era that is witnessing an alarming increase of drug resistance in human pathogens. The identification of new targets of known drugs is a promising approach, which has proven successful in several cases. Here, we describe a database that includes information on 5153 putative drug-target pairs for 150 human pathogens derived from available drug-target crystallographic complexes. AVAILABILITY AND IMPLEMENTATION: The TiPs database is freely available at http://biocomputing.it/tips. CONTACT: anna.tramontano@uniroma1.it or allegra.via@uniroma1.it. DOI: 10.1093/bioinformatics/btt289 PMCID: PMC3702258 PMID: 23698860 [Indexed for MEDLINE] 69. Methods Inf Med. 2007;46(3):360-6. Does drug-target have a likeness? Chen X(1), Fang Y, Yao L, Chen Y, Xu H. Author information: (1)College of Life Science, Zhejiang University, Hangzhou, Zhejiang, PR China. xinchen@zju.edu.cn OBJECTIVE: The discovery of new targets that are sufficiently robust to yield marketable therapeutics is an enormous challenge. Conventional target identification approaches are disease-dependent, which require heavy experimental workload and comprehensive domain knowledge. In this work, we propose that a disease-independent property of proteins, "drug-target likeness", can be explored to facilitate the genomic scale target screening in the post-genomic age. METHODS: A Support Vector Machine (SVM) classifier was trained to recognize target and non-target protein sequences compiled from the Therapeutic Target Database, DrugBank, and PFam. Protein sequences are encoded by their composition, transition and distribution features of residues and Gaussian kernel function was used in SVM classification. RESULTS: SVM with a fine-tuned kernel width records 66.4 +/- 5.1% of sensitivity and 97.2 +/- 0.6% of specificity, corresponding to an overall target prediction accuracy of 94.4 +/- 0.8%. CONCLUSIONS: Though primitive, these results suggest that, similar to the "drug likeness" for small chemicals, their binding partners, drug targets, also display shared features which are reflected in their sequences and can be captured by statistical learning approaches. Further research on how to accurately and interpretably measure the likeness of protein being a drug target is promising. Inspired by the progress of "drug likeness" studies, advances in protein descriptors, statistical learning algorithms and more comprehensive and accurate gold-standard data set from disease biology research may help to further define the "drug-target likeness" property of proteins. DOI: 10.1160/ME0425 PMID: 17492123 [Indexed for MEDLINE] 70. Biomed Res Int. 2015;2015:350983. doi: 10.1155/2015/350983. Epub 2015 Oct 12. Predicting Drug-Target Interactions via Within-Score and Between-Score. Shi JY(1), Liu Z(2), Yu H(2), Li YJ(2). Author information: (1)School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China. (2)School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China. Network inference and local classification models have been shown to be useful in predicting newly potential drug-target interactions (DTIs) for assisting in drug discovery or drug repositioning. The idea is to represent drugs, targets, and their interactions as a bipartite network or an adjacent matrix. However, existing methods have not yet addressed appropriately several issues, such as the powerless inference in the case of isolated subnetworks, the biased classifiers derived from insufficient positive samples, the need of training a number of local classifiers, and the unavailable relationship between known DTIs and unapproved drug-target pairs (DTPs). Designing more effective approaches to address those issues is always desirable. In this paper, after presenting better drug similarities and target similarities, we characterize each DTP as a feature vector of within-scores and between-scores so as to hold the following superiorities: (1) a uniform vector of all types of DTPs, (2) only one global classifier with less bias benefiting from adequate positive samples, and (3) more importantly, the visualized relationship between known DTIs and unapproved DTPs. The effectiveness of our approach is finally demonstrated via comparing with other popular methods under cross validation and predicting potential interactions for DTPs under the validation in existing databases. DOI: 10.1155/2015/350983 PMCID: PMC4620248 PMID: 26543857 [Indexed for MEDLINE] 71. Iran J Pharm Res. 2015 Winter;14(1):291-302. Protein Drug Targets of Lavandula angustifolia on treatment of Rat Alzheimer's Disease. Zali H(1), Zamanian-Azodi M(1), Rezaei Tavirani M(2), Akbar-Zadeh Baghban A(3). Author information: (1)Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. (2)Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. (3)Rehabilitation Faculty, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Different treatment strategies of Alzheimer's disease (AD) are being studied for treating or slowing the progression of AD. Many pharmaceutically important regulation systems operate through proteins as drug targets. Here, we investigate the drug target proteins in beta-amyloid (Aβ) injected rat hippocampus treated with Lavandula angustifolia (LA) by proteomics techniques. The reported study showed that lavender extract (LE) improves the spatial performance in AD animal model by diminishing Aβ production in histopathology of hippocampus, so in this study neuroprotective proteins expressed in Aβ injected rats treated with LE were scrutinized. Rats were divided into three groups including normal, Aβ injected, and Aβ injected that was treated with LE. Protein expression profiles of hippocampus tissue were determined by two-dimensional electrophoresis (2DE) method and dysregulated proteins such as Snca, NF-L, Hspa5, Prdx2, Apoa1, and Atp5a1were identified by MALDI-TOF/TOF. KEGG pathway and gene ontology (GO) categories were used by searching DAVID Bioinformatics Resources. All detected protein spots were used to determine predictedinteractions with other proteins in STRING online database. Different isoforms of important protein, Snca that exhibited neuroprotective effects by anti-apoptotic properties were expressed. NF-L involved in the maintenance of neuronal caliber. Hspa5 likewise Prdx2 displays as anti-apoptotic protein that Prdx2 also involved in the neurotrophic effects. Apoa1 has anti-inflammatory activity and Atp5a1, produces ATP from ADP. To sum up, these proteins as potential drug targets were expressed in hippocampus in response to effective components in LA may have therapeutic properties for the treatment of AD and other neurodegenerative diseases. PMCID: PMC4277642 PMID: 25561935 72. J Am Med Inform Assoc. 2014 Mar-Apr;21(2):238-44. doi: 10.1136/amiajnl-2013-001700. Epub 2013 Jun 11. Drug repurposing: mining protozoan proteomes for targets of known bioactive compounds. Sateriale A(1), Bessoff K, Sarkar IN, Huston CD. Author information: (1)Cell, Molecular, and Biomedical Sciences Graduate Program, University of Vermont College of Medicine, Burlington, Vermont, USA. OBJECTIVE: To identify potential opportunities for drug repurposing by developing an automated approach to pre-screen the predicted proteomes of any organism against databases of known drug targets using only freely available resources. MATERIALS AND METHODS: We employed a combination of Ruby scripts that leverage data from the DrugBank and ChEMBL databases, MySQL, and BLAST to predict potential drugs and their targets from 13 published genomes. Results from a previous cell-based screen to identify inhibitors of Cryptosporidium parvum growth were used to validate our in-silico prediction method. RESULTS: In-vitro validation of these results, using a cell-based C parvum growth assay, showed that the predicted inhibitors were significantly more likely than expected by chance to have confirmed activity, with 8.9-15.6% of predicted inhibitors confirmed depending on the drug target database used. This method was then used to predict inhibitors for the following 13 disease-causing protozoan parasites, including: C parvum, Entamoeba histolytica, Giardia intestinalis, Leishmania braziliensis, Leishmania donovani, Leishmania major, Naegleria gruberi (in proxy of Naegleria fowleri), Plasmodium falciparum, Plasmodium vivax, Toxoplasma gondii, Trichomonas vaginalis, Trypanosoma brucei and Trypanosoma cruzi. CONCLUSIONS: Although proteome-wide screens for drug targets have disadvantages, in-silico methods can be developed that are fast, broad, inexpensive, and effective. In-vitro validation of our results for C parvum indicate that the method presented here can be used to construct a library for more directed small molecule screening, or pipelined into structural modeling and docking programs to facilitate target-based drug development. DOI: 10.1136/amiajnl-2013-001700 PMCID: PMC3932453 PMID: 23757409 [Indexed for MEDLINE] 73. BMC Bioinformatics. 2016 Apr 12;17:160. doi: 10.1186/s12859-016-1005-x. Predicting drug target interactions using meta-path-based semantic network analysis. Fu G(1), Ding Y(2)(3), Seal A(2), Chen B(4), Sun Y(5), Bolton E(6). Author information: (1)National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA. gang.fu@nih.gov. (2)School of Informatics & Computing, Indiana University, 107 S. Indiana Ave, Bloomington, IN, USA. (3)School of Information Management, Wuhan University, Wuchang, Wuhan, Hubei, China. (4)Department of Medicine, Stanford University, 450 Serra Mall, Stanford, CA, USA. (5)College of Computer and Information Science, Northeastern University, 360 Huntington Avenue, Boston, MA, USA. (6)National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA. BACKGROUND: In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction. RESULTS: Supervised machine learning models were constructed based on meta-path topological features of an enriched semantic network, which was derived from Chem2Bio2RDF, and was expanded by adding compound and protein similarity neighboring links obtained from the PubChem databases. The additional semantic links significantly improved the predictive performance of the supervised learning models. The binary classification model built upon the enriched feature space using the Random Forest algorithm significantly outperformed an existing semantic link prediction algorithm, Semantic Link Association Prediction (SLAP), to predict unknown links between compounds and protein targets in an evolving network. In addition to link prediction, Random Forest also has an intrinsic feature ranking algorithm, which can be used to select the important topological features that contribute to link prediction. CONCLUSIONS: The proposed framework has been demonstrated as a powerful alternative to SLAP in order to predict DTIs using the semantic network that integrates chemical, pharmacological, genomic, biological, functional, and biomedical information into a unified framework. It offers the flexibility to enrich the feature space by using different normalization processes on the topological features, and it can perform model construction and feature selection at the same time. DOI: 10.1186/s12859-016-1005-x PMCID: PMC4830032 PMID: 27071755 [Indexed for MEDLINE] 74. Bioinformatics. 2013 Aug 15;29(16):2004-8. doi: 10.1093/bioinformatics/btt307. Epub 2013 May 29. Drug-target interaction prediction through domain-tuned network-based inference. Alaimo S(1), Pulvirenti A, Giugno R, Ferro A. Author information: (1)Department of Mathematics and Computer Science and Department of Clinical and Molecular Biomedicine, University of Catania, Catania, Italy. MOTIVATION: The identification of drug-target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do not take into account important features within the drug-target domain. RESULTS: In this article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs. AVAILABILITY: DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http://sites.google.com/site/ehybridalgo/. DOI: 10.1093/bioinformatics/btt307 PMCID: PMC3722516 PMID: 23720490 [Indexed for MEDLINE] 75. Bioinformatics. 2013 Jul 1;29(13):i126-34. doi: 10.1093/bioinformatics/btt234. Predicting drug-target interactions using restricted Boltzmann machines. Wang Y(1), Zeng J. Author information: (1)Department of Automation and Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China. MOTIVATION: In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. RESULTS: We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. AVAILABILITY: Software and datasets are available on request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. DOI: 10.1093/bioinformatics/btt234 PMCID: PMC3694663 PMID: 23812976 [Indexed for MEDLINE] 76. J Ginseng Res. 2017 Oct;41(4):534-539. doi: 10.1016/j.jgr.2016.10.005. Epub 2016 Nov 10. Using reverse docking to identify potential targets for ginsenosides. Park K(1), Cho AE(1). Author information: (1)Department of Bioinformatics, Korea University, Sejong, Republic of Korea. BACKGROUND: Ginsenosides are the main ingredients of ginseng, which, in traditional Eastern medicine, has been claimed to have therapeutic values for many diseases. In order to verify the effects of ginseng that have been empirically observed, we utilized the reverse docking method to screen for target proteins that are linked to specific diseases. METHODS: We constructed a target protein database including 1,078 proteins associated with various kinds of diseases, based on the Potential Drug Target Database, with an added list of kinase proteins. We screened 26 kinds of ginsenosides of this target protein database using docking. RESULTS: We found four potential target proteins for ginsenosides, based on docking scores. Implications of these "hit" targets are discussed. From this screening, we also found four targets linked to possible side effects and toxicities, based on docking scores. CONCLUSION: Our method and results can be helpful for finding new targets and developing new drugs from natural products. DOI: 10.1016/j.jgr.2016.10.005 PMCID: PMC5628352 PMID: 29021701 77. J Drug Target. 2009 Aug;17(7):524-32. doi: 10.1080/10611860903046610. The analysis of the drug-targets based on the topological properties in the human protein-protein interaction network. Zhu M(1), Gao L, Li X, Liu Z, Xu C, Yan Y, Walker E, Jiang W, Su B, Chen X, Lin H. Author information: (1)School of Biomedical Engineering, Capital University of Medical Sciences, Beijing, People's Republic of China. Analyzing topological properties of drug-target proteins in the biology network is very helpful in understanding the mechanism of drug action. However, comprehensive studies to elaborately characterize the biological network features of drug-target proteins are still lacking. In this paper, we compared the topological properties of drug-targets with those of the non-drug-target sets, by mapping the drug-targets in DrugBank to the human protein interaction network. The results indicate that the topological properties of drug-targets are significantly distinguishable from those of non-drug-targets. Moreover, the potential possibility of drug-target prediction based on these properties is discussed. All proteins in the interaction network were ranked by their topological properties. Among the top 200 proteins, 94 overlapped with drug-targets in DrugBank and some novel predictions were found to be drug-targets in public literatures and other databases. In conclusion, our method explores the topological properties of drug-targets in the human protein interaction network by exploiting the large-scale drug-targets and protein interaction data. DOI: 10.1080/10611860903046610 PMID: 19530902 [Indexed for MEDLINE] 78. BMC Genomics. 2013;14 Suppl 4:S1. doi: 10.1186/1471-2164-14-S4-S1. Epub 2013 Oct 1. Exploring drug-target interaction networks of illicit drugs. Atreya RV, Sun J, Zhao Z. BACKGROUND: Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care. RESULTS: In this study, we extracted 188 illicit substances and their related information from the DrugBank database. The data process revealed 86 illicit drugs targeting a total of 73 unique human genes, which forms an illicit drug-target network. Compared to the full drug-target network from DrugBank, illicit drugs and their target genes tend to cluster together and form four subnetworks, corresponding to four major medication categories: depressants, stimulants, analgesics, and steroids. External analysis of Anatomical Therapeutic Chemical (ATC) second sublevel classifications confirmed that the illicit drugs have neurological functions or act via mechanisms of stimulants, opioids, and steroids. To further explore other drugs potentially having associations with illicit drugs, we constructed an illicit-extended drug-target network by adding the drugs that have the same target(s) as illicit drugs to the illicit drug-target network. After analyzing the degree and betweenness of the network, we identified hubs and bridge nodes, which might play important roles in the development and treatment of drug addiction. Among them, 49 non-illicit drugs might have potential to be used to treat addiction or have addictive effects, including some results that are supported by previous studies. CONCLUSIONS: This study presents the first systematic review of the network characteristics of illicit drugs, their targets, and other drugs that share the targets of these illicit drugs. The results, though preliminary, provide some novel insights into the molecular mechanisms of drug addiction. The observation of illicit-related drugs, with partial verification from previous studies, demonstrated that the network-assisted approach is promising for the identification of drug repositioning. DOI: 10.1186/1471-2164-14-S4-S1 PMCID: PMC3849475 PMID: 24268016 [Indexed for MEDLINE] 79. J Biomol Struct Dyn. 2017 Feb;35(2):287-299. doi: 10.1080/07391102.2015.1137229. Epub 2016 Apr 21. Identification of Phosphoribosyl-AMP cyclohydrolase, as drug target and its inhibitors in Brucella melitensis bv. 1 16M using metabolic pathway analysis. Gupta M(1), Prasad Y(2), Sharma SK(1), Jain CK(1). Author information: (1)a Department of Biotechnology , Jaypee Institute of Information Technology , A-10, Sector-62, Noida , Uttar Pradesh 201307 , India. (2)b Department of Computer Science and Engineering , Indian Institute of Technology Delhi , New Delhi 110016 , India. Brucella melitensis is a pathogenic Gram-negative bacterium which is known for causing zoonotic diseases (Brucellosis). The organism is highly contagious and has been reported to be used as bioterrorism agent against humans. Several antibiotics and vaccines have been developed but these antibiotics have exhibited the sign of antibiotic resistance or ineffective at lower concentrations, which imposes an urgent need to identify the novel drugs/drug targets against this organism. In this work, metabolic pathways analysis has been performed with different filters such as non-homology with humans, essentially of genes and choke point analysis, leading to identification of novel drug targets. A total of 18 potential drug target proteins were filtered out and used to develop the high confidence protein-protein interaction network The Phosphoribosyl-AMP cyclohydrolase (HisI) protein has been identified as potential drug target on the basis of topological parameters. Further, a homology model of (HisI) protein has been developed using Modeller with multiple template (1W6Q (48%), 1ZPS (55%), and 2ZKN (48%)) approach and validated using PROCHECK and Verify3D. The virtual high throughput screening (vHTS) using DockBlaster tool has been performed against 16,11,889 clean fragments from ZINC database. Top 500 molecules from DockBlaster were docked using Vina. The docking analysis resulted in ZINC04880153 showing the lowest binding energy (-9.1 kcal/mol) with the drug target. The molecular dynamics study of the complex HisI-ZINC04880153 was conducted to analyze the stability and fluctuation of ligand within the binding pocket of HisI. The identified ligand could be analyzed in the wet-lab based experiments for future drug discovery. DOI: 10.1080/07391102.2015.1137229 PMID: 26725317 [Indexed for MEDLINE] 80. Arch Gynecol Obstet. 2014 Oct;290(4):749-55. doi: 10.1007/s00404-014-3264-y. Epub 2014 Jun 3. Construction of breast cancer gene regulatory networks and drug target optimization. Xie Y(1), Wang R, Zhu J. Author information: (1)Department of Oncology, Renmin Hospital of Wuhan University, No.238 Jiefang Road, Wuchang District, Wuchan, 430060, China. OBJECTIVE: The purpose of this study was to construct the breast cancer gene regulatory networks through the high-throughput techniques and optimize the drug target genes of breast cancer using bioinformatics analysis, and thus accelerate the process of drug development and improve the cure rate of breast cancer. METHODS: The gene expression profile data of breast cancer were downloaded from GEO database and the transcriptional regulation data were obtained from UCSC database. Then we identified the differentially expressed genes (DEGs) by SAM algorithm and built gene regulatory networks by the supervised algorithm SIRENE. Finally, the drug targets of the DEGs with changed regulation relations were optimized based on the CancerResource database. RESULTS: A total of 584 DEGs were identified and the gene regulatory networks in the normal state and tumorous state were constructed. By comparing the new predicted regulatory relation in cancer state and normal state, the regulatory relation of 18 genes was found to be changed in the two states, showing the possibility to be applied as drug target genes. After the searches in the CancerResources, 7 genes were screened as the drug target genes, such as PFKFB3. CONCLUSION: Our present findings shed new light on the molecular mechanism of breast cancer and provide some drug targets which have the potential to be used in clinic for the treatment of breast cancer in future. DOI: 10.1007/s00404-014-3264-y PMID: 24890807 [Indexed for MEDLINE] 81. PLoS One. 2013 Jun 26;8(6):e66952. doi: 10.1371/journal.pone.0066952. Print 2013. Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile. van Laarhoven T(1), Marchiori E. Author information: (1)Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands. In silico discovery of interactions between drug compounds and target proteins is of core importance for improving the efficiency of the laborious and costly experimental determination of drug-target interaction. Drug-target interaction data are available for many classes of pharmaceutically useful target proteins including enzymes, ion channels, GPCRs and nuclear receptors. However, current drug-target interaction databases contain a small number of drug-target pairs which are experimentally validated interactions. In particular, for some drug compounds (or targets) there is no available interaction. This motivates the need for developing methods that predict interacting pairs with high accuracy also for these 'new' drug compounds (or targets). We show that a simple weighted nearest neighbor procedure is highly effective for this task. We integrate this procedure into a recent machine learning method for drug-target interaction we developed in previous work. Results of experiments indicate that the resulting method predicts true interactions with high accuracy also for new drug compounds and achieves results comparable or better than those of recent state-of-the-art algorithms. Software is publicly available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2013/. DOI: 10.1371/journal.pone.0066952 PMCID: PMC3694117 PMID: 23840562 [Indexed for MEDLINE] 82. Biomed Res Int. 2015;2015:212061. doi: 10.1155/2015/212061. Epub 2015 Nov 5. Comparative Genome and Network Centrality Analysis to Identify Drug Targets of Mycobacterium tuberculosis H37Rv. Melak T(1), Gakkhar S(2). Author information: (1)Department of Computer Science, Dilla University, P.O. Box 419, Dilla, SNNPR, Ethiopia. (2)Department of Mathematics, IIT Roorkee, Roorkee, Uttarakhand 247667, India. Potential drug targets of Mycobacterium tuberculosis H37Rv were identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome of Mycobacterium tuberculosis H37Rv against Database of Essential Genes (DEG) yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery. DOI: 10.1155/2015/212061 PMCID: PMC4651637 PMID: 26618166 [Indexed for MEDLINE] 83. PLoS One. 2013 May 7;8(5):e62975. doi: 10.1371/journal.pone.0062975. Print 2013. A semi-supervised method for drug-target interaction prediction with consistency in networks. Chen H(1), Zhang Z. Author information: (1)School of Information Science and Engineering, Central South University, Changsha, China. Computational prediction of interactions between drugs and their target proteins is of great importance for drug discovery and design. The difficulties of developing computational methods for the prediction of such potential interactions lie in the rarity of known drug-protein interactions and no experimentally verified negative drug-target interaction sample. Furthermore, target proteins need also to be predicted for some new drugs without any known target interaction information. In this paper, a semi-supervised learning method NetCBP is presented to address this problem by using labeled and unlabeled interaction information. Assuming coherent interactions between the drugs ranked by their relevance to a query drug, and the target proteins ranked by their relevance to the hidden target proteins of the query drug, we formulate a learning framework maximizing the rank coherence with respect to the known drug-target interactions. When applied to four classes of important drug-target interaction networks, our method improves previous methods in terms of cross-validation and some strongly predicted interactions are confirmed by the publicly accessible drug target databases, which indicates the usefulness of our method. Finally, a comprehensive prediction of drug-target interactions enables us to suggest many new potential drug-target interactions for further studies. DOI: 10.1371/journal.pone.0062975 PMCID: PMC3646965 PMID: 23667553 [Indexed for MEDLINE] 84. PLoS One. 2014 Jan 24;9(1):e86499. doi: 10.1371/journal.pone.0086499. eCollection 2014. ASDCD: antifungal synergistic drug combination database. Chen X(1), Ren B(2), Chen M(3), Liu MX(4), Ren W(5), Wang QX(6), Zhang LX(7), Yan GY(1). Author information: (1)National Centre for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, P. R. China ; Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, P. R. China. (2)University of Chinese Academy of Sciences, Beijing, P. R. China ; South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, P. R. China. (3)Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, P. R. China. (4)Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, P. R. China ; University of Chinese Academy of Sciences, Beijing, P. R. China. (5)Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, P. R. China. (6)School of Life Science, University of Science and Technology of China, Hefei, P.R. China. (7)South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, P. R. China ; Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, P. R. China. Finding effective drugs to treat fungal infections has important clinical significance based on high mortality rates, especially in an immunodeficient population. Traditional antifungal drugs with single targets have been reported to cause serious side effects and drug resistance. Nowadays, however, drug combinations, particularly with respect to synergistic interaction, have attracted the attention of researchers. In fact, synergistic drug combinations could simultaneously affect multiple subpopulations, targets, and diseases. Therefore, a strategy that employs synergistic antifungal drug combinations could eliminate the limitations noted above and offer the opportunity to explore this emerging bioactive chemical space. However, it is first necessary to build a powerful database in order to facilitate the analysis of drug combinations. To address this gap in our knowledge, we have built the first Antifungal Synergistic Drug Combination Database (ASDCD), including previously published synergistic antifungal drug combinations, chemical structures, targets, target-related signaling pathways, indications, and other pertinent data. Its current version includes 210 antifungal synergistic drug combinations and 1225 drug-target interactions, involving 105 individual drugs from more than 12,000 references. ASDCD is freely available at http://ASDCD.amss.ac.cn. DOI: 10.1371/journal.pone.0086499 PMCID: PMC3901703 PMID: 24475134 [Indexed for MEDLINE] 85. BMC Genomics. 2018 Sep 24;19(Suppl 7):667. doi: 10.1186/s12864-018-5031-0. Deep learning-based transcriptome data classification for drug-target interaction prediction. Xie L(1), He S(2), Song X(2), Bo X(3), Zhang Z(4). Author information: (1)Xiamen University, Xiamen, 361005, China. (2)Beijing Institute of Radiation Medicine, Beijing, 100850, China. (3)Beijing Institute of Radiation Medicine, Beijing, 100850, China. boxiaoc@163.com. (4)Xiamen University, Xiamen, 361005, China. zhongnan_zhang@xmu.edu.cn. BACKGROUND: The ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded by the wide range of data platforms and data scarcity. RESULTS: In this paper, we modeled the prediction of drug-target interactions as a binary classification task. Using transcriptome data from the L1000 database of the LINCS project, we developed a framework based on a deep-learning algorithm to predict potential drug target interactions. Once fully trained, the model achieved over 98% training accuracy. The results of our research demonstrated that our framework could discover more reliable DTIs than found by other methods. This conclusion was validated further across platforms with a high percentage of overlapping interactions. CONCLUSIONS: Our model's capacity of integrating transcriptome data from drugs and genes strongly suggests the strength of its potential for DTI prediction, thereby improving the drug discovery process. DOI: 10.1186/s12864-018-5031-0 PMCID: PMC6156897 PMID: 30255785 [Indexed for MEDLINE] 86. Nucleic Acids Res. 2014 Jul;42(Web Server issue):W39-45. doi: 10.1093/nar/gku337. Epub 2014 May 16. DINIES: drug-target interaction network inference engine based on supervised analysis. Yamanishi Y(1), Kotera M(2), Moriya Y(3), Sawada R(4), Kanehisa M(3), Goto S(5). Author information: (1)Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan Institute for Advanced Study, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan. (2)Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. (3)Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. (4)Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. (5)Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan goto@kuicr.kyoto-u.ac.jp. DINIES (drug-target interaction network inference engine based on supervised analysis) is a web server for predicting unknown drug-target interaction networks from various types of biological data (e.g. chemical structures, drug side effects, amino acid sequences and protein domains) in the framework of supervised network inference. The originality of DINIES lies in prediction with state-of-the-art machine learning methods, in the integration of heterogeneous biological data and in compatibility with the KEGG database. The DINIES server accepts any 'profiles' or precalculated similarity matrices (or 'kernels') of drugs and target proteins in tab-delimited file format. When a training data set is submitted to learn a predictive model, users can select either known interaction information in the KEGG DRUG database or their own interaction data. The user can also select an algorithm for supervised network inference, select various parameters in the method and specify weights for heterogeneous data integration. The server can provide integrative analyses with useful components in KEGG, such as biological pathways, functional hierarchy and human diseases. DINIES (http://www.genome.jp/tools/dinies/) is publicly available as one of the genome analysis tools in GenomeNet. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gku337 PMCID: PMC4086078 PMID: 24838565 [Indexed for MEDLINE] 87. J Chem Inf Model. 2014 Feb 24;54(2):407-18. doi: 10.1021/ci4005354. Epub 2014 Feb 5. Pathway analysis for drug repositioning based on public database mining. Pan Y(1), Cheng T, Wang Y, Bryant SH. Author information: (1)National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , 8600 Rockville Pike, Bethesda, Maryland 20894, United States. Sixteen FDA-approved drugs were investigated to elucidate their mechanisms of action (MOAs) and clinical functions by pathway analysis based on retrieved drug targets interacting with or affected by the investigated drugs. Protein and gene targets and associated pathways were obtained by data-mining of public databases including the MMDB, PubChem BioAssay, GEO DataSets, and the BioSystems databases. Entrez E-Utilities were applied, and in-house Ruby scripts were developed for data retrieval and pathway analysis to identify and evaluate relevant pathways common to the retrieved drug targets. Pathways pertinent to clinical uses or MOAs were obtained for most drugs. Interestingly, some drugs identified pathways responsible for other diseases than their current therapeutic uses, and these pathways were verified retrospectively by in vitro tests, in vivo tests, or clinical trials. The pathway enrichment analysis based on drug target information from public databases could provide a novel approach for elucidating drug MOAs and repositioning, therefore benefiting the discovery of new therapeutic treatments for diseases. DOI: 10.1021/ci4005354 PMCID: PMC3956470 PMID: 24460210 [Indexed for MEDLINE] 88. J Cheminform. 2016 Jun 14;8:33. doi: 10.1186/s13321-016-0141-7. eCollection 2016. IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data. Legehar A(1), Xhaard H(2), Ghemtio L(1). Author information: (1)Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790 Helsinki, Finland. (2)Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790 Helsinki, Finland ; Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, P.O. Box 56, 00014 Helsinki, Finland. BACKGROUND: The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process. DESCRIPTION: We have built a relational database (IDAAPM) to integrate available approved drug data such as drug approval information, ADMET and adverse effects, chemical structures and molecular descriptors, targets, bioactivity and related references. The database has been coupled with a searchable web interface and modern data analytics platform (KNIME) to allow data access, data transformation, initial analysis and further predictive modeling. Data were extracted from FDA resources and supplemented from other publicly available databases. Currently, the database contains information regarding about 19,226 FDA approval applications for 31,815 products (small molecules and biologics) with their approval history, 2505 active ingredients, together with as many ADMET properties, 1629 molecular structures, 2.5 million adverse effects and 36,963 experimental drug-target bioactivity data. CONCLUSION: IDAAPM is a unique resource that, in a single relational database, provides detailed information on FDA approved drugs including their ADMET properties and adverse effects, the corresponding targets with bioactivity data, coupled with a data analytics platform. It can be used to perform basic to complex drug-target ADMET or adverse effects analysis and predictive modeling. IDAAPM is freely accessible at http://idaapm.helsinki.fi and can be exploited through a KNIME workflow connected to the database.Graphical abstractFDA approved drug data integration for predictive modeling. DOI: 10.1186/s13321-016-0141-7 PMCID: PMC4906584 PMID: 27303447 89. J Chem Inf Model. 2016 Jun 27;56(6):1175-83. doi: 10.1021/acs.jcim.5b00690. Epub 2016 May 31. Enhancing the Enrichment of Pharmacophore-Based Target Prediction for the Polypharmacological Profiles of Drugs. Wang X(1), Pan C(1), Gong J(1), Liu X(1), Li H(1). Author information: (1)Shanghai Key Laboratory of New Drug Design, School of Pharmacy, and ‡School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200237, China. PharmMapper is a web server for drug target identification by reversed pharmacophore matching the query compound against an annotated pharmacophore model database, which provides a computational polypharmacology prediction approach for drug repurposing and side effect risk evaluation. But due to the inherent nondiscriminative feature of the simple fit scores used for prediction results ranking, the signal/noise ratio of the prediction results is high, posing a challenge for predictive reliability. In this paper, we improved the predictive accuracy of PharmMapper by generating a ligand-target pairwise fit score matrix from profiling all the annotated pharmacophore models against corresponding ligands in the original complex structures that were used to extract these pharmacophore models. The matrix reflects the noise baseline of fit score distribution of the background database, thus enabling estimation of the probability of finding a given target randomly with the calculated ligand-pharmacophore fit score. Two retrospective tests were performed which confirmed that the probability-based ranking score outperformed the simple fit score in terms of identification of both known drug targets and adverse drug reaction related off-targets. DOI: 10.1021/acs.jcim.5b00690 PMID: 27187084 [Indexed for MEDLINE] 90. BMC Bioinformatics. 2014 Mar 11;15:68. doi: 10.1186/1471-2105-15-68. Drug2Gene: an exhaustive resource to explore effectively the drug-target relation network. Roider HG, Pavlova N, Kirov I, Slavov S, Slavov T, Uzunov Z, Weiss B(1). Author information: (1)Bayer Pharma AG, Müllerstr 178, 13342 Berlin, Germany. bertram.weiss@bayer.com. BACKGROUND: Information about drug-target relations is at the heart of drug discovery. There are now dozens of databases providing drug-target interaction data with varying scope, and focus. Therefore, and due to the large chemical space, the overlap of the different data sets is surprisingly small. As searching through these sources manually is cumbersome, time-consuming and error-prone, integrating all the data is highly desirable. Despite a few attempts, integration has been hampered by the diversity of descriptions of compounds, and by the fact that the reported activity values, coming from different data sets, are not always directly comparable due to usage of different metrics or data formats. DESCRIPTION: We have built Drug2Gene, a knowledge base, which combines the compound/drug-gene/protein information from 19 publicly available databases. A key feature is our rigorous unification and standardization process which makes the data truly comparable on a large scale, allowing for the first time effective data mining in such a large knowledge corpus. As of version 3.2, Drug2Gene contains 4,372,290 unified relations between compounds and their targets most of which include reported bioactivity data. We extend this set with putative (i.e. homology-inferred) relations where sufficient sequence homology between proteins suggests they may bind to similar compounds. Drug2Gene provides powerful search functionalities, very flexible export procedures, and a user-friendly web interface. CONCLUSIONS: Drug2Gene v3.2 has become a mature and comprehensive knowledge base providing unified, standardized drug-target related information gathered from publicly available data sources. It can be used to integrate proprietary data sets with publicly available data sets. Its main goal is to be a 'one-stop shop' to identify tool compounds targeting a given gene product or for finding all known targets of a drug. Drug2Gene with its integrated data set of public compound-target relations is freely accessible without restrictions at http://www.drug2gene.com. DOI: 10.1186/1471-2105-15-68 PMCID: PMC4234465 PMID: 24618344 [Indexed for MEDLINE] 91. J Chem Inf Model. 2014 Jan 27;54(1):69-78. doi: 10.1021/ci400539q. Epub 2014 Jan 6. Halogen bond: its role beyond drug-target binding affinity for drug discovery and development. Xu Z(1), Yang Z, Liu Y, Lu Y, Chen K, Zhu W. Author information: (1)Drug Discovery and Design Center, Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Shanghai, 201203, China. Halogen bond has attracted a great deal of attention in the past years for hit-to-lead-to-candidate optimization aiming at improving drug-target binding affinity. In general, heavy organohalogens (i.e., organochlorines, organobromines, and organoiodines) are capable of forming halogen bonds while organofluorines are not. In order to explore the possible roles that halogen bonds could play beyond improving binding affinity, we performed a detailed database survey and quantum chemistry calculation with close attention paid to (1) the change of the ratio of heavy organohalogens to organofluorines along the drug discovery and development process and (2) the halogen bonds between organohalogens and nonbiopolymers or nontarget biopolymers. Our database survey revealed that (1) an obviously increasing trend of the ratio of heavy organohalogens to organofluorines was observed along the drug discovery and development process, illustrating that more organofluorines are worn and eliminated than heavy organohalogens during the process, suggesting that heavy halogens with the capability of forming halogen bonds should have priority for lead optimization; and (2) more than 16% of the halogen bonds in PDB are formed between organohalogens and water, and nearly 20% of the halogen bonds are formed with the proteins that are involved in the ADME/T process. Our QM/MM calculations validated the contribution of the halogen bond to the binding between organohalogens and plasma transport proteins. Thus, halogen bonds could play roles not only in improving drug-target binding affinity but also in tuning ADME/T property. Therefore, we suggest that albeit halogenation is a valuable approach for improving ligand bioactivity, more attention should be paid in the future to the application of the halogen bond for ligand ADME/T property optimization. DOI: 10.1021/ci400539q PMID: 24372485 [Indexed for MEDLINE] 92. J Vet Sci. 2018 Mar 31;19(2):188-199. doi: 10.4142/jvs.2018.19.2.188. In silico analysis of putative drug and vaccine targets of the metabolic pathways of Actinobacillus pleuropneumoniae using a subtractive/comparative genomics approach. Birhanu BT(1), Lee SJ(1), Park NH(1), Song JB(2), Park SC(1). Author information: (1)Laboratory of Veterinary Pharmacokinetics and Pharmacodynamics, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Korea. (2)Department of Chemistry Education, Teachers College, Kyungpook National University, Daegu 41566, Korea. Actinobacillus pleuropneumoniae is a Gram-negative bacterium that resides in the respiratory tract of pigs and causes porcine respiratory disease complex, which leads to significant losses in the pig industry worldwide. The incidence of drug resistance in this bacterium is increasing; thus, identifying new protein/gene targets for drug and vaccine development is critical. In this study, we used an in silico approach, utilizing several databases including the Kyoto Encyclopedia of Genes and Genomes (KEGG), the Database of Essential Genes (DEG), DrugBank, and Swiss-Prot to identify non-homologous essential genes and prioritize these proteins for their druggability. The results showed 20 metabolic pathways that were unique and contained 273 non-homologous proteins, of which 122 were essential. Of the 122 essential proteins, there were 95 cytoplasmic proteins and 11 transmembrane proteins, which are potentially suitable for drug and vaccine targets, respectively. Among these, 25 had at least one hit in DrugBank, and three had similarity to metabolic proteins from Mycoplasma hyopneumoniae, another pathogen causing porcine respiratory disease complex; thus, they could serve as common therapeutic targets. In conclusion, we identified glyoxylate and dicarboxylate pathways as potential targets for antimicrobial therapy and tetra-acyldisaccharide 4'-kinase and 3-deoxy-D-manno-octulosonic-acid transferase as vaccine candidates against A. pleuropneumoniae. DOI: 10.4142/jvs.2018.19.2.188 PMCID: PMC5879067 PMID: 29032659 [Indexed for MEDLINE] 93. Curr Protoc Bioinformatics. 2016 Jun 20;54:14.4.1-14.4.31. doi: 10.1002/cpbi.1. Using DrugBank for In Silico Drug Exploration and Discovery. Wishart DS(1), Wu A(1). Author information: (1)Departments of Computing Science and Biological Sciences, University of Alberta, Edmonton, Alberta, Canada. DrugBank is a fully curated drug and drug target database that contains 8174 drug entries including 1944 FDA approved small-molecule drugs, 198 FDA-approved biotech (protein/peptide) drugs, 93 nutraceuticals, and over 6000 experimental drugs. Additionally, 4300 non-redundant protein (i.e., drug target/enzyme/transporter/carrier) sequences are linked to these drug entries. DrugBank is primarily focused on providing both the query/search tools and biophysical data needed to facilitate drug discovery and drug development. This unit provides readers with a detailed description of how to effectively use the DrugBank database and how to navigate through the DrugBank Web site. It also provides specific examples of how to find chemical homologs of potential drug leads and how to identify potential drug targets from newly sequenced tumor samples. The intent of this unit is to give readers an introduction to the field of Web-based drug discovery and to show how cheminformatics can be seamlessly integrated into the field of bioinformatics. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc. DOI: 10.1002/cpbi.1 PMID: 27322405 [Indexed for MEDLINE] 94. J Comput Biol. 2011 Feb;18(2):133-45. doi: 10.1089/cmb.2010.0213. Combining drug and gene similarity measures for drug-target elucidation. Perlman L(1), Gottlieb A, Atias N, Ruppin E, Sharan R. Author information: (1)The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv, Israel. Understanding drugs and their modes of action is a fundamental challenge in systems medicine. Key to addressing this challenge is the elucidation of drug targets, an important step in the search for new drugs or novel targets for existing drugs. Incorporating multiple biological information sources is of essence for improving the accuracy of drug target prediction. In this article, we introduce a novel framework--Similarity-based Inference of drug-TARgets (SITAR)--for incorporating multiple drug-drug and gene-gene similarity measures for drug target prediction. The framework consists of a new scoring scheme for drug-gene associations based on a given pair of drug-drug and gene-gene similarity measures, combined with a logistic regression component that integrates the scores of multiple measures to yield the final association score. We apply our framework to predict targets for hundreds of drugs using both commonly used and novel drug-drug and gene-gene similarity measures and compare our results to existing state of the art methods, markedly outperforming them. We then employ our framework to make novel target predictions for hundreds of drugs; we validate these predictions via curated databases that were not used in the learning stage. Our framework provides an extensible platform for incorporating additional emerging similarity measures among drugs and genes. Supplementary Material is available at www.liebertonline.com/cmb. DOI: 10.1089/cmb.2010.0213 PMID: 21314453 [Indexed for MEDLINE] 95. Mol Biosyst. 2009 Sep;5(9):1051-7. doi: 10.1039/b905821b. Epub 2009 Jul 8. The topology of drug-target interaction networks: implicit dependence on drug properties and target families. Mestres J(1), Gregori-Puigjané E, Valverde S, Solé RV. Author information: (1)Chemogenomics Laboratory, Research Unit on Biomedical Informatics, Institut Municipal d'Investigació Mèdica, Parc de Recerca Biomèdica, Doctor Aiguader 88, Catalonia, 08003 Barcelona, Spain. jmestres@imim.es The availability of interaction data between small molecule drugs and protein targets has increased substantially in recent years. Using seven different databases, we were able to assemble a total of 4767 unique interactions between 802 drugs and 480 targets, which means that on average every drug is currently acknowledged to interact with 6 targets. The application of network theory to the analysis of these data reveals an unexpectedly complex picture of drug-target interactions. The results confirm that the topology of drug-target networks depends implicitly on data completeness, drug properties, and target families. The implications for drug discovery are discussed. DOI: 10.1039/b905821b PMID: 19668871 [Indexed for MEDLINE] 96. Cell. 2017 Jul 13;170(2):260-272.e8. doi: 10.1016/j.cell.2017.06.030. Functional Profiling of a Plasmodium Genome Reveals an Abundance of Essential Genes. Bushell E(1), Gomes AR(1), Sanderson T(1), Anar B(1), Girling G(1), Herd C(1), Metcalf T(1), Modrzynska K(1), Schwach F(1), Martin RE(2), Mather MW(3), McFadden GI(4), Parts L(1), Rutledge GG(1), Vaidya AB(3), Wengelnik K(5), Rayner JC(6), Billker O(7). Author information: (1)Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK. (2)Research School of Biology, Australian National University, Canberra, Australia. (3)Drexel University College of Medicine, Philadelphia, PA, USA. (4)School of Biosciences, University of Melbourne, Royal Parade, Parkville, Australia. (5)DIMNP, CNRS, INSERM, University Montpellier, Montpellier, France. (6)Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK. Electronic address: julian.rayner@sanger.ac.uk. (7)Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK. Electronic address: oliver.billker@sanger.ac.uk. The genomes of malaria parasites contain many genes of unknown function. To assist drug development through the identification of essential genes and pathways, we have measured competitive growth rates in mice of 2,578 barcoded Plasmodium berghei knockout mutants, representing >50% of the genome, and created a phenotype database. At a single stage of its complex life cycle, P. berghei requires two-thirds of genes for optimal growth, the highest proportion reported from any organism and a probable consequence of functional optimization necessitated by genomic reductions during the evolution of parasitism. In contrast, extreme functional redundancy has evolved among expanded gene families operating at the parasite-host interface. The level of genetic redundancy in a single-celled organism may thus reflect the degree of environmental variation it experiences. In the case of Plasmodium parasites, this helps rationalize both the relative successes of drugs and the greater difficulty of making an effective vaccine. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved. DOI: 10.1016/j.cell.2017.06.030 PMCID: PMC5509546 PMID: 28708996 [Indexed for MEDLINE] 97. Nucleic Acids Res. 2012 Jan;40(Database issue):D1113-7. doi: 10.1093/nar/gkr912. Epub 2011 Nov 8. SuperTarget goes quantitative: update on drug-target interactions. Hecker N(1), Ahmed J, von Eichborn J, Dunkel M, Macha K, Eckert A, Gilson MK, Bourne PE, Preissner R. Author information: (1)Structural Bioinformatics Group, Institute for Physiology, Charité-University Medicine Berlin, Lindenberger Weg 80, 13125 Berlin, Germany. There are at least two good reasons for the on-going interest in drug-target interactions: first, drug-effects can only be fully understood by considering a complex network of interactions to multiple targets (so-called off-target effects) including metabolic and signaling pathways; second, it is crucial to consider drug-target-pathway relations for the identification of novel targets for drug development. To address this on-going need, we have developed a web-based data warehouse named SuperTarget, which integrates drug-related information associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. At present, the updated database contains >6000 target proteins, which are annotated with >330,000 relations to 196,000 compounds (including approved drugs); the vast majority of interactions include binding affinities and pointers to the respective literature sources. The user interface provides tools for drug screening and target similarity inclusion. A query interface enables the user to pose complex queries, for example, to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target proteins within a certain affinity range. SuperTarget is available at http://bioinformatics.charite.de/supertarget. DOI: 10.1093/nar/gkr912 PMCID: PMC3245174 PMID: 22067455 [Indexed for MEDLINE] 98. J Chem Inf Model. 2013 Dec 23;53(12):3343-51. doi: 10.1021/ci400457v. Epub 2013 Dec 10. Structural and energetic analyses of SNPs in drug targets and implications for drug therapy. Sun HY(1), Ji FQ, Fu LY, Wang ZY, Zhang HY. Author information: (1)National Key Laboratory of Crop Genetic Improvement, Center for Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University , Wuhan 430070, P.R. China. Mutations in drug targets can alter the therapeutic effects of drugs. Therefore, evaluating the effects of single-nucleotide polymorphisms (SNPs) on drug-target binding is of significant interest. This study focuses on the analysis of the structural and energy properties of SNPs in successful drug targets by using the data derived from HapMap and the Therapeutic Target Database. The results show the following: (i) Drug targets undergo strong purifying selection, and the majority (92.4%) of the SNPs are located far from the drug-binding sites (>12 Å). (ii) For SNPs near the drug-binding pocket (≤12 Å), nearly half of the drugs are weakly affected by the SNPs, and only a few drugs are significantly affected by the target mutations. These results have direct implications for population-based drug therapy and for chemical treatment of genetic diseases as well. DOI: 10.1021/ci400457v PMID: 24304102 [Indexed for MEDLINE] 99. Sci Rep. 2016 Apr 20;6:24245. doi: 10.1038/srep24245. Drug target identification using network analysis: Taking active components in Sini decoction as an example. Chen S(1), Jiang H(1), Cao Y(1), Wang Y(1), Hu Z(2), Zhu Z(1), Chai Y(1). Author information: (1)School of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai, 200433, China. (2)School of Pharmacy, University of Pittsburgh, 3501 Terrace Street, Pittsburgh, PA, 15261, USA. Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound. DOI: 10.1038/srep24245 PMCID: PMC4837341 PMID: 27095146 [Indexed for MEDLINE] 100. Nucleic Acids Res. 2006 Jan 1;34(Database issue):D668-72. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Wishart DS(1), Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J. Author information: (1)Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8. david.wishart@ualberta.ca DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug (i.e. chemical) data with comprehensive drug target (i.e. protein) information. The database contains >4100 drug entries including >800 FDA approved small molecule and biotech drugs as well as >3200 experimental drugs. Additionally, >14,000 protein or drug target sequences are linked to these drug entries. Each DrugCard entry contains >80 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data. Many data fields are hyperlinked to other databases (KEGG, PubChem, ChEBI, PDB, Swiss-Prot and GenBank) and a variety of structure viewing applets. The database is fully searchable supporting extensive text, sequence, chemical structure and relational query searches. Potential applications of DrugBank include in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. DrugBank is available at http://redpoll.pharmacy.ualberta.ca/drugbank/. DOI: 10.1093/nar/gkj067 PMCID: PMC1347430 PMID: 16381955 [Indexed for MEDLINE] 101. J Mol Graph Model. 2017 Mar;72:272-282. doi: 10.1016/j.jmgm.2016.12.019. Epub 2017 Jan 6. First protein drug target's appraisal of lead-likeness descriptors to unfold the intervening chemical space. Athar M(1), Lone MY(1), Jha PC(2). Author information: (1)CCG@CUG, School of Chemical Sciences, Central University of Gujarat, Gandhinagar 382030, Gujarat, India. (2)CCG@CUG, Centre for Applied Chemistry, Central University of Gujarat, Gandhinagar 382030, Gujarat, India. Electronic address: prakash.jha@cug.ac.in. Despite the advances in combinatorial chemistry, high throughput and virtual screening experiments, plethora of clinical studies disquiet due to lead and drug-likeness attritions. For mitigation, the knowledge of physicochemical properties are really useful for guiding and selection of compounds from libraries dictated by certain rule of thumbs. However, robust bio-technological and instrumental innovations have created exponential increase in novel compounds and databases which compelled rethinking of the evaluation procedures. Known descriptive molecular property filters proposed by Lipinski, Verber and Hann are not efficient enough to encompass long array of compounds. Moreover, these filters do not take into account the specificity of biological target. In this pursuit, we have tried to appraise eight molecular properties for two major classes of biological targets viz membrane proteins and ion channels binding ligands. These molecular properties were utilized to search for the specific attributes that can be identified as an intervening space for dictating the biological activity. Copyright © 2017 Elsevier Inc. All rights reserved. DOI: 10.1016/j.jmgm.2016.12.019 PMID: 28167312 [Indexed for MEDLINE] 102. Bioinformatics. 2012 Sep 15;28(18):2304-10. doi: 10.1093/bioinformatics/bts360. Epub 2012 Jun 23. Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization. Gönen M(1). Author information: (1)Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University School of Science, FI-00076 Aalto, Espoo, Finland. mehmet.gonen@aalto.fi MOTIVATION: Identifying interactions between drug compounds and target proteins has a great practical importance in the drug discovery process for known diseases. Existing databases contain very few experimentally validated drug-target interactions and formulating successful computational methods for predicting interactions remains challenging. RESULTS: In this study, we consider four different drug-target interaction networks from humans involving enzymes, ion channels, G-protein-coupled receptors and nuclear receptors. We then propose a novel Bayesian formulation that combines dimensionality reduction, matrix factorization and binary classification for predicting drug-target interaction networks using only chemical similarity between drug compounds and genomic similarity between target proteins. The novelty of our approach comes from the joint Bayesian formulation of projecting drug compounds and target proteins into a unified subspace using the similarities and estimating the interaction network in that subspace. We propose using a variational approximation in order to obtain an efficient inference scheme and give its detailed derivations. Finally, we demonstrate the performance of our proposed method in three different scenarios: (i) exploratory data analysis using low-dimensional projections, (ii) predicting interactions for the out-of-sample drug compounds and (iii) predicting unknown interactions of the given network. AVAILABILITY: Software and Supplementary Material are available at http://users.ics.aalto.fi/gonen/kbmf2k. CONTACT: mehmet.gonen@aalto.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. DOI: 10.1093/bioinformatics/bts360 PMID: 22730431 [Indexed for MEDLINE] 103. Protoplasma. 2011 Oct;248(4):799-804. doi: 10.1007/s00709-010-0255-0. Epub 2010 Dec 21. In silico prediction of drug targets in Vibrio cholerae. Katara P(1), Grover A, Kuntal H, Sharma V. Author information: (1)Department of Bioscience and Biotechnology, Banasthali University, Banasthali, 304022, India. pmkatara@gmail.com Identification of potential drug targets is the first step in the process of modern drug discovery, subjected to their validation and drug development. Whole genome sequences of a number of organisms allow prediction of potential drug targets using sequence comparison approaches. Here, we present a subtractive approach exploiting the knowledge of global gene expression along with sequence comparisons to predict the potential drug targets more efficiently. Based on the knowledge of 155 known virulence and their coexpressed genes mined from microarray database in the public domain, 357 coexpressed probable virulence genes for Vibrio cholerae were predicted. Based on screening of Database of Essential Genes using blastn, a total of 102 genes out of these 357 were enlisted as vitally essential genes, and hence good putative drug targets. As the effective drug target is a protein which is only present in the pathogen, similarity search of these 102 essential genes against human genome sequence led to subtraction of 66 genes, thus leaving behind a subset of 36 genes whose products have been called as potential drug targets. The gene ontology analysis using Blast2GO of these 36 genes revealed their roles in important metabolic pathways of V. cholerae or on the surface of the pathogen. Thus, we propose that the products of these genes be evaluated as target sites of drugs against V. cholerae in future investigations. DOI: 10.1007/s00709-010-0255-0 PMID: 21174131 [Indexed for MEDLINE] 104. PLoS Comput Biol. 2011 Sep;7(9):e1002139. doi: 10.1371/journal.pcbi.1002139. Epub 2011 Sep 1. A computational approach to finding novel targets for existing drugs. Li YY(1), An J, Jones SJ. Author information: (1)Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia, Canada. yli@bcgsc.ca Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects. DOI: 10.1371/journal.pcbi.1002139 PMCID: PMC3164726 PMID: 21909252 [Indexed for MEDLINE] 105. Front Cell Infect Microbiol. 2018 Dec 7;8:424. doi: 10.3389/fcimb.2018.00424. eCollection 2018. MDAD: A Special Resource for Microbe-Drug Associations. Sun YZ(1), Zhang DH(2), Cai SB(1), Ming Z(1), Li JQ(1), Chen X(2). Author information: (1)Department of Computer Science and Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. (2)School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China. The human-associated microbiota is diverse and complex. It takes an essential role in human health and behavior and is closely related to the occurrence and development of disease. Although the diversity and distribution of microbial communities have been widely studied, little is known about the function and dynamics of microbes in the human body or the complex mechanisms of interaction between them and drugs, which are important for drug discovery and design. A high-quality comprehensive microbe and drug association database will be extremely beneficial to explore the relationship between them. In this article, we developed the Microbe-Drug Association Database (MDAD), a collection of clinically or experimentally supported associations between microbes and drugs, collecting 5,055 entries that include 1,388 drugs and 180 microbes from multiple drug databases and related publications. Moreover, we provided detailed annotations for each record, including the molecular form of drugs or hyperlinks from DrugBank, microbe target information from Uniprot and the original reference links. We hope MDAD will be a useful resource for deeper understanding of microbe and drug interactions and will also be beneficial to drug design, disease therapy and human health. DOI: 10.3389/fcimb.2018.00424 PMCID: PMC6292923 PMID: 30581775 106. Sci Rep. 2016 Sep 7;6:32745. doi: 10.1038/srep32745. Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing. Udrescu L(1), Sbârcea L(1), Topîrceanu A(2), Iovanovici A(2), Kurunczi L(3), Bogdan P(4), Udrescu M(2). Author information: (1)"Victor Babeş" University of Medicine and Pharmacy Timişoara, Faculty of Pharmacy, Timişoara, 300041, Romania. (2)University Politehnica of Timişoara, Department of Computer and Information Technology, Timişoara, 300223, Romania. (3)Institute of Chemistry Timişoara of the Romanian Academy, Timişoara, 300223, Romania. (4)University of Southern California, Ming Hsieh Department of Electrical Engineering, Los Angeles, CA 90089-2563, USA. Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications. DOI: 10.1038/srep32745 PMCID: PMC5013446 PMID: 27599720 [Indexed for MEDLINE] 107. Expert Opin Drug Discov. 2009 Aug;4(8):857-72. doi: 10.1517/17460440903049290. Drug target central. Harland L(1), Gaulton A. Author information: (1)Pfizer Regenerative Medicine, Granta Park, Cambridge, UK +44 1304641575 ; Lee.Harland@pfizer.com. BACKGROUND: One of the primary pillars of drug discovery is the drug target, its relationship to both the drugs designed against it and the biological processes in which it is involved. Here we review the informatics approaches required to build a complete catalogue of known drug targets. OBJECTIVE: Using Pfizer's internal target database as a narrative, we review the steps involved in the construction of an integrated, enterprise target-informatics system. We consider how compiling the drug target universe requires integration across several resources such as competitor intelligence and pharmacological activity databases, as well as input from techniques such as text-mining. In particular, we address data standards and the complexities of representing targets in a structured ontology as well as opportunities for future development. CONCLUSION: Drug target-orientated databases address important areas of drug discovery such as chemogenomics, drug/candidate repurposing and business intelligence. As research in industry and academia drives continued expansion of the druggable genome, it is crucial that such systems be maintained to provide an accurate picture of the landscape. This power of this information stretches beyond drug discovery and into the wider scientific community where small molecule tool compounds can enable the dissection of complex cellular pathways. DOI: 10.1517/17460440903049290 PMID: 23496271 108. PLoS One. 2013;8(3):e59288. doi: 10.1371/journal.pone.0059288. Epub 2013 Mar 26. A systematic in silico search for target similarity identifies several approved drugs with potential activity against the Plasmodium falciparum apicoplast. Bispo NA(1), Culleton R, Silva LA, Cravo P. Author information: (1)Instituto de Patologia Tropical e Saúde Pública/Universidade Federal de Goiás/Goiânia, Brazil. Erratum in PLoS One. 2013;8(6). doi:10.1371/annotation/0bbd3579-5212-4dcf-a5ef-dd3d8e26f287. Most of the drugs in use against Plasmodium falciparum share similar modes of action and, consequently, there is a need to identify alternative potential drug targets. Here, we focus on the apicoplast, a malarial plastid-like organelle of algal source which evolved through secondary endosymbiosis. We undertake a systematic in silico target-based identification approach for detecting drugs already approved for clinical use in humans that may be able to interfere with the P. falciparum apicoplast. The P. falciparum genome database GeneDB was used to compile a list of ≈600 proteins containing apicoplast signal peptides. Each of these proteins was treated as a potential drug target and its predicted sequence was used to interrogate three different freely available databases (Therapeutic Target Database, DrugBank and STITCH3.1) that provide synoptic data on drugs and their primary or putative drug targets. We were able to identify several drugs that are expected to interact with forty-seven (47) peptides predicted to be involved in the biology of the P. falciparum apicoplast. Fifteen (15) of these putative targets are predicted to have affinity to drugs that are already approved for clinical use but have never been evaluated against malaria parasites. We suggest that some of these drugs should be experimentally tested and/or serve as leads for engineering new antimalarials. DOI: 10.1371/journal.pone.0059288 PMCID: PMC3608639 PMID: 23555651 [Indexed for MEDLINE] 109. J Am Med Inform Assoc. 2016 Jul;23(4):741-9. doi: 10.1093/jamia/ocw004. Epub 2016 Apr 23. A bioinformatics approach for precision medicine off-label drug drug selection among triple negative breast cancer patients. Cheng L(1), Schneider BP(2), Li L(3). Author information: (1)Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA. (2)Division of Hematology and Oncology, Department of Medicine, School of Medicine, Indiana University, Indianapolis, IN 46202, USA Indiana Institute of Personalized Medicine, School of Medicine, Indiana University, Indianapolis, IN 46202, USA. (3)Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA Indiana Institute of Personalized Medicine, School of Medicine, Indiana University, Indianapolis, IN 46202, USA lali@iu.edu. BACKGROUND: Cancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment. OBJECTIVE: The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data. METHODS: In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowledge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selection in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression. RESULTS: By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. Sixty-five drug targets are identified as TNBC treatment targets and 85 candidate drugs are recommended. Many existing TNBC candidate targets, such as Poly (ADP-Ribose) Polymerase 1 (PARP1), Cell division protein kinase 6 (CDK6), epidermal growth factor receptor, etc., were identified. On the other hand, we found some additional targets that are not yet fully investigated in the TNBC, such as Gamma-Glutamyl Hydrolase (GGH), Thymidylate Synthetase (TYMS), Protein Tyrosine Kinase 6 (PTK6), Topoisomerase (DNA) I, Mitochondrial (TOP1MT), Smoothened, Frizzled Class Receptor (SMO), etc. Our additional analysis of target and drug selection strategy is also fully supported by the drug screening data on TNBC cell lines in the Cancer Cell Line Encyclopedia. CONCLUSIONS: The proposed bioinformatics approach lays a foundation for cancer precision medicine. It supplies much needed knowledge base for the off-label cancer drug usage in clinics. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com. DOI: 10.1093/jamia/ocw004 PMCID: PMC4926742 PMID: 27107440 [Indexed for MEDLINE] 110. Bioinformation. 2012;8(14):664-72. doi: 10.6026/97320630008664. Epub 2012 Jul 21. Potential therapeutic drug target identification in Community Acquired-Methicillin Resistant Staphylococcus aureus (CA-MRSA) using computational analysis. Yadav PK(1), Singh G, Singh S, Gautam B, Saad EI. Author information: (1)Department of Computational Biology & Bioinformatics, JSBB, SHIATS (DU), Allahabad-211007, India. The emergence of multidrug-resistant strain of community-acquired methicillin resistant Staphylococcus aureus (CA-MRSA) strain has highlighted the urgent need for the alternative and effective therapeutic approach to combat the menace of this nosocomial pathogen. In the present work novel potential therapeutic drug targets have been identified through the metabolic pathways analysis. All the gene products involved in different metabolic pathways of CA-MRSA in KEGG database were searched against the proteome of Homo sapiens using the BLASTp program and the threshold of E-value was set to as 0.001. After database searching, 152 putative targets were identified. Among all 152 putative targets, 39 genes encoding for putative targets were identified as the essential genes from the DEG database which are indispensable for the survival of CA-MRSA. After extensive literature review, 7 targets were identified as potential therapeutic drug target. These targets are Fructose-bisphosphate aldolase, Phosphoglyceromutase, Purine nucleoside phosphorylase, Uridylate kinase, Tryptophan synthase subunit beta, Acetate kinase and UDP-N-acetylglucosamine 1-carboxyvinyltransferase. Except Uridylate kinase all the identified targets were involved in more than one metabolic pathways of CA-MRSA which underlines the importance of drug targets. These potential therapeutic drug targets can be exploited for the discovery of novel inhibitors for CA-MRSA using the structure based drug design (SBDD) strategy. DOI: 10.6026/97320630008664 PMCID: PMC3449366 PMID: 23055607 111. BMC Bioinformatics. 2012 Jun 11;13 Suppl 9:S7. doi: 10.1186/1471-2105-13-S9-S7. DTome: a web-based tool for drug-target interactome construction. Sun J(1), Wu Y, Xu H, Zhao Z. Author information: (1)Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA. BACKGROUND: Understanding drug bioactivities is crucial for early-stage drug discovery, toxicology studies and clinical trials. Network pharmacology is a promising approach to better understand the molecular mechanisms of drug bioactivities. With a dramatic increase of rich data sources that document drugs' structural, chemical, and biological activities, it is necessary to develop an automated tool to construct a drug-target network for candidate drugs, thus facilitating the drug discovery process. RESULTS: We designed a computational workflow to construct drug-target networks from different knowledge bases including DrugBank, PharmGKB, and the PINA database. To automatically implement the workflow, we created a web-based tool called DTome (Drug-Target interactome tool), which is comprised of a database schema and a user-friendly web interface. The DTome tool utilizes web-based queries to search candidate drugs and then construct a DTome network by extracting and integrating four types of interactions. The four types are adverse drug interactions, drug-target interactions, drug-gene associations, and target-/gene-protein interactions. Additionally, we provided a detailed network analysis and visualization process to illustrate how to analyze and interpret the DTome network. The DTome tool is publicly available at http://bioinfo.mc.vanderbilt.edu/DTome. CONCLUSIONS: As demonstrated with the antipsychotic drug clozapine, the DTome tool was effective and promising for the investigation of relationships among drugs, adverse interaction drugs, drug primary targets, drug-associated genes, and proteins directly interacting with targets or genes. The resultant DTome network provides researchers with direct insights into their interest drug(s), such as the molecular mechanisms of drug actions. We believe such a tool can facilitate identification of drug targets and drug adverse interactions. DOI: 10.1186/1471-2105-13-S9-S7 PMCID: PMC3372450 PMID: 22901092 [Indexed for MEDLINE] 112. PLoS One. 2018 Jun 8;13(6):e0198170. doi: 10.1371/journal.pone.0198170. eCollection 2018. Pathway based therapeutic targets identification and development of an interactive database CampyNIBase of Campylobacter jejuni RM1221 through non-redundant protein dataset. Hossain MU(1), Omar TM(2), Alam I(3), Das KC(4), Mohiuddin AKM(2), Keya CA(5), Salimullah M(4). Author information: (1)Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, Bangladesh. (2)Department of Biotechnology and Genetic Engineering, Life Science Faculty, Mawlana Bhashani Science and Technology University, Santosh, Tangail, Bangladesh. (3)Plant Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, Bangladesh. (4)Molecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, Bangladesh. (5)Department of Biochemistry and Microbiology, North south University, Bashundhara, Dhaka, Bangladesh. The bacterial species Campylobacter jejuni RM1221 (CjR) is the primary cause of campylobacteriosis which poses a global threat for human health. Over the years the efficacy of antibiotic treatment is becoming more fruitless due to the development of multiple drug resistant strains. Therefore, identification of new drug targets is a valuable tool for the development of new treatments for affected patients and can be obtained by targeting essential protein(s) of CjR. We conducted this in silico study in order to identify therapeutic targets by subtractive CjR proteome analysis. The most important proteins of the CjR proteome, which includes chokepoint enzymes, plasmid, virulence and antibiotic resistant proteins were annotated and subjected to subtractive analyses to filter out the CjR essential proteins from duplicate or human homologous proteins. Through the subtractive and characterization analysis we have identified 38 eligible therapeutic targets including 1 potential vaccine target. Also, 12 potential targets were found in interactive network, 5 targets to be dealt with FDA approved drugs and one pathway as potential pathway based drug target. In addition, a comprehensive database 'CampyNIBase' has also been developed. Besides the results of this study, the database is enriched with other information such as 3D models of the identified targets, experimental structures and Expressed Sequence Tag (EST) sequences. This study, including the database might be exploited for future research and the identification of effective therapeutics against campylobacteriosis. URL: (http://nib.portal.gov.bd/site/page/4516e965-8935-4129-8c3f-df95e754c562#Banner). DOI: 10.1371/journal.pone.0198170 PMCID: PMC5993290 PMID: 29883471 [Indexed for MEDLINE] Conflict of interest statement: The authors have declared that no competing interests exist. 113. Pharmacogenomics J. 2017 Mar;17(2):128-136. doi: 10.1038/tpj.2015.97. Epub 2016 Jan 26. Impact of germline and somatic missense variations on drug binding sites. Yan C(1), Pattabiraman N(2), Goecks J(3), Lam P(1), Nayak A(1), Pan Y(1), Torcivia-Rodriguez J(1), Voskanian A(1), Wan Q(1), Mazumder R(1)(4). Author information: (1)Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC, USA. (2)MolBox LLC, Silver Spring, MD, USA. (3)The Computational Biology Institute, George Washington University, Ashburn, VA, USA. (4)McCormick Genomic and Proteomic Center, George Washington University, Washington, DC, USA. Advancements in next-generation sequencing (NGS) technologies are generating a vast amount of data. This exacerbates the current challenge of translating NGS data into actionable clinical interpretations. We have comprehensively combined germline and somatic nonsynonymous single-nucleotide variations (nsSNVs) that affect drug binding sites in order to investigate their prevalence. The integrated data thus generated in conjunction with exome or whole-genome sequencing can be used to identify patients who may not respond to a specific drug because of alterations in drug binding efficacy due to nsSNVs in the target protein's gene. To identify the nsSNVs that may affect drug binding, protein-drug complex structures were retrieved from Protein Data Bank (PDB) followed by identification of amino acids in the protein-drug binding sites using an occluded surface method. Then, the germline and somatic mutations were mapped to these amino acids to identify which of these alter protein-drug binding sites. Using this method we identified 12 993 amino acid-drug binding sites across 253 unique proteins bound to 235 unique drugs. The integration of amino acid-drug binding sites data with both germline and somatic nsSNVs data sets revealed 3133 nsSNVs affecting amino acid-drug binding sites. In addition, a comprehensive drug target discovery was conducted based on protein structure similarity and conservation of amino acid-drug binding sites. Using this method, 81 paralogs were identified that could serve as alternative drug targets. In addition, non-human mammalian proteins bound to drugs were used to identify 142 homologs in humans that can potentially bind to drugs. In the current protein-drug pairs that contain somatic mutations within their binding site, we identified 85 proteins with significant differential gene expression changes associated with specific cancer types. Information on protein-drug binding predicted drug target proteins and prevalence of both somatic and germline nsSNVs that disrupt these binding sites can provide valuable knowledge for personalized medicine treatment. A web portal is available where nsSNVs from individual patient can be checked by scanning against DrugVar to determine whether any of the SNVs affect the binding of any drug in the database. DOI: 10.1038/tpj.2015.97 PMCID: PMC5380835 PMID: 26810135 [Indexed for MEDLINE] 114. Protein J. 2018 Oct;37(5):444-453. doi: 10.1007/s10930-018-9790-x. Multifunctional Proteins: Involvement in Human Diseases and Targets of Current Drugs. Franco-Serrano L(1), Huerta M(1), Hernández S(1), Cedano J(2), Perez-Pons J(1), Piñol J(1), Mozo-Villarias A(3), Amela I(1), Querol E(4). Author information: (1)Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Barcelona, Spain. (2)Laboratorio de Inmunología, Universidad de la República Regional Norte-Salto, Rivera 1350, 50000, Salto, Uruguay. (3)Departament de Medicina Experimental and Institut de Recerca Biomèdica, Universitat de Lleida, 25198, Lleida, Spain. (4)Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Barcelona, Spain. enric.querol@uab.cat. Multifunctionality or multitasking is the capability of some proteins to execute two or more biochemical functions. The objective of this work is to explore the relationship between multifunctional proteins, human diseases and drug targeting. The analysis of the proportion of multitasking proteins from the MultitaskProtDB-II database shows that 78% of the proteins analyzed are involved in human diseases. This percentage is much higher than the 17.9% found in human proteins in general. A similar analysis using drug target databases shows that 48% of these analyzed human multitasking proteins are targets of current drugs, while only 9.8% of the human proteins present in UniProt are specified as drug targets. In almost 50% of these proteins, both the canonical and moonlighting functions are related to the molecular basis of the disease. A procedure to identify multifunctional proteins from disease databases and a method to structurally map the canonical and moonlighting functions of the protein have also been proposed here. Both of the previous percentages suggest that multitasking is not a rare phenomenon in proteins causing human diseases, and that their detailed study might explain some collateral drug effects. DOI: 10.1007/s10930-018-9790-x PMCID: PMC6132618 PMID: 30123928 [Indexed for MEDLINE] 115. Pak J Pharm Sci. 2018 Mar;31(2):485-489. Drug-target network of taxanes revealed by data mining. Chen SJ(1), Wei-Cai -(1), Li ZH(1). Author information: (1)Department of Traditional Chinese Medicine, Zhejiang Pharmaceutical College, Ningbo, China. Taxanes, mainly group paclitaxel and docetaxel, are amongst the most promising anticancer agents that are widely used for a variety of tumor types. It is a great challenge to gain a quick overview of the molecular mechanisms of taxanes, owning to the massive amounts of data have been produced. Network pharmacology will be a powerful tool to uncover the drug-targets network of taxanes. In this study, drug-targets network of paclitaxel and docetaxel were constructed via STITCH by database mining, and its topological parameters and important nodes were analyzed. All will provide a systematic understanding for molecular mechanisms of pacltaxel and docetaxel in a quick and visual way. PMID: 29618439 116. Anal Chim Acta. 2016 Feb 25;909:41-50. doi: 10.1016/j.aca.2016.01.014. Epub 2016 Jan 14. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique. Hao M(1), Wang Y(2), Bryant SH(3). Author information: (1)National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. (2)National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. Electronic address: ywang@ncbi.nlm.nih.gov. (3)National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. Electronic address: bryant@ncbi.nlm.nih.gov. Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision-recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. Published by Elsevier B.V. DOI: 10.1016/j.aca.2016.01.014 PMCID: PMC4744621 [Available on 2017-02-25] PMID: 26851083 [Indexed for MEDLINE] 117. Nucleic Acids Res. 2011 Jan;39(Database issue):D1035-41. doi: 10.1093/nar/gkq1126. Epub 2010 Nov 8. DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Knox C(1), Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS. Author information: (1)Department of Computing Science, University of Alberta, Edmonton, AB, Canada. DrugBank (http://www.drugbank.ca) is a richly annotated database of drug and drug target information. It contains extensive data on the nomenclature, ontology, chemistry, structure, function, action, pharmacology, pharmacokinetics, metabolism and pharmaceutical properties of both small molecule and large molecule (biotech) drugs. It also contains comprehensive information on the target diseases, proteins, genes and organisms on which these drugs act. First released in 2006, DrugBank has become widely used by pharmacists, medicinal chemists, pharmaceutical researchers, clinicians, educators and the general public. Since its last update in 2008, DrugBank has been greatly expanded through the addition of new drugs, new targets and the inclusion of more than 40 new data fields per drug entry (a 40% increase in data 'depth'). These data field additions include illustrated drug-action pathways, drug transporter data, drug metabolite data, pharmacogenomic data, adverse drug response data, ADMET data, pharmacokinetic data, computed property data and chemical classification data. DrugBank 3.0 also offers expanded database links, improved search tools for drug-drug and food-drug interaction, new resources for querying and viewing drug pathways and hundreds of new drug entries with detailed patent, pricing and manufacturer data. These additions have been complemented by enhancements to the quality and quantity of existing data, particularly with regard to drug target, drug description and drug action data. DrugBank 3.0 represents the result of 2 years of manual annotation work aimed at making the database much more useful for a wide range of 'omics' (i.e. pharmacogenomic, pharmacoproteomic, pharmacometabolomic and even pharmacoeconomic) applications. DOI: 10.1093/nar/gkq1126 PMCID: PMC3013709 PMID: 21059682 [Indexed for MEDLINE] 118. PLoS One. 2016 Dec 22;11(12):e0168812. doi: 10.1371/journal.pone.0168812. eCollection 2016. Drug Repositioning for Alzheimer's Disease Based on Systematic 'omics' Data Mining. Zhang M(1), Schmitt-Ulms G(1), Sato C(1), Xi Z(1), Zhang Y(1), Zhou Y(1), St George-Hyslop P(1)(2)(3), Rogaeva E(1)(2). Author information: (1)Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada. (2)Department of Medicine, Division of Neurology, University of Toronto, Toronto, Ontario, Canada. (3)Cambridge Institute for Medical Research, and the Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom. Traditional drug development for Alzheimer's disease (AD) is costly, time consuming and burdened by a very low success rate. An alternative strategy is drug repositioning, redirecting existing drugs for another disease. The large amount of biological data accumulated to date warrants a comprehensive investigation to better understand AD pathogenesis and facilitate the process of anti-AD drug repositioning. Hence, we generated a list of anti-AD protein targets by analyzing the most recent publically available 'omics' data, including genomics, epigenomics, proteomics and metabolomics data. The information related to AD pathogenesis was obtained from the OMIM and PubMed databases. Drug-target data was extracted from the DrugBank and Therapeutic Target Database. We generated a list of 524 AD-related proteins, 18 of which are targets for 75 existing drugs-novel candidates for repurposing as anti-AD treatments. We developed a ranking algorithm to prioritize the anti-AD targets, which revealed CD33 and MIF as the strongest candidates with seven existing drugs. We also found 7 drugs inhibiting a known anti-AD target (acetylcholinesterase) that may be repurposed for treating the cognitive symptoms of AD. The CAD protein and 8 proteins implicated by two 'omics' approaches (ABCA7, APOE, BIN1, PICALM, CELF1, INPP5D, SPON1, and SOD3) might also be promising targets for anti-AD drug development. Our systematic 'omics' mining suggested drugs with novel anti-AD indications, including drugs modulating the immune system or reducing neuroinflammation that are particularly promising for AD intervention. Furthermore, the list of 524 AD-related proteins could be useful not only as potential anti-AD targets but also considered for AD biomarker development. DOI: 10.1371/journal.pone.0168812 PMCID: PMC5179106 PMID: 28005991 [Indexed for MEDLINE] Conflict of interest statement: The authors declare no competing financial interests. 119. Curr Drug Metab. 2017;18(6):556-565. doi: 10.2174/1389200218666170316093301. Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools. Issa NT(1), Wathieu H(1), Ojo A(2), Byers SW(1), Dakshanamurthy S(1). Author information: (1)Georgetown-Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Washington DC, 20057, United States. (2)College of Pharmacy, Howard University, Washington, DC 20059, United States. BACKGROUND: While establishing efficacy in translational models and humans through clinically-relevant endpoints for disease is of great interest, assessing the potential toxicity of a putative therapeutic drug is critical. Toxicological assessments in the pre-clinical discovery phase help to avoid future failure in the clinical phases of drug development. Many in vitro assays exist to aid in modular toxicological assessment, such as hepatotoxicity and genotoxicity. While these methods have provided tremendous insight into human toxicity by investigational new drugs, they are expensive, require substantial resources, and do not account for pharmacogenomics as well as critical ADME properties. Computational tools can fill this niche in toxicology if in silico models are accurate in relating drug molecular properties to toxicological endpoints as well as reliable in predicting important drug-target interactions that mediate known adverse events or adverse outcome pathways (AOPs). METHODS: We undertook an unstructured search of multiple bibliographic databases for peer-reviewed literature regarding computational methods in predictive toxicology for in silico drug discovery. As this review paper is meant to serve as a survey of available methods for the interested reader, no focused criteria were applied. Literature chosen was based on the writers' expertise and intent in communicating important aspects of in silico toxicology to the interested reader. CONCLUSION: This review provides a purview of computational methods of pre-clinical toxicologic assessments for novel small molecule drugs that may be of use for novice and experienced investigators as well as academic and commercial drug discovery entities. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1389200218666170316093301 PMCID: PMC5892202 PMID: 28302026 [Indexed for MEDLINE] 120. Bioinformatics. 2016 Jan 15;32(2):226-34. doi: 10.1093/bioinformatics/btv528. Epub 2015 Sep 28. Computational probing protein-protein interactions targeting small molecules. Wang YC(1), Chen SL(1), Deng NY(2), Wang Y(3). Author information: (1)Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China. (2)College of Science, China Agricultural University, Beijing 100083, China and. (3)National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. MOTIVATION: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein-protein interaction (PPI) is promising to improve the specificity with fewer adverse side-effects. Also it greatly broadens the drug target search space, which makes the drug target discovery difficult. Computational methods are highly desired to efficiently provide candidates for further experiments and hold the promise to greatly accelerate the discovery of novel drug targets. RESULTS: Here, we propose a machine learning method to predict PPI targets in a genomic-wide scale. Specifically, we develop a computational method, named as PrePPItar, to Predict PPIs as drug targets by uncovering the potential associations between drugs and PPIs. First, we survey the databases and manually construct a gold-standard positive dataset for drug and PPI interactions. This effort leads to a dataset with 227 associations among 63 PPIs and 113 FDA-approved drugs and allows us to build models to learn the association rules from the data. Second, we characterize drugs by profiling in chemical structure, drug ATC-code annotation, and side-effect space and represent PPI similarity by a symmetrical S-kernel based on protein amino acid sequence. Then the drugs and PPIs are correlated by Kronecker product kernel. Finally, a support vector machine (SVM), is trained to predict novel associations between drugs and PPIs. We validate our PrePPItar method on the well-established gold-standard dataset by cross-validation. We find that all chemical structure, drug ATC-code, and side-effect information are predictive for PPI target. Moreover, we can increase the PPI target prediction coverage by integrating multiple data sources. Follow-up database search and pathway analysis indicate that our new predictions are worthy of future experimental validation. CONCLUSION: In conclusion, PrePPItar can serve as a useful tool for PPI target discovery and provides a general heterogeneous data integrative framework. AVAILABILITY AND IMPLEMENTATION: PrePPItar is available at http://doc.aporc.org/wiki/PrePPItar. CONTACT: ycwang@nwipb.cas.cn or ywang@amss.ac.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. DOI: 10.1093/bioinformatics/btv528 PMID: 26415726 [Indexed for MEDLINE] 121. Nucleic Acids Res. 2014 Jan;42(Database issue):D1091-7. doi: 10.1093/nar/gkt1068. Epub 2013 Nov 6. DrugBank 4.0: shedding new light on drug metabolism. Law V(1), Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V, Tang A, Gabriel G, Ly C, Adamjee S, Dame ZT, Han B, Zhou Y, Wishart DS. Author information: (1)Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Department Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9. DrugBank (http://www.drugbank.ca) is a comprehensive online database containing extensive biochemical and pharmacological information about drugs, their mechanisms and their targets. Since it was first described in 2006, DrugBank has rapidly evolved, both in response to user requests and in response to changing trends in drug research and development. Previous versions of DrugBank have been widely used to facilitate drug and in silico drug target discovery. The latest update, DrugBank 4.0, has been further expanded to contain data on drug metabolism, absorption, distribution, metabolism, excretion and toxicity (ADMET) and other kinds of quantitative structure activity relationships (QSAR) information. These enhancements are intended to facilitate research in xenobiotic metabolism (both prediction and characterization), pharmacokinetics, pharmacodynamics and drug design/discovery. For this release, >1200 drug metabolites (including their structures, names, activity, abundance and other detailed data) have been added along with >1300 drug metabolism reactions (including metabolizing enzymes and reaction types) and dozens of drug metabolism pathways. Another 30 predicted or measured ADMET parameters have been added to each DrugCard, bringing the average number of quantitative ADMET values for Food and Drug Administration-approved drugs close to 40. Referential nuclear magnetic resonance and MS spectra have been added for almost 400 drugs as well as spectral and mass matching tools to facilitate compound identification. This expanded collection of drug information is complemented by a number of new or improved search tools, including one that provides a simple analyses of drug-target, -enzyme and -transporter associations to provide insight on drug-drug interactions. DOI: 10.1093/nar/gkt1068 PMCID: PMC3965102 PMID: 24203711 [Indexed for MEDLINE] 122. Genomics Inform. 2017 Mar;15(1):19-27. doi: 10.5808/GI.2017.15.1.19. Epub 2017 Mar 29. Use of Graph Database for the Integration of Heterogeneous Biological Data. Yoon BH(1)(2), Kim SK(1), Kim SY(1)(2). Author information: (1)Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea. (2)Department of Functional Genomics, University of Science and Technology (UST), Daejeon 34113, Korea. Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data. DOI: 10.5808/GI.2017.15.1.19 PMCID: PMC5389944 PMID: 28416946 123. Nucleic Acids Res. 2008 Jan;36(Database issue):D919-22. Epub 2007 Oct 16. SuperTarget and Matador: resources for exploring drug-target relationships. Günther S(1), Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, Ahmed J, Urdiales EG, Gewiess A, Jensen LJ, Schneider R, Skoblo R, Russell RB, Bourne PE, Bork P, Preissner R. Author information: (1)Structural Bioinformatics Group, Institute of Molecular Biology and Bioinformatics, Charité-University Medicine Berlin, Arnimallee 22, 14195 Berlin, Germany. The molecular basis of drug action is often not well understood. This is partly because the very abundant and diverse information generated in the past decades on drugs is hidden in millions of medical articles or textbooks. Therefore, we developed a one-stop data warehouse, SuperTarget that integrates drug-related information about medical indication areas, adverse drug effects, drug metabolization, pathways and Gene Ontology terms of the target proteins. An easy-to-use query interface enables the user to pose complex queries, for example to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target the same protein but are metabolized by different enzymes. Furthermore, we provide tools for 2D drug screening and sequence comparison of the targets. The database contains more than 2500 target proteins, which are annotated with about 7300 relations to 1500 drugs; the vast majority of entries have pointers to the respective literature source. A subset of these drugs has been annotated with additional binding information and indirect interactions and is available as a separate resource called Matador. SuperTarget and Matador are available at http://insilico.charite.de/supertarget and http://matador.embl.de. DOI: 10.1093/nar/gkm862 PMCID: PMC2238858 PMID: 17942422 [Indexed for MEDLINE] 124. Expert Opin Drug Discov. 2009 Nov;4(11):1177-89. doi: 10.1517/17460440903322234. Epub 2009 Oct 13. Building a drug-target network and its applications. Lee S(1), Park K, Kim D. Author information: (1)KAIST, Department of Bio and Brain Engineering, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701 Korea, Republic of Korea +82 42 350 4317 ; +82 42 350 4310 ; kds@kaist.ac.kr. BACKGROUND: One of the most recent and important developments in drug discovery is a new drug development approach of building and analyzing networks that contain relationships among drugs and targets, diseases, genes and other components. These networks and their integrations provide useful information for finding new targets as well as new drugs. OBJECTIVE: This review article aims to review recent developments in various types of networks and suggest the future direction of these network studies for drug discovery. METHODS: Databases and networks are integrated into a more complete network to better present the relationships among drugs, targets, genes, phenotypes and diseases. After discussing the limitations and obstacles of the recent research, we suggest several strategies to build a successful and practical drug-target network. RESULTS/CONCLUSION: A useful, integrated network can be built from various databases and networks by resolving several issues, such as limited coverage and inconsistency. This integrated network can be completed by the prediction of missing links, biological network comparison and drug target identification. Possible applications are multi-target drug development, drug repurposing, estimation of drug effect on target perturbations in the whole system and extraction of the suitable purpose of the drug-target sub-network. DOI: 10.1517/17460440903322234 PMID: 23480435 125. Gene. 2015 Nov 15;573(1):153-9. doi: 10.1016/j.gene.2015.07.033. Epub 2015 Jul 15. Role of the anti-glioma drug AT13148 in the inhibition of Notch signaling pathway. Min W(1), Li Y(1), Zhang Y(1), Dai D(1), Cao Y(1), Yue Z(2), Liu J(1). Author information: (1)Department of Neurosurgery, Changhai Hospital, Second Military Medical University, Shanghai 200433, China. (2)Department of Neurosurgery, Changhai Hospital, Second Military Medical University, Shanghai 200433, China. Electronic address: zjianyuezs@163.com. OBJECTIVE: To investigate the drug targets related to Notch signaling pathway for glioma treatment. METHODS: Gene expression profiles GSE44561, GSE48079 and GSE22772GSE48079GSE22772 of glioma cells samples with activated Notch signaling pathway and control samples were downloaded from Gene Expression Omnibus database to screen the differentially expressed genes (DEGs) using limma package. GO (Gene Oncology) function and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses were conducted using DAVID tools to predict the underlying function of these DEGs. Sequentially, drug target genes recorded in DrugBank database were collected and matched with the selected DEGs to identify the potential drug targets for glioma. Further, these targets were verified by the screened DEGs in the anti-glioma drug (AT13148) treated samples of microarray data of GSE38008. RESULTS: A total of 75,645,497 DEGs were respectively identified in GSE44561, GSE48079 and GSE22772GSE48079GSE22772 datasets and these DEGs could well distinguish the glioma samples from controls. The DEGs were mainly enriched in classical functions and pathways, such as cell cycle, and DNA replication. A total of 122 DEGs were found to be potential drug targets for glioma, among which GLIPR1 was targeted by drug XL820, PDGFRB and KDR were targeted by SOT-107. Efficacy validation of the other 119 drug targets by GSE38008 data showed that ACSS1, ASL, GCLM, ROCK2, IMPA1, and TFPI may be targeted by the anti-glioma drug of AT13148. CONCLUSION: AT13148 may inhibit glioma progression by suppressing the Notch signaling genes, including GLIPR1, PDGFRB, ACSS1, and ASL. Copyright © 2015. Published by Elsevier B.V. DOI: 10.1016/j.gene.2015.07.033 PMID: 26187072 [Indexed for MEDLINE] 126. Drug Discov Today. 2015 Nov;20(11):1398-406. doi: 10.1016/j.drudis.2015.06.012. Epub 2015 Jul 6. Olfactory drug effects approached from human-derived data. Lötsch J(1), Knothe C(2), Lippmann C(3), Ultsch A(4), Hummel T(5), Walter C(2). Author information: (1)Institute of Clinical Pharmacology, Goethe University, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany; Fraunhofer Project Group Translational Medicine and Pharmacology (IME-TMP), Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany. Electronic address: j.loetsch@em.uni-frankfurt.de. (2)Institute of Clinical Pharmacology, Goethe University, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany. (3)Fraunhofer Project Group Translational Medicine and Pharmacology (IME-TMP), Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany; DataBionics Research Group, University of Marburg, Hans-Meerwein-Strabe, 35032 Marburg, Germany. (4)DataBionics Research Group, University of Marburg, Hans-Meerwein-Strabe, 35032 Marburg, Germany. (5)Smell & Taste Clinic, Department of Otorhinolaryngology, TU Dresden, Fetscherstrasse 74, 01307 Dresden, Germany. The complexity of the sense of smell makes adverse olfactory effects of drugs highly likely, which can impact a patient's quality of life. Here, we present a bioinformatics approach that identifies drugs with potential olfactory effects by connecting drug target expression patterns in human olfactory tissue with drug-related information and the underlying molecular drug targets taken from publically available databases. We identified 71 drugs with listed olfactory effects and 147 different targets. Taking the target-based approach further, we found additional drugs with potential olfactory effects, including 152 different substances interacting with genes expressed in the human olfactory bulb. Our proposed bioinformatics approach provides plausible hypotheses about mechanistic drug effects for drug discovery and repurposing and, thus, would be appropriate for use during drug development. Copyright © 2015 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.drudis.2015.06.012 PMID: 26160059 [Indexed for MEDLINE] 127. Curr Pharm Biotechnol. 2012 Jul;13(9):1632-9. In silico search for drug targets of natural compounds. Yao L(1). Author information: (1)Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. lixia.yao@dbmi.columbia.ed Natural compounds represent a significant source for the development of novel medicines. Finding the target proteins for a natural compound is the most important step towards understanding its molecular mechanism for therapeutic usage. In fact, the search for target proteins could be considered the first step of the drug discovery and development pipeline. While experimental determination of compound-protein interactions remains very challenging, effective in silico approaches have been developed and have demonstrated appealing advantages, including their low-cost and capability to scale up easily. The goal of this article is to provide an introduction to in silico search for drug targets of natural compounds. I first review currently available natural compounds databases and human gene/protein databases, and the rapidly emerging databases for known drug-target interactions. These resources provide the 'materials' for in silico approaches and define the gold standard of 'positives' for evaluating them. I then introduce three classes of computational methods for target identification of natural compounds, namely molecular docking, quantitative structure-activity relationship (QSAR) modeling, and data mining and integrative analysis. Use of these methods is explained using real examples, and the advantages and disadvantages of each method are compared. As these state-of-the-art methods continue to mature amid significant challenges, this field appears poised for a period of significant growth, with untold benefits to drug discovery and natural product development. PMID: 22039820 [Indexed for MEDLINE] 128. Nucleic Acids Res. 2017 Jan 4;45(D1):D932-D939. doi: 10.1093/nar/gkw993. Epub 2016 Oct 26. DrugCentral: online drug compendium. Ursu O(1), Holmes J(1), Knockel J(2), Bologa CG(1), Yang JJ(1), Mathias SL(1), Nelson SJ(1), Oprea TI(3). Author information: (1)Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA. (2)Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA. (3)Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA toprea@salud.unm.edu. DrugCentral (http://drugcentral.org) is an open-access online drug compendium. DrugCentral integrates structure, bioactivity, regulatory, pharmacologic actions and indications for active pharmaceutical ingredients approved by FDA and other regulatory agencies. Monitoring of regulatory agencies for new drugs approvals ensures the resource is up-to-date. DrugCentral integrates content for active ingredients with pharmaceutical formulations, indexing drugs and drug label annotations, complementing similar resources available online. Its complementarity with other online resources is facilitated by cross referencing to external resources. At the molecular level, DrugCentral bridges drug-target interactions with pharmacological action and indications. The integration with FDA drug labels enables text mining applications for drug adverse events and clinical trial information. Chemical structure overlap between DrugCentral and five online drug resources, and the overlap between DrugCentral FDA-approved drugs and their presence in four different chemical collections, are discussed. DrugCentral can be accessed via the web application or downloaded in relational database format. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gkw993 PMCID: PMC5210665 PMID: 27789690 [Indexed for MEDLINE] 129. Chem Biol. 2012 Dec 21;19(12):1620-30. doi: 10.1016/j.chembiol.2012.10.014. Morphobase, an encyclopedic cell morphology database, and its use for drug target identification. Futamura Y(1), Kawatani M, Kazami S, Tanaka K, Muroi M, Shimizu T, Tomita K, Watanabe N, Osada H. Author information: (1)Chemical Biology Core Facility, Chemical Biology Department, RIKEN Advanced Science Institute, Wako-shi, Saitama 351-0198, Japan. Visual observation is a powerful approach for screening bioactive compounds that can facilitate the discovery of attractive druggable targets following their chemicobiological validation. So far, many high-content approaches, using sophisticated imaging technology and bioinformatics, have been developed. In our study, we aimed to develop a simpler method that focuses on intact cell images because we found that dynamic changes in morphology are informative, often reflecting the mechanism of action of a drug. Here, we constructed a chemical-genetic phenotype profiling system, based on the high-content cell morphology database Morphobase. This database compiles the phenotypes of cancer cell lines that are induced by hundreds of reference compounds, wherein those of well-characterized anticancer drugs are classified by mode of action. Furthermore, we demonstrate the applicability of this system in identifying NPD6689, NPD8617, and NPD8969 as tubulin inhibitors. Copyright © 2012 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.chembiol.2012.10.014 PMID: 23261605 [Indexed for MEDLINE] 130. Drug Des Devel Ther. 2015 Oct 1;9:5439-45. doi: 10.2147/DDDT.S89861. eCollection 2015. The clinicopathological significance and drug target potential of FHIT in breast cancer, a meta-analysis and literature review. Su Y(1), Wang X(2), Li J(1), Xu J(3), Xu L(1). Author information: (1)Department of Cardiothoracic Surgery, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China. (2)Department of Cardiothoracic Surgery, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China ; Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China. (3)Department of General Surgery, Shanghai First People's Hospital, Shanghai Jiaotong University, Shanghai, People's Republic of China. FHIT is a bona fide tumor-suppressor gene and its loss contributes to tumorigenesis of epithelial cancers including breast cancer (BC). However, the association and clinicopathological significance between FHIT promoter hypermethylation and BC remains unclear. The purpose of this study is to conduct a meta-analysis and literature review to investigate the clinicopathological significance of FHIT methylation in BC. A detailed literature search was performed in PubMed, EMBASE, Web of Science, and Google Scholar databases. The data were extracted and assessed by two reviewers independently. Odds ratios with 95% corresponding confidence intervals were calculated. A total of seven relevant articles were available for meta-analysis, which included 985 patients. The frequency of FHIT hypermethylation was significantly increased in invasive ductal carcinoma compared to benign breast disease, the pooled odds ratio was 8.43, P<0.00001. The rate of FHIT hypermethylation was not significantly different between stage I/II and stage III/IV, odds ratio was 2.98, P=0.06. In addition, FHIT hypermethylation was not significantly associated with ER and PR status. FHIT hypermethylation was not significantly correlated with premenopausal and postmenopausal patients with invasive ductal carcinoma. In summary, our meta-analysis indicated that the frequency of FHIT hypermethylation was significantly increased in BC compared to benign breast disease. The rate of FHIT hypermethylation in advanced stages of BC was higher than in earlier stages; however, the difference was not statistically significant. Our data suggested that FHIT methylation could be a diagnostic biomarker of BC carcinogenesis. FHIT is a potential drug target for development of demethylation treatment for patients with BC. DOI: 10.2147/DDDT.S89861 PMCID: PMC4598219 PMID: 26491255 [Indexed for MEDLINE] 131. In Silico Biol. 2006;6(6):485-93. T-iDT : tool for identification of drug target in bacteria and validation by Mycobacterium tuberculosis. Singh NK(1), Selvam SM, Chakravarthy P. Author information: (1)S.R.M Institute of Science and Technology, Chennai, India. nitesh_summi@yahoo.com With the completion of the Human Genome Project in 2003, many new projects to sequence bacterial genomes were started and soon many complete bacterial genome sequences were available. The sequenced genomes of pathogenic bacteria provide useful information for understanding host-pathogen interactions. These data prove to be a new weapon in fighting against pathogenic bacteria by providing information about potential drug targets. But the limitation of computational tools for finding potential drug targets has hindered the process and further experimental analysis. There are many in silico approaches proposed for finding drug targets but only few have been automated. One such approach finds essential genes in bacterial genomes with no human homologue and predicts these as potential drug targets. The same approach is used in our tool. T-iDT, a tool for the identification of drug targets, finds essential genes by comparing a bacterial gene set against DEG (Database of Essential Genes) and excludes homologue genes by comparing against a human protein database. The tool predicts both the set of essential genes as well as potential target genes for the given genome. The tool was tested with Mycobacterium tuberculosis and results were validated. With default parameters, the tool predicted 236 essential genes and 52 genes to encode potential drug targets. A pathway-based approach was used to validate these potential drug target genes. The pathway in which the products of these genes are involved was determined. Our analysis shows that almost all these pathways are very essential for the bacterial survival and hence these genes encode possible drug targets. Our tool provides a fast method for finding possible drug targets in bacterial genomes with varying stringency level. The tool will be helpful in finding possible drug targets in various pathogenic organisms and can be used for further analysis in novel therapeutic drug development. The tool can be downloaded from http://www.milser.co.in/research.htm and http://www.srmbioinformatics.edu.in/ forum.htm. PMID: 17518759 [Indexed for MEDLINE] 132. J Theor Biol. 2014 Nov 21;361:152-8. doi: 10.1016/j.jtbi.2014.07.031. Epub 2014 Aug 5. Potential non homologous protein targets of mycobacterium tuberculosis H37Rv identified from protein-protein interaction network. Melak T(1), Gakkhar S(2). Author information: (1)Department of Mathematics, IIT Roorkee, India. Electronic address: the_melak@yahoo.com. (2)Department of Mathematics, IIT Roorkee, India. Electronic address: sungkfma@gmail.com. Bacillus mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis and H37Rv is the most studied strain. Identification of new drug targets for Mtb is among one of the priorities since it is still a major global health problem by being a cause of morbidity and mortality for millions of people each year. We used centrality measures to identify the most central proteins from protein-protein interaction network of mycobacterium tuberculosis H37Rv which was retrieved from STRING database by hypothesizing these proteins would be important to alter the function of the network. We then refined the result by using a dataset obtained from Drug Target Protein Database to identify non-human homologous proteins since in host-parasite diseases like tuberculosis; non-homologous proteins (enzymes) as drug target are the primary choices. We also tried to compare our proposed potential non-human homologous protein target lists against previously reported targets. Moreover, the structural coverage of the proposed target list has been identified. The analysis shows that 807 proteins in mycobacterium tuberculosis H37Rv were found at the center of gravity of the functional network of which 390 were non-human homologous, which are thought to be potential drug targets. 119 (30.51%) of the 390 proteins were reported as drug targets and only 33 (8.46%) of the non-human homologous proposed target lists have solved structure. Copyright © 2014 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.jtbi.2014.07.031 PMID: 25106794 [Indexed for MEDLINE] 133. BMC Syst Biol. 2017 Apr 19;11(1):50. doi: 10.1186/s12918-017-0426-0. SSER: Species specific essential reactions database. Labena AA(1)(2)(3), Ye YN(4), Dong C(1)(2), Zhang FZ(1)(2), Guo FB(5)(6)(7). Author information: (1)Center of Bioinformatics, Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. (2)Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China. (3)College of Computational and Natural Sciences, Dilla University, Dilla, Ethiopia. (4)School of Biology and Engineering, Guizhou Medical University, Guiyang Shi, China. (5)Center of Bioinformatics, Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. fbguo@uestc.edu.cn. (6)Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China. fbguo@uestc.edu.cn. (7)Bioinformatics Center in School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North JianShe Road, Chengdu, 610054, China. fbguo@uestc.edu.cn. BACKGROUND: Essential reactions are vital components of cellular networks. They are the foundations of synthetic biology and are potential candidate targets for antimetabolic drug design. Especially if a single reaction is catalyzed by multiple enzymes, then inhibiting the reaction would be a better option than targeting the enzymes or the corresponding enzyme-encoding gene. The existing databases such as BRENDA, BiGG, KEGG, Bio-models, Biosilico, and many others offer useful and comprehensive information on biochemical reactions. But none of these databases especially focus on essential reactions. Therefore, building a centralized repository for this class of reactions would be of great value. DESCRIPTION: Here, we present a species-specific essential reactions database (SSER). The current version comprises essential biochemical and transport reactions of twenty-six organisms which are identified via flux balance analysis (FBA) combined with manual curation on experimentally validated metabolic network models. Quantitative data on the number of essential reactions, number of the essential reactions associated with their respective enzyme-encoding genes and shared essential reactions across organisms are the main contents of the database. CONCLUSION: SSER would be a prime source to obtain essential reactions data and related gene and metabolite information and it can significantly facilitate the metabolic network models reconstruction and analysis, and drug target discovery studies. Users can browse, search, compare and download the essential reactions of organisms of their interest through the website http://cefg.uestc.edu.cn/sser . DOI: 10.1186/s12918-017-0426-0 PMCID: PMC5395902 PMID: 28420402 [Indexed for MEDLINE] 134. Interdiscip Sci. 2016 Dec;8(4):388-394. Epub 2016 Jan 11. An Approach for Identification of Novel Drug Targets in Streptococcus pyogenes SF370 Through Pathway Analysis. Singh S(1), Singh DB(2), Singh A(3), Gautam B(1), Ram G(4), Dwivedi S(5), Ramteke PW(6). Author information: (1)Department of Computational Biology and Bioinformatics, SHIATS, Allahabad, 211007, India. (2)Department of Biotechnology, Institute of Biosciences and Biotechnology, Chhatrapati Shahu Ji Maharaj University, Kanpur, 208024, India. answer.dev@gmail.com. (3)Maitreyi College, University of Delhi, Delhi, India. (4)Department of Molecular and Cellular Engineering, SHIATS, Allahabad, 211007, India. (5)School of Biotechnology, Gautam Buddha University, Greater Noida, 201308, Uttar Pradesh, India. (6)Department of Biological Sciences, SHIATS, Allahabad, 211007, India. Streptococcus pyogenes is one of the most important pathogens as it is involved in various infections affecting upper respiratory tract and skin. Due to the emergence of multidrug resistance and cross-resistance, S. Pyogenes is becoming more pathogenic and dangerous. In the present study, an in silico comparative analysis of total 65 metabolic pathways of the host (Homo sapiens) and the pathogen was performed. Initially, 486 paralogous enzymes were identified so that they can be removed from possible drug target list. The 105 enzymes of the biochemical pathways of S. pyogenes from the KEGG metabolic pathway database were compared with the proteins from the Homo sapiens by performing a BLASTP search against the non-redundant database restricted to the Homo sapiens subset. Out of these, 83 enzymes were identified as non-human homologous while 30 enzymes of inadequate amino acid length were removed for further processing. Essential enzymes were finally mined from remaining 53 enzymes. Finally, 28 essential enzymes were identified in S. pyogenes SF370 (serotype M1). In subcellular localization study, 18 enzymes were predicted with cytoplasmic localization and ten enzymes with the membrane localization. These ten enzymes with putative membrane localization should be of particular interest. Acyl-carrier-protein S-malonyltransferase, DNA polymerase III subunit beta and dihydropteroate synthase are novel drug targets and thus can be used to design potential inhibitors against S. pyogenes infection. 3D structure of dihydropteroate synthase was modeled and validated that can be used for virtual screening and interaction study of potential inhibitors with the target enzyme. DOI: 10.1007/s12539-015-0139-2 PMID: 26750924 [Indexed for MEDLINE] 135. Bioinformatics. 2017 Apr 15;33(8):1187-1196. doi: 10.1093/bioinformatics/btw770. LRSSL: predict and interpret drug-disease associations based on data integration using sparse subspace learning. Liang X, Zhang P, Yan L, Fu Y, Peng F, Qu L, Shao M, Chen Y, Chen Z. Motivation: : Exploring the potential curative effects of drugs is crucial for effective drug development. Previous studies have indicated that integration of multiple types of information could be conducive to discovering novel indications of drugs. However, how to efficiently identify the mechanism behind drug-disease associations while integrating data from different sources remains a challenging problem. Results: : In this research, we present a novel method for indication prediction of both new drugs and approved drugs. This method is based on Laplacian regularized sparse subspace learning (LRSSL), which integrates drug chemical information, drug target domain information and target annotation information. Experimental results show that the proposed method outperforms several recent approaches for predicting drug-disease associations. Some drug therapeutic effects predicted by the method could be validated by database records or literatures. Moreover, with L1-norm constraint, important drug features have been extracted from multiple drug feature profiles. Case studies suggest that the extracted drug features could be beneficial to interpretation of the predicted results. Availability and Implementation: https://github.com/LiangXujun/LRSSL. Contact: proteomics@csu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com DOI: 10.1093/bioinformatics/btw770 PMID: 28096083 [Indexed for MEDLINE] 136. Proteins. 2015 Jan;83(1):25-36. doi: 10.1002/prot.24605. Epub 2014 Nov 18. Survey of phosphorylation near drug binding sites in the Protein Data Bank (PDB) and their effects. Smith KP(1), Gifford KM, Waitzman JS, Rice SE. Author information: (1)Department of Cell and Molecular Biology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, 60611. While it is currently estimated that 40 to 50% of eukaryotic proteins are phosphorylated, little is known about the frequency and local effects of phosphorylation near pharmaceutical inhibitor binding sites. In this study, we investigated how frequently phosphorylation may affect the binding of drug inhibitors to target proteins. We examined the 453 non-redundant structures of soluble mammalian drug target proteins bound to inhibitors currently available in the Protein Data Bank (PDB). We cross-referenced these structures with phosphorylation data available from the PhosphoSitePlus database. Three hundred twenty-two of 453 (71%) of drug targets have evidence of phosphorylation that has been validated by multiple methods or labs. For 132 of 453 (29%) of those, the phosphorylation site is within 12 Å of the small molecule-binding site, where it would likely alter small molecule binding affinity. We propose a framework for distinguishing between drug-phosphorylation site interactions that are likely to alter the efficacy of drugs versus those that are not. In addition we highlight examples of well-established drug targets, such as estrogen receptor alpha, for which phosphorylation may affect drug affinity and clinical efficacy. Our data suggest that phosphorylation may affect drug binding and efficacy for a significant fraction of drug target proteins. © 2014 Wiley Periodicals, Inc. DOI: 10.1002/prot.24605 PMCID: PMC4233198 PMID: 24833420 [Indexed for MEDLINE] 137. Methods. 2016 Nov 1;110:14-25. doi: 10.1016/j.ymeth.2016.07.023. Epub 2016 Jul 30. Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation. Tan Y(1), Hu Y(1), Liu X(1), Yin Z(1), Chen XW(2), Liu M(3). Author information: (1)Big Data Decision Institute, The First Affiliated Hospital, International Immunology Center, The Biomedical Translational Research Institute, Jinan University, Guangzhou, Guangdong, China. (2)Department of Computer Science, Wayne State University, Detroit, USA. (3)Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, USA. Electronic address: meiliu@kumc.edu. Adverse drug reactions (ADRs) are a major public health concern, causing over 100,000 fatalities in the United States every year with an annual cost of $136 billion. Early detection and accurate prediction of ADRs is thus vital for drug development and patient safety. Multiple scientific disciplines, namely pharmacology, pharmacovigilance, and pharmacoinformatics, have been addressing the ADR problem from different perspectives. With the same goal of improving drug safety, this article summarizes and links the research efforts in the multiple disciplines into a single framework from comprehensive understanding of the interactions between drugs and biological system and the identification of genetic and phenotypic predispositions of patients susceptible to higher ADR risks and finally to the current state of implementation of medication-related decision support systems. We start by describing available computational resources for building drug-target interaction networks with biological annotations, which provides a fundamental knowledge for ADR prediction. Databases are classified by functions to help users in selection. Post-marketing surveillance is then introduced where data-driven approach can not only enhance the prediction accuracy of ADRs but also enables the discovery of genetic and phenotypic risk factors of ADRs. Understanding genetic risk factors for ADR requires well organized patient genetics information and analysis by pharmacogenomic approaches. Finally, current state of clinical decision support systems is presented and described how clinicians can be assisted with the integrated knowledgebase to minimize the risk of ADR. This review ends with a discussion of existing challenges in each of disciplines with potential solutions and future directions. Copyright © 2016 Elsevier Inc. All rights reserved. DOI: 10.1016/j.ymeth.2016.07.023 PMID: 27485605 [Indexed for MEDLINE] 138. Front Pharmacol. 2015 Mar 31;6:65. doi: 10.3389/fphar.2015.00065. eCollection 2015. The histamine H4 receptor: from orphan to the clinic. Thurmond RL(1). Author information: (1)Janssen Research & Development, LLC San Diego, CA, USA. The histamine H4 receptor (H4R) was first noted as a sequence in genomic databases that had features of a class A G-protein coupled receptor. This putative receptor was found to bind histamine consistent with its homology to other histamine receptors and thus became the fourth member of the histamine receptor family. Due to the previous success of drugs that target the H1 and H2 receptors, an effort was made to understand the function of this new receptor and determine if it represented a viable drug target. Taking advantage of the vast literature on the function of histamine, a search for histamine activity that did not appear to be mediated by the other three histamine receptors was undertaken. From this asthma and pruritus emerged as areas of particular interest. Histamine has long been suspected to play a role in the pathogenesis of asthma, but antihistamines that target the H1 and H2 receptors have not been shown to be effective for this condition. The use of selective ligands in animal models of asthma has now potentially filled this gap by showing a role for the H4R in mediating lung function and inflammation. A similar story exists for chronic pruritus associated with conditions such as atopic dermatitis. Antihistamines that target the H1 receptor are effective in reducing acute pruritus, but are ineffective in pruritus experienced by patients with atopic dermatitis. As for asthma, animal models have now suggested a role for the H4R in mediating pruritic responses, with antagonists of the H4R reducing pruritus in a number of different conditions. The anti-pruritic effect of H4R antagonists has recently been shown in human clinical studies, validating the preclinical findings in the animal models. A selective H4R antagonist inhibited histamine-induced pruritus in health volunteers and reduced pruritus in patients with atopic dermatitis. The history to date of the H4R provides an excellent example of the deorphanization of a novel receptor and the translation of this into clinical efficacy in humans. DOI: 10.3389/fphar.2015.00065 PMCID: PMC4379874 PMID: 25873897 139. BioData Min. 2016 May 26;9:21. doi: 10.1186/s13040-016-0097-1. eCollection 2016. Data integration to prioritize drugs using genomics and curated data. Louhimo R(1), Laakso M(1), Belitskin D(2), Klefström J(2), Lehtonen R(1), Hautaniemi S(1). Author information: (1)Genome Scale Biology Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, P.O. Box 63 (Haartmaninkatu 8), Helsinki, FI-00014 Finland. (2)Translational Cancer Biology Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, P.O. Box 63 (Haartmaninkatu 8), Helsinki, FI-00014 Finland. BACKGROUND: Genomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Increasingly, patients likely to benefit from targeted cancer therapy are selected based on molecular alterations. The selection of a precision therapy benefiting most patients is challenging but can be enhanced with integration of multiple types of molecular data. Data integration approaches for drug prioritization have successfully integrated diverse molecular data but do not take full advantage of existing data and literature. RESULTS: We have built a knowledge-base which connects data from public databases with molecular results from over 2200 tumors, signaling pathways and drug-target databases. Moreover, we have developed a data mining algorithm to effectively utilize this heterogeneous knowledge-base. Our algorithm is designed to facilitate retargeting of existing drugs by stratifying samples and prioritizing drug targets. We analyzed 797 primary tumors from The Cancer Genome Atlas breast and ovarian cancer cohorts using our framework. FGFR, CDK and HER2 inhibitors were prioritized in breast and ovarian data sets. Estrogen receptor positive breast tumors showed potential sensitivity to targeted inhibitors of FGFR due to activation of FGFR3. CONCLUSIONS: Our results suggest that computational sample stratification selects potentially sensitive samples for targeted therapies and can aid in precision medicine drug repositioning. Source code is available from http://csblcanges.fimm.fi/GOPredict/. DOI: 10.1186/s13040-016-0097-1 PMCID: PMC4881054 PMID: 27231484 140. Eur J Med Chem. 2011 Apr;46(4):1074-94. doi: 10.1016/j.ejmech.2011.01.023. Epub 2011 Jan 21. Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica. Prado-Prado F(1), García-Mera X, Abeijón P, Alonso N, Caamaño O, Yáñez M, Gárate T, Mezo M, González-Warleta M, Muiño L, Ubeira FM, González-Díaz H. Author information: (1)Department of Organic Chemistry, Faculty of Pharmacy, USC 15782, Spain. fenol1@hotmail.com There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately, most QSAR models predict activity against only one protein. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 32:32-15-1:1. This MLP classifies correctly 623 out of 678 DTPs (Sensitivity = 91.89%) and 2995 out of 3234 nDTPs (Specificity = 92.61%), corresponding to training Accuracy = 92.48%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 313 out of 338 DTPs (Sensitivity = 92.60%) and 1411 out of 1534 nDTP (Specificity = 91.98%) in validation series, corresponding to total Accuracy = 92.09% for validation series (Predictability). This model favorably compares with other LDA and ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. These mt-QSARs offer also a good opportunity to construct drug-protein Complex Networks (CNs) that can be used to explore large and complex drug-protein receptors databases. Finally, we illustrated two practical uses of this model with two different experiments. In experiment 1, we report prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of 10 rasagiline derivatives promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, SEC and 1DE sample preparation, MALDI-TOF MS and MS/MS analysis, MASCOT search, MM/MD 3D structure modeling, and QSAR prediction for different peptides of hemoglobin found in the proteome of the human parasite Fasciola hepatica; which is promising for anti-parasite drug targets discovery. Copyright © 2011 Elsevier Masson SAS. All rights reserved. DOI: 10.1016/j.ejmech.2011.01.023 PMID: 21315497 [Indexed for MEDLINE] 141. Tumour Biol. 2015 Aug;36(8):6139-48. doi: 10.1007/s13277-015-3298-1. Epub 2015 Mar 11. The clinicopathological significance and potential drug target of E-cadherin in NSCLC. Zhong K(1), Chen W, Xiao N, Zhao J. Author information: (1)Department of Thoracic Surgery, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, China. Human epithelial cadherin (E-cadherin), a member of transmembrane glycoprotein family, encoded by the E-cadherin gene, plays a key role in cell-cell adhesion, adherent junction in normal epithelial tissues, contributing to tissue differentiation and homeostasis. Although previous studies indicated that inactivation of the E-cadherin is mainly induced by hypermethylation of E-cadherin gene, evidence concerning E-cadherin hypermethylation in the carcinogenesis and development of non-small cell lung carcinoma (NSCLC) remains controversial. In this study, we conducted a meta-analysis to quantitatively evaluate the effects of E-cadherin hypermethylation on the incidence and clinicopathological characteristics of NSCLC. A comprehensive search of PubMed and Embase databases was performed up to October 2014. Analyses of pooled data were performed. Odds ratios (ORs) were calculated and summarized. Our meta-analysis combining 18 published articles demonstrated that the hypermethylation frequencies in NSCLC were significantly higher than those in normal control tissues, OR = 3.55, 95 % confidence interval (CI) = 1.98-6.36, p < 0.0001. Further analysis showed that E-cadherin hypermethylation was not strongly associated with the sex or smoking status in NSCLC patients. In addition, E-cadherin hypermethylation was also not strongly associated with pathological types, differentiated status, clinical stages, or metastatic status in NSCLC patients. The results from the current study indicate that the hypermethylation frequency of E-cadherin in NSCLC is strongly associated with NSCLC incidence and it may be an early event in carcinogenesis of NSCLC. We also discussed the potential value of E-cadherin as a drug target that may bring new direction and hope for cancer treatment through gene-targeted therapy. DOI: 10.1007/s13277-015-3298-1 PMID: 25758052 [Indexed for MEDLINE] 142. J Biomed Semantics. 2017 Jun 6;8(1):20. doi: 10.1186/s13326-017-0131-3. Literature evidence in open targets - a target validation platform. Kafkas Ş(1)(2), Dunham I(3)(4), McEntyre J(3)(4). Author information: (1)European Molecular Biology Laboratory (EMBL-EBI), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK. kafkas@ebi.ac.uk. (2)Open Targets, Wellcome Genome Campus, Hinxton, CB10 1SD, UK. kafkas@ebi.ac.uk. (3)European Molecular Biology Laboratory (EMBL-EBI), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK. (4)Open Targets, Wellcome Genome Campus, Hinxton, CB10 1SD, UK. BACKGROUND: We present the Europe PMC literature component of Open Targets - a target validation platform that integrates various evidence to aid drug target identification and validation. The component identifies target-disease associations in documents and ranks the documents based on their confidence from the Europe PMC literature database, by using rules utilising expert-provided heuristic information. The confidence score of a given document represents how valuable the document is in the scope of target validation for a given target-disease association by taking into account the credibility of the association based on the properties of the text. The component serves the platform regularly with the up-to-date data since December, 2015. RESULTS: Currently, there are a total number of 1168365 distinct target-disease associations text mined from >26 million PubMed abstracts and >1.2 million Open Access full text articles. Our comparative analyses on the current available evidence data in the platform revealed that 850179 of these associations are exclusively identified by literature mining. CONCLUSIONS: This component helps the platform's users by providing the most relevant literature hits for a given target and disease. The text mining evidence along with the other types of evidence can be explored visually through https://www.targetvalidation.org and all the evidence data is available for download in json format from https://www.targetvalidation.org/downloads/data . DOI: 10.1186/s13326-017-0131-3 PMCID: PMC5461726 PMID: 28587637 [Indexed for MEDLINE] 143. Interdiscip Sci. 2014 Mar;6(1):48-56. doi: 10.1007/s12539-014-0188-y. Epub 2014 Jan 28. Application of a subtractive genomics approach for in silico identification and characterization of novel drug targets in Mycobacterium tuberculosis F11. Hosen MI(1), Tanmoy AM, Mahbuba DA, Salma U, Nazim M, Islam MT, Akhteruzzaman S. Author information: (1)Department of Biochemistry and Molecular Biology, Faculty of Biological Science, University of Dhaka, Dhaka, 1000, Bangladesh, milon_ismail_02@yahoo.com. Extensive dead ends or host toxicity of the conventional approaches of drug development can be avoided by applying the in silico subtractive genomics approach in the designing of potential drug target against bacterial diseases. This study utilizes the advanced in silico genome subtraction methodology to design potential and pathogen specific drug targets against Mycobacterium tuberculosis, causal agent of deadly tuberculosis. The whole proteome of Mycobacterium tuberculosis F11 containing 3941 proteins have been analyzed through a series of subtraction methodologies to remove paralogous proteins and proteins that show extensive homology with human. The subsequent exclusion of these proteins ensured the absence of host cytotoxicity and cross reaction in the identified drug targets. The high stringency (expectation value 10(-100)) analysis of the remaining 2935 proteins against database of essential genes resulted in 274 proteins to be essential for Mycobacterium tuberculosis F11. Comparative analysis of the metabolic pathways of human and Mycobacterium tuberculosis F11 by KAAS at the KEGG server sorted out 20 unique metabolic pathways in Mycobacterium tuberculosis F11 that involve the participation of 30 essential proteins. Subcellular localization analysis of these 30 essential proteins revealed 7 proteins with outer membrane potentialities. All these proteins can be used as a potential therapeutic target against Mycobacterium tuberculosis F11 infection. 66 of the 274 essential proteins were uncharacterized (described as hypothetical) and functional classification of these proteins showed that they belonged to a wide variety of protein classes including zinc binding proteins, transferases, transmembrane proteins, other metal ion binding proteins, oxidoreductase, and primary active transporters etc. 2D and 3D structures of these 15 membrane associated proteins were predicted using PRED-TMBB and homology modeling by Swiss model workspace respectively. The identified drug targets are expected to be of great potential for designing novel anti-tuberculosis drugs and further screening of the compounds against these newly targets may result in discovery of novel therapeutic compounds that can be effective against Mycobacterium tuberculosis. DOI: 10.1007/s12539-014-0188-y PMID: 24464704 [Indexed for MEDLINE] 144. Front Physiol. 2015 Dec 18;6:371. doi: 10.3389/fphys.2015.00371. eCollection 2015. Historeceptomic Fingerprints for Drug-Like Compounds. Shmelkov E(1), Grigoryan A(1), Swetnam J(2), Xin J(1), Tivon D(3), Shmelkov SV(4), Cardozo T(1). Author information: (1)Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine New York, NY, USA. (2)Google Inc., Mountain View CA, USA. (3)GeneCentrix Inc. New York, NY, USA. (4)Department of Neuroscience and Physiology, New York University School of MedicineNew York, NY, USA; Department of Psychiatry, New York University School of MedicineNew York, NY, USA. Most drugs exert their beneficial and adverse effects through their combined action on several different molecular targets (polypharmacology). The true molecular fingerprint of the direct action of a drug has two components: the ensemble of all the receptors upon which a drug acts and their level of expression in organs/tissues. Conversely, the fingerprint of the adverse effects of a drug may derive from its action in bystander tissues. The ensemble of targets is almost always only partially known. Here we describe an approach improving upon and integrating both components: in silico identification of a more comprehensive ensemble of targets for any drug weighted by the expression of those receptors in relevant tissues. Our system combines more than 300,000 experimentally determined bioactivity values from the ChEMBL database and 4.2 billion molecular docking scores. We integrated these scores with gene expression data for human receptors across a panel of human tissues to produce drug-specific tissue-receptor (historeceptomics) scores. A statistical model was designed to identify significant scores, which define an improved fingerprint representing the unique activity of any drug. These multi-dimensional historeceptomic fingerprints describe, in a novel, intuitive, and easy to interpret style, the holistic, in vivo picture of the mechanism of any drug's action. Valuable applications in drug discovery and personalized medicine, including the identification of molecular signatures for drugs with polypharmacologic modes of action, detection of tissue-specific adverse effects of drugs, matching molecular signatures of a disease to drugs, target identification for bioactive compounds with unknown receptors, and hypothesis generation for drug/compound phenotypes may be enabled by this approach. The system has been deployed at drugable.org for access through a user-friendly web site. DOI: 10.3389/fphys.2015.00371 PMCID: PMC4683199 PMID: 26733872 145. Autophagy. 2019 Jan 22:1-16. doi: 10.1080/15548627.2019.1571717. [Epub ahead of print] AutophagySMDB: a curated database of small molecules that modulate protein targets regulating autophagy. Nanduri R(1), Kalra R(1), Bhagyaraj E(1), Chacko AP(1), Ahuja N(1), Tiwari D(1), Kumar S(1), Jain M(1), Parkesh R(1), Gupta P(1). Author information: (1)a Department of Molecular Biology , CSIR-Institute of Microbial Technology , Chandigarh , India. Macroautophagy/autophagy is a complex self-degradative mechanism responsible for clearance of non functional organelles and proteins. A range of factors influences the autophagic process, and disruptions in autophagy-related mechanisms lead to disease states, and further exacerbation of disease. Despite in-depth research into autophagy and its role in pathophysiological processes, the resources available to use it for therapeutic purposes are currently lacking. Herein we report the Autophagy Small Molecule Database (AutophagySMDB; http://www.autophagysmdb.org/ ) of small molecules and their cognate protein targets that modulate autophagy. Presently, AutophagySMDB enlists ~10,000 small molecules which regulate 71 target proteins. All entries are comprised of information such as EC50 (half maximal effective concentration), IC50 (half maximal inhibitory concentration), Kd (dissociation constant) and Ki (inhibition constant), IUPAC name, canonical SMILE, structure, molecular weight, QSAR (quantitative structure activity relationship) properties such as hydrogen donor and acceptor count, aromatic rings and XlogP. AutophagySMDB is an exhaustive, cross-platform, manually curated database, where either the cognate targets for small molecule or small molecules for a target can be searched. This database is provided with different search options including text search, advanced search and structure search. Various computational tools such as tree tool, cataloging tools, and clustering tools have also been implemented for advanced analysis. Data and the tools provided in this database helps to identify common or unique scaffolds for designing novel drugs or to improve the existing ones for autophagy small molecule therapeutics. The approach to multitarget drug discovery by identifying common scaffolds has been illustrated with experimental validation. Abbreviations: AMPK: AMP-activated protein kinase; ATG: autophagy related; AutophagySMDB: autophagy small molecule database; BCL2: BCL2, apoptosis regulator; BECN1: beclin 1; CAPN: calpain; MTOR: mechanistic target of rapamycin kinase; PPARG: peroxisome proliferator activated receptor gamma; SMILES: simplified molecular input line entry system; SQSTM1: sequestosome 1; STAT3: signal transducer and activator of transcription. DOI: 10.1080/15548627.2019.1571717 PMID: 30669929 146. Oncol Lett. 2018 Jul;16(1):113-122. doi: 10.3892/ol.2018.8634. Epub 2018 May 4. Integrated multi-omics data analysis identifying novel drug sensitivity-associated molecular targets of hepatocellular carcinoma cells. Yildiz G(1). Author information: (1)Department of Medical Biology, Faculty of Medicine, Karadeniz Technical University, Trabzon 61080, Turkey. Hepatocellular carcinoma (HCC) is the most common type of liver cancer and the third-leading cause of malignancy-associated mortality worldwide. HCC cells are highly resistant to chemotherapeutic agents. Therefore, there are currently only two US Food and Drug Administration-approved drugs available for the treatment of HCC. The objective of the present study was to analyze the results of previously published high-throughput drug screening, and in vitro genomic and transcriptomic data from HCC cell lines, and to integrate the obtained results to define the underlying molecular mechanisms of drug sensitivity and resistance in HCC cells. The results of treatment with 225 different small molecules on 14 different HCC cell lines were retrieved from the Genomics of Drug Sensitivity in Cancer database and analyzed. Cluster analysis using the treatment results determined that HCC cell lines consist of two groups, according to their drug response profiles. Continued analyses of these two groups with Gene Set Enrichment Analysis method revealed 6 treatment-sensitive molecular targets (epidermal growth factor receptor, mechanistic target of rapamycin, deoxyribonucleic acid-dependent protein kinase, the Aurora kinases, Bruton's tyrosine kinase and phosphoinositide 3-kinase; all P<0.05) and partially effective drugs. Genetic and genome-wide gene expression data analyses of the determined targets and their known biological partners revealed 2 somatically mutated and 13 differentially expressed genes, which differed between drug-resistant and drug-sensitive HCC cells. Integration of the obtained data into a short molecular pathway revealed a drug treatment-sensitive signaling axis in HCC cells. In conclusion, the results of the present study provide novel drug sensitivity-associated molecular targets for the development of novel personalized and targeted molecular therapies against HCC. DOI: 10.3892/ol.2018.8634 PMCID: PMC6006500 PMID: 29930714 147. Sci Signal. 2011 May 17;4(173):pt3. doi: 10.1126/scisignal.2001950. Network-based tools for the identification of novel drug targets. Farkas IJ(1), Korcsmáros T, Kovács IA, Mihalik Á, Palotai R, Simkó GI, Szalay KZ, Szalay-Beko M, Vellai T, Wang S, Csermely P. Author information: (1)Statistical and Biological Physics Group of the Hungarian Academy of Sciences, Pázmány P. s. 1A, H-1117 Budapest, Hungary. In the past few years, network-based tools have become increasingly important in the identification of novel molecular targets for drug development. Systems-based approaches to predict signal transduction-related drug targets have developed into an especially promising field. Here, we summarize our studies, which indicate that modular bridges and overlaps of protein-protein interaction and signaling networks may be of key importance in future drug design. Intermodular nodes are very efficient in mediating the transmission of perturbations between signaling modules and are important in network cooperation. The analysis of stress-induced rearrangements of the yeast interactome by the ModuLand modularization algorithm indicated that components of modular overlap are key players in cellular adaptation to stress. Signaling crosstalk was much more pronounced in humans than in Caenorhabditis elegans or Drosophila melanogaster in the SignaLink (http://www.SignaLink.org) database, a uniformly curated database of eight major signaling pathways. We also showed that signaling proteins that participate in multiple pathways included multiple established drug targets and drug target candidates. Lastly, we caution that the pervasive overlap of cellular network modules implies that wider use of multitarget drugs to partially inhibit multiple individual proteins will be necessary to modify specific cellular functions, because targeting single proteins for complete disruption usually affects multiple cellular functions with little specificity for a particular process. Tools for analyzing network topology and especially network dynamics have great potential to identify alternative sets of targets for developing multitarget drugs. DOI: 10.1126/scisignal.2001950 PMID: 21586727 [Indexed for MEDLINE] 148. Methods Mol Med. 2008;142:1-9. doi: 10.1007/978-1-59745-246-5_1. Biocomputational strategies for microbial drug target identification. Sakharkar KR(1), Sakharkar MK, Chow VT. Author information: (1)Human Genome Laboratory, Department of Microbiology, Yong loo Lin School of Medicine, National University of Singapore, Singapore. The complete genome sequences of about 300 bacteria (mostly pathogenic) have been determined, and many more such projects are currently in progress. The detection of bacterial genes that are non-homologous to human genes and are essential for the survival of the pathogen represent a promising means of identifying novel drug targets. We present a subtractive genomics approach for the identification of putative drug targets in microbial genomes and demonstrate its execution using Pseudomonas aeruginosa as an example. The resultant analyses are in good agreement with the results of systematic gene deletion experiments. This strategy enables rapid potential drug target identification, thereby greatly facilitating the search for new antibiotics. It should be recognized that there are limitations to this computational approach for drug target identification. Distant gene relationships may be missed since the alignment scores are likely to have low statistical significance. In conclusion, the results of such a strategy underscore the utility of large genomic databases for in silico systematic drug target identification in the post-genomic era. DOI: 10.1007/978-1-59745-246-5_1 PMID: 18437301 [Indexed for MEDLINE] 149. Tumour Biol. 2015 Aug;36(8):5839-48. doi: 10.1007/s13277-015-3254-0. Epub 2015 Feb 27. Clinicopathological significance and potential drug target of O6-methylguanine-DNA methyltransferase in colorectal cancer: a meta-analysis. Zheng CG(1), Jin C, Ye LC, Chen NZ, Chen ZJ. Author information: (1)Department of Coloproctology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China. Emerging evidence indicates that O(6)-methylguanine-DNA methyltransferase (MGMT) is a candidate for tumor suppression in several types of human tumors including colorectal cancer (CRC). However, the correlation between MGMT hypermethylation and clinicopathological characteristics of CRC remains unclear. In this study, we conducted a systematic review and meta-analysis to quantitatively evaluate the effects of MGMT hypermethylation on the incidence of CRC and clinicopathological characteristics. A comprehensive literature search was done from Web of Science, the Cochrane Library Database, PubMed, EMBASE, CINAHL, and the Chinese Biomedical Database for related research publications written in English and Chinese. Methodological quality of the studies was also evaluated. Analyses of pooled data were performed with Review Manager 5.2. Odds ratio (OR) and hazard ratio (HR) were calculated and summarized, respectively. Final analysis from 28 eligible studies was performed. MGMT hypermethylation is found to be significantly higher in CRC than in normal colorectal mucosa, the pooled OR from 13 studies including 1085 CRC and 899 normal colorectal mucosa, OR = 6.04, 95 % confidence interval (CI) = 4.69-7.77, p < 0.00001. MGMT hypermethylation is also significantly higher in colorectal adenoma than in normal colorectal mucosa, but it is significantly less compared to that in CRC patients. Interestingly, MGMT hypermethylation is correlated with sex status and is significantly higher in female than in male. MGMT hypermethylation is also associated with high levels of microsatellite instability (MSI). The pooled HR for overall survival (OS) shows that MGMT hypermethylation is not associated with worse survival in CRC patients. The results of this meta-analysis suggest that MGMT hypermethylation is associated with an increased risk and high levels of MSI and may play an important role in CRC initiation. However, MGMT hypermethylation may play an important role in the early stage of CRC progression and development, as well as having limited value in prediction of prognosis in CRC patients. We also discussed that MGMT may serve as a potential drug target of CRC. DOI: 10.1007/s13277-015-3254-0 PMID: 25716203 [Indexed for MEDLINE] 150. Expert Opin Ther Targets. 2007 Mar;11(3):411-21. Pathway analysis software as a tool for drug target selection, prioritization and validation of drug mechanism. Sivachenko AY(1), Yuryev A. Author information: (1)Ariadne Genomics, Inc., 9430 Key West Avenue, Rockville, MD 20850, USA. sivachenko@ariadnegenomics.com One of the major challenges of drug discovery today is the poor understanding of the detailed molecular mechanisms underlying both disease progression and drug action. Insufficient drug specificity and side effects are often discovered during the late stages of drug development, sometimes after the drug is released on the market. These discoveries result in a high target attrition rate, a slow drug design pipeline and high development costs. Recent advances in systems biology and pathway analysis can help make true rational design a reality through the integration of experimental observations with underlying cellular regulation and metabolic networks. It should enable the formulation of better and more informed testable hypotheses with regard to the most efficient target candidates. In this article, the authors overview the broad and heterogeneous field of molecular interaction databases and pathway analysis tools, and the challenges existing in the field. The authors describe and classify different approaches for data acquisition, storage and navigation, give a detailed description of the integrative technology behind the Pathway Studio software solution, and present a comparison with other integrative pathway analysis platforms suitable for drug discovery tasks. DOI: 10.1517/14728222.11.3.411 PMID: 17298298 [Indexed for MEDLINE] 151. Curr Cancer Drug Targets. 2015;15(9):836-46. In Silico Designing and Screening of Antagonists against Cancer Drug Target XIAP. Kumar R, Chauhan JS, Raghava GP(1). Author information: (1)Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, India. raghava@imtech.res.in. X-linked inhibitor of apoptosis (XIAP) is a member of inhibitor of apoptosis (IAP) family and involved in the suppression of apoptosis in cancer cells. This property makes it a therapeutic target for the cancer therapy. In the present study, we have developed QSAR models using chemical descriptors, fingerprints, principal components, docking energy parameters and similarity-based approach against XIAP. We have achieved correlation (R) of 0.803 with R(2) value of 0.645 at 10-fold cross validation using SMOreg algorithm. We have evaluated these models on independent dataset to ascertain its robustness and achieved correlation (R) of 0.793 with R(2) value of 0.628. Further, we have used these models for the screening of FDA approved drugs and drug-like molecules from ZINC database and prioritized them on the basis of their predicted pIC50 values. Docking studies of top hits with XIAP-BIR3 domain shows that Iodixanol (DB01249) and ZINC68678304 have higher binding affinities than well-known tetrapeptide inhibitor, AVPI. We have integrated these models in a web server named as "XIAPin". We hope that this web server will contribute in the designing of nifty antagonists against XIAP. PMID: 26143944 [Indexed for MEDLINE] 152. World Acad Sci Eng Technol. 2015 Jun;9(6):587-591. Bioinformatics and Molecular Biological Characterization of a Hypothetical Protein SAV1226 as a Potential Drug Target for Methicillin/Vancomycin-Staphylococcus aureus Infections. Haag N(1), Velk K(2), McCune T(3), Wu C(1). Author information: (1)The Natural Sciences Division, Mount Marty College, Yankton, SD 57078 USA. (2)Mount Marty College, Yankton, SD 57078 USA, She is now with Yankton High School. (3)Mount Marty College, Yankton, SD 57078 USA. Methicillin/multiple-resistant Staphylococcus aureus (MRSA) are infectious bacteria that are resistant to common antibiotics. A previous in silico study in our group has identified a hypothetical protein SAV1226 as one of the potential drug targets. In this study, we reported the bioinformatics characterization, as well as cloning, expression, purification and kinetic assays of hypothetical protein SAV1226 from methicillin/vancomycin-resistant Staphylococcus aureus Mu50 strain. MALDI-TOF/MS analysis revealed a low degree of structural similarity with known proteins. Kinetic assays demonstrated that hypothetical protein SAV1226 is neither a domain of an ATP dependent dihydroxyacetone kinase nor of a phosphotransferase system (PTS) dihydroxyacetone kinase, suggesting that the function of hypothetical protein SAV1226 might be misannotated on public databases such as UniProt and InterProScan 5. PMCID: PMC4572700 PMID: 26388980 153. FEBS Lett. 2012 Jul 16;586(15):2157-63. doi: 10.1016/j.febslet.2012.01.057. Epub 2012 Feb 8. A Synthetic Biology Project - Developing a single-molecule device for screening drug-target interactions. Firman K(1), Evans L, Youell J. Author information: (1)IBBS Biophysics Laboratories, School of Biological Sciences, University of Portsmouth, King Henry Building, King Henry I Street, Portsmouth PO1 2DY, United Kingdom. This review describes a European-funded project in the area of Synthetic Biology. The project seeks to demonstrate the application of engineering techniques and methodologies to the design and construction of a biosensor for detecting drug-target interactions at the single-molecule level. Production of the proteins required for the system followed the principle of previously described "bioparts" concepts (a system where a database of biological parts - promoters, genes, terminators, linking tags and cleavage sequences - is used to construct novel gene assemblies) and cassette-type assembly of gene expression systems (the concept of linking different "bioparts" to produce functional "cassettes"), but problems were quickly identified with these approaches. DNA substrates for the device were also constructed using a cassette-system. Finally, micro-engineering was used to build a magnetoresistive Magnetic Tweezer device for detection of single molecule DNA modifying enzymes (motors), while the possibility of constructing a Hall Effect version of this device was explored. The device is currently being used to study helicases from Plasmodium as potential targets for anti-malarial drugs, but we also suggest other potential uses for the device. Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved. DOI: 10.1016/j.febslet.2012.01.057 PMID: 22710185 [Indexed for MEDLINE] 154. Pharmacogenomics. 2008 Aug;9(8):1155-62. doi: 10.2217/14622416.9.8.1155. DrugBank and its relevance to pharmacogenomics. Wishart DS(1). Author information: (1)Departments of Computing Science & Biological Sciences, University of Alberta, Edmonton ABT6G2E8, Canada. david.wishart@ualberta.ca DrugBank is a freely available web-enabled database that combines detailed drug data with comprehensive drug-target and drug-action information. It was specifically designed to facilitate in silico drug-target discovery, drug design, drug-metabolism prediction, drug-interaction prediction, and general pharmaceutical education. One of the most unique and useful components of the DrugBank database is the information it contains on drug metabolism, drug-metabolizing enzymes and drug-target polymorphisms. As pharmacogenomics is fundamentally concerned with the role of genes and genetic variation of how an individual responds to a drug, DrugBank is able to offer a convenient venue to explore pharmacogenomic questions in silico. This paper provides a brief overview on DrugBank and how it can facilitate pharmacogenomic research. DOI: 10.2217/14622416.9.8.1155 PMID: 18681788 [Indexed for MEDLINE] 155. Brief Bioinform. 2018 Jul 31. doi: 10.1093/bib/bby061. [Epub ahead of print] Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Rifaioglu AS(1)(2), Atas H(3), Martin MJ(4), Cetin-Atalay R(1), Atalay V(1), Dogan T(5). Author information: (1)Department of Computer Engineering, Middle East Technical University, Ankara, Turkey. (2)Department of Computer Engineering, İskenderun Technical University, Hatay, Turkey. (3)Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey. (4)European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, Hinxton CB10 1SD, UK. (5)Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey and European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, Hinxton CB10 1SD, UK. The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance.The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods. DOI: 10.1093/bib/bby061 PMID: 30084866 156. Adv Exp Med Biol. 2018;1094:109-115. doi: 10.1007/978-981-13-0719-5_11. Prediction of Non-coding RNAs as Drug Targets. Jiang W(1)(2), Lv Y(3), Wang S(3). Author information: (1)Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. weijiang@nuaa.edu.cn. (2)College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. weijiang@nuaa.edu.cn. (3)College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. MiRNA is a class of small non-coding RNA molecule that regulates gene expression at post-transcriptional level. Increasing evidences show aberrant expression of miRNAs in a variety of diseases. Targeting the dysregulated miRNAs with small molecule drugs has become a novel therapeutics for many human diseases, especially cancers. In this chapter, we introduced a series of computational studies for prediction of small molecule and miRNA associations. Based on different hypotheses, such as transcriptional response similarity, functional consistence or network closeness, the small molecule-miRNA networks were constructed and further analyzed. In addition, several resources that collected experimentally validated relationships or computational predicted associations between small molecules and miRNAs were provided. Collectively, these computational frameworks and databases pave a new way for miRNA-targeted therapy and drug repositioning. DOI: 10.1007/978-981-13-0719-5_11 PMID: 30191492 157. Mt Sinai J Med. 2007 Apr;74(1):27-32. Network analysis of FDA approved drugs and their targets. Ma'ayan A(1), Jenkins SL, Goldfarb J, Iyengar R. Author information: (1)Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, New York 10029, USA. The global relationship between drugs that are approved for therapeutic use and the human genome is not known. We employed graph-theory methods to analyze the Federal Food and Drug Administration (FDA) approved drugs and their known molecular targets. We used the FDA Approved Drug Products with Therapeutic Equivalence Evaluations 26(th) Edition Electronic Orange Book (EOB) to identify all FDA approved drugs and their active ingredients. We then connected the list of active ingredients extracted from the EOB to those known human protein targets included in the DrugBank database and constructed a bipartite network. We computed network statistics and conducted Gene Ontology analysis on the drug targets and drug categories. We find that drug to drug-target relationship in the bipartite network is scale-free. Several classes of proteins in the human genome appear to be better targets for drugs since they appear to be selectively enriched as drug targets for the currently FDA approved drugs. These initial observations allow for development of an integrated research methodology to identify general principles of the drug discovery process. Copyright (c) 2007 Mount Sinai School of Medicine. DOI: 10.1002/msj.20002 PMCID: PMC2561141 PMID: 17516560 [Indexed for MEDLINE] 158. BMC Bioinformatics. 2012 Nov 12;13:294. doi: 10.1186/1471-2105-13-294. Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction. Mora A(1), Donaldson IM. Author information: (1)Department for Molecular Biosciences, University of Oslo, Norway. BACKGROUND: Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes, and data reliability assumptions. Here we examined how the use of different data integration and representation techniques, or different notions of reliability, may affect the efficacy of degree and centrality as features in drug target prediction. RESULTS: Fifty percent of drug targets have a degree of less than nine, and ninety-five percent have a degree of less than ninety. We found that drug targets are over-represented in higher degree bins - this relationship is only seen for the consolidated interactome and it is not dependent on n-ary interaction data or its representation. Degree acts as a weak predictive feature for drug-target status and using more reliable subsets of the data does not increase this performance. However, performance does increase if only cancer-related drug targets are considered. We also note that a protein's membership in pathway records can act as a predictive feature that is better than degree and that high-centrality may be an indicator of a drug that is more likely to be withdrawn. CONCLUSIONS: These results show that protein interaction data integration and cleaning is an important consideration when incorporating network properties as predictive features for drug-target status. The provided scripts and data sets offer a starting point for further studies and cross-comparison of methods. DOI: 10.1186/1471-2105-13-294 PMCID: PMC3534413 PMID: 23146171 [Indexed for MEDLINE] 159. Expert Opin Ther Targets. 2013 Nov;17(11):1303-28. doi: 10.1517/14728222.2013.830105. Epub 2013 Oct 7. gp130: a promising drug target for cancer therapy. Xu S(1), Neamati N. Author information: (1)University of Michigan, Department of Medicinal Chemistry, College of Pharmacy , North Campus Research Complex, 2800 Plymouth Road, Building 520, Ann Arbor, MI 48109 , USA neamati@umich.edu. INTRODUCTION: Ubiquitously expressed in the human body, glycoprotein 130 (gp130) is a shared subunit of receptor complexes for at least nine cytokines (IL-6, OSM, LIF, IL-11, CNTF, CLC, IL-27, CT-1, and NP) that mediate highly diverse biological processes. Dysregulation of gp130 expression, activation, or associated signaling pathways are implicated in a variety of human diseases, including cancer. Accumulating evidence indicates that the gp130-mediated signaling networks play important roles in the progression of multiple types of cancer. AREAS COVERED: This review discusses the structural basis of gp130 in signal transduction activity and its role in physiological and pathological conditions, particularly cancer. We analyzed the currently available databases to illustrate the expression of gp130, its coexpression with other molecules involved in the gp130 signaling pathways, and the role of gp130 in cancer progression. Finally, we highlight strategies for blocking gp130 signaling and the currently available antagonists. EXPERT OPINION: As gp130 signaling mediates cancer progression, inhibition of gp130 activity offers a potential and promising approach to cancer therapy. Compared to antibodies blocking individual cytokines, gp130-targeted small-molecule inhibitors present multiple advantages. To achieve successful clinical outcomes for gp130-targeted cancer therapy, dosage determination, duration of therapy, and patient selection are some of the critical factors to be considered. DOI: 10.1517/14728222.2013.830105 PMID: 24099136 [Indexed for MEDLINE] 160. Nucleic Acids Res. 2015 Jan;43(Database issue):D963-7. doi: 10.1093/nar/gku1139. Epub 2014 Nov 11. PubAngioGen: a database and knowledge for angiogenesis and related diseases. Li P(1), Liu Y(1), Wang H(1), He Y(1), Wang X(1), He Y(1), Lv F(1), Chen H(1), Pang X(1), Liu M(2), Shi T(3), Yi Z(4). Author information: (1)The center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China. (2)The center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China Center for Cancer and Stem Cell Biology, Institute of Biosciences and Technology, Texas A&M University Health Science Center, Houston, TX 77030, USA. (3)The center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China tieliushi01@gmail.com. (4)The center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China zfyi@bio.ecnu.edu.cn. Angiogenesis is the process of generating new blood vessels based on existing ones, which is involved in many diseases including cancers, cardiovascular diseases and diabetes mellitus. Recently, great efforts have been made to explore the mechanisms of angiogenesis in various diseases and many angiogenic factors have been discovered as therapeutic targets in anti- or pro-angiogenic drug development. However, the resulted information is sparsely distributed and no systematical summarization has been made. In order to integrate these related results and facilitate the researches for the community, we conducted manual text-mining from published literature and built a database named as PubAngioGen (http://www.megabionet.org/aspd/). Our online application displays a comprehensive network for exploring the connection between angiogenesis and diseases at multilevels including protein-protein interaction, drug-target, disease-gene and signaling pathways among various cells and animal models recorded through text-mining. To enlarge the scope of the PubAngioGen application, our database also links to other common resources including STRING, DrugBank and OMIM databases, which will facilitate understanding the underlying molecular mechanisms of angiogenesis and drug development in clinical therapy. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gku1139 PMCID: PMC4383947 PMID: 25392416 [Indexed for MEDLINE] 161. Front Genet. 2013 Dec 17;4:293. doi: 10.3389/fgene.2013.00293. eCollection 2013. A tale of two drug targets: the evolutionary history of BACE1 and BACE2. Southan C(1), Hancock JM(2). Author information: (1)IUPHAR Database and Guide to Pharmacology Web Portal Group, University/BHF Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh Edinburgh, UK. (2)Department of Physiology, Development and Neuroscience, University of Cambridge Cambridge, UK. The beta amyloid (APP) cleaving enzyme (BACE1) has been a drug target for Alzheimer's Disease (AD) since 1999 with lead inhibitors now entering clinical trials. In 2011, the paralog, BACE2, became a new target for type II diabetes (T2DM) having been identified as a TMEM27 secretase regulating pancreatic β cell function. However, the normal roles of both enzymes are unclear. This study outlines their evolutionary history and new opportunities for functional genomics. We identified 30 homologs (UrBACEs) in basal phyla including Placozoans, Cnidarians, Choanoflagellates, Porifera, Echinoderms, Annelids, Mollusks and Ascidians (but not Ecdysozoans). UrBACEs are predominantly single copy, show 35-45% protein sequence identity with mammalian BACE1, are ~100 residues longer than cathepsin paralogs with an aspartyl protease domain flanked by a signal peptide and a C-terminal transmembrane domain. While multiple paralogs in Trichoplax and Monosiga pre-date the nervous system, duplication of the UrBACE in fish gave rise to BACE1 and BACE2 in the vertebrate lineage. The latter evolved more rapidly as the former maintained the emergent neuronal role. In mammals, Ka/Ks for BACE2 is higher than BACE1 but low ratios for both suggest purifying selection. The 5' exons show higher Ka/Ks than the catalytic section. Model organism genomes show the absence of certain BACE human substrates when the UrBACE is present. Experiments could thus reveal undiscovered substrates and roles. The human protease double-target status means that evolutionary trajectories and functional shifts associated with different substrates will have implications for the development of clinical candidates for both AD and T2DM. A rational basis for inhibition specificity ratios and assessing target-related side effects will be facilitated by a more complete picture of BACE1 and BACE2 functions informed by their evolutionary context. DOI: 10.3389/fgene.2013.00293 PMCID: PMC3865767 PMID: 24381583 162. In Silico Biol. 2004;4(3):355-60. A novel genomics approach for the identification of drug targets in pathogens, with special reference to Pseudomonas aeruginosa. Sakharkar KR(1), Sakharkar MK, Chow VT. Author information: (1)BioInformatics Institute, Singapore. Complete genome sequences of several pathogenic bacteria have been determined, and many more such projects are currently under way. While these data potentially contain all the determinants of host-pathogen interactions and possible drug targets, computational tools for selecting suitable candidates for further experimental analyses are currently limited. Detection of bacterial genes that are non-homologous to human genes, and are essential for the survival of the pathogen represents a promising means of identifying novel drug targets. We have used three-way genome comparisons to identify essential genes from Pseudomonas aeruginosa. Our approach identified 306 essential genes that may be considered as potential drug targets. The resultant analyses are in good agreement with the results of systematic gene deletion experiments. This approach enables rapid potential drug target identification, thereby greatly facilitating the search for new antibiotics. These results underscore the utility of large genomic databases for in silico systematic drug target identification in the post-genomic era. PMID: 15724285 [Indexed for MEDLINE] 163. Int J Mol Med. 2016 Jan;37(1):3-10. doi: 10.3892/ijmm.2015.2411. Epub 2015 Nov 13. Repositioning of drugs using open-access data portal DTome: A test case with probenecid (Review). Ahmed MU(1), Bennett DJ(1), Hsieh TC(1), Doonan BB(1), Ahmed S(1), Wu JM(1). Author information: (1)Department of Biochemistry and Molecular Biology, New York Medical College, Valhalla, NY 10595, USA. The one gene-one enzyme hypothesis, first introduced by Beadle and Tatum in the 1940s and based on their genetic analysis and observation of phenotype changes in Neurospora crassa challenged by various experimental conditions, has witnessed significant advances in recent decades. Much of our understanding of the association between genes and their phenotype expression has benefited from the completion of the human genome project, and has shown continual transformation guided by the effort directed at the annotation and characterization of human genes. Similarly, the idea of one drug‑one primary disease indication that traditionally has been the benchmark for the labeling and usage of drugs has also undergone evident progressive refinements; in recent years the science and practice of pharmaceutical development has notable success in the strategy of drug repurposing. Drug repurposing is an innovative approach where, instead of de novo synthesis and discovery of new drugs with novel indications, drug candidates with the desired usage are identified by a process of re‑profiling using an open‑source database or knowledge of known or failed drugs already in existence. In the present study, the repurposing drug strategy employing open‑access data portal drug‑target interactome (DTome) is applied to the uncovering of new clinical usage for probenecid. DOI: 10.3892/ijmm.2015.2411 PMID: 26572802 [Indexed for MEDLINE] 164. Clin Ther. 2016 Apr;38(4):688-701. doi: 10.1016/j.clinthera.2015.12.001. Epub 2016 Apr 21. IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research. Chen Y(1), Elenee Argentinis JD(2), Weber G(1). Author information: (1)IBM Almaden Research Center, San Jose, California. (2)IBM Watson, New York, New York. Electronic address: eargent@us.ibm.com. Life sciences researchers are under pressure to innovate faster than ever. Big data offer the promise of unlocking novel insights and accelerating breakthroughs. Ironically, although more data are available than ever, only a fraction is being integrated, understood, and analyzed. The challenge lies in harnessing volumes of data, integrating the data from hundreds of sources, and understanding their various formats. New technologies such as cognitive computing offer promise for addressing this challenge because cognitive solutions are specifically designed to integrate and analyze big datasets. Cognitive solutions can understand different types of data such as lab values in a structured database or the text of a scientific publication. Cognitive solutions are trained to understand technical, industry-specific content and use advanced reasoning, predictive modeling, and machine learning techniques to advance research faster. Watson, a cognitive computing technology, has been configured to support life sciences research. This version of Watson includes medical literature, patents, genomics, and chemical and pharmacological data that researchers would typically use in their work. Watson has also been developed with specific comprehension of scientific terminology so it can make novel connections in millions of pages of text. Watson has been applied to a few pilot studies in the areas of drug target identification and drug repurposing. The pilot results suggest that Watson can accelerate identification of novel drug candidates and novel drug targets by harnessing the potential of big data. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved. DOI: 10.1016/j.clinthera.2015.12.001 PMID: 27130797 [Indexed for MEDLINE] 165. BMC Bioinformatics. 2017 Sep 13;18(Suppl 10):393. doi: 10.1186/s12859-017-1785-7. MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection. He C(1), Micallef L(2), Tanoli ZU(3), Kaski S(2), Aittokallio T(3), Jacucci G(4). Author information: (1)Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2b, Helsinki, 00560, Finland. chen.he@helsinki.fi. (2)Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Konemiehentie 2, Espoo, 02150, Finland. (3)Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, 00014, Finland. (4)Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2b, Helsinki, 00560, Finland. BACKGROUND: Dispersed biomedical databases limit user exploration to generate structured knowledge. Linked Data unifies data structures and makes the dispersed data easy to search across resources, but it lacks supporting human cognition to achieve insights. In addition, potential errors in the data are difficult to detect in their free formats. Devising a visualization that synthesizes multiple sources in such a way that links between data sources are transparent, and uncertainties, such as data conflicts, are salient is challenging. RESULTS: To investigate the requirements and challenges of uncertainty-aware visualizations of linked data, we developed MediSyn, a system that synthesizes medical datasets to support drug treatment selection. It uses a matrix-based layout to visually link drugs, targets (e.g., mutations), and tumor types. Data uncertainties are salient in MediSyn; for example, (i) missing data are exposed in the matrix view of drug-target relations; (ii) inconsistencies between datasets are shown via overlaid layers; and (iii) data credibility is conveyed through links to data provenance. CONCLUSIONS: Through the synthesis of two manually curated datasets, cancer treatment biomarkers and drug-target bioactivities, a use case shows how MediSyn effectively supports the discovery of drug-repurposing opportunities. A study with six domain experts indicated that MediSyn benefited the drug selection and data inconsistency discovery. Though linked publication sources supported user exploration for further information, the causes of inconsistencies were not easy to find. Additionally, MediSyn could embrace more patient data to increase its informativeness. We derive design implications from the findings. DOI: 10.1186/s12859-017-1785-7 PMCID: PMC5606218 PMID: 28929971 [Indexed for MEDLINE] 166. Nucleic Acids Res. 2018 Jan 4;46(D1):D918-D924. doi: 10.1093/nar/gkx877. CR2Cancer: a database for chromatin regulators in human cancer. Ru B(1), Sun J(1), Tong Y(1), Wong CN(1), Chandra A(1), Tang ATS(1), Chow LKY(1), Wun WL(1), Levitskaya Z(1), Zhang J(1). Author information: (1)School of Biological Sciences, The University of Hong Kong, Hong Kong 999077, China. Chromatin regulators (CRs) can dynamically modulate chromatin architecture to epigenetically regulate gene expression in response to intrinsic and extrinsic signalling cues. Somatic alterations or misexpression of CRs might reprogram the epigenomic landscape of chromatin, which in turn lead to a wide range of common diseases, notably cancer. Here, we present CR2Cancer, a comprehensive annotation and visualization database for CRs in human cancer constructed by high throughput data analysis and literature mining. We collected and integrated genomic, transcriptomic, proteomic, clinical and functional information for over 400 CRs across multiple cancer types. We also built diverse types of CR-associated relations, including cancer type dependent (CR-target and miRNA-CR) and independent (protein-protein interaction and drug-target) ones. Furthermore, we manually curated around 6000 items of aberrant molecular alterations and interactions of CRs in cancer development from 5007 publications. CR2Cancer provides a user-friendly web interface to conveniently browse, search and download data of interest. We believe that this database would become a valuable resource for cancer epigenetics investigation and potential clinical application. CR2Cancer is freely available at http://cis.hku.hk/CR2Cancer. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gkx877 PMCID: PMC5753221 PMID: 29036683 167. Sci Rep. 2018 Mar 15;8(1):4624. doi: 10.1038/s41598-018-22834-4. Rare variants in drug target genes contributing to complex diseases, phenome-wide. Verma SS(1), Josyula N(2), Verma A(1), Zhang X(1), Veturi Y(1), Dewey FE(3), Hartzel DN(4), Lavage DR(4), Leader J(2)(4), Ritchie MD(1), Pendergrass SA(5). Author information: (1)Perelman School of Medicine, Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA. (2)Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, 17221, USA. (3)Regeneron Genetics Center, Tarrytown, NY, 10591, USA. (4)Phenomic Analytics and Clinical Data Core, Geisinger, Danville, PA, USA. (5)Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, 17221, USA. spendergrass@geisinger.edu. Erratum in Sci Rep. 2018 Oct 23;8(1):15911. The DrugBank database consists of ~800 genes that are well characterized drug targets. This list of genes is a useful resource for association testing. For example, loss of function (LOF) genetic variation has the potential to mimic the effect of drugs, and high impact variation in these genes can impact downstream traits. Identifying novel associations between genetic variation in these genes and a range of diseases can also uncover new uses for the drugs that target these genes. Phenome Wide Association Studies (PheWAS) have been successful in identifying genetic associations across hundreds of thousands of diseases. We have conducted a novel gene based PheWAS to test the effect of rare variants in DrugBank genes, evaluating associations between these genes and more than 500 quantitative and dichotomous phenotypes. We used whole exome sequencing data from 38,568 samples in Geisinger MyCode Community Health Initiative. We evaluated the results of this study when binning rare variants using various filters based on potential functional impact. We identified multiple novel associations, and the majority of the significant associations were driven by functionally annotated variation. Overall, this study provides a sweeping exploration of rare variant associations within functionally relevant genes across a wide range of diagnoses. DOI: 10.1038/s41598-018-22834-4 PMCID: PMC5854600 PMID: 29545597 168. Nucleic Acids Res. 2018 Jan 4;46(D1):D1-D7. doi: 10.1093/nar/gkx1235. The 2018 Nucleic Acids Research database issue and the online molecular biology database collection. Rigden DJ(1), Fernández XM(2). Author information: (1)Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK. (2)Institut Curie, 25 rue d'Ulm, 75005 Paris, France. The 2018 Nucleic Acids Research Database Issue contains 181 papers spanning molecular biology. Among them, 82 are new and 84 are updates describing resources that appeared in the Issue previously. The remaining 15 cover databases most recently published elsewhere. Databases in the area of nucleic acids include 3DIV for visualisation of data on genome 3D structure and RNArchitecture, a hierarchical classification of RNA families. Protein databases include the established SMART, ELM and MEROPS while GPCRdb and the newcomer STCRDab cover families of biomedical interest. In the area of metabolism, HMDB and Reactome both report new features while PULDB appears in NAR for the first time. This issue also contains reports on genomics resources including Ensembl, the UCSC Genome Browser and ENCODE. Update papers from the IUPHAR/BPS Guide to Pharmacology and DrugBank are highlights of the drug and drug target section while a number of proteomics databases including proteomicsDB are also covered. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, reviewing 138 entries, adding 88 new resources and eliminating 47 discontinued URLs, bringing the current total to 1737 databases. It is available at http://www.oxfordjournals.org/nar/database/c/. © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gkx1235 PMCID: PMC5753253 PMID: 29316735 169. Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W219-24. TarFisDock: a web server for identifying drug targets with docking approach. Li H(1), Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen J, Wang X, Jiang H. Author information: (1)Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China. TarFisDock is a web-based tool for automating the procedure of searching for small molecule-protein interactions over a large repertoire of protein structures. It offers PDTD (potential drug target database), a target database containing 698 protein structures covering 15 therapeutic areas and a reverse ligand-protein docking program. In contrast to conventional ligand-protein docking, reverse ligand-protein docking aims to seek potential protein targets by screening an appropriate protein database. The input file of this web server is the small molecule to be tested, in standard mol2 format; TarFisDock then searches for possible binding proteins for the given small molecule by use of a docking approach. The ligand-protein interaction energy terms of the program DOCK are adopted for ranking the proteins. To test the reliability of the TarFisDock server, we searched the PDTD for putative binding proteins for vitamin E and 4H-tamoxifen. The top 2 and 10% candidates of vitamin E binding proteins identified by TarFisDock respectively cover 30 and 50% of reported targets verified or implicated by experiments; and 30 and 50% of experimentally confirmed targets for 4H-tamoxifen appear amongst the top 2 and 5% of the TarFisDock predicted candidates, respectively. Therefore, TarFisDock may be a useful tool for target identification, mechanism study of old drugs and probes discovered from natural products. TarFisDock and PDTD are available at http://www.dddc.ac.cn/tarfisdock/. DOI: 10.1093/nar/gkl114 PMCID: PMC1538869 PMID: 16844997 [Indexed for MEDLINE] 170. Sci China C Life Sci. 2009 Apr;52(4):398-404. doi: 10.1007/s11427-009-0055-y. Epub 2009 Apr 21. Identifying drug-target proteins based on network features. Zhu M(1), Gao L, Li X, Liu Z. Author information: (1)School of Biomedical Engineering, Capital Medical University, Beijing 100069, China. Proteins rarely function in isolation inside and outside cells, but operate as part of a highly interconnected cellular network called the interaction network. Therefore, the analysis of the properties of drug-target proteins in the biological network is especially helpful for understanding the mechanism of drug action in terms of informatics. At present, no detailed characterization and description of the topological features of drug-target proteins have been available in the human protein-protein interaction network. In this work, by mapping the drug-targets in DrugBank onto the interaction network of human proteins, five topological indices of drug-targets were analyzed and compared with those of the whole protein interactome set and the non-drug-target set. The experimental results showed that drug-target proteins have higher connectivity and quicker communication with each other in the PPI network. Based on these features, all proteins in the interaction network were ranked. The results showed that, of the top 100 proteins, 48 are covered by DrugBank; of the remaining 52 proteins, 9 are drug-target proteins covered by the TTD, Matador and other databases, while others have been demonstrated to be drug-target proteins in the literature. DOI: 10.1007/s11427-009-0055-y PMID: 19381466 [Indexed for MEDLINE] 171. J Clin Bioinforma. 2012 Jan 13;2(1):1. doi: 10.1186/2043-9113-2-1. A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer. Yeh SH(#)(1), Yeh HY(#)(2), Soo VW(2)(1). Author information: (1)Institute of Information Systems and Applications, National Tsing Hua University, HsinChu 300, Taiwan. (2)Department of Computer Science, National Tsing Hua University, HsinChu 300, Taiwan. (#)Contributed equally BACKGROUND: Systematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases. METHODS: We integrate gene expression, disease genes and interaction networks to identify the effective drug targets which have a strong influence on disease genes using network flow approach. In the experiments, we adopt the microarray dataset containing 62 prostate cancer samples and 41 normal samples, 108 known prostate cancer genes and 322 approved drug targets treated in human extracted from DrugBank database to be candidate proteins as our test data. Using our method, we prioritize the candidate proteins and validate them to the known prostate cancer drug targets. RESULTS: We successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets which raise the attention to biologists at present. We denote that it is hard to discover drug targets based only on differential expression changes due to the fact that those genes used to be drug targets may not always have significant expression changes. Comparing to previous methods that depend on the network topology attributes, they turn out that the genes having potential as drug targets are weakly correlated to critical points in a network. In comparison with previous methods, our results have highest mean average precision and also rank the position of the truly drug targets higher. It thereby verifies the effectiveness of our method. CONCLUSIONS: Our method does not know the real ideal routes in the disease network but it tries to find the feasible flow to give a strong influence to the disease genes through possible paths. We successfully formulate the identification of drug target prediction as a maximum flow problem on biological networks and discover potential drug targets in an accurate manner. DOI: 10.1186/2043-9113-2-1 PMCID: PMC3285036 PMID: 22239822 172. Yakugaku Zasshi. 2008 Nov;128(11):1525-35. [Bio-database literacy and its application with cis-regulatory modules to find novel drug target proteins]. [Article in Japanese] Miyazaki S(1). Author information: (1)Department of Medicinal and Life Science, Faculty of Pharmaceutical Sciences, Tokyo University of Science, Yamazaki, Noda City, Japan. smiyazak@rs.noda.tus.ac.jp We have expected Bioinformatics as tools to extract new knowledge from whole genome sequences of various organisms. In the post-genome era, to find some knowledge of the gene regulation including locations of cis-regulatory elements, modules and those combinations became one of the big challenges on Bioinformatics field. Because, it is difficult and inefficient to determine all possible combinations of cis-regulatory elements by bio-chemical approach. However, computational ways might allow us to find out all cis-elements within a time frame. In this review, we introduce the current status of public available databases on Internet comparing our original database for the cis-modules. We also explain our new mathematical measurement to characterize sequence patterns for cis-elements of each transcription factors and its application to predict the gene expression regulation network. PMID: 18981686 [Indexed for MEDLINE] 173. Front Pharmacol. 2015 Aug 25;6:179. doi: 10.3389/fphar.2015.00179. eCollection 2015. Computational drug repositioning for peripheral arterial disease: prediction of anti-inflammatory and pro-angiogenic therapeutics. Chu LH(1), Annex BH(2), Popel AS(1). Author information: (1)Department of Biomedical Engineering, School of Medicine, Johns Hopkins University Baltimore, MD, USA. (2)Division of Cardiovascular Medicine, Department of Medicine and Robert M. Berne Cardiovascular Research Center, University of Virginia School of Medicine Charlottesville, VA, USA. Peripheral arterial disease (PAD) results from atherosclerosis that leads to blocked arteries and reduced blood flow, most commonly in the arteries of the legs. PAD clinical trials to induce angiogenesis to improve blood flow conducted in the last decade have not succeeded. We have recently constructed PADPIN, protein-protein interaction network (PIN) of PAD, and here we combine it with the drug-target relations to identify potential drug targets for PAD. Specifically, the proteins in the PADPIN were classified as belonging to the angiome, immunome, and arteriome, characterizing the processes of angiogenesis, immune response/inflammation, and arteriogenesis, respectively. Using the network-based approach we predict the candidate drugs for repositioning that have potential applications to PAD. By compiling the drug information in two drug databases DrugBank and PharmGKB, we predict FDA-approved drugs whose targets are the proteins annotated as anti-angiogenic and pro-inflammatory, respectively. Examples of pro-angiogenic drugs are carvedilol and urokinase. Examples of anti-inflammatory drugs are ACE inhibitors and maraviroc. This is the first computational drug repositioning study for PAD. DOI: 10.3389/fphar.2015.00179 PMCID: PMC4548203 PMID: 26379552 174. Bioinformatics. 2015 Sep 15;31(18):3035-42. doi: 10.1093/bioinformatics/btv302. Epub 2015 May 13. GLASS: a comprehensive database for experimentally validated GPCR-ligand associations. Chan WK(1), Zhang H(1), Yang J(1), Brender JR(1), Hur J(1), Özgür A(1), Zhang Y(2). Author information: (1)Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey. (2)Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey. MOTIVATION: G protein-coupled receptors (GPCRs) are probably the most attractive drug target membrane proteins, which constitute nearly half of drug targets in the contemporary drug discovery industry. While the majority of drug discovery studies employ existing GPCR and ligand interactions to identify new compounds, there remains a shortage of specific databases with precisely annotated GPCR-ligand associations. RESULTS: We have developed a new database, GLASS, which aims to provide a comprehensive, manually curated resource for experimentally validated GPCR-ligand associations. A new text-mining algorithm was proposed to collect GPCR-ligand interactions from the biomedical literature, which is then crosschecked with five primary pharmacological datasets, to enhance the coverage and accuracy of GPCR-ligand association data identifications. A special architecture has been designed to allow users for making homologous ligand search with flexible bioactivity parameters. The current database contains ∼500 000 unique entries, of which the vast majority stems from ligand associations with rhodopsin- and secretin-like receptors. The GLASS database should find its most useful application in various in silico GPCR screening and functional annotation studies. AVAILABILITY AND IMPLEMENTATION: The website of GLASS database is freely available at http://zhanglab.ccmb.med.umich.edu/GLASS/. CONTACT: zhng@umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. DOI: 10.1093/bioinformatics/btv302 PMCID: PMC4668776 PMID: 25971743 [Indexed for MEDLINE] 175. Bioinformation. 2012;8(3):134-41. Epub 2012 Feb 3. Metabolic pathway analysis and molecular docking analysis for identification of putative drug targets in Toxoplasma gondii: novel approach. Gautam B, Singh G, Wadhwa G, Farmer R, Singh S, Singh AK, Jain PA, Yadav PK. Toxoplasma gondii is an obligate intracellular apicomplexan parasite that can infect a wide range of warm-blooded animals including humans. In humans and other intermediate hosts, toxoplasma develops into chronic infection that cannot be eliminated by host's immune response or by currently used drugs. In most cases, chronic infections are largely asymptomatic unless the host becomes immune compromised. Thus, toxoplasma is a global health problem and the situation has become more precarious due to the advent of HIV infections and poor toleration of drugs used to treat toxoplasma infection, having severe side effects and also resistance have been developed to the current generation of drugs. The emergence of these drug resistant varieties of T. gondii has led to a search for novel drug targets. We have performed a comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen T. gondii. The enzymes in the unique pathways of T. gondii, which do not show similarity to any protein from the host, represent attractive potential drug targets. We have listed out 11 such potential drug targets which are playing some important work in more than one pathway. Out of these, one important target is Glutamate dehydrogenase enzyme; it plays crucial part in oxidation reduction, metabolic process and amino acid metabolic process. As this is also present in the targets of tropical diseases of TDR (Tropical disease related Drug) target database and no PDB and MODBASE 3D structural model is available, homology models for Glutamate dehydrogenase enzyme were generated using MODELLER9v6. The model was further explored for the molecular dynamics simulation study with GROMACS, virtual screening and docking studies with suitable inhibitors against the NCI diversity subset molecules from ZINC database, by using AutoDock-Vina. The best ten docking solutions were selected (ZINC01690699, ZINC17465979, ZINC17465983, ZINC18141294_03, ZINC05462670, ZINC01572309, ZINC18055497_01, ZINC18141294, ZINC05462674 and ZINC13152284_01). Further the Complexes were analyzed through LIGPLOT. On the basis of Complex scoring and binding ability it is deciphered that these NCI diversity set II compounds, specifically ZINC01690699 (as it has minimum energy score and one of the highest number of interactions with the active site residue), could be promising inhibitors for T. gondii using Glutamate dehydrogenase as Drug target. PMCID: PMC3283885 PMID: 22368385 176. Sci Rep. 2017 Mar 6;7:43738. doi: 10.1038/srep43738. A Bayesian Target Predictor Method based on Molecular Pairing Energies estimation. Oliver A(1), Canals V(1), Rosselló JL(1). Author information: (1)Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Spain. Virtual screening (VS) is applied in the early drug discovery phases for the quick inspection of huge molecular databases to identify those compounds that most likely bind to a given drug target. In this context, there is the necessity of the use of compact molecular models for database screening and precise target prediction in reasonable times. In this work we present a new compact energy-based model that is tested for its application to Virtual Screening and target prediction. The model can be used to quickly identify active compounds in huge databases based on the estimation of the molecule's pairing energies. The greatest molecular polar regions along with its geometrical distribution are considered by using a short set of smart energy vectors. The model is tested using similarity searches within the Directory of Useful Decoys (DUD) database. The results obtained are considerably better than previously published models. As a Target prediction methodology we propose the use of a Bayesian Classifier that uses a combination of different active compounds to build an energy-dependent probability distribution function for each target. DOI: 10.1038/srep43738 PMCID: PMC5338323 PMID: 28263323 [Indexed for MEDLINE] 177. OMICS. 2012 Oct;16(10):513-26. doi: 10.1089/omi.2011.0160. Epub 2012 Jul 9. Identification of SRC as a potent drug target for asthma, using an integrative approach of protein interactome analysis and in silico drug discovery. Randhawa V(1), Bagler G. Author information: (1)Biotechnology Division, Institute of Himalayan Bioresource Technology, Council of Scientific and Industrial Research (CSIR-IHBT), Palampur, India. Network-biology inspired modeling of interactome data and computational chemistry have the potential to revolutionize drug discovery by complementing conventional methods. We consider asthma, a complex disease characterized by intricate molecular mechanisms, for our study. We aim to integrate prediction of potent drug targets using graph-theoretical methods and subsequent identification of small molecules capable of modulating activity of the best target. In this work, we construct the protein interactome underlying this disease: Asthma Protein Interactome (API). Using a strategy based on network analysis of the interactome, we identify a set of potential drug targets for asthma. Topologically and dynamically, v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (SRC) emerges as the most central target in API. SRC is known to play an important role in promoting airway smooth muscle cell growth and facilitating migration in airway remodeling. From interactome analysis, and with the reported role in respiratory mechanisms, SRC emerges as a promising drug target for asthma. Further, we proceed to identify leads for SRC from a public database of small molecules. We predict two potential leads for SRC using ligand-based virtual screening methodology. DOI: 10.1089/omi.2011.0160 PMID: 22775150 [Indexed for MEDLINE] 178. Comb Chem High Throughput Screen. 2011 Jul;14(6):532-47. Comparative modeling: the state of the art and protein drug target structure prediction. Liu T(1), Tang GW, Capriotti E. Author information: (1)Department of Bioengineering, Stanford University, 318 Campus Dr, Room S240 Mail code: 5448, Stanford, CA 94305, USA. The goal of computational protein structure prediction is to provide three-dimensional (3D) structures with resolution comparable to experimental results. Comparative modeling, which predicts the 3D structure of a protein based on its sequence similarity to homologous structures, is the most accurate computational method for structure prediction. In the last two decades, significant progress has been made on comparative modeling methods. Using the large number of protein structures deposited in the Protein Data Bank (~65,000), automatic prediction pipelines are generating a tremendous number of models (~1.9 million) for sequences whose structures have not been experimentally determined. Accurate models are suitable for a wide range of applications, such as prediction of protein binding sites, prediction of the effect of protein mutations, and structure-guided virtual screening. In particular, comparative modeling has enabled structure-based drug design against protein targets with unknown structures. In this review, we describe the theoretical basis of comparative modeling, the available automatic methods and databases, and the algorithms to evaluate the accuracy of predicted structures. Finally, we discuss relevant applications in the prediction of important drug target proteins, focusing on the G protein-coupled receptor (GPCR) and protein kinase families. PMID: 21521153 [Indexed for MEDLINE] 179. PLoS Negl Trop Dis. 2015 Jan 8;9(1):e3435. doi: 10.1371/journal.pntd.0003435. eCollection 2015 Jan. In silico repositioning-chemogenomics strategy identifies new drugs with potential activity against multiple life stages of Schistosoma mansoni. Neves BJ(1), Braga RC(2), Bezerra JC(3), Cravo PV(4), Andrade CH(1). Author information: (1)LabMol - Laboratory for Drug Design and Modeling, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Brazil; Instituto de Patologia Tropical e Saúde Pública, Universidade Federal de Goiás, Goiânia, Brazil. (2)LabMol - Laboratory for Drug Design and Modeling, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Brazil; Instituto de Química, Universidade Federal de Goiás, Goiaânia, Brazil. (3)Instituto de Patologia Tropical e Saúde Pública, Universidade Federal de Goiás, Goiânia, Brazil. (4)Instituto de Patologia Tropical e Saúde Pública, Universidade Federal de Goiás, Goiânia, Brazil; Centro de Malária e Doenças Tropicais, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisboa, Portugal. Erratum in PLoS Negl Trop Dis. 2015 Feb;9(2):e0003554. Morbidity and mortality caused by schistosomiasis are serious public health problems in developing countries. Because praziquantel is the only drug in therapeutic use, the risk of drug resistance is a concern. In the search for new schistosomicidal drugs, we performed a target-based chemogenomics screen of a dataset of 2,114 proteins to identify drugs that are approved for clinical use in humans that may be active against multiple life stages of Schistosoma mansoni. Each of these proteins was treated as a potential drug target, and its amino acid sequence was used to interrogate three databases: Therapeutic Target Database (TTD), DrugBank and STITCH. Predicted drug-target interactions were refined using a combination of approaches, including pairwise alignment, conservation state of functional regions and chemical space analysis. To validate our strategy, several drugs previously shown to be active against Schistosoma species were correctly predicted, such as clonazepam, auranofin, nifedipine, and artesunate. We were also able to identify 115 drugs that have not yet been experimentally tested against schistosomes and that require further assessment. Some examples are aprindine, gentamicin, clotrimazole, tetrabenazine, griseofulvin, and cinnarizine. In conclusion, we have developed a systematic and focused computer-aided approach to propose approved drugs that may warrant testing and/or serve as lead compounds for the design of new drugs against schistosomes. DOI: 10.1371/journal.pntd.0003435 PMCID: PMC4287566 PMID: 25569258 [Indexed for MEDLINE] 180. PLoS Comput Biol. 2015 Mar 31;11(3):e1004153. doi: 10.1371/journal.pcbi.1004153. eCollection 2015 Mar. Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens. Lo YC(1), Senese S(2), Li CM(3), Hu Q(4), Huang Y(3), Damoiseaux R(5), Torres JZ(6). Author information: (1)Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America; Program in Bioengineering, University of California, Los Angeles, Los Angeles, California, United States of America. (2)Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America. (3)Drug Studies Unit, Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, California, United States of America. (4)Institute for Digital Research and Education, University of California, Los Angeles, Los Angeles, California, United States of America. (5)California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California, United States of America. (6)Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California, United States of America; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, United States of America. Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60-70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/). DOI: 10.1371/journal.pcbi.1004153 PMCID: PMC4380459 PMID: 25826798 [Indexed for MEDLINE] 181. J Chem Inf Model. 2013 Apr 22;53(4):753-62. doi: 10.1021/ci400010x. Epub 2013 Apr 8. Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. Cheng F(1), Li W, Wu Z, Wang X, Zhang C, Li J, Liu G, Tang Y. Author information: (1)Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China. Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug-target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 ± 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug-target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs. DOI: 10.1021/ci400010x PMID: 23527559 [Indexed for MEDLINE] 182. J Bioinform Comput Biol. 2013 Aug;11(4):1350003. doi: 10.1142/S0219720013500030. Epub 2013 Feb 25. Drug target prioritization in Plasmodium falciparum through metabolic network analysis, and inhibitor designing using virtual screening and docking approach. Yadav MK(1), Pandey SK, Swati D. Author information: (1)Department of Bioinformatics, MMV, Banaras Hindu University, Varanasi 221005, India. manojiids@gmail.com The genome sequence of Plasmodium falciparum reveals that many metabolic pathways are unique as compared to its human host. Metabolic Network Analysis was carried out to find the essential enzymes critical for the survival of the pathogen. In the present study, choke point and load point analysis was used to locate putative targets. The identified targets were further checked to confirm that no alternate pathway or human homolog exists. Among the top 15 enzymes obtained from this analysis, we have selected P. falciparum orotidine-5'-monophosphate decarboxylase (PfODCase) enzyme as it is sequentially and structurally different from that of humans, for searching novel inhibitors. A five-point 3D pharmacophore was generated for the crystal structure of PfODCase complexes with uridine-5'-monophosphate (U5P). The binding site environment shows three H-bond acceptors, one H-bond donor and one negative ionizable feature. This pharmacophore model was used as a 3D query to perform virtual screening experiments against 2,664,779 standard lead compounds obtained from the freely available ZINC database. Top 10 hits obtained from virtual screening were selected for molecular docking experiments against PfODCase in order to verify their results and to have a better insight into their binding modes. Here, docking of U5P with PfODCase is used as a control. We have identified six compounds, among them, few are U5P analogs and others are novel ones with diverse scaffolds. The key residues: Lys42, Asp20, Lys72, Ser127, Ala184, Gln185 and Arg203 at the main binding pocket of PfODCase are responsible for better stability of diverse ligands. These compounds according to their free energy of binding could serve as potent leads for designing novel inhibitors against malarial ODCase enzyme. DOI: 10.1142/S0219720013500030 PMID: 23859267 [Indexed for MEDLINE] 183. J Microbiol Methods. 2014 Jun;101:1-8. doi: 10.1016/j.mimet.2014.03.009. Epub 2014 Mar 29. Comparative genomics study for the identification of drug and vaccine targets in Staphylococcus aureus: MurA ligase enzyme as a proposed candidate. Ghosh S(1), Prava J(2), Samal HB(2), Suar M(1), Mahapatra RK(3). Author information: (1)School of Biotechnology, Campus-11, KIIT University, Odisha 751024, India. (2)Bioinformatics Department, BJB Autonomous College, Bhubaneswar, Odisha 751001, India. (3)School of Biotechnology, Campus-11, KIIT University, Odisha 751024, India. Electronic address: rmahapatra@kiitbiotech.ac.in. Now-a-days increasing emergence of antibiotic-resistant pathogenic microorganisms is one of the biggest challenges for management of disease. In the present study comparative genomics, metabolic pathways analysis and additional parameters were defined for the identification of 94 non-homologous essential proteins in Staphylococcus aureus genome. Further study prioritized 19 proteins as vaccine candidates where as druggability study reports 34 proteins suitable as drug targets. Enzymes from peptidoglycan biosynthesis, folate biosynthesis were identified as candidates for drug development. Furthermore, bacterial secretory proteins and few hypothetical proteins identified in our analysis fulfill the criteria of vaccine candidates. As a case study, we built a homology model of one of the potential drug target, MurA ligase, using MODELLER (9v12) software. The model has been further selected for in silico docking study with inhibitors from the DrugBank database. Results from this study could facilitate selection of proteins for entry into drug design and vaccine production pipelines. Copyright © 2014 Elsevier B.V. All rights reserved. DOI: 10.1016/j.mimet.2014.03.009 PMID: 24685600 [Indexed for MEDLINE] 184. J Proteome Res. 2018 May 4;17(5):1749-1760. doi: 10.1021/acs.jproteome.7b00702. Epub 2018 Apr 6. In Silico Enhancing M. tuberculosis Protein Interaction Networks in STRING To Predict Drug-Resistance Pathways and Pharmacological Risks. Mei S(1). Author information: (1)Software College , Shenyang Normal University , Shenyang 110034 , China. Bacterial protein-protein interaction (PPI) networks are significant to reveal the machinery of signal transduction and drug resistance within bacterial cells. The database STRING has collected a large number of bacterial pathogen PPI networks, but most of the data are of low quality without being experimentally or computationally validated, thus restricting its further biomedical applications. We exploit the experimental data via four solutions to enhance the quality of M. tuberculosis H37Rv (MTB) PPI networks in STRING. Computational results show that the experimental data derived jointly by two-hybrid and copurification approaches are the most reliable to train an L2-regularized logistic regression model for MTB PPI network validation. On the basis of the validated MTB PPI networks, we further study the three problems via breadth-first graph search algorithm: (1) discovery of MTB drug-resistance pathways through searching for the paths between known drug-target genes and drug-resistance genes, (2) choosing potential cotarget genes via searching for the critical genes located on multiple pathways, and (3) choosing essential drug-target genes via analysis of network degree distribution. In addition, we further combine the validated MTB PPI networks with human PPI networks to analyze the potential pharmacological risks of known and candidate drug-target genes from the point of view of system pharmacology. The evidence from protein structure alignment demonstrates that the drugs that act on MTB target genes could also adversely act on human signaling pathways. DOI: 10.1021/acs.jproteome.7b00702 PMID: 29611419 185. J Cheminform. 2015 Dec 29;7:63. doi: 10.1186/s13321-015-0110-6. eCollection 2015. Accurate and efficient target prediction using a potency-sensitive influence-relevance voter. Lusci A(1), Browning M(2), Fooshee D(1), Swamidass J(2), Baldi P(1). Author information: (1)School of Information and Computer Sciences, University of California, Irvine, Irvine, USA. (2)Pathology and Immunology, Washington University in St. Louis, St. Louis, USA. BACKGROUND: A number of algorithms have been proposed to predict the biological targets of diverse molecules. Some are structure-based, but the most common are ligand-based and use chemical fingerprints and the notion of chemical similarity. These methods tend to be computationally faster than others, making them particularly attractive tools as the amount of available data grows. RESULTS: Using a ChEMBL-derived database covering 490,760 molecule-protein interactions and 3236 protein targets, we conduct a large-scale assessment of the performance of several target-prediction algorithms at predicting drug-target activity. We assess algorithm performance using three validation procedures: standard tenfold cross-validation, tenfold cross-validation in a simulated screen that includes random inactive molecules, and validation on an external test set composed of molecules not present in our database. CONCLUSIONS: We present two improvements over current practice. First, using a modified version of the influence-relevance voter (IRV), we show that using molecule potency data can improve target prediction. Second, we demonstrate that random inactive molecules added during training can boost the accuracy of several algorithms in realistic target-prediction experiments. Our potency-sensitive version of the IRV (PS-IRV) obtains the best results on large test sets in most of the experiments. Models and software are publicly accessible through the chemoinformatics portal at http://chemdb.ics.uci.edu/. DOI: 10.1186/s13321-015-0110-6 PMCID: PMC4696267 PMID: 26719774 186. Interdiscip Sci. 2013 Dec;5(4):296-311. doi: 10.1007/s12539-013-0180-y. Epub 2014 Jan 10. Uridine monophosphate kinase as potential target for tuberculosis: from target to lead identification. Arvind A(1), Jain V, Saravanan P, Mohan CG. Author information: (1)Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, 160062, India. Mycobacterium tuberculosis (Mtb) is a causative agent of tuberculosis (TB) disease, which has affected approximately 2 billion people worldwide. Due to the emergence of resistance towards the existing drugs, discovery of new anti-TB drugs is an important global healthcare challenge. To address this problem, there is an urgent need to identify new drug targets in Mtb. In the present study, the subtractive genomics approach has been employed for the identification of new drug targets against TB. Screening the Mtb proteome using the Database of Essential Genes (DEG) and human proteome resulted in the identification of 60 key proteins which have no eukaryotic counterparts. Critical analysis of these proteins using Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways database revealed uridine monophosphate kinase (UMPK) enzyme as a potential drug target for developing novel anti-TB drugs. Homology model of Mtb-UMPK was constructed for the first time on the basis of the crystal structure of E. coli-UMPK, in order to understand its structure-function relationships, and which would in turn facilitate to perform structure-based inhibitor design. Furthermore, the structural similarity search was carried out using physiological inhibitor UTP of Mtb-UMPK to virtually screen ZINC database. Retrieved hits were further screened by implementing several filters like ADME and toxicity followed by molecular docking. Finally, on the basis of the Glide docking score and the mode of binding, 6 putative leads were identified as inhibitors of this enzyme which can potentially emerge as future drugs for the treatment of TB. DOI: 10.1007/s12539-013-0180-y PMID: 24402823 [Indexed for MEDLINE] 187. Comp Biochem Physiol Part D Genomics Proteomics. 2007 Mar;2(1):9-17. doi: 10.1016/j.cbd.2006.01.003. Epub 2006 Nov 24. Discovering drug targets through the web. Wishart DS(1). Author information: (1)Departments of Computing Science and Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8. Traditionally, drug-target discovery is a "wet-bench" experimental process, depending on carefully designed genetic screens, biochemical tests and cellular assays to identify proteins and genes that are associated with a particular disease or condition. However, recent advances in DNA sequencing, transcript profiling, protein identification and protein quantification are leading to a flood of genomic and proteomic data that is, or potentially could be, linked to disease data. The quantity of data generated by these high throughput methods is forcing scientists to re-think the way they do traditional drug-target discovery. In particular it is leading them more and more towards identifying potential drug targets using computers. In fact, drug-target identification is now being done as much on the desk-top as on the bench-top. This review focuses on describing how drug-target discovery can be done in silico (i.e. via computer) using a variety of bioinformatic resources that are freely available on the web. Specifically, it highlights a number of web-accessible sequence databases, automated genome annotation tools, text mining tools; and integrated drug/sequence databases that can be used to identify drug targets for both endogenous (genetic and epigenetic) diseases as well as exogenous (infectious) diseases. DOI: 10.1016/j.cbd.2006.01.003 PMID: 20483274 188. Curr Protoc Bioinformatics. 2014;45:8.8.1-39. doi: 10.1002/0471250953.bi0808s45. Using VisANT to Analyze Networks. Hu Z(1). Author information: (1)Bioinformatics Program, Boston University, Boston, Massachusetts. VisANT is a Web-based workbench for the integrative analysis of biological networks with unique features such as exploratory navigation of interaction network and multi-scale visualization and inference with integrated hierarchical knowledge. It provides functionalities for convenient construction, visualization, and analysis of molecular and higher order networks based on functional (e.g., expression profiles, phylogenetic profiles) and physical (e.g., yeast two-hybrid, chromatin-immunoprecipitation and drug target) relations from either the Predictome database or user-defined data sets. Analysis capabilities include network structure analysis, overrepresentation analysis, expression enrichment analysis etc. Additionally, network can be saved, accessed, and shared online. VisANT is able to develop and display meta-networks for meta-nodes that are structural complexes or pathways or any kind of subnetworks. Further, VisANT supports a growing number of standard exchange formats and database referencing standards, e.g., PSI-MI, KGML, BioPAX, SBML(in progress) Multiple species are supported to the extent that interactions or associations are available (i.e., public datasets or Predictome database). DOI: 10.1002/0471250953.bi0808s45 PMCID: PMC4240741 PMID: 25422679 [Indexed for MEDLINE] 189. Curr Cancer Drug Targets. 2001 May;1(1):73-83. Drug target discovery by gene expression analysis: cell cycle genes. Walker MG(1). Author information: (1)Incyte Genomics and Department of Medicine, Stanford University, 1475 Flamingo Way, Sunnyvale, CA, USA. mwalker@stanfordalumni.org Gene expression microarrays and gene expression databases provide new opportunities for the discovery of drug targets and for determination of a drug's mode of action. We review gene expression analysis methods and describe studies that have identified cell cycle genes using differential expression analysis and co-expression analysis. We present an example of the identification of previously-unrecognized human cell cycle genes, CDCA1 through CDCA8, that are co-expressed with known cell cycle genes including CDC2, CDC7, CDC23, cyclin, MCAK, mki67a, topoisomerase II, and others. PMID: 12188893 [Indexed for MEDLINE] 190. Curr Protoc Bioinformatics. 2018 Mar;61(1):1.34.1-1.34.46. doi: 10.1002/cpbi.46. Accessing Expert-Curated Pharmacological Data in the IUPHAR/BPS Guide to PHARMACOLOGY. Sharman JL(1), Harding SD(1), Southan C(1), Faccenda E(1), Pawson AJ(1), Davies JA(1); NC-IUPHAR(1). Author information: (1)Deanery of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom. The IUPHAR/BPS Guide to PHARMACOLOGY is an expert-curated, open-access database of information on drug targets and the substances that act on them. This unit describes the procedures for searching and downloading ligand-target binding data and for finding detailed annotations and the most relevant literature. The database includes concise overviews of the properties of 1,700 data-supported human drug targets and related proteins, divided into families, and 9,000 small molecule and peptide experimental ligands and approved drugs that bind to those targets. More detailed descriptions of pharmacology, function, and pathophysiology are provided for a subset of important targets. The information is reviewed regularly by expert subcommittees of the IUPHAR Committee on Receptor Nomenclature and Drug Classification. A new immunopharmacology portal has recently been added, drawing together data on immunological targets, ligands, cell types, processes and diseases. The data are available for download and can be accessed computationally via Web services. © 2018 by John Wiley & Sons, Inc. © 2018 John Wiley & Sons, Inc. DOI: 10.1002/cpbi.46 PMID: 30040201 191. Mol Biosyst. 2017 Aug 22;13(9):1788-1796. doi: 10.1039/c7mb00059f. Identifying the common genetic networks of ADR (adverse drug reaction) clusters and developing an ADR classification model. Hwang Y(1), Oh M, Jang G, Lee T, Park C, Ahn J, Yoon Y. Author information: (1)Dept. of Computer Science, University of Southern California, USA. youhyeoh@usc.edu. Adverse drug reactions (ADRs) are one of the major concerns threatening public health and have resulted in failures in drug development. Thus, predicting ADRs and discovering the mechanisms underlying ADRs have become important tasks in pharmacovigilance. Identification of potential ADRs by computational approaches in the early stages would be advantageous in drug development. Here we propose a computational method that elucidates the action mechanisms of ADRs and predicts potential ADRs by utilizing ADR genes, drug features, and protein-protein interaction (PPI) networks. If some ADRs share similar features, there is a high possibility that they may appear together in a drug and share analogous mechanisms. Proceeding from this assumption, we clustered ADRs according to interactions of ADR genes in the PPI networks and the frequency of co-occurrence of ADRs in drugs. ADR clusters were verified based on a side effect database and literature data regarding whether ADRs have relevance to other ADRs in the same cluster. Gene networks shared by ADRs in each cluster were constructed by cumulating the shortest paths between drug target genes and ADR genes in the PPI network. We developed a classification model to predict potential ADRs using these gene networks shared by ADRs and calculated cross-validation AUC (area under the curve) values for each ADR cluster. In addition, in order to demonstrate correlations between gene networks shared by ADRs and ADRs in a cluster, we applied the Wilcoxon rank sum statistical test to the literature data and results of a Google query search. We attained statistically meaningful p-values (<0.05) for every ADR cluster. The results suggest that our approach provides insights into discovering the action mechanisms of ADRs and is a novel attempt to predict ADRs in a biological aspect. DOI: 10.1039/c7mb00059f PMID: 28702565 [Indexed for MEDLINE] 192. Curr Top Med Chem. 2015;15(1):57-64. Multiclass comparative virtual screening to identify novel Hsp90 inhibitors: a therapeutic breast cancer drug target. Dunna NR, Bandaru S, Akare UR, Rajadhyax S, Gutlapalli VR, Yadav M, Nayarisseri A(1). Author information: (1)In silico Research Laboratory, Eminent Biosciences, Vijaynagar, Indore - 452010, India. anuraj@eminentbio.com. Since the discovery of Hsp90, a decade ago, it has surfaced as a potential target in breast cancer therapy along with other cancers. In present study, we have selected seven established Hsp inhibitors viz., PU3, CCT-018159, CNF-2024, SNX-5422, NVP (AUY-922), EGCG and IPI-504 used in the treatment of cancer. Considering these seven inhibitors as a parent compound, ligand based search was carried out with 90% similarity in Pubchem database (31 million compounds). All the similar molecules belonging to respective parent compound along with similar compound were subjected to virtual screening using MolDock and PLP algorithm aided molecular docking. Compounds with highest docking rerank scores were selected and filtered through Lipinski's drug-likeness filters and toxicity parameters. New candidate (Pubchem CID: 11363378) qualified to demonstrate considerable affinity towards Hsp90. The selected compound was further pharmcophorically incited for receptor- ligand interactions like H-bond, electrostatic, hydrophobic interactions etc. PMID: 25579569 [Indexed for MEDLINE] 193. Molecules. 2014 Jul 11;19(7):10150-76. doi: 10.3390/molecules190710150. Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Grinter SZ(1), Zou X(2). Author information: (1)Informatics Institute, University of Missouri, Columbia, MO 65211, USA. szg4y4@mail.missouri.edu. (2)Informatics Institute, University of Missouri, Columbia, MO 65211, USA. zoux@missouri.edu. The docking methods used in structure-based virtual database screening offer the ability to quickly and cheaply estimate the affinity and binding mode of a ligand for the protein receptor of interest, such as a drug target. These methods can be used to enrich a database of compounds, so that more compounds that are subsequently experimentally tested are found to be pharmaceutically interesting. In addition, like all virtual screening methods used for drug design, structure-based virtual screening can focus on curated libraries of synthesizable compounds, helping to reduce the expense of subsequent experimental verification. In this review, we introduce the protein-ligand docking methods used for structure-based drug design and other biological applications. We discuss the fundamental challenges facing these methods and some of the current methodological topics of interest. We also discuss the main approaches for applying protein-ligand docking methods. We end with a discussion of the challenging aspects of evaluating or benchmarking the accuracy of docking methods for their improvement, and discuss future directions. DOI: 10.3390/molecules190710150 PMCID: PMC6270832 PMID: 25019558 [Indexed for MEDLINE] 194. J Mol Graph Model. 2013 Jul;44:278-85. doi: 10.1016/j.jmgm.2013.07.005. Epub 2013 Jul 23. A combined molecular docking-based and pharmacophore-based target prediction strategy with a probabilistic fusion method for target ranking. Li GB(1), Yang LL, Xu Y, Wang WJ, Li LL, Yang SY. Author information: (1)State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, China. Herein, a combined molecular docking-based and pharmacophore-based target prediction strategy is presented, in which a probabilistic fusion method is suggested for target ranking. Establishment and validation of the combined strategy are described. A target database, termed TargetDB, was firstly constructed, which contains 1105 drug targets. Based on TargetDB, the molecular docking-based target prediction and pharmacophore-based target prediction protocols were established. A probabilistic fusion method was then developed by constructing probability assignment curves (PACs) against a set of selected targets. Finally the workflow for the combined molecular docking-based and pharmacophore-based target prediction strategy was established. Evaluations of the performance of the combined strategy were carried out against a set of structurally different single-target compounds and a well-known multi-target drug, 4H-tamoxifen, which results showed that the combined strategy consistently outperformed the sole use of docking-based and pharmacophore-based methods. Overall, this investigation provides a possible way for improving the accuracy of in silico target prediction and a method for target ranking. Copyright © 2013 Elsevier Inc. All rights reserved. DOI: 10.1016/j.jmgm.2013.07.005 PMID: 23933279 [Indexed for MEDLINE] 195. J Clin Pharm Ther. 2014 Dec;39(6):621-7. doi: 10.1111/jcpt.12206. Epub 2014 Sep 17. DNA methylation and personalized medicine. Tang J(1), Xiong Y, Zhou HH, Chen XP. Author information: (1)Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China; Pharmacogenetics Research Institute, Institute of Clinical Pharmacology, Hunan Key laboratory of Pharmacogenetics, Central South University, Changsha, China. WHAT IS KNOWN AND OBJECTIVE: Variation in the expression of drug-response-related genes contributes significantly to interindividual differences in drug response. DNA methylation is one of the most common epigenetic modifications that control gene expression. DNA methylation may occur in genes encoding drug metabolizing enzymes (DMEs), drug transporters and drug targets, and can thereby alter the pharmacokinetics and pharmacodynamics of drugs. In this review, we discuss recent advances in pharmacoepigenetics with a focus on DNA methylation. METHODS: The literature search focusing on DNA methylation of drug-response-related genes and DNA methylation-related SNPs in pharmacogenomics was carried out using the PUBMED database and a combination of keywords including DNA methylation, drug response, DMEs, drug transporters, drug target and SNPs. RESULTS AND DISCUSSION: An extensive range of research has contributed to our understanding of how DNA methylation of drug-response-related genes alters their function. This is particularly well studied in cancer chemotherapy and drug resistance. The impact of polymorphisms of miRNAs in these processes requires further study. WHAT IS NEW AND CONCLUSION: DNA methylation-related genetic variation is an increasingly recognized mechanism for altered drug-response and disease susceptibility. These new discoveries require assimilation into the practice of personalized medicine. © 2014 John Wiley & Sons Ltd. DOI: 10.1111/jcpt.12206 PMID: 25230364 [Indexed for MEDLINE] 196. Nat Commun. 2018 Nov 8;9(1):4699. doi: 10.1038/s41467-018-07239-1. Systemic neurotransmitter responses to clinically approved and experimental neuropsychiatric drugs. Noori HR(1)(2)(3)(4), Mervin LH(5), Bokharaie V(6), Durmus Ö(7), Egenrieder L(7), Fritze S(7), Gruhlke B(7), Reinhardt G(7), Schabel HH(7), Staudenmaier S(7), Logothetis NK(6), Bender A(5), Spanagel R(7). Author information: (1)Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159, Mannheim, Germany. hamid.noori@tuebingen.mpg.de. (2)Max Planck Institute for Biological Cybernetics, Max Planck Ring 8, 72076, Tübingen, Germany. hamid.noori@tuebingen.mpg.de. (3)Courant Institute for Mathematical Sciences, New York University, 251 Mercer Street, New York, NY, 10012, USA. hamid.noori@tuebingen.mpg.de. (4)Neuronal Convergence Group, Max Planck Institute for Biological Cybernetics, Max Planck Ring 8, 72076, Tübingen, Germany. hamid.noori@tuebingen.mpg.de. (5)Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK. (6)Max Planck Institute for Biological Cybernetics, Max Planck Ring 8, 72076, Tübingen, Germany. (7)Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159, Mannheim, Germany. Neuropsychiatric disorders are the third leading cause of global disease burden. Current pharmacological treatment for these disorders is inadequate, with often insufficient efficacy and undesirable side effects. One reason for this is that the links between molecular drug action and neurobehavioral drug effects are elusive. We use a big data approach from the neurotransmitter response patterns of 258 different neuropsychiatric drugs in rats to address this question. Data from experiments comprising 110,674 rats are presented in the Syphad database [ www.syphad.org ]. Chemoinformatics analyses of the neurotransmitter responses suggest a mismatch between the current classification of neuropsychiatric drugs and spatiotemporal neurostransmitter response patterns at the systems level. In contrast, predicted drug-target interactions reflect more appropriately brain region related neurotransmitter response. In conclusion the neurobiological mechanism of neuropsychiatric drugs are not well reflected by their current classification or their chemical similarity, but can be better captured by molecular drug-target interactions. DOI: 10.1038/s41467-018-07239-1 PMCID: PMC6224407 PMID: 30410047 197. BMC Genomics. 2014 Nov 5;15:955. doi: 10.1186/1471-2164-15-955. Comparative transcriptomics reveals striking similarities between the bovine and feline isolates of Tritrichomonas foetus: consequences for in silico drug-target identification. Morin-Adeline V, Lomas R, O'Meally D, Stack C, Conesa A(1), Šlapeta J. Author information: (1)Faculty of Veterinary Science, University of Sydney, New South Wales 2006, Australia. aconesa@cipf.es. BACKGROUND: Few, if any, protozoan parasites are reported to exhibit extreme organ tropism like the flagellate Tritrichomonas foetus. In cattle, T. foetus infects the reproductive system causing abortion, whereas the infection in cats results in chronic large bowel diarrhoea. In the absence of a T. foetus genome, we utilized a de novo approach to assemble the transcriptome of the bovine and feline genotype to identify host-specific adaptations and virulence factors specific to each genotype. Furthermore, a subset of orthologs was used to characterize putative druggable targets and expose complications of in silico drug target mining in species with indefinite host-ranges. RESULTS: Illumina RNA-seq reads were assembled into two representative bovine and feline transcriptomes containing 42,363 and 36,559 contigs, respectively. Coding and non-coding regions of the genome libraries revealed striking similarities, with 24,620 shared homolog pairs reduced down to 7,547 coding orthologs between the two genotypes. The transcriptomes were near identical in functional category distribution; with no indication of selective pressure acting on orthologs despite differences in parasite origins/host. Orthologs formed a large proportion of highly expressed transcripts in both genotypes (bovine genotype: 76%, feline genotype: 56%). Mining the libraries for protease virulence factors revealed the cysteine proteases (CP) to be the most common. In total, 483 and 445 bovine and feline T. foetus transcripts were identified as putative proteases based on MEROPS database, with 9 hits to putative protease inhibitors. In bovine T. foetus, CP8 is the preferentially transcribed CP while in the feline genotype, transcription of CP7 showed higher abundance. In silico druggability analysis of the two genotypes revealed that when host sequences are taken into account, drug targets are genotype-specific. CONCLUSION: Gene discovery analysis based on RNA-seq data analysis revealed prominent similarities between the bovine and feline T. foetus, suggesting recent adaptation to their respective host/niche. T. foetus represents a unique case of a mammalian protozoan expanding its parasitic grasp across distantly related host lineages. Consequences of the host-range for in silico drug targeting are exposed here, demonstrating that targets of the parasite in one host are not necessarily ideal for the same parasite in another host. DOI: 10.1186/1471-2164-15-955 PMCID: PMC4247702 PMID: 25374366 [Indexed for MEDLINE] 198. Comb Chem High Throughput Screen. 2015;18(5):492-504. Screening and Identification of Inhibitors Against Glutathione Synthetase, A Potential Drug Target of Plasmodium falciparum. Kumar YN(1), Jeyakodi G, Thulasibabu R, Gunasekaran K, Jambulingam P. Author information: (1)Biomedical Informatics Centre, Vector Control Research Centre (ICMR), Indira Nagar, Pondicherry, 605006, India. nandakumaryellapu@gmail.com. Malaria is the world's most fatal disease - causing up to 2.7 million deaths annually all over the world. The ability of organisms to develop resistance against existing antimalarial drugs exacerbates the problem. There is a clear cut need for more effective, affordable and accessible drugs that act by novel modes of action. Glutathione synthetase (GS) from Plasmodium falciparum represents an important potential drug target due to its defensive role; hence ceasing the respective metabolic step will destroy the parasite. A three dimensional model of Plasmodium GS was constructed by de novo modelling method and potential GS inhibitors were identified from a library of glutathione (GSH) analogues retrieved from Ligand-info database and filtered using Lipinski and ADME rules. Two common feature pharmacophore models were generated from the individual inhibitor clusters to provide insight into the key pharmacophore features that are crucial for the GS inhibition. Molecular docking of selective compounds into the predicted GS binding site revealed that the compound CMBMB was the best GS inhibitor when compared to the standard reference Chloroquine (CQ). This was taken as indicating that CMBMB was the best effective and safest drug against P. falciparum. PMID: 26220832 [Indexed for MEDLINE] 199. BMC Med Genomics. 2014;7 Suppl 1:S8. doi: 10.1186/1755-8794-7-S1-S8. Epub 2014 May 8. Identification of novel therapeutics for complex diseases from genome-wide association data. Grover MP, Ballouz S, Mohanasundaram KA, George RA, Sherman CD, Crowley TM, Wouters MA. BACKGROUND: Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials. METHODS: We previously used Gentrepid (http://www.gentrepid.org) as a platform to predict 1,497 candidate genes for the seven complex diseases considered in the Wellcome Trust Case-Control Consortium genome-wide association study; namely Type 2 Diabetes, Bipolar Disorder, Crohn's Disease, Hypertension, Type 1 Diabetes, Coronary Artery Disease and Rheumatoid Arthritis. Here, we adopted a simple approach to integrate drug data from three publicly available drug databases: the Therapeutic Target Database, the Pharmacogenomics Knowledgebase and DrugBank; with candidate gene predictions from Gentrepid at the systems level. RESULTS: Using the publicly available drug databases as sources of drug-target association data, we identified a total of 428 candidate genes as novel therapeutic targets for the seven phenotypes of interest, and 2,130 drugs feasible for repositioning against the predicted novel targets. CONCLUSIONS: By integrating genetic, bioinformatic and drug data, we have demonstrated that currently available drugs may be repositioned as novel therapeutics for the seven diseases studied here, quickly taking advantage of prior work in pharmaceutics to translate ground-breaking results in genetics to clinical treatments. DOI: 10.1186/1755-8794-7-S1-S8 PMCID: PMC4101352 PMID: 25077696 [Indexed for MEDLINE] 200. J Ethnopharmacol. 2014;151(1):93-107. doi: 10.1016/j.jep.2013.07.001. Epub 2013 Jul 9. Systems pharmacology strategies for drug discovery and combination with applications to cardiovascular diseases. Li P(1), Chen J(2), Wang J(1), Zhou W(1), Wang X(1), Li B(1), Tao W(1), Wang W(3), Wang Y(1), Yang L(4). Author information: (1)Center of Bioinformatics, Northwest A&F University, Yangling 712100, Shaanxi, China; College of Life Sciences, Northwest A&F University, Yangling 712100, Shaanxi, China. (2)Beijing University of Chinese Medicine, Beijing 100029, China. (3)Beijing University of Chinese Medicine, Beijing 100029, China. Electronic address: wangwei@bucm.edu.cn. (4)Laboratory of Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning, China. ETHNOPHARMACOLOGICAL RELEVANCE: Multi-target therapeutics is a promising paradigm for drug discovery which is expected to produce greater levels of efficacy with fewer adverse effects and toxicity than monotherapies. Medical herbs featuring multi-components and multi-targets may serve as valuable resources for network-based multi-target drug discovery. MATERIALS AND METHODS: In this study, we report an integrated systems pharmacology platform for drug discovery and combination, with a typical example applied to herbal medicines in the treatment of cardiovascular diseases. RESULTS: First, a disease-specific drug-target network was constructed and examined at systems level to capture the key disease-relevant biology for discovery of multi-targeted agents. Second, considering an integration of disease complexity and multilevel connectivity, a comprehensive database of literature-reported associations, chemicals and pharmacology for herbal medicines was designed. Third, a large-scale systematic analysis combining pharmacokinetics, chemogenomics, pharmacology and systems biology data through computational methods was performed and validated experimentally, which results in a superior output of information for systematic drug design strategies for complex diseases. CONCLUSIONS: This strategy integrating different types of technologies is expected to help create new opportunities for drug discovery and combination. Copyright © 2013. Published by Elsevier Ireland Ltd. DOI: 10.1016/j.jep.2013.07.001 PMID: 23850710 [Indexed for MEDLINE] 201. Interdiscip Sci. 2012 Dec;4(4):273-81. doi: 10.1007/s12539-012-0138-5. Epub 2013 Jan 26. In silico exploration of novel phytoligands against probable drug target of Clostridium tetani. Skariyachan S(1), Prakash N, Bharadwaj N. Author information: (1)Research & Development Centre, Department of Biotechnology, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India. sinoshskariya@gmail.com Though tetanus is an old disease with well known medicines, its complications are still a serious issue worldwide. Tetanus is mainly due to a powerful neurotoxin, tetanolysin-O, produced by a Gram positive anaerobic bacterium, Clostridium tetani. The toxin has a thiol-activated cytolysin which causes lysis of human platelets, lysosomes and a variety of subcellular membranes. The existing therapy seems to have challenged as available vaccines are not so effective and the bacteria developed resistance to many drugs. Computer aided approach is a novel platform to screen drug targets and design potential inhibitors. The three dimensional structure of the toxin is essential for structure based drug design. But the structure of tetanolysin-O is not available in its native form. Moreover, the interaction and pharmacological activities of current drugs against tetanolysin-O is not clear. Hence, there is need for three dimensional model of the toxin. The model was generated by homology modeling using crystal structure of perfringolysin-O, chain-A (PDB ID: 1PFO) as the template. The modeled structure has 22.7% α helices, 27.51% β sheets and 41.75% random coils. A thiol-activated cytolysin was predicted in the region of 105 to 1579, which acts as a functional domain of the toxin. The hypothetical model showed the backbone root mean square deviation (RMSD) value of 0.6 Å and the model was validated by ProCheck. The Ramachandran plot of the model accounts for 92.3% residues in the most allowed region. The model was further refined by various tools and deposited to Protein Model Database (PMDB ID: PM0077550). The model was used as the drug target and the interaction of various lead molecules with protein was studied by molecular docking. We have selected phytoligands based on literatures and pharmacophoric studies. The efficiency of herbal compounds and chemical leads was compared. Our study concluded that herbal derivatives such as berberine (7, 8, 13, 13a-tetradehydro-9,10-dimethoxy-2,3 [methylenebis(oxy)] berbinium), curcumin ((1E,6E)-1,7-bis (4-hydroxy-3-methoxyphenyl)-1,6-heptadiene-3,5-dione), coumarin (2H-chromen-2-one), catechol (Benzene-1,2-diol) and diosphenol (2-hydroxy-3-methyl-6-propan-2-ylcyclohex-2-en-1-one) are the best inhibitors compared to known chemicals. Hence, these leads can be used as potential inhibitors against tetanolysin. DOI: 10.1007/s12539-012-0138-5 PMID: 23354816 [Indexed for MEDLINE] 202. Antiinflamm Antiallergy Agents Med Chem. 2011;10(2):92-120. Current and future therapeutic targets of rheumatoid arthritis. Di YM, Zhou ZW, Guang Li C, Zhou SF(1). Author information: (1)Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, 12901 Bruce B. Downs Blvd., Tampa, FL 33612-4799. szhou@health.usf.edu. Rheumatoid arthritis (RA) is a chronic systematic autoimmune disease which affects about 1% of the population world wide. This article aimed to identify current therapeutic targets for RA based on data from the literature and drug target related databases. Identified targets were further analysed using a powerful bioinformatics tool, PANTHER (Protein ANalysis THrough Evolutionary Relationships). Additionally, we explored future possible therapeutic targets for RA and discussed the possibility of discovering novel drugs with improved efficacy and reduced toxicity for RA treatment. Data on current clinical drugs for RA treatment were extracted from the US Food and Drugs Administration (FDA) website. Candidate targets of RA were extracted from three online databases: Drugbank, Therapeutic Target Database (TTD) and Potential Drug Target Database (PDTD). A total of 95 clinical protein targets for RA have been identified and were analysed using the PANTHER Classification System. According to the PANTHER analysis, most commonly involved pathways in current RA targeting includes inflammation mediated by chemokine and cytokine signalling pathways, angiogenesis, p53 pathway, de novo purine biosynthesis, T-cell activation, apoptosis signalling pathway and vascular endothelial growth factor (VEGF) receptor signalling pathway. Accordingly, current clinical agents for the treatment of RA mainly include corticosteroids, non-steriodal anti-inflammatory drugs (NSAIDs) and disease-modifying antirheumatic drugs (DMARDs). In addition, a number of investigational targets for RA have been identified and many novel drugs for RA therapy are under investigation. Current approaches to handle RA aim to ameliorate inflammation, to relieve pain, and most importantly to protect the cartilage, joints and bones from further damage by blocking proinflammatory molecules and inhibit the production of matrix-degrading factors. New drugs for RA with improved efficacy and safety should be developed. PMID: 25182058 203. Mediators Inflamm. 2017;2017:3709874. doi: 10.1155/2017/3709874. Epub 2017 Jan 16. Elucidation of the Anti-Inflammatory Mechanisms of Bupleuri and Scutellariae Radix Using System Pharmacological Analyses. Shen X(1), Zhao Z(1), Wang H(1), Guo Z(2), Hu B(1), Zhang G(1). Author information: (1)College of Pharmacy, Shaanxi University of Chinese Medicine, Xi'an, Shaanxi, China. (2)Bioinformatics Center, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China. Objective. This study was aimed at elucidating the molecular mechanisms underlying the anti-inflammatory effect of the combined application of Bupleuri Radix and Scutellariae Radix and explored the potential therapeutic efficacy of these two drugs on inflammation-related diseases. Methods. After searching the databases, we collected the active ingredients of Bupleuri Radix and Scutellariae Radix and calculated their oral bioavailability (OB) and drug-likeness (DL) based on the absorption-distribution-metabolism-elimination (ADME) model. In addition, we predicted the drug targets of the selected active components based on weighted ensemble similarity (WES) and used them to construct a drug-target network. Gene ontology (GO) analysis and KEGG mapper tools were performed on these predicted target genes. Results. We obtained 30 compounds from Bupleuri Radix and Scutellariae Radix of good quality as indicated by ADME assays, which possess potential pharmacological activity. These 30 ingredients have a total of 121 potential target genes, which are involved in 24 biological processes related to inflammation. Conclusions. Combined application of Bupleuri Radix and Scutellariae Radix was found not only to directly inhibit the synthesis and release of inflammatory cytokines, but also to have potential therapeutic effects against inflammation-induced pain. In addition, a combination therapy of these two drugs exhibited systemic treatment efficacy and provided a theoretical basis for the development of drugs against inflammatory diseases. DOI: 10.1155/2017/3709874 PMCID: PMC5278517 PMID: 28190938 [Indexed for MEDLINE] Conflict of interest statement: The authors declare that there is no conflict of interests regarding the publication of this paper. 204. BMC Bioinformatics. 2013 Jun 6;14:181. doi: 10.1186/1471-2105-14-181. Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing. Xu R(1), Wang Q. Author information: (1)Medical Informatics Division, Case Western Reserve, Cleveland, OH, USA. rxx@case.edu BACKGROUND: A large-scale, highly accurate, machine-understandable drug-disease treatment relationship knowledge base is important for computational approaches to drug repurposing. The large body of published biomedical research articles and clinical case reports available on MEDLINE is a rich source of FDA-approved drug-disease indication as well as drug-repurposing knowledge that is crucial for applying FDA-approved drugs for new diseases. However, much of this information is buried in free text and not captured in any existing databases. The goal of this study is to extract a large number of accurate drug-disease treatment pairs from published literature. RESULTS: In this study, we developed a simple but highly accurate pattern-learning approach to extract treatment-specific drug-disease pairs from 20 million biomedical abstracts available on MEDLINE. We extracted a total of 34,305 unique drug-disease treatment pairs, the majority of which are not included in existing structured databases. Our algorithm achieved a precision of 0.904 and a recall of 0.131 in extracting all pairs, and a precision of 0.904 and a recall of 0.842 in extracting frequent pairs. In addition, we have shown that the extracted pairs strongly correlate with both drug target genes and therapeutic classes, therefore may have high potential in drug discovery. CONCLUSIONS: We demonstrated that our simple pattern-learning relationship extraction algorithm is able to accurately extract many drug-disease pairs from the free text of biomedical literature that are not captured in structured databases. The large-scale, accurate, machine-understandable drug-disease treatment knowledge base that is resultant of our study, in combination with pairs from structured databases, will have high potential in computational drug repurposing tasks. DOI: 10.1186/1471-2105-14-181 PMCID: PMC3702428 PMID: 23742147 [Indexed for MEDLINE] 205. Bioinformation. 2013 Jun 8;9(10):518-23. doi: 10.6026/97320630009518. Print 2013. in Silico analysis of Escherichia coli polyphosphate kinase (PPK) as a novel antimicrobial drug target and its high throughput virtual screening against PubChem library. Saha SB(1), Verma V. Author information: (1)Department of Computational Biology and Bioinformatics, JSBB, SHIATS, Allahabad -211007, Uttar Pradesh, India. Multiple drug resistance (MDR) in bacteria is a global health challenge that needs urgent attention. The 2011 outbreak caused by Escherichia coli O104:H4 in Europe has exposed the inability of present antibiotic arsenal to tackle the problem of antimicrobial infections. It has further posed a tremendous burden on entire pharmaceutical industry to find novel drugs and/or drug targets. Polyphosphate kinase (PPK) in bacteria plays a crucial role in helping latter to adapt to stringent conditions of low nutritional availability thus making it a good target for antibacterials. In spite of this critical role, to best of our knowledge no in-silico work has been carried out to develop PPK as an antibiotic target. In the present study, virtual screening of PPK was carried out against all the 3D compounds with pharmacological action present in PubChem database. Our screening results were further refined by interaction maps to eliminate the false positive data respectively. From our results, compound number 5281927 (PubChem ID) has been found to have significant affinity towards affinity towards PPK active ATP-binding site indicating its therapeutic relevance. DOI: 10.6026/97320630009518 PMCID: PMC3705627 PMID: 23861568 206. BMC Med Genomics. 2015;8 Suppl 2:S1. doi: 10.1186/1755-8794-8-S2-S1. Epub 2015 May 29. Novel therapeutics for coronary artery disease from genome-wide association study data. Grover MP, Ballouz S, Mohanasundaram KA, George RA, Goscinski A, Crowley TM, Sherman CD, Wouters MA. BACKGROUND: Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD. METHODS: Previously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (http://www.gentrepid.org), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatic modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD. RESULTS: Our analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p < 0.05). CONCLUSIONS: We have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies. DOI: 10.1186/1755-8794-8-S2-S1 PMCID: PMC4460746 PMID: 26044129 [Indexed for MEDLINE] 207. Bioinformatics. 2010 Aug 15;26(16):2042-50. doi: 10.1093/bioinformatics/btq310. Epub 2010 Jun 11. Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery. Korcsmáros T(1), Farkas IJ, Szalay MS, Rovó P, Fazekas D, Spiró Z, Böde C, Lenti K, Vellai T, Csermely P. Author information: (1)Department of Genetics, Eötvös University, Budapest, Hungary. MOTIVATION: Signaling pathways control a large variety of cellular processes. However, currently, even within the same database signaling pathways are often curated at different levels of detail. This makes comparative and cross-talk analyses difficult. RESULTS: We present SignaLink, a database containing eight major signaling pathways from Caenorhabditis elegans, Drosophila melanogaster and humans. Based on 170 review and approximately 800 research articles, we have compiled pathways with semi-automatic searches and uniform, well-documented curation rules. We found that in humans any two of the eight pathways can cross-talk. We quantified the possible tissue- and cancer-specific activity of cross-talks and found pathway-specific expression profiles. In addition, we identified 327 proteins relevant for drug target discovery. CONCLUSIONS: We provide a novel resource for comparative and cross-talk analyses of signaling pathways. The identified multi-pathway and tissue-specific cross-talks contribute to the understanding of the signaling complexity in health and disease, and underscore its importance in network-based drug target selection. AVAILABILITY: http://SignaLink.org. DOI: 10.1093/bioinformatics/btq310 PMID: 20542890 [Indexed for MEDLINE] 208. Bioinformation. 2010 Jan 17;4(7):278-89. Identification and modeling of a drug target for Clostridium perfringens SM101. Chhabra G(1), Sharma P, Anant A, Deshmukh S, Kaushik H, Gopal K, Srivastava N, Sharma N, Garg LC. Author information: (1)Gene Regulation Laboratory, National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi – 110067, India. In the present study, comparative genome analysis between Clostridium perfringens and the human genome was carried out to identify genes that are essential for the pathogen's survival, and non-homologous to the genes of human host, that can be used as potential drug targets. The study resulted in the identification of 426 such genes. The number of these potential drug targets thus identified is significantly lower than the genome's protein coding capacity (2558 protein coding genes). The 426 genes of C. perfringens were further analyzed for overall similarities with the essential genes of 14 different bacterial species present in Database of Essential Genes (DEG). Our results show that there are only 5 essential genes of C. perfringens that exhibit similarity with 12 species of the 14 different bacterial species present in DEG database. Of these, 1 gene was similar in 12 species and 4 genes were similar in 11 species. Thus, the study opens a new avenue for the development of potential drugs against the highly pathogenic bacterium. Further, by selecting these essential genes of C. perfringens, which are common and essential for other pathogenic microbial species, a broad spectrum anti-microbial drug can be developed. As a case study, we have built a homology model of one of the potential drug targets, ABC transporter-ATP binding protein, which can be employed for in silico docking studies by suitable inhibitors. PMCID: PMC2957761 PMID: 20978600 209. J Chem Inf Model. 2013 Oct 28;53(10):2525-37. doi: 10.1021/ci400240u. Epub 2013 Sep 24. Training based on ligand efficiency improves prediction of bioactivities of ligands and drug target proteins in a machine learning approach. Sugaya N(1). Author information: (1)Drug Discovery Department, Research & Development Division, PharmaDesign, Inc. , Hatchobori 2-19-8, Chuo-ku, Tokyo, 104-0032, Japan. Machine learning methods based on ligand-protein interaction data in bioactivity databases are one of the current strategies for efficiently finding novel lead compounds as the first step in the drug discovery process. Although previous machine learning studies have succeeded in predicting novel ligand-protein interactions with high performance, all of the previous studies to date have been heavily dependent on the simple use of raw bioactivity data of ligand potencies measured by IC50, EC50, K(i), and K(d) deposited in databases. ChEMBL provides us with a unique opportunity to investigate whether a machine-learning-based classifier created by reflecting ligand efficiency other than the IC50, EC50, K(i), and Kd values can also offer high predictive performance. Here we report that classifiers created from training data based on ligand efficiency show higher performance than those from data based on IC50 or K(i) values. Utilizing GPCRSARfari and KinaseSARfari databases in ChEMBL, we created IC50- or K(i)-based training data and binding efficiency index (BEI) based training data then constructed classifiers using support vector machines (SVMs). The SVM classifiers from the BEI-based training data showed slightly higher area under curve (AUC), accuracy, sensitivity, and specificity in the cross-validation tests. Application of the classifiers to the validation data demonstrated that the AUCs and specificities of the BEI-based classifiers dramatically increased in comparison with the IC50- or K(i)-based classifiers. The improvement of the predictive power by the BEI-based classifiers can be attributed to (i) the more separated distributions of positives and negatives, (ii) the higher diversity of negatives in the BEI-based training data in a feature space of SVMs, and (iii) a more balanced number of positives and negatives in the BEI-based training data. These results strongly suggest that training data based on ligand efficiency as well as data based on classical IC50, EC50, K(d), and K(i) values are important when creating a classifier using a machine learning approach based on bioactivity data. DOI: 10.1021/ci400240u PMID: 24020509 [Indexed for MEDLINE] 210. Adv Protein Chem Struct Biol. 2018;111:263-282. doi: 10.1016/bs.apcsb.2017.09.002. Epub 2017 Nov 6. Human Interactomics: Comparative Analysis of Different Protein Interaction Resources and Construction of a Cancer Protein-Drug Bipartite Network. De Las Rivas J(1), Alonso-López D(2), Arroyo MM(3). Author information: (1)Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain. Electronic address: jrivas@usal.es. (2)Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain. (3)Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain; Pontifical Catholic University of Puerto Rico (PCUPR), Ponce, Puerto Rico. Unraveling the protein interaction wiring that occurs in human cells as a scaffold of biological processes requires the identification of all elements that constitute such molecular interaction networks. Proteome-wide experimental studies and bioinformatic comprehensive efforts have provided reliable and updated compendiums of the human protein interactome. In this work, we present a current view of available databases of human protein-protein interactions (PPIs) that allow building protein interaction networks. We also investigate human proteins as targets of specific drugs to analyze how chemicals interact with different target proteins, placing also the study in a network relational space. Hence, we undertake a description of several major drug-target resources to provide a present perspective of the associations between human proteins and specific chemicals. The identification of molecular targets for specific drugs is a critical step to improve disease therapy. As different diseases have different biomolecular scenarios, we addressed the identification of drug-targeted genes focusing our investigations on cancer and cancer genes. So, a description of resources that provide curated compendiums of human cancer genes is presented. Cancer is a complex disease where multiple genetic changes rewire cellular networks during carcinogenesis. This indicates that cancer drug therapy needs the implementation of network-driven studies to reveal multiplex interactions between cancer genes and drugs. To make progress in this direction, in the last part of this work we provide a bipartite network of cancer genes and their drugs shown in a graph landscape that disclose the existence of specific drug-target modules. © 2018 Elsevier Inc. All rights reserved. DOI: 10.1016/bs.apcsb.2017.09.002 PMID: 29459035 211. SAR QSAR Environ Res. 2009 Oct;20(7-8):755-66. doi: 10.1080/10629360903438628. In silico method for identification of promising anticancer drug targets. Koborova ON(1), Filimonov DA, Zakharov AV, Lagunin AA, Ivanov SM, Kel A, Poroikov VV. Author information: (1)Institute of Biomedical Chemistry of Russian Academy of Medical Sciences, Moscow, Russia. okoborova@gmail.com In recent years, the accumulation of the genomics, proteomics, transcriptomics data for topological and functional organization of regulatory networks in a cell has provided the possibility of identifying the potential targets involved in pathological processes and of selecting the most promising targets for future drug development. We propose an approach for anticancer drug target identification, which, using microarray data, allows discrete modelling of regulatory network behaviour. The effect of drugs inhibiting a particular protein or a combination of proteins in a regulatory network is analysed by simulation of a blockade of single nodes or their combinations. The method was applied to the four groups of breast cancer, HER2/neu-positive breast carcinomas, ductal carcinoma, invasive ductal carcinoma and/or a nodal metastasis, and to generalized breast cancer. As a result, some promising specific molecular targets and their combinations were identified. Inhibitors of some identified targets are known as potential drugs for therapy of malignant diseases; for some other targets we identified hits in the commercially available sample databases. DOI: 10.1080/10629360903438628 PMID: 20024808 [Indexed for MEDLINE] 212. AMIA Annu Symp Proc. 2015 Nov 5;2015:1342-51. eCollection 2015. tcTKB: an integrated cardiovascular toxicity knowledge base for targeted cancer drugs. Xu R(1), Wang Q(2). Author information: (1)Department of Epidemiology and Biostatistics, Institute of Computational Biology, School of Medicine, Case Western Reserve University, Cleveland OH 44106. (2)ThinTek, LLC, Palo Alto, CA 94306. Targeted cancer drugs are often associated with unexpectedly high cardiovascular (CV) adverse events. Systematic approaches to studying CV events associated with targeted anticancer drugs have high potential for elucidating the complex pathways underlying targeted anti-cancer drugs. In this study, we built tcTKB, a comprehensive CV toxicity knowledge base for targeted cancer drugs, by extracting drug-CV pairs from five large-scale and complementary data sources. The data sources include FDA drug labels (44,979 labels), the FDA Adverse Event Reporting System (FAERS) (4,285,097 records), the Canada Vigilance Adverse Reaction Online Database (CVAROD) (1,107,752 records), published biomedical literature (21,354,075 records), and published full-text articles from the Journal of Oncology (JCO) (13,855 articles). tcTKB contains 14,351 drug-CV pairs for 45 targeted anticancer drugs and 1,842 CV events. We demonstrate that CV events positively correlate with drug target genes and drug metabolism genes, demonstrating that tcTKB in combination with other data resources, could facilitate our understanding of targeted anticancer drugs and their associated CV toxicities. PMCID: PMC4765587 PMID: 26958275 [Indexed for MEDLINE] 213. Exp Parasitol. 2017 Sep;180:33-44. doi: 10.1016/j.exppara.2017.03.006. Epub 2017 Mar 27. Transcript and protein expression analysis of proteases in the blood stages of Plasmodium falciparum. Weißbach T(1), Golzmann A(1), Bennink S(1), Pradel G(1), Julius Ngwa C(2). Author information: (1)Division of Cellular and Applied Infection Biology, Institute of Zoology, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany. (2)Division of Cellular and Applied Infection Biology, Institute of Zoology, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany. Electronic address: ngwa.che@bio2.rwth-aachen.de. Proteases are crucial enzymes with varying roles in living organisms. In the malaria parasite Plasmodium falciparum, the role of proteases has been deciphered mainly in the asexual blood stages and shown to represent promising drug targets. However, little is known about their functions in the sexual blood stages, which are important for transmission of the disease from the human to the mosquito vector. Determination of their stage-specific expression during the malaria life-cycle is crucial for the effective design of multi-stage anti-malaria drugs aimed at eradicating the disease. In this study, we screened the P. falciparum genome database for putative proteases and determined the transcript and protein expression profiles of selected proteases in the plasmodial blood stages using semi-quantitative RT-PCR and indirect immunofluorescence assay. Database mining identified a total of 148 putative proteases, out of which 18 were demonstrated to be expressed in the blood stages on the transcript level; for 12 of these proteins synthesis was confirmed. While three of these proteases exhibit gametocyte-specific expression, two are restricted to the asexual blood stages and seven are found in both stages, making them interesting multi-stage drug targets. Copyright © 2017 Elsevier Inc. All rights reserved. DOI: 10.1016/j.exppara.2017.03.006 PMID: 28351685 [Indexed for MEDLINE] 214. Bioinformatics. 2013 May 15;29(10):1317-24. doi: 10.1093/bioinformatics/btt158. Epub 2013 Apr 5. Network predicting drug's anatomical therapeutic chemical code. Wang YC(1), Chen SL, Deng NY, Wang Y. Author information: (1)Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China. MOTIVATION: Discovering drug's Anatomical Therapeutic Chemical (ATC) classification rules at molecular level is of vital importance to understand a vast majority of drugs action. However, few studies attempt to annotate drug's potential ATC-codes by computational approaches. RESULTS: Here, we introduce drug-target network to computationally predict drug's ATC-codes and propose a novel method named NetPredATC. Starting from the assumption that drugs with similar chemical structures or target proteins share common ATC-codes, our method, NetPredATC, aims to assign drug's potential ATC-codes by integrating chemical structures and target proteins. Specifically, we first construct a gold-standard positive dataset from drugs' ATC-code annotation databases. Then we characterize ATC-code and drug by their similarity profiles and define kernel function to correlate them. Finally, we use a kernel method, support vector machine, to automatically predict drug's ATC-codes. Our method was validated on four drug datasets with various target proteins, including enzymes, ion channels, G-protein couple receptors and nuclear receptors. We found that both drug's chemical structure and target protein are predictive, and target protein information has better accuracy. Further integrating these two data sources revealed more experimentally validated ATC-codes for drugs. We extensively compared our NetPredATC with SuperPred, which is a chemical similarity-only based method. Experimental results showed that our NetPredATC outperforms SuperPred not only in predictive coverage but also in accuracy. In addition, database search and functional annotation analysis support that our novel predictions are worthy of future experimental validation. CONCLUSION: In conclusion, our new method, NetPredATC, can predict drug's ATC-codes more accurately by incorporating drug-target network and integrating data, which will promote drug mechanism understanding and drug repositioning and discovery. AVAILABILITY: NetPredATC is available at http://doc.aporc.org/wiki/NetPredATC. CONTACT: ycwang@nwipb.cas.cn or ywang@amss.ac.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. DOI: 10.1093/bioinformatics/btt158 PMID: 23564845 [Indexed for MEDLINE] 215. Expert Opin Ther Targets. 2013 Mar;17(3):265-79. doi: 10.1517/14728222.2012.741122. Epub 2013 Jan 7. The potential of carboxypeptidase M as a therapeutic target in cancer. Denis CJ(1), Lambeir AM. Author information: (1)University of Antwerp, Pharmaceutical Sciences, Laboratory of Medical Biochemistry, Universiteitsplein 1, Antwerp, B-2610, Belgium. INTRODUCTION: In the recent literature, carboxypeptidase M (CPM) emerged as a potential cancer biomarker. CPM modulates receptor signaling of kinins, anaphylatoxins, and chemokines. These CPM substrates affect proliferation, angiogenesis, and apoptosis of cancer cells. What is the evidence that CPM is a drug target for cancer therapy? AREAS COVERED: The literature was searched using PubMed with the search terms "carboxypeptidase M" and/or "chromosome 12q13-15" eventually combined with general terms related to cancer. Information was retrieved from the GEO database and material of gene expression and proteomic studies. EXPERT OPINION: CPM is a part of the molecular signature of many cancers. There is good evidence that it is useful for the discrimination and stratification of cancer types, possibly in combination with other markers such as EGFR and MDM2. Whether it is also a drug target remains to be determined. Lung, kidney, brain, and the reproductive system contain relatively high levels of CPM, but its functions in those tissues are largely unknown. CPM is expressed on tumor-associated macrophages. To facilitate the investigation of CPM in tumor-associated inflammation and in the other aspects of tumor biology, it is necessary to develop potent and selective CPM inhibitors. DOI: 10.1517/14728222.2012.741122 PMID: 23294303 [Indexed for MEDLINE] 216. Comb Chem High Throughput Screen. 2015;18(6):528-43. Role of Open Source Tools and Resources in Virtual Screening for Drug Discovery. Karthikeyan M(1), Vyas R. Author information: (1)Digital Information Resource Centre (DIRC) & Centre of Excellence in Scientific Computing (CoESC), CSIR-National Chemical Laboratory, Pune - 411008, India. m.karthikeyan@ncl.res.in. Advancement in chemoinformatics research in parallel with availability of high performance computing platform has made handling of large scale multi-dimensional scientific data for high throughput drug discovery easier. In this study we have explored publicly available molecular databases with the help of open-source based integrated in-house molecular informatics tools for virtual screening. The virtual screening literature for past decade has been extensively investigated and thoroughly analyzed to reveal interesting patterns with respect to the drug, target, scaffold and disease space. The review also focuses on the integrated chemoinformatics tools that are capable of harvesting chemical data from textual literature information and transform them into truly computable chemical structures, identification of unique fragments and scaffolds from a class of compounds, automatic generation of focused virtual libraries, computation of molecular descriptors for structure-activity relationship studies, application of conventional filters used in lead discovery along with in-house developed exhaustive PTC (Pharmacophore, Toxicophores and Chemophores) filters and machine learning tools for the design of potential disease specific inhibitors. A case study on kinase inhibitors is provided as an example. PMID: 26138575 [Indexed for MEDLINE] 217. J Cell Biochem. 2018 Sep;119(9):7328-7338. doi: 10.1002/jcb.27033. Epub 2018 May 15. Computational discovery of potent drugs to improve the treatment of pyrazinamide resistant Mycobacterium tuberculosis mutants. Jagadeb M(1), Rath SN(2), Sonawane A(1)(3). Author information: (1)School of Biotechnology, KIIT University, Bhubaneswar, Odisha, India. (2)Department of Bioinformatics, Orissa University of Agriculture and Technology, Bhubaneswar, Odisha, India. (3)Centre for Bioscience and Biomedical Engineering, IIT Indore, Simrol, Madhya Pradesh, India. Emergence of multi-drug resistance tuberculosis has become a serious health problem globally. Accumulation of mutations in the drug target led to the development of multi-drug resistant mycobacterial strains that have made most of the conventional drugs ineffective. Hence, there is desperate need for the development of new therapeutic strategies. Here, we focused on the analysis of mutations in Mycobacterium tuberculosis (Mtb) PncA (pyrazinamidase) that is responsible for resistance against first-line anti-tuberculosis pyrazinamide (PZA) drug. First, PZA and its two isoforms were analyzed for their binding affinity toward ligand binding cavity of Mtb wild-type and mutant PncA proteins. The observations suggested that some drug resistant mutations cause strong binding of PncA with the active form of PZA and impair its release, which is required to inhibit the growth of Mtb. To improve the treatment of PZA resistant Mtb, high throughput virtual drug screening was performed to identify potent drug molecules from a library of compounds derived from ChEMBL database. From this library, we predicted a lead molecule (terta-butyl(2S,4S)-4-amino-2-cyclopropyl-6-(trifluoromethyl)-3,4-dihydro-2H-quin oline-1-carboxylate) to be more effective against PZA resistant Mtb strains in comparison to PZA. The lead molecule showed better drug-like properties such as high affinity and atomic interactions with wild-type and drug-resistant mutations in Mtb PncA proteins. Further, molecular dynamic simulation studies showed that this lead molecule has better conformational stability and compatibility with drug-resistant PncA proteins in comparison to PZA drug. We hypothesized that the predicted lead compound could be more effective, and thus may improve the treatment of PZA resistant tuberculosis. © 2018 Wiley Periodicals, Inc. DOI: 10.1002/jcb.27033 PMID: 29761826 218. Chem Biol Drug Des. 2017 Nov;90(5):909-918. doi: 10.1111/cbdd.13012. Epub 2017 Jun 12. Documenting and harnessing the biological potential of molecules in Distributed Drug Discovery (D3) virtual catalogs. Abraham MM(1), Denton RE(1), Harper RW(1), Scott WL(1), O'Donnell MJ(1), Durrant JD(2). Author information: (1)Department of Chemistry and Chemical Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA. (2)Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA. Virtual molecular catalogs have limited utility if member compounds are (i) difficult to synthesize or (ii) unlikely to have biological activity. The Distributed Drug Discovery (D3) program addresses the synthesis challenge by providing scientists with a free virtual D3 catalog of 73,024 easy-to-synthesize N-acyl unnatural α-amino acids, their methyl esters, and primary amides. The remaining challenge is to document and exploit the bioactivity potential of these compounds. In the current work, a search process is described that retrospectively identifies all virtual D3 compounds classified as bioactive hits in PubChem-cataloged experimental assays. The results provide insight into the broad range of drug-target classes amenable to inhibition and/or agonism by D3-accessible molecules. To encourage computer-aided drug discovery centered on these compounds, a publicly available virtual database of D3 molecules prepared for use with popular computer docking programs is also presented. © 2017 John Wiley & Sons A/S. DOI: 10.1111/cbdd.13012 PMID: 28453915 [Indexed for MEDLINE] 219. Bioinformation. 2013;9(2):89-93. doi: 10.6026/97320630009089. Epub 2013 Jan 18. A two-step drug repositioning method based on a protein-protein interaction network of genes shared by two diseases and the similarity of drugs. Fukuoka Y(1), Takei D, Ogawa H. Author information: (1)Department of Electrical Engineering, Faculty of Engineering, Kogakuin University, 1-24-2 Nishi-Shinjuku, Shinjuku, Tokyo 163-8677, Japan. The present study proposed a two-step drug repositioning method based on a protein-protein interaction (PPI) network of two diseases and the similarity of the drugs prescribed for one of the two. In the proposed method, first, lists of disease related genes were obtained from a meta-database called Genotator. Then genes shared by a pair of diseases were sought. At the first step of the method, if a drug having its target(s) in the PPI network, the drug was deemed a repositioning candidate. Because targets of many drugs are still unknown, the similarities between the prescribed drugs for a specific disease were used to infer repositioning candidates at the second step. As a first attempt, we applied the proposed method to four different types of diseases: hypertension, diabetes mellitus, Crohn disease, and autism. Some repositioning candidates were found both at the first and second steps. DOI: 10.6026/97320630009089 PMCID: PMC3563404 PMID: 23390352 220. BMC Microbiol. 2016 May 12;16:84. doi: 10.1186/s12866-016-0700-0. Target identification in Fusobacterium nucleatum by subtractive genomics approach and enrichment analysis of host-pathogen protein-protein interactions. Kumar A(1), Thotakura PL(1), Tiwary BK(2), Krishna R(3). Author information: (1)Centre for Bioinformatics, Pondicherry University, Puducherry, 605014, India. (2)Centre Head, Centre for Bioinformatics, Pondicherry University, Puducherry, 605014, India. (3)Centre for Bioinformatics, Pondicherry University, Puducherry, 605014, India. krishstrucbio@gmail.com. BACKGROUND: Fusobacterium nucleatum, a well studied bacterium in periodontal diseases, appendicitis, gingivitis, osteomyelitis and pregnancy complications has recently gained attention due to its association with colorectal cancer (CRC) progression. Treatment with berberine was shown to reverse F. nucleatum-induced CRC progression in mice by balancing the growth of opportunistic pathogens in tumor microenvironment. Intestinal microbiota imbalance and the infections caused by F. nucleatum might be regulated by therapeutic intervention. Hence, we aimed to predict drug target proteins in F. nucleatum, through subtractive genomics approach and host-pathogen protein-protein interactions (HP-PPIs). We also carried out enrichment analysis of host interacting partners to hypothesize the possible mechanisms involved in CRC progression due to F. nucleatum. RESULTS: In subtractive genomics approach, the essential, virulence and resistance related proteins were retrieved from RefSeq proteome of F. nucleatum by searching against Database of Essential Genes (DEG), Virulence Factor Database (VFDB) and Antibiotic Resistance Gene-ANNOTation (ARG-ANNOT) tool respectively. A subsequent hierarchical screening to identify non-human homologous, metabolic pathway-independent/pathway-specific and druggable proteins resulted in eight pathway-independent and 27 pathway-specific druggable targets. Co-aggregation of F. nucleatum with host induces proinflammatory gene expression thereby potentiates tumorigenesis. Hence, proteins from IBDsite, a database for inflammatory bowel disease (IBD) research and those involved in colorectal adenocarcinoma as interpreted from The Cancer Genome Atlas (TCGA) were retrieved to predict drug targets based on HP-PPIs with F. nucleatum proteome. Prediction of HP-PPIs exhibited 186 interactions contributed by 103 host and 76 bacterial proteins. Bacterial interacting partners were accounted as putative targets. And enrichment analysis of host interacting partners showed statistically enriched terms that were in positive correlation with CRC, atherosclerosis, cardiovascular, osteoporosis, Alzheimer's and other diseases. CONCLUSION: Subtractive genomics analysis provided a set of target proteins suggested to be indispensable for survival and pathogenicity of F. nucleatum. These target proteins might be considered for designing potent inhibitors to abrogate F. nucleatum infections. From enrichment analysis, it was hypothesized that F. nucleatum infection might enhance CRC progression by simultaneously regulating multiple signaling cascades which could lead to up-regulation of proinflammatory responses, oncogenes, modulation of host immune defense mechanism and suppression of DNA repair system. DOI: 10.1186/s12866-016-0700-0 PMCID: PMC4866016 PMID: 27176600 [Indexed for MEDLINE] 221. Chin J Nat Med. 2014 Jun;12(6):443-8. doi: 10.1016/S1875-5364(14)60069-8. The forecast of anticancer targets of cryptotanshinone based on reverse pharmacophore-based screening technology. Yuan DP(1), Long J(2), Lu Y(3), Lin J(4), Tong L(5). Author information: (1)College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China. (2)College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China. Electronic address: long_ydp@aliyun.com. (3)Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210029, China. (4)School of Life Science, Yunnan University, Kunming 650091, China. (5)Traditional Chinese and Tibetan Medicine Research Centre, Medical College of Qinghai Univercity, Xining, 810001, China. Anticancer targets of cryptotanshinone were evaluated and rapidly forecasted with PharmMapper, a reverse pharmacophore-based screening platform, as well as drug target databases, including PDTD, DrugBank and TTD. The pathway analyses for the collection of anticancer targets screened were carried out based on the KEGG pathway database, followed by the forecast of potential pharmacological activities and pathways of the effects of cryptotanshinone, and verification of some of the targets screened using whole cell tests. The results showed that a total of eight targets with anticancer potential were screened, including MAP2K1, RARα, RXRα, PDK1, CHK1, AR, Ang-1 R, and Kif11. These targets are mainly related to four aspects of the cancer growth: the cell cycle, angiogenesis, apoptosis, and androgen receptor. The cell tests showed that cryptotanshinone can inhibit the viability of human hepatoma cells SMMC-7721, which is related to the reduction of expression of MAP2K1 mRNA. This method provides a strong clue for the study of the anticancer effects and mechanisms of action of cryptotanshinone in the future. Copyright © 2014 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved. DOI: 10.1016/S1875-5364(14)60069-8 PMID: 24969525 [Indexed for MEDLINE] 222. J Biomed Inform. 2015 Feb;53:128-35. doi: 10.1016/j.jbi.2014.10.002. Epub 2014 Oct 13. Combining automatic table classification and relationship extraction in extracting anticancer drug-side effect pairs from full-text articles. Xu R(1), Wang Q(2). Author information: (1)Medical Informatics Program, Center for Clinical Investigation, Case Western Reserve University, Cleveland, OH 44106, United States. Electronic address: rxx@case.edu. (2)ThinTek, LLC, Palo Alto, CA 94306, United States. Electronic address: qwang@thintek.com. Anticancer drug-associated side effect knowledge often exists in multiple heterogeneous and complementary data sources. A comprehensive anticancer drug-side effect (drug-SE) relationship knowledge base is important for computation-based drug target discovery, drug toxicity predication and drug repositioning. In this study, we present a two-step approach by combining table classification and relationship extraction to extract drug-SE pairs from a large number of high-profile oncological full-text articles. The data consists of 31,255 tables downloaded from the Journal of Oncology (JCO). We first trained a statistical classifier to classify tables into SE-related and -unrelated categories. We then extracted drug-SE pairs from SE-related tables. We compared drug side effect knowledge extracted from JCO tables to that derived from FDA drug labels. Finally, we systematically analyzed relationships between anti-cancer drug-associated side effects and drug-associated gene targets, metabolism genes, and disease indications. The statistical table classifier is effective in classifying tables into SE-related and -unrelated (precision: 0.711; recall: 0.941; F1: 0.810). We extracted a total of 26,918 drug-SE pairs from SE-related tables with a precision of 0.605, a recall of 0.460, and a F1 of 0.520. Drug-SE pairs extracted from JCO tables is largely complementary to those derived from FDA drug labels; as many as 84.7% of the pairs extracted from JCO tables have not been included a side effect database constructed from FDA drug labels. Side effects associated with anticancer drugs positively correlate with drug target genes, drug metabolism genes, and disease indications. Copyright © 2014 Elsevier Inc. All rights reserved. DOI: 10.1016/j.jbi.2014.10.002 PMCID: PMC4586056 PMID: 25445920 [Indexed for MEDLINE] 223. Curr Drug Targets. 2018 Dec 10. doi: 10.2174/1389450120666181211111815. [Epub ahead of print] Heat shock proteins (HSPs): A novel target for cancer metastasis prevention. Narayanankutty V(1), Narayanankutty A(2), Nair A(3). Author information: (1)Government Medical College, Manjeri, Malappuram, Kerala. India. (2)Postgraduate & Research Department of Zoology, St. Joseph's College, Devagiri (Autonomous), Calicut, Kerala- 673 008. India. (3)Scientific Officer in charge, Cell and Tissue Culture Department, Micro labs, Bangalore. India. BACKGROUND: Heat shock proteins (HSPs) are predominant molecular chaperone which is actively involved in the protein folding; which is essential in protecting the structure and functioning of proteins during various stress conditions. Though HSPs have important physiological roles, they have been well known for their roles in various pathogenic conditions such as carcinogenesis; however, limited literature has consolidated its potential as an anti-metastatic drug target. OBJECTIVES: The present review outlines the role of different HSPs on cancer progression and metastasis; possible role of HSP inhibitors as anti-neoplastic agents are also discussed. METHODS: The data was collected from PubMed/Medline and other reputed journal databases. The literature that is too old and has no significant role to the review has been then omitted. RESULTS: Despite their strong physiological functions, HSPs are considered as good markers for cancer prognosis and diagnosis. They have controls survival, proliferation and progression events of cancer including drug resistance, metastasis, and angiogenesis. Since neoplastic cells are more dependent on HSPs for survival and proliferation, the selectivity and specificity of HSP-targeted cancer drugs remain high. This has made various HSPs are potential clinical and experimental target for cancer prevention. An array of HSP inhibitors has been in trials and many others are in experimental conditions as anticancer and anti-metastatic agents. Several natural products are also being investigated for their efficacy for anticancer and anti-metastatic agents by modulating HSPs. CONCLUSION: Apart from their role as an anticancer drug target, HSPs have shown to be promising targets for the prevention of cancer progression. There need extensive studies for the use of these molecules as anti-metastatic agents. Further studies in this line may yield specific and effective anti-metastatic agents. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1389450120666181211111815 PMID: 30526455 224. Reprod Toxicol. 2011 May;31(4):562-9. doi: 10.1016/j.reprotox.2010.11.008. Epub 2010 Nov 27. Drug target-gene signatures that predict teratogenicity are enriched for developmentally related genes. Schachter AD(1), Kohane IS. Author information: (1)Division of Nephrology, Children's Hospital Boston, Boston, MA 02115, USA. asher.schachter@gmail.com Drugs prescribed during pregnancy affect two populations simultaneously: fetuses and their mothers. Drug-induced fetal injury (teratogenicity) has a significant impact on current and future public health. Teratogenic risk designation of many drugs relies on associating rare fetal events with rare environmental exposures. Therefore we aim to develop preclinical predictive models of clinical teratogenicity. We collated public databases for drug-target-gene relationships for 619 drugs spanning the 5 pregnancy risk classes. Genes targeted by high risk but not low risk drugs demonstrated 79% accuracy (p < 0.0001 vs. random) for predicting high vs. low fetal risk on cross validation. Functional enrichment analysis revealed that target genes of drugs known to be safe in pregnancy contained no developmentally related terms, while target genes of known teratogens contained 85 developmentally related terms. Drug target gene signatures that are enriched for known developmental genes may provide valuable preclinical predictive information regarding drug pregnancy risk. Copyright © 2010 Elsevier Inc. All rights reserved. DOI: 10.1016/j.reprotox.2010.11.008 PMCID: PMC3139687 PMID: 21115113 [Indexed for MEDLINE] 225. Curr Drug Targets Infect Disord. 2004 Mar;4(1):25-40. Computer assisted searches for drug targets with emphasis on malarial proteases and their inhibitors. Wang Y(1), Wu Y. Author information: (1)Department of Biology, University of Texas at San Antonio, 6900 North Loop 1604 West, San Antonio, Texas 78249, USA. ywang@utsa.edu The creation of databases that make enormous and diverse amounts of information available, the coding of algorithms that allow the collection and investigation of these data and the wide availability of desktop computers capable of handling the data and running the algorithms have set the stage for innovative approaches to drug target identification. Here we review the main currents in this new field, providing an overview of some of the databases and software used to generate and shorten the lists of potential drug targets using in silico methods. As a case study, we look at the identification and investigation of malarial proteases as therapeutic targets in the Plasmodium spp. parasites. PMID: 15032632 [Indexed for MEDLINE] 226. CPT Pharmacometrics Syst Pharmacol. 2015 Feb;4(2):e9. doi: 10.1002/psp4.9. Epub 2015 Feb 19. A New Drug Combinatory Effect Prediction Algorithm on the Cancer Cell Based on Gene Expression and Dose-Response Curve. Goswami CP(1), Cheng L(2), Alexander PS(3), Singal A(3), Li L(4). Author information: (1)Molecular Lab, Thomas Jefferson University Hospitals Philadelphia, Pennsylvania, USA. (2)Centers for Computational Biology and Bioinformatics, School of Medicine, Indiana University Indianapolis, Indiana, USA ; Department of Medical and Molecular Genetics, School of Medicine, Indiana University Indianapolis, Indiana, USA ; State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute Shanghai, China. (3)Centers for Computational Biology and Bioinformatics, School of Medicine, Indiana University Indianapolis, Indiana, USA. (4)Centers for Computational Biology and Bioinformatics, School of Medicine, Indiana University Indianapolis, Indiana, USA ; Department of Medical and Molecular Genetics, School of Medicine, Indiana University Indianapolis, Indiana, USA. Gene expression data before and after treatment with an individual drug and the IC20 of dose-response data were utilized to predict two drugs' interaction effects on a diffuse large B-cell lymphoma (DLBCL) cancer cell. A novel drug interaction scoring algorithm was developed to account for either synergistic or antagonistic effects between drug combinations. Different core gene selection schemes were investigated, which included the whole gene set, the drug-sensitive gene set, the drug-sensitive minus drug-resistant gene set, and the known drug target gene set. The prediction scores were compared with the observed drug interaction data at 6, 12, and 24 hours with a probability concordance (PC) index. The test result shows the concordance between observed and predicted drug interaction ranking reaches a PC index of 0.605. The scoring reliability and efficiency was further confirmed in five drug interaction studies published in the GEO database. DOI: 10.1002/psp4.9 PMCID: PMC4360667 PMID: 26225234 227. J Chem Inf Model. 2014 Mar 24;54(3):735-43. doi: 10.1021/ci400709d. Epub 2014 Feb 21. Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. Tang J(1), Szwajda A, Shakyawar S, Xu T, Hintsanen P, Wennerberg K, Aittokallio T. Author information: (1)Institute for Molecular Medicine Finland (FIMM), University of Helsinki , Tukholmankatu 8, FI-00290, Helsinki, Finland. We carried out a systematic evaluation of target selectivity profiles across three recent large-scale biochemical assays of kinase inhibitors and further compared these standardized bioactivity assays with data reported in the widely used databases ChEMBL and STITCH. Our comparative evaluation revealed relative benefits and potential limitations among the bioactivity types, as well as pinpointed biases in the database curation processes. Ignoring such issues in data heterogeneity and representation may lead to biased modeling of drugs' polypharmacological effects as well as to unrealistic evaluation of computational strategies for the prediction of drug-target interaction networks. Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), we also introduce a model-based integration approach, termed KIBA, and demonstrate here how it can be used to classify kinase inhibitor targets and to pinpoint potential errors in database-reported drug-target interactions. An integrated drug-target bioactivity matrix across 52,498 chemical compounds and 467 kinase targets, including a total of 246,088 KIBA scores, has been made freely available. DOI: 10.1021/ci400709d PMID: 24521231 [Indexed for MEDLINE] 228. Front Pharmacol. 2017 Jan 9;7:531. doi: 10.3389/fphar.2016.00531. eCollection 2016. In silico Approach for Anti-Thrombosis Drug Discovery: P2Y1R Structure-Based TCMs Screening. Yi F(1), Sun L(1), Xu LJ(1), Peng Y(1), Liu HB(1), He CN(1), Xiao PG(1). Author information: (1)Institute of Medicinal Plant Development, Peking Union Medical College, Chinese Academy of Medical SciencesBeijing, China; Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of EducationBeijing, China. Cardiovascular diseases (CVDs), including thrombosis, which is induced by platelet aggregation, are the leading cause of mortality worldwide. The P2Y1 receptor (P2Y1R) facilitates platelet aggregation and is thus an important potential anti-thrombotic drug target. The P2Y1R protein structure contains a binding site for receptor antagonist MRS2500 within its seven-transmembrane bundle, which also provides suitable pockets for numerous other ligands to act as nucleotide antagonists of P2Y1R. The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) comprises 499 Chinese Pharmacopoeia-registered herbs and the structure information for 29,384 ingredients. In silico docking of these compounds into the P2Y1R protein structure within the MRS2500 pocket can identify potential antithrombotic drugs from natural medicinal plants. Docking studies were performed and scored to evaluate ligand-binding affinities. In this study, a total of 8987 compounds from Traditional Chinese Medicine (TCM) were filtered by Lipinski's rule of five, and their ideal oral-intake properties were evaluated. Of these, 1656 compounds distributed in 443 herbs docked into the P2Y1R-MRS2500 structure in 16,317 poses. A total of 38 compounds were ranked with a DockScore above 70, and these may have significant potential for development into anti-thrombosis drugs. These computational results suggested that licorice (Glycyrrhiza uralensis Fisch), cimicifugae (Cimicifuga foetida L.), and ganoderma (Ganoderma lucidum Karst) and their chemical constituents, which have not previously been widely used for anti-thrombosis, may have unexpected effects on platelet aggregation. Moreover, two types of triterpene scaffolds summarized from 10 compounds were distributed in these three herbs and also docked into P2Y1R. These scaffold structures may be utilized for the development of drugs to inhibit platelet aggregation. DOI: 10.3389/fphar.2016.00531 PMCID: PMC5220089 PMID: 28119608 229. BMC Complement Altern Med. 2013 Apr 15;13:85. doi: 10.1186/1472-6882-13-85. Computational repositioning of ethno medicine elucidated gB-gH-gL complex as novel anti herpes drug target. Basha SH(1), Talluri D, Raminni NP. Author information: (1)Montessori Siva Sivani Institute of Science and Technology-College of Pharmacy, Mylavaram, Vijayawada, 521 230, India. hassainbasha53@gmail.com BACKGROUND: Herpes viruses are important human pathogens that can cause mild to severe lifelong infections with high morbidity. They remain latent in the host cells and can cause recurrent infections that might prove fatal. These viruses are known to infect the host cells by causing the fusion of viral and host cell membrane proteins. Fusion is achieved with the help of conserved fusion machinery components, glycoproteins gB, heterodimer gH-gL complex along with other non-conserved components. Whereas, another important glycoprotein gD without which viral entry to the cell is not possible, acts as a co-activator for the gB-gH-gL complex formation. Thus, this complex formation interface is the most promising drug target for the development of novel anti-herpes drug candidates. In the present study, we propose a model for binding of gH-gL to gB glycoprotein leading from pre to post conformational changes during gB-gH-gL complex formation and reported the key residues involved in this binding activity along with possible binding site locations. To validate the drug targetability of our proposed binding site, we have repositioned some of the most promising in vitro, in vivo validated anti-herpes molecules onto the proposed binding site of gH-gL complex in a computational approach. METHODS: Hex 6.3 standalone software was used for protein-protein docking studies. Arguslab 4.0.1 and Accelrys® Discovery Studio 3.1 Visualizer softwares were used for semi-flexible docking studies and visualizing the interactions respectively. Protein receptors and ethno compounds were retrieved from Protein Data Bank (PDB) and Pubchem databases respectively. Lipinski's Filter, Osiris Property Explorer and Lazar online servers were used to check the pharmaceutical fidelity of the drug candidates. RESULTS: Through protein-protein docking studies, it was identified that the amino acid residues VAL342, GLU347, SER349, TYR355, SER388, ASN395, HIS398 and ALA387 of gH-gL complex play an active role in its binding activity with gB. Semi flexible docking analysis of the most promising in vitro, in vivo validated anti-herpes molecules targeting the above mentioned key residues of gH-gL complex showed that all the analyzed ethno medicinal compounds have successfully docked into the proposed binding site of gH-gL glycoprotein with binding energy range between -10.4 to -6.4 K.cal./mol. CONCLUSIONS: Successful repositioning of the analyzed compounds onto the proposed binding site confirms the drug targetability of gH-gL complex. Based on the free binding energy and pharmacological properties, we propose (3-chloro phenyl) methyl-3,4,5 trihydroxybenzoate as worth a small ethno medicinal lead molecule for further development as potent anti-herpes drug candidate targeting gB-gH-gL complex formation interface. DOI: 10.1186/1472-6882-13-85 PMCID: PMC3662606 PMID: 23587166 [Indexed for MEDLINE] 230. 99mTc-Labeled doxorubicin. Shan L(1). In: Molecular Imaging and Contrast Agent Database (MICAD) [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2004-2013. 2012 Dec 03. Author information: (1)National Center for Biotechnology Information, NLM, NIH Molecular imaging is considered to be a decision-making tool throughout the drug discovery and initial stages of clinical trials (1, 2). Molecular imaging techniques have been used to validate the potential drug targets, determine the drug pharmacokinetics and biodistribution, and assess the drug–target interaction (3). One notable example is the phase 0 clinical trial, which has been developed in response to the exploratory Investigational New Drug (IND) guidance of the United States Food and Drug Administration (1, 4). Phase 0 clinical trials perform first-in-human testing of IND drugs at microdoses (<100 ug) and allow the demonstration of drug–target effects and assessment of pharmacokinetic–pharmacodynamic relationships in humans earlier in clinical development (2, 3). Phase 0 trials are expected to reduce the high failure rate of new drug candidates in clinical trials and to decrease the prolonged timeline and high cost due to the low predictability of toxicity and efficacy with traditional preclinical studies. The drug and target information in these studies is obtained by directly labeling the drugs themselves and/or by imaging the drug targets with specific molecular probes. On the other hand, some well-studied, clinically used drugs have been explored as molecular imaging agents for tumor detection and other purposes. These studies take advantage of the fact that these drugs are already well understood in terms of their targeting, pharmacokinetics, toxicity, and other characteristics. Examples include the studies performed by Kumar et al., who tested the feasibility of doxorubicin as an imaging agent for tumor detection (5). Doxorubicin is a well-investigated anthracycline antibiotic and widely used in cancer chemotherapy (6). The planar aromatic chromophore portion of doxorubicin intercalates between two base pairs of DNA, while the six-membered daunosamine sugar sits in the minor groove and interacts with the flanking base pairs immediately adjacent to the intercalation site (6, 7). The doxorubicin–DNA intercalation inhibits the progression of the enzyme topoisomerase II and stabilizes the topoisomerase II complex after it breaks the DNA chain; this prevents the DNA double helix from being resealed and thereby stops the process of replication. It is estimated that several thousand doxorubicin analogs have been synthesized (8). This chapter summarizes the data obtained in studies of 99mTc-doxorubicin performed by Kumar et al. (5). PMID: 23304756 231. Molecules. 2016 May 5;21(5). pii: E589. doi: 10.3390/molecules21050589. Arginase Flavonoid Anti-Leishmanial in Silico Inhibitors Flagged against Anti-Targets. Glisic S(1), Sencanski M(2), Perovic V(3), Stevanovic S(4), García-Sosa AT(5). Author information: (1)Center for Multidisciplinary Research, Institute of Nuclear Sciences VINCA, University of Belgrade, P.O. Box 522, 11001 Belgrade, Serbia. sanja@vin.bg.ac.rs. (2)Center for Multidisciplinary Research, Institute of Nuclear Sciences VINCA, University of Belgrade, P.O. Box 522, 11001 Belgrade, Serbia. sencanski@vin.bg.ac.rs. (3)Center for Multidisciplinary Research, Institute of Nuclear Sciences VINCA, University of Belgrade, P.O. Box 522, 11001 Belgrade, Serbia. vladaper@vinca.rs. (4)Center for Multidisciplinary Research, Institute of Nuclear Sciences VINCA, University of Belgrade, P.O. Box 522, 11001 Belgrade, Serbia. strahinja.stevanovic@protonmail.com. (5)Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia. alfonsog@ut.ee. Arginase, a drug target for the treatment of leishmaniasis, is involved in the biosynthesis of polyamines. Flavonoids are interesting natural compounds found in many foods and some of them may inhibit this enzyme. The MetIDB database containing 5667 compounds was screened using an EIIP/AQVN filter and 3D QSAR to find the most promising candidate compounds. In addition, these top hits were screened in silico versus human arginase and an anti-target battery consisting of cytochromes P450 2a6, 2c9, 3a4, sulfotransferase, and the pregnane-X-receptor in order to flag their possible interactions with these proteins involved in the metabolism of substances. The resulting compounds may have promise to be further developed for the treatment of leishmaniasis. DOI: 10.3390/molecules21050589 PMCID: PMC6274217 PMID: 27164067 [Indexed for MEDLINE] 232. Int J Comput Biol Drug Des. 2012;5(2):164-79. doi: 10.1504/IJCBDD.2012.048311. Epub 2012 Jul 31. Genome wide search for identification of potential drug targets in Bacillus anthracis. Gutlapalli RV(1), Ambaru JL, Darla P, Rao KR. Author information: (1)Department of Biotechnology, Acharya Nagarjuna University Nagarjuna Nagar, Guntur Andhra Pradesh, India. raviupanishad@gmail.com With the heightened interest in Bacillus anthracis as a potential biological threat agent, novel drug targets identification is of great importance in drug discovery. This study considered a genome-wide approach to identify 270 non-redundant, non-human homologous genes and 103 essential genes of the bacteria as putative drug targets. Sub-cellular localisation of each drug target was annotated using PSORTb 3.0 and confirmation by a hybrid support vector machine analysis identified 16 membrane-bound genes with reliability index ≥4. SPAAN analysis predicted 3 adhesion-like proteins and BLAST against the MEROPS database identified 7 peptidases with inhibitors. As a case study, a homology model was built for the ptsG gene using Modeller 9v8. The work reported here identified a small subset of potential drug targets involved in vital aspects of the metabolism of pathogen, persistence, virulence and cell wall biosynthesis. Thus, this manifold workflow can speed up the process of drug target discovery. DOI: 10.1504/IJCBDD.2012.048311 PMID: 22854124 [Indexed for MEDLINE] 233. Nucleic Acids Res. 2016 Jan 4;44(D1):D959-68. doi: 10.1093/nar/gkv1128. Epub 2015 Oct 30. ccmGDB: a database for cancer cell metabolism genes. Kim P(1), Cheng F(1), Zhao J(1), Zhao Z(2). Author information: (1)Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA. (2)Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN 37212, USA zhongming.zhao@vanderbilt.edu. Accumulating evidence has demonstrated that rewiring of metabolism in cells is an important hallmark of cancer. The percentage of patients killed by metabolic disorder has been estimated to be 30% of the advanced-stage cancer patients. Thus, a systematic annotation of cancer cell metabolism genes is imperative. Here, we present ccmGDB (Cancer Cell Metabolism Gene DataBase), a comprehensive annotation database for cell metabolism genes in cancer, available at http://bioinfo.mc.vanderbilt.edu/ccmGDB. We assembled, curated, and integrated genetic, genomic, transcriptomic, proteomic, biological network and functional information for over 2000 cell metabolism genes in more than 30 cancer types. In total, we integrated over 260 000 somatic alterations including non-synonymous mutations, copy number variants and structural variants. We also integrated RNA-Seq data in various primary tumors, gene expression microarray data in over 1000 cancer cell lines and protein expression data. Furthermore, we constructed cancer or tissue type-specific, gene co-expression based protein interaction networks and drug-target interaction networks. Using these systematic annotations, the ccmGDB portal site provides 6 categories: gene summary, phenotypic information, somatic mutations, gene and protein expression, gene co-expression network and drug pharmacological information with a user-friendly interface for browsing and searching. ccmGDB is developed and maintained as a useful resource for the cancer research community. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gkv1128 PMCID: PMC4702820 PMID: 26519468 [Indexed for MEDLINE] 234. Curr Comput Aided Drug Des. 2017 Nov 10;13(4):303-310. doi: 10.2174/1573409913666170301121110. CAPi: Computational Model for Apicoplast Inhibitors Prediction Against Plasmodium Parasite. Dixit S(1), Singla D(2). Author information: (1)Infectious Diseases Laboratory, National Institute of Immunology, New Delhi, India. (2)Center for Microbial Biotechnology, Panjab University, Chandigarh, India. BACKGROUND: Discovery of apicoplast as a drug target offers a new direction in the development of novel anti-malarial compounds, especially against the drug-resistant strains. Drugs such as azithromycin were reported to block the apicoplast development that leads to unusual phenotypes affecting the parasite. This phenomenon suggests that identification of new apicoplast inhibitors will aid in the anti-malarial drug discovery. Therefore, in this study, we developed a computational model to predict apicoplast inhibitors by applying state-of-the-art machine learning techniques. METHODS: We have used two high-throughput chemical screening data (AID-504850, AID-504848) from PubChem BioAssay database and applied machine learning techniques. The performance of the models were assessed on various types of binary fingerprints. RESULTS: In this study, we developed a robust computational algorithm for the prediction of apicoplast inhibition. We observed 73.7% sensitivity and 84% specificity along with 81.4% accuracy rate only on 41 PubChem fingerprints on 48 hrs dataset. Similarly, an accuracy rate of 75.8% was observed for 96 hrs dataset. Additionally, we observed that our model has ~70% positive prediction rate on the independent dataset obtained from ChEMBL-NTD database. Furthermore, the fingerprint analysis suggested that compounds with at least one heteroatom containing hexagonal ring would most likely belong to the antimalarial category as compared to simple aliphatic compounds. We also observed that aromatic compounds with oxygen and chlorine atoms were preferred in inhibitors class as compared to sulphur. Additionally, the compounds with average molecular weight >380Da and XlogP>4 were most likely to belong to the inhibitor category. CONCLUSION: This study highlighted the significance of simple interpretable molecular properties along with some preferred substructure in designing the novel anti-malarial compounds. In addition to that, robustness and accuracy of models developed in the present work could be utilized to screen a large chemical library. Based on this study, we developed freely available software at http://deepaklab. com/capi. This study would provide the best alternative for searching the novel apicoplast inhibitors against Plasmodium. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1573409913666170301121110 PMID: 28260517 [Indexed for MEDLINE] 235. J Chem Inf Model. 2014 Oct 27;54(10):2834-45. doi: 10.1021/ci5003872. Epub 2014 Oct 7. Application of the 4D fingerprint method with a robust scoring function for scaffold-hopping and drug repurposing strategies. Hamza A(1), Wagner JM, Wei NN, Kwiatkowski S, Zhan CG, Watt DS, Korotkov KV. Author information: (1)Department of Molecular and Cellular Biochemistry, ‡Center for Structural Biology, §Center for Pharmaceutical Research and Innovation, College of Pharmacy, ∥Molecular Modeling and Biopharmaceutical Center, and ⊥Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky , Lexington, Kentucky 40536, United States. Two factors contribute to the inefficiency associated with screening pharmaceutical library collections as a means of identifying new drugs: [1] the limited success of virtual screening (VS) methods in identifying new scaffolds; [2] the limited accuracy of computational methods in predicting off-target effects. We recently introduced a 3D shape-based similarity algorithm of the SABRE program, which encodes a consensus molecular shape pattern of a set of active ligands into a 4D fingerprint descriptor. Here, we report a mathematical model for shape similarity comparisons and ligand database filtering using this 4D fingerprint method and benchmarked the scoring function HWK (Hamza-Wei-Korotkov), using the 81 targets of the DEKOIS database. Subsequently, we applied our combined 4D fingerprint and HWK scoring function VS approach in scaffold-hopping and drug repurposing using the National Cancer Institute (NCI) and Food and Drug Administration (FDA) databases, and we identified new inhibitors with different scaffolds of MycP1 protease from the mycobacterial ESX-1 secretion system. Experimental evaluation of nine compounds from the NCI database and three from the FDA database displayed IC50 values ranging from 70 to 100 μM against MycP1 and possessed high structural diversity, which provides departure points for further structure-activity relationship (SAR) optimization. In addition, this study demonstrates that the combination of our 4D fingerprint algorithm and the HWK scoring function may provide a means for identifying repurposed drugs for the treatment of infectious diseases and may be used in the drug-target profile strategy. DOI: 10.1021/ci5003872 PMCID: PMC4210175 PMID: 25229183 [Indexed for MEDLINE] 236. Cell Rep. 2018 Oct 9;25(2):523-535.e5. doi: 10.1016/j.celrep.2018.09.031. Accurate Drug Repositioning through Non-tissue-Specific Core Signatures from Cancer Transcriptomes. Xu C(1), Ai D(2), Suo S(2), Chen X(1), Yan Y(1), Cao Y(1), Sun N(2), Chen W(2), McDermott J(2), Zhang S(1), Zeng Y(1), Han JJ(3). Author information: (1)Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China. (2)Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. (3)Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. Electronic address: jdhan@picb.ac.cn. Experimental large-scale screens for drug repositioning are limited by restriction to in vitro conditions and lack of applicability to real human conditions. Here, we developed an in silico screen in human in vivo conditions using a reference of single gene mutations' non-tissue-specific "core transcriptome signatures" (CSs) of 8,476 genes generated from the TCGA database. We developed the core-signature drug-to-gene (csD2G) software to scan 3,546 drug treatment profiles against the reference signatures. csD2G significantly outperformed conventional cell line-based gene perturbation signatures and existing drug-repositioning methods in both coverage and specificity. We highlight this with 3 demonstrated applications: (1) repositioned category of psychiatric drugs to inhibit the TGF-β pathway; (2) antihypertensive calcium channel blockers predicted to activate AMPK and inhibit AKT pathways, and validated by clinical electronic medical records; and (3) 7 drugs predicted and validated to selectively target the AKT-FOXO and AMPK pathways and thus regulate worm lifespan. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved. DOI: 10.1016/j.celrep.2018.09.031 PMID: 30304690 237. Curr Drug Metab. 2014;15(4):414-28. QSPR and flow cytometry analysis (QSPR-FCA): review and new findings on parallel study of multiple interactions of chemical compounds with immune cellular and molecular targets. Tenorio-Borroto E, Ramirez FR, Speck-Planche A, Cordeiro MN, Luan F, Gonzalez-Diaz H(1). Author information: (1)Universidad Tecnologica del Valle de Toluca, 52050, Santa Maria Atarasquillo, Lerma, Mexico; or Department of Organic Chemistry II, University of the Basque Country (UPV/EHU), 48940, Bilbao, Spain. humberto.gonzalezdiaz@ehu.es. The immune system helps to halt the infections caused by pathogenic microbial and parasitic agents. The ChEMBL database lists very large datasets of cytotoxicity of organic compounds but notably, a large number of compounds have unknown effects over molecular and cellular targets in the immune system. Flow Cytometry Analysis (FCA) is a very important technique to determine the effect of organic compounds over these molecular and cellular targets in the immune system. In addition, multi-target Quantitative Structure- Property Relationship (mt-QSPR) models can predict drug-target interactions, networks. The objectives of this paper are the following. Firstly, we carried out a review of general aspects and some examples of applications of FCA to study the effect of drugs over different cellular targets. However, we focused more on methods, materials, and experimental results obtained in previous works reported by our group in the study of the drug Dermofural. We also reviewed different mt-QSPR models useful to predict the immunotoxicity and/or the effects of drugs over immune system targets including immune cell lineages or proteins. Secondly, we included new results not published before. Initially, we used ChEMBL data to train and validate a new model but with emphasis in the effect of drugs over lymphocytes. Lastly, we report unpublished results of the computational and FCA study of a new nitro-vinyl-furan compound over thymic lymphocytes T helpers (CD4+) and T cytotoxic (CD8+) population. PMID: 25204826 [Indexed for MEDLINE] 238. Interdiscip Sci. 2018 Jun;10(2):271-281. doi: 10.1007/s12539-016-0188-1. Epub 2016 Oct 1. Structure-Based Drug Designing and Simulation Studies for Finding Novel Inhibitors of Heat Shock Protein (HSP70) as Suppressors for Psoriasis. Mishra S(1), Kumar A(2), Varadwaj PK(1), Misra K(3). Author information: (1)Indian Institute of Information Technology, Allahabad, India. (2)Gautam Buddha University, New Delhi, India. (3)Indian Institute of Information Technology, Allahabad, India. kmisra@iiita.ac.in. Psoriasis is a chronic immune-mediated inflammatory skin disorder. Heat shock proteins (HSPs) have been witnessed as a potential drug target for inhibition of psoriatic cell differentiation. The expression level of HSP is increased when the cells get exposed to elevated temperature, oxidative stress and nutritional deficiencies and thus plays major role in psoriatic progression pathway. Immunoreactivity intensity distribution index scores for HSP70 expression is significantly higher in psoriatic patients compared to normal. In the present work, the 3D structure of human Hsp70 has been taken. Inhibition of HSP70 can control the severity of psoriasis up to many folds; thus, virtual screening was performed against lead-like, drug-like and some natural product of ZINC database. The screened ligands were further introduced to ADMET prediction and simulations to see the drug proficiency and likeness property. The molecular dynamic of system was found stable during simulation trajectory and not much of significant changes occurred in the conformation of the protein-ligand complex. Thus, present study in all probability might prove useful for future design of new derivatives with higher potency and specificity. DOI: 10.1007/s12539-016-0188-1 PMID: 27696208 [Indexed for MEDLINE] 239. Bioinformation. 2010 Mar 31;4(9):392-5. Computational genome analyses of metabolic enzymes in Mycobacterium leprae for drug target identification. Shanmugam A(1), Natarajan J. Author information: (1)Department of Bioinformatics, VMKV Engineering College, Vinayaka Missions University, Salem. Leprosy is an infectious disease caused by Mycobacterium leprae. M. leprae has undergone a major reductive evolution leaving a minimal set of functional genes for survival. It remains non-cultivable. As M. leprae develops resistance against most of the drugs, novel drug targets are required in order to design new drugs. As most of the essential genes mediate several biosynthetic and metabolic pathways, the pathway predictions can predict essential genes. We used comparative genome analysis of metabolic enzymes in M. leprae and H. sapiens using KEGG pathway database and identified 179 non-homologues enzymes. On further comparison of these 179 non-homologous enzymes to the list of minimal set of 48 essential genes required for cell-wall biosynthesis of M. leprae reveals eight common enzymes. Interestingly, six of these eight common enzymes map to that of peptidoglycan biosynthesis and they all belong to Mur enzymes. The machinery for peptidoglycan biosynthesis is a rich source of crucial targets for antibacterial chemotherapy and thus targeting these enzymes is a step towards facilitating the search for new antibiotics. PMCID: PMC2951640 PMID: 20975887 240. Pac Symp Biocomput. 2016;21:321-32. PATIENT-SPECIFIC DATA FUSION FOR CANCER STRATIFICATION AND PERSONALISED TREATMENT. Gligorijević V(1), Malod-Dognin N, Pržulj N. Author information: (1)Department of Computing, Imperial College London, London, SW7 2AZ, United Kingdom. According to Cancer Research UK, cancer is a leading cause of death accounting for more than one in four of all deaths in 2011. The recent advances in experimental technologies in cancer research have resulted in the accumulation of large amounts of patient-specific datasets, which provide complementary information on the same cancer type. We introduce a versatile data fusion (integration) framework that can effectively integrate somatic mutation data, molecular interactions and drug chemical data to address three key challenges in cancer research: stratification of patients into groups having different clinical outcomes, prediction of driver genes whose mutations trigger the onset and development of cancers, and repurposing of drugs treating particular cancer patient groups. Our new framework is based on graph-regularised non-negative matrix tri-factorization, a machine learning technique for co-clustering heterogeneous datasets. We apply our framework on ovarian cancer data to simultaneously cluster patients, genes and drugs by utilising all datasets.We demonstrate superior performance of our method over the state-of-the-art method, Network-based Stratification, in identifying three patient subgroups that have significant differences in survival outcomes and that are in good agreement with other clinical data. Also, we identify potential new driver genes that we obtain by analysing the gene clusters enriched in known drivers of ovarian cancer progression. We validated the top scoring genes identified as new drivers through database search and biomedical literature curation. Finally, we identify potential candidate drugs for repurposing that could be used in treatment of the identified patient subgroups by targeting their mutated gene products. We validated a large percentage of our drug-target predictions by using other databases and through literature curation. PMID: 26776197 [Indexed for MEDLINE] 241. Bioorg Med Chem Lett. 2017 Feb 15;27(4):1055-1061. doi: 10.1016/j.bmcl.2016.12.058. Epub 2016 Dec 26. Virtual screening and experimental validation identify novel modulators of nuclear receptor RXRα from Drugbank database. Xu D(1), Cai L(1), Guo S(1), Xie L(1), Yin M(1), Chen Z(1), Zhou H(1), Su Y(2), Zeng Z(3), Zhang X(4). Author information: (1)School of Pharmaceutical Sciences, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361005, China. (2)School of Pharmaceutical Sciences, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361005, China; Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA. (3)School of Pharmaceutical Sciences, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361005, China. Electronic address: zengzhiping@xmu.edu.cn. (4)School of Pharmaceutical Sciences, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361005, China; Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA. Electronic address: xkzhang@xmu.edu.cn. Retinoid X receptor alpha (RXRα), an important ligand-dependent transcription factor, plays a critical role in the development of various cancers and metabolic and neurodegenerative diseases. Therefore, RXRα represents one of the most important targets in modern drug discovery. In this study, Drugbank 2.0 with 1280 old drugs were virtually screened by Glide according to the crystal structure of ligand-binding domain (LBP) of RXRα. 15 compounds selected were tested for their binding and transcriptional activity toward RXRα by Biacore and reporter gene assay, respectively. The identified new scafford ligand of RXRα, Pitavastatin (1), was chemically optimized. Our results demonstrated that statin compounds Pitavastatin (1) and Fluvastatin (4) could bind to the LBP of RXRα (KD=13.30μM and 11.04μM, respectively) and serve as transcriptional antagonists of RXRα. On the contrary, compound (12) (domperidone) and (13) (rosiglitazone maleate) could bind to the LBP of RXRα (KD=8.80μM and 15.01μM, respectively) but serve as transcriptional agonists of RXRα. Copyright © 2016 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.bmcl.2016.12.058 PMID: 28089347 [Indexed for MEDLINE] 242. J Cheminform. 2011 May 13;3(1):14. doi: 10.1186/1758-2946-3-14. Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: Ranking 1654 human protein targets by assayed compounds and molecular scaffolds. Southan C(1), Boppana K, Jagarlapudi SA, Muresan S. Author information: (1)DECS Global Compound Sciences, Computational Chemistry, AstraZeneca R&D Mölndal, S-431 83 Mölndal, Sweden. sorel.muresan@astrazeneca.com. BACKGROUND: Since the classic Hopkins and Groom druggable genome review in 2002, there have been a number of publications updating both the hypothetical and successful human drug target statistics. However, listings of research targets that define the area between these two extremes are sparse because of the challenges of collating published information at the necessary scale. We have addressed this by interrogating databases, populated by expert curation, of bioactivity data extracted from patents and journal papers over the last 30 years. RESULTS: From a subset of just over 27,000 documents we have extracted a set of compound-to-target relationships for biochemical in vitro binding-type assay data for 1,736 human proteins and 1,654 gene identifiers. These are linked to 1,671,951 compound records derived from 823,179 unique chemical structures. The distribution showed a compounds-per-target average of 964 with a maximum of 42,869 (Factor Xa). The list includes non-targets, failed targets and cross-screening targets. The top-278 most actively pursued targets cover 90% of the compounds. We further investigated target ranking by determining the number of molecular frameworks and scaffolds. These were compared to the compound counts as alternative measures of chemical diversity on a per-target basis. CONCLUSIONS: The compounds-per-protein listing generated in this work (provided as a supplementary file) represents the major proportion of the human drug target landscape defined by published data. We supplemented the simple ranking by the number of compounds assayed with additional rankings by molecular topology. These showed significant differences and provide complementary assessments of chemical tractability. DOI: 10.1186/1758-2946-3-14 PMCID: PMC3118229 PMID: 21569515 243. Zhonghua Liu Xing Bing Xue Za Zhi. 2016 Feb;37(2):291-3. doi: 10.3760/cma.j.issn.0254-6450.2016.02.028. [Bioinformatics analysis on molecular mechanism of ribavirin and interferon-α in treating MERS-CoV]. [Article in Chinese] Zheng Y(1), Wang QY. Author information: (1)Beijing Center for Disease Control and Prevention, Beijing 100013, China. OBJECTIVE: To study the molecular mechanism of ribavirin and interferon-α in the treatment of Middle East Respiratory Syndrome (MERS) by bio-informatic methods. METHODS: MERS-CoV-related microarray data was searched from Array Express database and analyzed by Agilent GeneSpring GX software. Target genes of ribavirin and interferon-α were acquired from Comparative Toxicogenomics Database (CTD). PANTHER TOOL and DAVID platform were used for the analysis of GO and Pathway. RESULTS: One set of MERS-CoV related microarray data and 27 target genes of ribavirin and interferon-α were acquired from the online databases. Data on Genes from microarray were divided into two time-related gene clusters by using the Unsupervised Hierarchical Clustering. Data from the GO analysis indicated that the target genes of ribavirin and interferon-α as well as the genes from the microarray were mainly enriched in 10 biological processes, including cellular process, metabolic process, immune system process and biological regulation, et al. Data on drug target genes, first and second cluster of microarray would involve 9, 3 and 23 signaling pathways respectively, and only the former two showed 7 common pathways, which were related to pathogen recognition, cytokine release and autoimmune response. CONCLUSION: Ribavirin in combination with interferon-α might have therapeutic effects on MERS patients through several signaling pathways. Genes in the second cluster might serve as target genes to be used for screening of new drugs in treating the MERS-CoV infection. DOI: 10.3760/cma.j.issn.0254-6450.2016.02.028 PMID: 26917533 [Indexed for MEDLINE] 244. J Pharm Sci. 2009 Jun;98(6):1928-34. doi: 10.1002/jps.21649. Drug delivery trends in clinical trials and translational medicine: Updated analysis of ClinicalTrials.gov database. Ho RJ(1), Chien JY. Author information: (1)Department of Pharmaceutics, University of Washington, and Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. rodneyho@u.washington.edu While the number of clinical trials has continued to grow by about 20% in the past six months, no corresponding growth in product approval by the food and drug administration is seen or anticipated in the near future. Late-stage clinical failures due to lack of efficacy or toxicity continues to be a challenge. The optimization of absorption, distribution, metabolism and elimination (ADME) has improved drug candidate selection and reduced early clinical failure. The current challenge is how to avoid late stage clinical failures. Expanded knowledge of drug target distribution, pharmacokinetics and validated biomarkers will allow implementation of appropriate drug delivery and clinical trial designs to reduce drug exposure to off-target organs such as the liver and kidney and could reduce potential untoward effects. In essence, integration of drug delivery and targeting to reduce exposure in off-target tissues in the preclinical and clinical program may hold the key to increasing the odds of success in drug development. In this update, we briefly review data on clinical trials pertinent to drug delivery in the current regulatory environment. It also provides our analysis on the emerging trends in second generation antibody therapeutics in drug delivery and targeting. (c) 2008 Wiley-Liss, Inc. DOI: 10.1002/jps.21649 PMID: 19117050 [Indexed for MEDLINE] 245. Curr Med Chem. 2017;24(42):4873-4904. doi: 10.2174/0929867323666160829145055. Oncogene Expression Modulation in Cancer Cell Lines by DNA G-Quadruplex-Interactive Small Molecules. Francisco AP(1), Paulo A(1). Author information: (1)Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa; Av. Prof. Gama Pinto, 1649 003 Lisbon. Portugal. Nucleic acids are prone to structural polymorphism and a number of structures may be formed in addition to the well-known DNA double helix. Among these is a family of nucleic acid four-stranded structures known as G-quadruplexes (G4). These quadruplex structures can be formed by sequences containing repetitive guanine-rich tracks and the analysis of Non-B-DNA database indicated an enrichment of these sequences in genomic regions controlling cellular proliferation, such as for example in the promoter regions of c- MYC, k-RAS, c-KIT, HSP90 and VEGF among others. The broad concept of G4 targeting with small molecules is now generally accepted as a promising novel approach to anticancer therapy and several small molecules with antiproliferative activity in cancer cell lines have also been shown to stabilize these DNA structures, thus suggesting a potential application of G4-interactive small molecules as new anticancer drugs. Herein we review, by targeted oncogene and main chemical scaffold, those G4-interactive small molecules with reported gene expression modulatory activity in cancer cell lines. The data obtained so far are encouraging but further efforts are needed to validate G4 as drug targets and optimize the structure of G4- interactive small molecules into new anticancer drugs. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/0929867323666160829145055 PMID: 27573064 [Indexed for MEDLINE] 246. PLoS One. 2013 Apr 17;8(4):e61327. doi: 10.1371/journal.pone.0061327. Print 2013. Prospecting for novel plant-derived molecules of Rauvolfia serpentina as inhibitors of Aldose Reductase, a potent drug target for diabetes and its complications. Pathania S(1), Randhawa V, Bagler G. Author information: (1)Biotechnology Division, Institute of Himalayan Bioresource Technology, Council of Scientific and Industrial Research, Palampur, Himachal Pradesh, India. Aldose Reductase (AR) is implicated in the development of secondary complications of diabetes, providing an interesting target for therapeutic intervention. Extracts of Rauvolfia serpentina, a medicinal plant endemic to the Himalayan mountain range, have been known to be effective in alleviating diabetes and its complications. In this study, we aim to prospect for novel plant-derived inhibitors from R. serpentina and to understand structural basis of their interactions. An extensive library of R. serpentina molecules was compiled and computationally screened for inhibitory action against AR. The stability of complexes, with docked leads, was verified using molecular dynamics simulations. Two structurally distinct plant-derived leads were identified as inhibitors: indobine and indobinine. Further, using these two leads as templates, 16 more leads were identified through ligand-based screening of their structural analogs, from a small molecules database. Thus, we obtained plant-derived indole alkaloids, and their structural analogs, as potential AR inhibitors from a manually curated dataset of R. serpentina molecules. Indole alkaloids reported herein, as a novel structural class unreported hitherto, may provide better insights for designing potential AR inhibitors with improved efficacy and fewer side effects. DOI: 10.1371/journal.pone.0061327 PMCID: PMC3629236 PMID: 23613832 [Indexed for MEDLINE] 247. Comput Biol Chem. 2019 Feb;78:353-358. doi: 10.1016/j.compbiolchem.2018.12.023. Epub 2019 Jan 11. dbHDPLS: A database of human disease-related protein-ligand structures. Zhu M(1), Song X(1), Chen P(2), Wang W(3), Wang B(4). Author information: (1)Institutes of Physical Science and Information Technology, Anhui University, 230601 Hefei, Anhui, China. (2)School of Electrical and Information Engineering, Anhui University of Technology, 243032 Ma'anshan, Anhui, China; Institutes of Physical Science and Information Technology, Anhui University, 230601 Hefei, Anhui, China. Electronic address: pchen.ustc10@yahoo.com. (3)School of Electrical and Information Engineering, Anhui University of Technology, 243032 Ma'anshan, Anhui, China. (4)School of Electrical and Information Engineering, Anhui University of Technology, 243032 Ma'anshan, Anhui, China. Electronic address: wangbing@ustc.edu. Protein-ligand complexes perform specific functions, most of which are related to human diseases. The database, called as human disease-related protein-ligand structures (dbHDPLS), collected 8833 structures which were extracted from protein data bank (PDB) and other related databases. The database is annotated with comprehensive information involving ligands and drugs, related human diseases and protein-ligand interaction information, with the information of protein structures. The database may be a reliable resource for structure-based drug target discoveries and druggability predictions of protein-ligand binding sites, drug-disease relationships based on protein-ligand complex structures. It can be publicly accessed at the website: http://DeepLearner.ahu.edu.cn/web/dbDPLS/. Copyright © 2019 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.compbiolchem.2018.12.023 PMID: 30665056 248. Trends Parasitol. 2004 Aug;20(8):355-8. Genomics meets transgenics in search of the elusive Cryptosporidium drug target. Striepen B(1), Kissinger JC. Author information: (1)Center for Tropical and Emerging Global Diseases, University of Georgia, 623 Biological Sciences Building, Athens, GA 30602, USA. striepen@cb.uga.edu Cryptosporidium is an important pathogen of humans, and a challenging model for the laboratory. The parasite genome sequence, accessible through a comprehensive database, now provides exciting opportunities for urgently needed advances. Comparative genomics, combined with the genetic system in the related parasite Toxoplasma gondii, outlines a detailed Cryptosporidium parvum metabolic map and facilitates cell biological analyses. New targets for Cryptosporidium drug and vaccine development can be identified and validated based on this approach. DOI: 10.1016/j.pt.2004.06.003 PMID: 15246316 [Indexed for MEDLINE] 249. PLoS One. 2013 Nov 11;8(11):e78518. doi: 10.1371/journal.pone.0078518. eCollection 2013. Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data. Wang Y(1), Chen S, Deng N, Wang Y. Author information: (1)Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China. Erratum in PLoS One. 2013;8(12). doi:10.1371/annotation/fe02e998-6a38-4fd7-9df6-241bc4d0f267. Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems. DOI: 10.1371/journal.pone.0078518 PMCID: PMC3823875 PMID: 24244318 [Indexed for MEDLINE] 250. Curr Top Med Chem. 2012;12(8):927-60. From QSAR models of drugs to complex networks: state-of-art review and introduction of new Markov-spectral moments indices. Riera-Fernández P(1), Martín-Romalde R, Prado-Prado FJ, Escobar M, Munteanu CR, Concu R, Duardo-Sanchez A, González-Díaz H. Author information: (1)Department of Microbiology & Parasitology, University of Santiago de Compostela (USC), Santiago de Compostela, 15782, Spain. Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure. On the other hand, Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures (molecular graphs used in classic QSAR) to large systems. We can cite for instance, drug-target interaction networks, protein structure networks, protein interaction networks (PINs), or drug treatment in large geographical disease spreading networks. In any case, all complex networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and links (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks irrespective the nature of the object they represent and use these TIs to develop QSAR/QSPR models beyond the classic frontiers of drugs small-sized molecules. The goal of this work, in first instance, is to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most used software and databases, common types of QSAR/QSPR models, and complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and/or larger complex networks. In second instance, we use for the first time a Markov chain model to generalize Spectral moments to higher order analogues coined here as the Stochastic Spectral Moments TIs of order k (πk). Lastly, we report for the first time different QSAR/QSPR models for different classes of networks found in drug research, nature, technology, and social-legal sciences using πk values. This work updates our previous reviews Gonzalez-Diaz et al. Curr Top Med Chem. 2007; 7(10): 1015-29 and Gonzalez-Diaz et al. Curr Top Med Chem. 2008; 8(18):1676-90. It has been prepared in response to the kind invitation of the editor Prof. AB Reitz in commemoration of the 10th anniversary of this journal in 2010. PMID: 22352918 [Indexed for MEDLINE] 251. Comb Chem High Throughput Screen. 2018;21(3):182-193. doi: 10.2174/1386207321666180330114457. Virtual Screening of Novel Glucosamine-6-Phosphate Synthase Inhibitors. Lather A(1), Sharma S(2), Khatkar A(3). Author information: (1)Vaish Institute of Pharmaceutical Education and Research, Rohtak, India. (2)Department of Pharmaceutical Sciences, G.J.U.S.&T., Hisar, India. (3)Laboratory for Preservation Technology and Enzyme Inhibition Studies, Department of Pharmaceutical Sciences, M.D. University, Rohtak, India. BACKGROUND: Infections caused by microorganisms are the major cause of death today. The tremendous and improper use of antimicrobial agents leads to antimicrobial resistance. AIM AND OBJECTIVE: Various currently available antimicrobial drugs are inadequate to control the infections and lead to various adverse drug reactions. Efforts based on computer-aided drug design (CADD) can excavate a large number of databases to generate new, potent hits and minimize the requirement of time as well as money for the discovery of newer antimicrobials. Pharmaceutical sciences also have made development with advances in drug designing concepts. The current research article focuses on the study of various G-6-P synthase inhibitors from literature cited molecular database. Docking analysis was conducted and ADMET data of various molecules was evaluated by Schrodinger Glide and PreADMET software, respectively. Here, the results presented efficacy of various inhibitors towards enzyme G-6-P synthase. Docking scores, binding energy and ADMET data of various molecules showed good inhibitory potential toward G-6-P synthase as compared to standard antibiotics. This novel antimicrobial drug target G-6-P synthase has not so extensively been explored for its application in antimicrobial therapy, so the work done so far proved highly essential. This article has helped the drug researchers and scientists to intensively explore about this wonderful antimicrobial drug target. MATERIALS AND METHODS: The Schrodinger, Inc. (New York, USA) software was utilized to carry out the computational calculations and docking studies. The hardware configuration was Intel® core (TM) i5-4210U CPU @ 2.40GHz, RAM memory 4.0 GB under 64-bit window operating system. The ADMET data was calculated by using the PreADMET tool (PreADMET ver. 2.0). All the computational work was completed in the Laboratory for Enzyme Inhibition Studies, Department of Pharmaceutical Sciences, M.D. University, Rohtak, INDIA. RESULTS: Molecular docking studies were carried out to identify the binding affinities and interaction between the inhibitors and the target proteins (G-6-P synthase) by using Glide software (Schrodinger Inc. U.S.A.-Maestro version 10.2). Grid-based Ligand Docking with Energetic (Glide) is one of the most accurate docking softwares available for ligand-protein, protein-protein binding studies. A library of hundreds of available ligands was docked against targeted proteins G-6-P synthase having PDB ID 1moq. Results of docking are shown in Table 1 and Table 2. Results of G-6-P synthase docking showed that some compounds were found to have comparable docking score and binding energy (kj/mol) as compared to standard antibiotics. Many of the ligands showed hydrogen bond interaction, hydrophobic interactions, electrostatic interactions, ionic interactions and π- π stacking with the various amino acid residues in the binding pockets of G-6-P synthase. CONCLUSION: The docking study estimated free energy of binding, binding pose andglide score and all these parameters provide a promising tool for the discovery of new potent natural inhibitors of G-6-P synthase. These G-6-P synthase inhibitors could further be used as antimicrobials. Here, a detailed binding analysis and new insights of inhibitors from various classes of molecules were docked in binding cavity of G-6-P synthase. ADME and toxicity prediction of these compounds will further accentuate us to study these compounds in vivo. This information will possibly present further expansion of effective antimicrobials against several microbial infections. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1386207321666180330114457 PMID: 29600755 252. Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W332-5. doi: 10.1093/nar/gkn289. Epub 2008 May 14. MADNet: microarray database network web server. Segota I(1), Bartonicek N, Vlahovicek K. Author information: (1)Bioinformatics Group, Division of Biology, Faculty of Science, Zagreb University, Horvatovac 102a, 10000 Zagreb, Croatia. MADNet is a user-friendly data mining and visualization tool for rapid analysis of diverse high-throughput biological data such as microarray, phage display or even metagenome experiments. It presents biological information in the context of metabolic and signalling pathways, transcription factors and drug targets through minimal user input, consisting only of the file with the experimental data. These data are integrated with information stored in various biological databases such as NCBI nucleotide and protein databases, metabolic and signalling pathway databases (KEGG), transcription regulation (TRANSFAC(c)) and drug target database (DrugBank). MADNet is freely available for academic use at http://www.bioinfo.hr/madnet. DOI: 10.1093/nar/gkn289 PMCID: PMC2447778 PMID: 18480121 [Indexed for MEDLINE] 253. J Pharmacopuncture. 2015 Sep;18(3):11-8. doi: 10.3831/KPI.2015.18.020. Systems Biology - A Pivotal Research Methodology for Understanding the Mechanisms of Traditional Medicine. Lee S(1). Author information: (1)Department of Physiology, College of Korean Medicine, Sangji University, Wonju, Korea. OBJECTIVES: Systems biology is a novel subject in the field of life science that aims at a systems' level understanding of biological systems. Because of the significant progress in high-throughput technologies and molecular biology, systems biology occupies an important place in research during the post-genome era. METHODS: The characteristics of systems biology and its applicability to traditional medicine research have been discussed from three points of view: data and databases, network analysis and inference, and modeling and systems prediction. RESULTS: The existing databases are mostly associated with medicinal herbs and their activities, but new databases reflecting clinical situations and platforms to extract, visualize and analyze data easily need to be constructed. Network pharmacology is a key element of systems biology, so addressing the multi-component, multi-target aspect of pharmacology is important. Studies of network pharmacology highlight the drug target network and network target. Mathematical modeling and simulation are just in their infancy, but mathematical modeling of dynamic biological processes is a central aspect of systems biology. Computational simulations allow structured systems and their functional properties to be understood and the effects of herbal medicines in clinical situations to be predicted. CONCLUSION: Systems biology based on a holistic approach is a pivotal research methodology for understanding the mechanisms of traditional medicine. If systems biology is to be incorporated into traditional medicine, computational technologies and holistic insights need to be integrated. DOI: 10.3831/KPI.2015.18.020 PMCID: PMC4573803 PMID: 26388998 254. Appl Biochem Biotechnol. 2018 Apr;184(4):1421-1440. doi: 10.1007/s12010-017-2625-y. Epub 2017 Oct 23. Discovery of Potent Neuraminidase Inhibitors Using a Combination of Pharmacophore-Based Virtual Screening and Molecular Simulation Approach. K R(1), V S(2). Author information: (1)Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, Tamil Nadu, 632014, India. (2)Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, Tamil Nadu, 632014, India. shanthi.v@vit.ac.in. Neuraminidase (NA), a surface protein, facilitates the release of nascent virus and thus spreads infection. It has been renowned as a potential drug target for influenza A virus infection. The drugs such as oseltamivir, zanamivir, peramivir, and laninamivir are approved for the treatment of influenza infection. Additionally, investigational drugs namely MK2206, tamiphosphor, crenatoside, and dehydroepiandrosterone (DHEA) are also available for the treatment. However, recent outbreaks of highly pathogenic and drug-resistant influenza A strains highlighted the need to discover novel NA inhibitor. Keeping this in mind, in the current investigation, an effort was made to ascertain potent inhibitors using pharmacophore-based virtual screening and docking approach. A 3D pharmacophore model was generated based on the chemical features of approved and investigational NA inhibitors using PHASE module of Schrödinger suite. The model consists of two hydrogen bond acceptors (A), one hydrogen bond donor (D), and one positively charged group (P), AADP. Subsequently, molecules with same pharmacophoric features were screened from among the two million compounds available in the ZINC database using the generated pharmacophore hypothesis. Ligand filtration was also done to obtain an efficient collection of hit molecules by employing Lipinski "rule of five" using Qikprop module. Finally, the screened molecule was subjected to docking and molecular dynamic simulations to examine the inhibiting activity of the compounds. The results of our analysis suggest that "acebutolol hydrochloride" (156792) could be the promising candidates for the treatment of influenza A virus infection. DOI: 10.1007/s12010-017-2625-y PMID: 29063410 [Indexed for MEDLINE] 255. Recent Pat Antiinfect Drug Discov. 2019 Feb 11. doi: 10.2174/1574891X14666190211162403. [Epub ahead of print] Promising role of Wolbachia as anti-parasitic drug target and eco-friendly biocontrol agent. Chegeni TN(1), Fakhar M(2). Author information: (1)Student Research Committee, Department of Parasitology, School of Medicine, Mazandaran University of Medical Sciences, Sari. Iran. (2)Department of Parasitology, Toxoplasmosis Research Center, School of Medicine, Mazandaran University of Medical Sciences, Sari. Iran. BACKGROUND: Wolbachia is the most common endosymbiotic bacteria in insect-borne parasites and it is the most common reproductive parasite in the world. Wolbachia has been found worldwide in numerous arthropod and parasite species, including: insects, terrestrial isopods, spiders, mites and filarial nematodes. There is a complicated relationship between Wolbachia and its hosts and in some cases, they create a mutual relationship instead of a parasitic relationship. Some species are not able to reproduce in the absence of infection with Wolbachia. Thus, use of existing strains of Wolbachia bacteria offers a potential strategy for control the population of mosquitoes and other pests and diseases. METHODS: We searched ten databases and reviewed published papers regarding the role of Wolbachia as promising drug target and emerging biological control agent of parasitic diseases between 1996 and 2017 (22 years) were considered eligible. Also, in the current study several patents (WO008652), (US7723062), (US 0345249 A1) were reviewed. RESULTS: Endosymbiotic Wolbachia bacteria, which is inherited from mothers, is transmitted to mosquitoes and interfere with pathogen transmission. They can change the reproduction of their host. Wolbachia is transmitted through the cytoplasm of eggs and have evolved different mechanisms for manipulating reproduction of its hosts, including induction of reproductive incompatibility, parthenogenesis, and feminization. Wolbachia extensive effects on reproduction and host fitness have made Wolbachia the issue of growing attention as a potential biocontrol agent. CONCLUSION: Wolbachia has opened a new window to design costly, potent and eco-friendly for effective treatment and elimination of vector-borne parasitic diseases. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net. DOI: 10.2174/1574891X14666190211162403 PMID: 30747079 256. Tuberculosis (Edinb). 2012 Mar;92(2):133-8. doi: 10.1016/j.tube.2011.08.006. Epub 2011 Sep 22. Informatics resources for tuberculosis--towards drug discovery. Sundaramurthi JC(1), Brindha S, Reddy TB, Hanna LE. Author information: (1)ICMR-Biomedical Informatics Centre, Tuberculosis Research Centre (ICMR), Chennai 600031, India. Integration of biological data on gene sequence, genome annotation, gene expression, metabolic pathways, protein structure, drug target prioritization and selection, has resulted in several online bioinformatics databases and tools for Mycobacterium tuberculosis. Alongside there has been a growth in the list of cheminformatics databases for small molecules and tools to facilitate drug discovery. In spite of these efforts there is a noticeable lag in the drug discovery process which is an urgent need in the case of emerging and re-emerging infectious diseases. For example, more than 25 online databases are available freely for tuberculosis and yet these resources have not been exploited optimally. Informatics-centered drug discovery based on the integration and analysis of both bioinformatics and cheminformatics data could fill in the gap and help to accelerate the process of drug discovery. This article aims to review the current standing of developments in tuberculosis-bioinformatics and highlight areas where integration of existing resources could lead to acceleration of drug discovery against tuberculosis. Such an approach could be adapted for other diseases as well. Copyright © 2011 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.tube.2011.08.006 PMID: 21943870 [Indexed for MEDLINE] 257. Database (Oxford). 2018 Jan 1;2018. doi: 10.1093/database/bay121. ANCO-GeneDB: annotations and comprehensive analysis of candidate genes for alcohol, nicotine, cocaine and opioid dependence. Hu R(1), Dai Y(1), Jia P(1), Zhao Z(1)(2)(3). Author information: (1)Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA. (2)Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA. (3)Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. Studies have shown that genetic factors play an important role in the risk to substance addiction and abuse. So far, various genetic and genomic studies have reported the related evidence. These rich, but highly heterogeneous, data provide us an unprecedented opportunity to systematically collect, curate and assess the genetic and genomic signals from published studies and to perform a comprehensive analysis of their features, functional roles and druggability. Such genetic data resources have been made available for other disease or phenotypes but not for major substance dependence yet. Here, we report comprehensive data collection and secondary analyses of four phenotypes of dependence: alcohol dependence, nicotine dependence, cocaine dependence and opioid dependence, collectively named as Alcohol, Nicotine, Cocaine and Opioid (ANCO) dependence. We built the ANCO-GeneDB, an ANCO-dependence-associated gene resource database. ANCO-GeneDB includes resources from genome-wide association studies and candidate gene-based studies, transcriptomic studies, methylation studies, literature mining and drug-target data, as well as the derived data such as spatial-temporal gene expression, promoters, enhancers and expression quantitative trait loci. All associated genes and genetic variants are well annotated by using the collected evidence. Based on the collected data, we performed integrative, secondary analyses to prioritize genes, pathways, eQTLs and tissues that are significantly enriched in ANCO-related phenotypes. DOI: 10.1093/database/bay121 PMCID: PMC6310508 PMID: 30403795 [Indexed for MEDLINE] 258. Adv Appl Bioinform Chem. 2014 Nov 25;7:45-54. doi: 10.2147/AABC.S67336. eCollection 2014. Identification and analysis of potential targets in Streptococcus sanguinis using computer aided protein data analysis. Chowdhury MR(1), Bhuiyan MI(2), Saha A(2), Mosleh IM(2), Mondol S(2), Ahmed CM(3). Author information: (1)Department of Pharmacy, University of Science and Technology Chittagong, Chittagong, Bangladesh. (2)Department of Genetic Engineering and Biotechnology, University of Chittagong, Chittagong, Bangladesh. (3)Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna, Bangladesh. PURPOSE: Streptococcus sanguinis is a Gram-positive, facultative aerobic bacterium that is a member of the viridans streptococcus group. It is found in human mouths in dental plaque, which accounts for both dental cavities and bacterial endocarditis, and which entails a mortality rate of 25%. Although a range of remedial mediators have been found to control this organism, the effectiveness of agents such as penicillin, amoxicillin, trimethoprim-sulfamethoxazole, and erythromycin, was observed. The emphasis of this investigation was on finding substitute and efficient remedial approaches for the total destruction of this bacterium. MATERIALS AND METHODS: In this computational study, various databases and online software were used to ascertain some specific targets of S. sanguinis. Particularly, the Kyoto Encyclopedia of Genes and Genomes databases were applied to determine human nonhomologous proteins, as well as the metabolic pathways involved with those proteins. Different software such as Phyre2, CastP, DoGSiteScorer, the Protein Function Predictor server, and STRING were utilized to evaluate the probable active drug binding site with its known function and protein-protein interaction. RESULTS: In this study, among 218 essential proteins of this pathogenic bacterium, 81 nonhomologous proteins were accrued, and 15 proteins that are unique in several metabolic pathways of S. sanguinis were isolated through metabolic pathway analysis. Furthermore, four essentially membrane-bound unique proteins that are involved in distinct metabolic pathways were revealed by this research. Active sites and druggable pockets of these selected proteins were investigated with bioinformatic techniques. In addition, this study also mentions the activity of those proteins, as well as their interactions with the other proteins. CONCLUSION: Our findings helped to identify the type of protein to be considered as an efficient drug target. This study will pave the way for researchers to develop and discover more effective and specific therapeutic agents against S. sanguinis. DOI: 10.2147/AABC.S67336 PMCID: PMC4250024 PMID: 25473301 259. Trends Parasitol. 2016 Jan;32(1):7-9. doi: 10.1016/j.pt.2015.10.003. Epub 2015 Oct 31. Malaria Parasite Metabolic Pathways (MPMP) Upgraded with Targeted Chemical Compounds. Ginsburg H(1), Abdel-Haleem AM(2). Author information: (1)Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel. Electronic address: hagai.ginsburg@gmail.com. (2)Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; Biological and Environmental Sciences and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia. Malaria Parasite Metabolic Pathways (MPMP) is the website for the functional genomics of intraerythrocytic Plasmodium falciparum. All the published information about targeted chemical compounds has now been added. Users can find the drug target and publication details linked to a drug database for further information about the medicinal properties of each compound. Copyright © 2015 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.pt.2015.10.003 PMID: 26530861 [Indexed for MEDLINE] 260. Curr Protoc Bioinformatics. 2007 Jun;Chapter 14:Unit 14.4. doi: 10.1002/0471250953.bi1404s18. In silico drug exploration and discovery using DrugBank. Wishart DS(1). Author information: (1)University of Alberta and the National Institute of Nanotechnology (NINT) National Research Council, Edmonton, Alberta, Canada. DrugBank is a fully curated drug and drug target database that contains information on nearly 5000 drugs, including > 1200 FDA-approved small molecule and biotech drugs as well as >3200 experimental drugs. Additionally, more than 14,000 protein or drug target sequences are linked to these drug entries. DrugBank is primarily focused on providing both the query/search tools and the biophysical data needed to facilitate drug discovery and drug development. This unit provides readers with a detailed description of how to effectively use the DrugBank database and how to navigate through the DrugBank Web site. It also provides specific examples of how to find chemical homologs of potential drug leads and how to identify potential drug targets from newly sequenced pathogens. The intent of this unit is to give readers some introduction into the field of cheminformatics (the study of chemical information) and to show how cheminformatics can be seamlessly integrated into the field of bioinformatics. DOI: 10.1002/0471250953.bi1404s18 PMID: 18428789 [Indexed for MEDLINE] 261. Biotechnol Annu Rev. 2005;11:1-68. Towards quantitative biology: integration of biological information to elucidate disease pathways and to guide drug discovery. Fischer HP(1). Author information: (1)Genedata AG, Basel, Switzerland. Hans-Peter.Fischer@genedata.com Developing a new drug is a tedious and expensive undertaking. The recently developed high-throughput experimental technologies, summarised by the terms genomics, transcriptomics, proteomics and metabolomics provide for the first time ever the means to comprehensively monitor the molecular level of disease processes. The "-omics" technologies facilitate the systematic characterisation of a drug target's physiology, thereby helping to reduce the typically high attrition rates in discovery projects, and improving the overall efficiency of pharmaceutical research processes. Currently, the bottleneck for taking full advantage of the new experimental technologies are the rapidly growing volumes of automatically produced biological data. A lack of scalable database systems and computational tools for target discovery has been recognised as a major hurdle. In this review, an overview will be given on recent progress in computational biology that has an impact on drug discovery applications. The focus will be on novel in silico methods to reconstruct regulatory networks, signalling cascades, and metabolic pathways, with an emphasis on comparative genomics and microarray-based approaches. Promising methods, such as the mathematical simulation of pathway dynamics are discussed in the context of applications in discovery projects. The review concludes by exemplifying concrete data-driven studies in pharmaceutical research that demonstrate the value of integrated computational systems for drug target identification and validation, screening assay development, as well as drug candidate efficacy and toxicity evaluations. DOI: 10.1016/S1387-2656(05)11001-1 PMID: 16216773 [Indexed for MEDLINE] 262. BMC Bioinformatics. 2019 Feb 8;20(1):69. doi: 10.1186/s12859-019-2664-1. Predicting clinically promising therapeutic hypotheses using tensor factorization. Yao J(1), Hurle MR(2), Nelson MR(3), Agarwal P(2). Author information: (1)Computational Biology, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA, USA. jin.8.yao@gsk.com. (2)Computational Biology, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA, USA. (3)Genetics, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA, USA. BACKGROUND: Determining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. With current informatics technology and machine learning algorithms, it is now possible to computationally discover therapeutic hypotheses by predicting clinically promising drug targets based on the evidence associating drug targets with disease indications. We have collected this evidence from Open Targets and additional databases that covers 17 sources of evidence for target-indication association and represented the data as a tensor of 21,437 × 2211 × 17. RESULTS: As a proof-of-concept, we identified examples of successes and failures of target-indication pairs in clinical trials across 875 targets and 574 disease indications to build a gold-standard data set of 6140 known clinical outcomes. We designed and executed three benchmarking strategies to examine the performance of multiple machine learning models: Logistic Regression, LASSO, Random Forest, Tensor Factorization and Gradient Boosting Machine. With 10-fold cross-validation, tensor factorization achieved AUROC = 0.82 ± 0.02 and AUPRC = 0.71 ± 0.03. Across multiple validation schemes, this was comparable or better than other methods. CONCLUSION: In this work, we benchmarked a machine learning technique called tensor factorization for the problem of predicting clinical outcomes of therapeutic hypotheses. Results have shown that this method can achieve equal or better prediction performance compared with a variety of baseline models. We demonstrate one application of the method to predict outcomes of trials on novel indications of approved drug targets. This work can be expanded to targets and indications that have never been clinically tested and proposing novel target-indication hypotheses. Our proposed biologically-motivated cross-validation schemes provide insight into the robustness of the prediction performance. This has significant implications for all future methods that try to address this seminal problem in drug discovery. DOI: 10.1186/s12859-019-2664-1 PMCID: PMC6368709 PMID: 30736745 263. Nucleic Acids Res. 2015 Jan;43(Database issue):D946-55. doi: 10.1093/nar/gku1086. Epub 2014 Nov 20. EHFPI: a database and analysis resource of essential host factors for pathogenic infection. Liu Y(1), Xie D(1), Han L(1), Bai H(2), Li F(3), Wang S(1), Bo X(4). Author information: (1)Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, P.R.China. (2)Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, P.R.China No. 451 Hospital of Chinese People's Liberation Army, Xi'an 710054, China huibai13@hotmail.com. (3)Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, P.R.China lifei@bmi.ac.cn. (4)Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, P.R.China boxc@bmi.ac.cn. High-throughput screening and computational technology has greatly changed the face of microbiology in better understanding pathogen-host interactions. Genome-wide RNA interference (RNAi) screens have given rise to a new class of host genes designated as Essential Host Factors (EHFs), whose knockdown effects significantly influence pathogenic infections. Therefore, we present the first release of a manually-curated bioinformatics database and analysis resource EHFPI (Essential Host Factors for Pathogenic Infection, http://biotech.bmi.ac.cn/ehfpi). EHFPI captures detailed article, screen, pathogen and phenotype annotation information for a total of 4634 EHF genes of 25 clinically important pathogenic species. Notably, EHFPI also provides six powerful and data-integrative analysis tools, i.e. EHF Overlap Analysis, EHF-pathogen Network Analysis, Gene Enrichment Analysis, Pathogen Interacting Proteins (PIPs) Analysis, Drug Target Analysis and GWAS Candidate Gene Analysis, which advance the comprehensive understanding of the biological roles of EHF genes, as in diverse perspectives of protein-protein interaction network, drug targets and diseases/traits. The EHFPI web interface provides appropriate tools that allow efficient query of EHF data and visualization of custom-made analysis results. EHFPI data and tools shall keep available without charge and serve the microbiology, biomedicine and pharmaceutics research communities, to finally facilitate the development of diagnostics, prophylactics and therapeutics for human pathogens. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gku1086 PMCID: PMC4383917 PMID: 25414353 [Indexed for MEDLINE] 264. Comput Biol Chem. 2016 Dec;65:80-90. doi: 10.1016/j.compbiolchem.2016.10.003. Epub 2016 Oct 8. Protein-protein interaction and molecular dynamics analysis for identification of novel inhibitors in Burkholderia cepacia GG4. Gupta M(1), Chauhan R(1), Prasad Y(2), Wadhwa G(3), Jain CK(4). Author information: (1)Department of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector-62, Noida, Uttar Pradesh, 201307, India. (2)Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India. (3)Department of Biotechnology (DBT), Ministry of Science & Technology, New Delhi-110003, India. (4)Department of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector-62, Noida, Uttar Pradesh, 201307, India. Electronic address: ckj522@yahoo.com. The lack of complete treatments and appearance of multiple drug-resistance strains of Burkholderia cepacia complex (Bcc) are causing an increased risk of lung infections in cystic fibrosis patients. Bcc infection is a big risk to human health and demands an urgent need to identify new therapeutics against these bacteria. Network biology has emerged as one of the prospective hope in identifying novel drug targets and hits. We have applied protein-protein interaction methodology to identify new drug-target candidates (orthologs) in Burkhloderia cepacia GG4, which is an important strain for studying the quorum-sensing phenomena. An evolutionary based ortholog mapping approach has been applied for generating the large scale protein-protein interactions in B. Cepacia. As a case study, one of the identified drug targets; GEM_3202, a NH (3)-dependent NAD synthetase protein has been studied and the potential ligand molecules were screened using the ZINC database. The three dimensional structure (NH (3)-dependent NAD synthetase protein) has been predicted from MODELLERv9.11 tool using multiple PDB templates such as 3DPI, 2PZ8 and 1NSY with sequence identity of 76%, 50% and 50% respectively. The structure has been validated with Ramachandaran plot having 100% residues of NadE in allowed region and overall quality factor of 81.75 using ERRAT tool. High throughput screening and Vina resulted in two potential hits against NadE such as ZINC83103551 and ZINC38008121. These molecules showed lowest binding energy of -5.7kcalmol-1 and high stability in the binding pockets during molecular dynamics simulation analysis. The similar approach for target identification could be applied for clinical strains of other pathogenic microbes. Copyright © 2016 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.compbiolchem.2016.10.003 PMID: 27776248 [Indexed for MEDLINE] 265. Pathogens. 2017 Jul 20;6(3). pii: E32. doi: 10.3390/pathogens6030032. ProtozoaDB 2.0: A Trypanosoma Brucei Case Study. Jardim R(1), Tschoeke D(2)(3), Da Vila AMR(4). Author information: (1)Computational and Systems Biology Laboratory, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro 21040-900, Brazil. rodrigo_jardim@fiocruz.br. (2)Microbiology Laboratory, Rio de Janeiro Federal University, Rio de Janeiro 21941-901, Brazil. diogoat@gmail.com. (3)Nucleus in Ecology and Socio-Environmental Development of Macaé (NUPEM), Rio de Janeiro Federal University, Macaé, Rio de Janeiro 21941-901, Brazil. diogoat@gmail.com. (4)Computational and Systems Biology Laboratory, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro 21040-900, Brazil. davila@fiocruz.br. Over the last decade new species of Protozoa have been sequenced and deposited in GenBank. Analyzing large amounts of genomic data, especially using Next Generation Sequencing (NGS), is not a trivial task, considering that researchers used to deal or focus their studies on few genes or gene families or even small genomes. To facilitate the information extraction process from genomic data, we developed a database system called ProtozoaDB that included five genomes of Protozoa in its first version. In the present study, we present a new version of ProtozoaDB called ProtozoaDB 2.0, now with the genomes of 22 pathogenic Protozoa. The system has been fully remodeled to allow for new tools and a more expanded view of data, and now includes a number of analyses such as: (i) similarities with other databases (model organisms, the Conserved Domains Database, and the Protein Data Bank); (ii) visualization of KEGG metabolic pathways; (iii) the protein structure from PDB; (iv) homology inferences; (v) the search for related publications in PubMed; (vi) superfamily classification; and (vii) phenotype inferences based on comparisons with model organisms. ProtozoaDB 2.0 supports RESTful Web Services to make data access easier. Those services were written in Ruby language using Ruby on Rails (RoR). This new version also allows a more detailed analysis of the object of study, as well as expanding the number of genomes and proteomes available to the scientific community. In our case study, a group of prenyltransferase proteinsalready described in the literature was found to be a good drug target for Trypanosomatids. DOI: 10.3390/pathogens6030032 PMCID: PMC5617989 PMID: 28726736 Conflict of interest statement: The authors declare no conflict of interest. 266. Nucleic Acids Res. 2008 Jan;36(Database issue):D480-4. Epub 2007 Dec 12. KEGG for linking genomes to life and the environment. Kanehisa M(1), Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y. Author information: (1)Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan. KEGG (http://www.genome.jp/kegg/) is a database of biological systems that integrates genomic, chemical and systemic functional information. KEGG provides a reference knowledge base for linking genomes to life through the process of PATHWAY mapping, which is to map, for example, a genomic or transcriptomic content of genes to KEGG reference pathways to infer systemic behaviors of the cell or the organism. In addition, KEGG provides a reference knowledge base for linking genomes to the environment, such as for the analysis of drug-target relationships, through the process of BRITE mapping. KEGG BRITE is an ontology database representing functional hierarchies of various biological objects, including molecules, cells, organisms, diseases and drugs, as well as relationships among them. KEGG PATHWAY is now supplemented with a new global map of metabolic pathways, which is essentially a combined map of about 120 existing pathway maps. In addition, smaller pathway modules are defined and stored in KEGG MODULE that also contains other functional units and complexes. The KEGG resource is being expanded to suit the needs for practical applications. KEGG DRUG contains all approved drugs in the US and Japan, and KEGG DISEASE is a new database linking disease genes, pathways, drugs and diagnostic markers. DOI: 10.1093/nar/gkm882 PMCID: PMC2238879 PMID: 18077471 [Indexed for MEDLINE] 267. Nucleic Acids Res. 2018 Jan 4;46(D1):D447-D453. doi: 10.1093/nar/gkx1041. iUUCD 2.0: an update with rich annotations for ubiquitin and ubiquitin-like conjugations. Zhou J(1), Xu Y(1), Lin S(1), Guo Y(1), Deng W(1), Zhang Y(1), Guo A(1), Xue Y(1). Author information: (1)Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China. Here, we described the updated database iUUCD 2.0 (http://iuucd.biocuckoo.org/) for ubiquitin-activating enzymes (E1s), ubiquitin-conjugating enzymes (E2s), ubiquitin-protein ligases (E3s), deubiquitinating enzymes (DUBs), ubiquitin/ubiquitin-like binding domains (UBDs) and ubiquitin-like domains (ULDs), which act as key regulators in modulating ubiquitin and ubiquitin-like (UB/UBL) conjugations. In total, iUUCD 2.0 contained 136 512 UB/UBL regulators, including 1230 E1s, 5636 E2s, 93 343 E3s, 9548 DUBs, 30 173 UBDs and 11 099 ULDs in 148 eukaryotic species. In particular, we provided rich annotations for regulators of eight model organisms, especially in humans, by compiling and integrating the knowledge from nearly 70 widely used public databases that cover cancer mutations, single nucleotide polymorphisms (SNPs), mRNA expression, DNA and RNA elements, protein-protein interactions, protein 3D structures, disease-associated information, drug-target relations, post-translational modifications, DNA methylation and protein expression/proteomics. Compared with our previously developed UUCD 1.0 (∼0.41 GB), iUUCD 2.0 has a size of ∼32.1 GB of data with a >75-fold increase in data volume. We anticipate that iUUCD 2.0 can be a more useful resource for further study of UB/UBL conjugations. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gkx1041 PMCID: PMC5753239 PMID: 29106644 268. Biochim Biophys Acta Proteins Proteom. 2017 Nov;1865(11 Pt A):1416-1422. doi: 10.1016/j.bbapap.2017.08.009. Epub 2017 Aug 26. Conformations of the HIV-1 protease: A crystal structure data set analysis. Palese LL(1). Author information: (1)University of Bari "Aldo Moro", Department of Basic Medical Sciences, Neurosciences and Sense Organs (SMBNOS), Bari 70124, Italy. Electronic address: luigileonardo.palese@uniba.it. The HIV protease is an important drug target for HIV/AIDS therapy, and its structure and function have been extensively investigated. This enzyme performs an essential role in viral maturation by processing specific cleavage sites in the Gag and Gag-Pol precursor polyproteins so as to release their mature forms. This 99 amino acid aspartic protease works as a homodimer, with the active site localized in a central cavity capped by two flexible flap regions. The dimer presents closed or open conformations, which are involved in the substrate binding and release. Here the results of the analysis of a HIV-1 protease data set containing 552 dimer structures are reported. Different dimensionality reduction methods have been used in order to get information from this multidimensional database. Most of the structures in the data set belong to two conformational clusters. An interesting observation that comes from the analysis of these data is that some protease sequences are localized preferentially in specific areas of the conformational landscape of this protein. Copyright © 2017 Elsevier B.V. All rights reserved. DOI: 10.1016/j.bbapap.2017.08.009 PMID: 28846854 [Indexed for MEDLINE] 269. Oncotarget. 2017 Aug 24;8(53):91379-91390. doi: 10.18632/oncotarget.20557. eCollection 2017 Oct 31. Matrix metalloproteinase-1 expression in breast carcinoma: a marker for unfavorable prognosis. Wang J(1)(2), Ye C(3), Lu D(3)(4), Chen Y(1)(2), Jia Y(1)(2), Ying X(1)(2), Xiong H(1)(2), Zhao W(1)(2), Zhou J(1)(2), Wang L(1)(2). Author information: (1)Department of Surgical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310016, China. (2)Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Hangzhou, Zhejiang, 310016, China. (3)Cancer Institute, Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310009, China. (4)Department of Medical Oncology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310009, China. Matrix metalloproteinase-1 (MMP1) is a member of the matrix metalloproteinases family, and its aberrant expression is implicated in tumor invasion and metastasis. However, the relationship between MMP1 abnormal expression and clinical outcome in breast cancer patients remains to be elucidated. To address this issue, we conducted immunohistochemistry in breast cancer and adjacent normal tissues, and mined the transcriptional and survival data of MMP1 in breast cancer patients through Oncomine, Kaplan-Meier Plotter, bc-GenExMiner, COSMIC and cBioPortal databases. First, we found that both protein and mRNA levels of MMP1 expression were significantly higher in breast cancer tissues. Second, high MMP1 mRNA expression correlated with worse overall survival among grade II (HR = 1.75; p = 0.011), nodal-negative (HR = 2.00; p = 0.00028), ER-positive (HR = 1.61; p = 0.00027) and HER2-negative (HR = 3.17; p = 0.029) patients with breast cancer by using Kaplan-Meier plotter database. Third, the overexpression of MMP1 was associated with unfavorable survival results including overall survival (HR = 1.6; p = 1.6e-05), relapse free survival (HR = 1.78; p < 1e-16) and distant metastasis free survival (HR = 1.65; p = 5.3e-05) in patients with breast cancer. Taken together, the expression status of MMP1 is a significant prognostic indicator and a potential drug target for breast cancer. DOI: 10.18632/oncotarget.20557 PMCID: PMC5710931 PMID: 29207651 Conflict of interest statement: CONFLICTS OF INTEREST The authors declare that they have no competing interests. 270. Comput Biol Chem. 2015 Oct;58:158-66. doi: 10.1016/j.compbiolchem.2015.06.004. Epub 2015 Jun 30. In silico identification of novel IL-1β inhibitors to target protein-protein interfaces. Halim SA(1), Jawad M(2), Ilyas M(2), Mir Z(2), Mirza AA(2), Husnain T(2). Author information: (1)National Center of Excellence in Molecular Biology, University of the Punjab, Lahore 53700, Pakistan. Electronic address: sobia.halim@cemb.edu.pk. (2)National Center of Excellence in Molecular Biology, University of the Punjab, Lahore 53700, Pakistan. Interleukin-1β is a drug target in rheumatoid arthritis and several auto-immune disorders. In this study, a set of 48 compounds with the determined IC50 values were used for QSAR analysis by MOE. The QSAR model was developed by using training set of 41 compounds, based on 12 unique descriptors. Model was validated by predicting the IC50 values for a test set of 7 compounds. A correlation analysis was carried out comparing the statistics of the measured IC50 values with predicted ones. Subsequently, model was used for the screening of a large data set of 7,397,957 compounds obtained from "Drugs Now" category of ZINC database. The activities of those compounds were predicted by developed model. 708,960 compounds that showed best predicted activities were chosen for further studies. Additionally this set of 708,960 compounds was screened by pharmacophore modeling that led to the retrieval of 1809 molecules. Finally docking of 1809 molecules was conducted at the IL-1β receptor binding site using MOE and FRED docking program. Several new compounds were predicted as IL-1β inhibitors in silico. This study provides valuable insight for designing more potent and selective inhibitors for the treatment of inflammatory diseases. Copyright © 2015 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.compbiolchem.2015.06.004 PMID: 26253030 [Indexed for MEDLINE] 271. Chang Gung Med J. 2005 Apr;28(4):201-11. Clinical bioinformatics. Chang PL(1). Author information: (1)Division of Urology, Department of Surgery and Bioinformatics Center, Chang Gung Memorial Hospital, Taipei, Taiwan. henryc@cgmh.org.tw Clinical bioinformatics provides biological and medical information to allow for individualized healthcare. In this review, we describe the uses of clinical bioinformatics. After the analysis of the complete human genome sequences, clinical bioinformatics enables researchers to search online biological databases and use the biological information in their medical practices. The data obtained from using microarray is extremely complicated. In clinical bioinformatics, selecting appropriate software to analyze the microarray data for medical decision making is crucial. Proteomics strategy tools usually focus on similarity searches, structure prediction, and protein modeling. In clinical bioinformatics, the proteomic data only have meaning if they are integrated with clinical data. In pharmacogenomics, clinical bioinformatics includes elaborate studies of bioinformatics tools and various facets of proteomics related to drug target identification and clinical validation. Using clinical bioinformatics, researchers apply computational and high-throughput experimental techniques to cancer research and systems biology. Meanwhile, researchers of bioinformatics and medical information have incorporated clinical bioinformatics to improve health care, using biological and medical information. Using the high volume of biological information from clinical bioinformatics will contribute to changes in practice standards in the healthcare system. We believe that clinical bioinformatics provides benefits of improving healthcare, disease prevention and health maintenance as we move toward the era of personalized medicine. PMID: 16013339 [Indexed for MEDLINE] 272. Genome Inform. 2008;20:252-9. Network analysis of adverse drug interactions. Takarabe M(1), Okuda S, Itoh M, Tokimatsu T, Goto S, Kanehisa M. Author information: (1)Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan. takarabe@kuicr.kyoto-u.ac.jp Harmful effects associated with use of drugs are caused as a result of their side effects and combined use of different drugs. These drug interactions result in increased or decreased drug effects, or produce other new unwanted effects and are serious problems for medical institutions and pharmaceutical companies. In this study, we created a drug-drug interaction network from drug package inserts and characterized drug interactions. The known information about the potential risk of drug interactions is described in drug package inserts. Japanese drug package inserts are stored in the JAPIC (Japan Pharmaceutical Information Center) database and GenomeNet provides the GenomeNet pharmaceutical products database, which integrate the JAPIC and KEGG databases. We extracted drug interaction data from GenomeNet, where interactions are classified according to risks, contraindications or cautions for coadministration, and some entries include information about enzymes metabolizing the drugs. We defined drug target and drug-metabolizing enzymes as interaction factors using information on them in KEGG DRUG, and classified drugs into pharmacological/chemical subgroups. In the resulting drug-drug interaction network, the drugs that are associated with the same interaction factors are closely interconnected. Mechanisms of these interactions were then identified by each interaction factor. To characterize other interactions without interaction factors, we used the ATC classification system and found an association between interaction mechanisms and pharmacological/chemical subgroups. PMID: 19425139 [Indexed for MEDLINE] 273. Philos Trans R Soc Lond B Biol Sci. 2002 Jan 29;357(1417):101-7. Metabolic pathway analysis in trypanosomes and malaria parasites. Fairlamb AH(1). Author information: (1)Division of Biological Chemistry and Molecular Microbiology, The Wellcome Trust Biocentre, University of Dundee, Dundee DD1 5EH, UK. a.h.fairlamb@dundee.ac.uk Identification of novel drug targets is required for the development of new classes of drugs to overcome drug resistance and replace less efficacious treatments. In theory, knowledge of the entire genome of a pathogen identifies every potential drug target in any given microbe. In practice, the sheer complexity and the inadequate or inaccurate annotation of genomic information makes target identification and selection somewhat more difficult. Analysis of metabolic pathways provides a useful conceptual framework for the identification of potential drug targets and also for improving our understanding of microbial responses to nutritional, chemical and other environmental stresses. A number of metabolic databases are available as tools for such analyses. The strengths and weaknesses of this approach are discussed. DOI: 10.1098/rstb.2001.1040 PMCID: PMC1692913 PMID: 11839187 [Indexed for MEDLINE] 274. BMC Bioinformatics. 2012;13 Suppl 15:S7. doi: 10.1186/1471-2105-13-S15-S7. Epub 2012 Sep 11. IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis. Zhang F(1), Drabier R. Author information: (1)Department of Academic and Institutional Resources and Technology, University of North Texas Health Science Center, Fort Worth, USA. BACKGROUND: Next-Generation Sequencing (NGS) technologies and Genome-Wide Association Studies (GWAS) generate millions of reads and hundreds of datasets, and there is an urgent need for a better way to accurately interpret and distill such large amounts of data. Extensive pathway and network analysis allow for the discovery of highly significant pathways from a set of disease vs. healthy samples in the NGS and GWAS. Knowledge of activation of these processes will lead to elucidation of the complex biological pathways affected by drug treatment, to patient stratification studies of new and existing drug treatments, and to understanding the underlying anti-cancer drug effects. There are approximately 141 biological human pathway resources as of Jan 2012 according to the Pathguide database. However, most currently available resources do not contain disease, drug or organ specificity information such as disease-pathway, drug-pathway, and organ-pathway associations. Systematically integrating pathway, disease, drug and organ specificity together becomes increasingly crucial for understanding the interrelationships between signaling, metabolic and regulatory pathway, drug action, disease susceptibility, and organ specificity from high-throughput omics data (genomics, transcriptomics, proteomics and metabolomics). RESULTS: We designed the Integrated Pathway Analysis Database for Systematic Enrichment Analysis (IPAD, http://bioinfo.hsc.unt.edu/ipad), defining inter-association between pathway, disease, drug and organ specificity, based on six criteria: 1) comprehensive pathway coverage; 2) gene/protein to pathway/disease/drug/organ association; 3) inter-association between pathway, disease, drug, and organ; 4) multiple and quantitative measurement of enrichment and inter-association; 5) assessment of enrichment and inter-association analysis with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources; and 6) cross-linking of multiple available data sources.IPAD is a comprehensive database covering about 22,498 genes, 25,469 proteins, 1956 pathways, 6704 diseases, 5615 drugs, and 52 organs integrated from databases including the BioCarta, KEGG, NCI-Nature curated, Reactome, CTD, PharmGKB, DrugBank, PharmGKB, and HOMER. The database has a web-based user interface that allows users to perform enrichment analysis from genes/proteins/molecules and inter-association analysis from a pathway, disease, drug, and organ.Moreover, the quality of the database was validated with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources. Two case studies were also presented to demonstrate: 1) self-validation of enrichment analysis and inter-association analysis on brain-specific markers, and 2) identification of previously undiscovered components by the enrichment analysis from a prostate cancer study. CONCLUSIONS: IPAD is a new resource for analyzing, identifying, and validating pathway, disease, drug, organ specificity and their inter-associations. The statistical method we developed for enrichment and similarity measurement and the two criteria we described for setting the threshold parameters can be extended to other enrichment applications. Enriched pathways, diseases, drugs, organs and their inter-associations can be searched, displayed, and downloaded from our online user interface. The current IPAD database can help users address a wide range of biological pathway related, disease susceptibility related, drug target related and organ specificity related questions in human disease studies. DOI: 10.1186/1471-2105-13-S15-S7 PMCID: PMC3439721 PMID: 23046449 [Indexed for MEDLINE] 275. PLoS Comput Biol. 2013;9(11):e1003353. doi: 10.1371/journal.pcbi.1003353. Epub 2013 Nov 14. Substrate-driven mapping of the degradome by comparison of sequence logos. Fuchs JE(1), von Grafenstein S, Huber RG, Kramer C, Liedl KR. Author information: (1)Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria. Sequence logos are frequently used to illustrate substrate preferences and specificity of proteases. Here, we employed the compiled substrates of the MEROPS database to introduce a novel metric for comparison of protease substrate preferences. The constructed similarity matrix of 62 proteases can be used to intuitively visualize similarities in protease substrate readout via principal component analysis and construction of protease specificity trees. Since our new metric is solely based on substrate data, we can engraft the protease tree including proteolytic enzymes of different evolutionary origin. Thereby, our analyses confirm pronounced overlaps in substrate recognition not only between proteases closely related on sequence basis but also between proteolytic enzymes of different evolutionary origin and catalytic type. To illustrate the applicability of our approach we analyze the distribution of targets of small molecules from the ChEMBL database in our substrate-based protease specificity trees. We observe a striking clustering of annotated targets in tree branches even though these grouped targets do not necessarily share similarity on protein sequence level. This highlights the value and applicability of knowledge acquired from peptide substrates in drug design of small molecules, e.g., for the prediction of off-target effects or drug repurposing. Consequently, our similarity metric allows to map the degradome and its associated drug target network via comparison of known substrate peptides. The substrate-driven view of protein-protein interfaces is not limited to the field of proteases but can be applied to any target class where a sufficient amount of known substrate data is available. DOI: 10.1371/journal.pcbi.1003353 PMCID: PMC3828135 PMID: 24244149 [Indexed for MEDLINE] 276. Curr Pharm Des. 2014;20(32):5202-11. Predicting protein-ligand interactions based on chemical preference features with its application to new D-amino acid oxidase inhibitor discovery. Zhao M, Chang HT, Zhou Q, Zeng T, Shih CS, Liu ZP, Chen L, Wei DQ(1). Author information: (1)State Key Laboratory of Microbial Metabolism and College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China. zpliu@sibs.ac.cn. In silico prediction of the new drug-target interactions from existing databases is of important value for the drug discovery process. Currently, the amount of protein targets that have been identified experimentally is still very small compared with the entire human proteins. In order to predict protein-ligand interactions in an accurate manner, we have developed a support vector machine (SVM) model based on the chemical-protein interactions from STITCH. New features from ligand chemical space and interaction networks have been selected and encoded as the feature vectors for SVM analysis. Both the 5-fold cross validation and independent test show high predictive accuracy that outperforms the state-of-the-art method based on ligand similarity. Moreover, 91 distinct pairs of features have been selected to rebuild a simplifier model, which still maintains the same performance as that based on all 332 features. Then, this refined model is used to search for the potential D-amino acid oxidase inhibitors from STITCH database and the predicted results are finally validated by our wet experiments. Out of 10 candidates obtained, seven D-amino acid oxidase inhibitors have been verified, in which four are newly found in the present study, and one may have a new application in therapy of psychiatric disorders other than being an antineoplastic agent. Clearly, our model is capable of predicting potential new drugs or targets on a large scale with high efficiency. PMID: 24410568 [Indexed for MEDLINE] 277. Bioinformation. 2013;9(4):187-92. doi: 10.6026/97320630009187. Epub 2013 Feb 21. Identification of potential targets in Staphylococcus aureus N315 using computer aided protein data analysis. Hossain M(1), Chowdhury DU, Farhana J, Akbar MT, Chakraborty A, Islam S, Mannan A. Author information: (1)Department of Genetic Engineering & Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong -4331, Bangladesh. Staphylococcus aureus is a gram positive bacterium, responsible for both community-acquired and hospital-acquired infection, resulting in a mortality rate of 39%. 43.2% resistance to methicilin and emerging resistance to Fluroquinolone and Oxazolidinone, have evoked the necessity of the establishment of alternative and effective therapeutic approach to treat this bacteria. In this computational study, various database and online software are used to determine some specific targets of Staphylococcus aureus N315 other than those used by Penicillin, Quinolone and Oxazolidinone. For this purpose, among 302 essential proteins, 101 nonhomologous proteins were accrued and 64 proteins which are unique in several metabolic pathways of S. aureus were isolated by using metabolic pathway analysis tools. Furthermore, 7 essentially unique enzymes involved in exclusive metabolic pathways were revealed by this research, which can be potential drug target. Along with these important enzymes, 15 non-homologous proteins located on membrane were identified, which can play a vital role as potential therapeutic targets for the future researchers. DOI: 10.6026/97320630009187 PMCID: PMC3602888 PMID: 23519164 278. Curr Pharm Des. 2018;24(28):3347-3358. doi: 10.2174/1381612824666180607124038. Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review. Carpenter KA(1), Huang X(1). Author information: (1)Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, United States. BACKGROUND: Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered training set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity. OBJECTIVE: The study aims to review ML-based methods used for VS and applications to Alzheimer's Disease (AD) drug discovery. METHODS: To update the current knowledge on ML for VS, we review thorough backgrounds, explanations, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN). RESULTS: All techniques have found success in VS, but the future of VS is likely to lean more largely toward the use of neural networks - and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for conducting ML-based VS for potential therapeutics for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation. CONCLUSION: Different ML techniques are powerful tools for VS, and they have advantages and disadvantages albeit. ML-based VS can be applied to AD drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1381612824666180607124038 PMCID: PMC6327115 PMID: 29879881 279. Bioinformatics. 2008 Jan 15;24(2):225-33. Epub 2007 Nov 23. Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor. Faulon JL(1), Misra M, Martin S, Sale K, Sapra R. Author information: (1)Sandia National Laboratories, Computational Biosciences Department, P.O. Box 5800, Albuquerque, NM 87185-1413, USA. jfaulon@sandia.gov MOTIVATION: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. There is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein-chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. RESULTS: Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets. DOI: 10.1093/bioinformatics/btm580 PMID: 18037612 [Indexed for MEDLINE] 280. Eur J Med Chem. 2011 Dec;46(12):5838-51. doi: 10.1016/j.ejmech.2011.09.045. Epub 2011 Oct 1. 2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins. Prado-Prado F(1), García-Mera X, Escobar M, Sobarzo-Sánchez E, Yañez M, Riera-Fernandez P, González-Díaz H. Author information: (1)Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela 15782, Spain. francisco.prado@usc.e There are many pairs of possible Drug-Proteins Interactions that may take place or not (DPIs/nDPIs) between drugs with high affinity/non-affinity for different proteins. This fact makes expensive in terms of time and resources, for instance, the determination of all possible ligands-protein interactions for a single drug. In this sense, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out rational DPIs prediction. Unfortunately, almost all QSAR models predict activity against only one target. To solve this problem we can develop multi-target QSAR (mt-QSAR) models. In this work, we introduce the technique 2D MI-DRAGON a new predictor for DPIs based on two different well-known software. We use the software MARCH-INSIDE (MI) to calculate 3D structural parameters for targets and the software DRAGON was used to calculated 2D molecular descriptors all drugs showing known DPIs present in the Drug Bank (US FDA benchmark dataset). Both classes of parameters were used as input of different Artificial Neural Network (ANN) algorithms to seek an accurate non-linear mt-QSAR predictor. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 21:21-31-1:1. This MLP classifies correctly 303 out of 339 DPIs (Sensitivity = 89.38%) and 480 out of 510 nDPIs (Specificity = 94.12%), corresponding to training Accuracy = 92.23%. The validation of the model was carried out by means of external predicting series with Sensitivity = 92.18% (625/678 DPIs; Specificity = 90.12% (730/780 nDPIs) and Accuracy = 91.06%. 2D MI-DRAGON offers a good opportunity for fast-track calculation of all possible DPIs of one drug enabling us to re-construct large drug-target or DPIs Complex Networks (CNs). For instance, we reconstructed the CN of the US FDA benchmark dataset with 855 nodes 519 drugs+336 targets). We predicted CN with similar topology (observed and predicted values of average distance are equal to 6.7 vs. 6.6). These CNs can be used to explore large DPIs databases in order to discover both new drugs and/or targets. Finally, we illustrated in one theoretic-experimental study the practical use of 2D MI-DRAGON. We reported the prediction, synthesis, and pharmacological assay of 10 different oxoisoaporphines with MAO-A inhibitory activity. The more active compound OXO5 presented IC(50) = 0.00083 μM, notably better than the control drug Clorgyline. Copyright © 2011 Elsevier Masson SAS. All rights reserved. DOI: 10.1016/j.ejmech.2011.09.045 PMID: 22005185 [Indexed for MEDLINE] 281. Curr Drug Metab. 2018;19(5):469-476. doi: 10.2174/1389200219666180305151011. A System Pharmacology Study for Deciphering Anti Depression Activity of Nardostachys jatamansi. Jalali S(1)(2), Zarrinhaghighi A(1)(2), Sadraei S(1)(2), Ghasemi Y(1), Sakhteman A(3), Faridi P(1)(4)(5). Author information: (1)Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. (2)Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran. (3)Department of Medicinal Chemistry, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran. (4)Department of Phytopharmaceutical, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran. (5)Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia. BACKGROUND: The plant Nardostachys jatamansi from Valerianaceae family is a well known antidepressant plant and has historically been used in traditional medicine. As N. jatamansi contains many different compounds, to identify its mechanisms of action, we need a network-based study. Network-based studies are becoming an increasingly important tool in understanding the mechanisms of actions of drugs. Systems pharmacology (SP) and bioinformatics are two emerging tools that use computation to develop an understanding of drug actions in molecular and cellular levels. SP can provide mechanistic understanding of protein-protein (drug-target) interaction involved in a common biological pathway. The present study was undertaken to identify unknown targets and mechanisms of antidepressant activity of N. jatamansi according to a systems pharmacology approach. METHOD: First of all a list of all the targets (receptors and metabolites) involved in depression process were provided based on KEGG database. The 3D structures of protein targets were collected as PDB files and their active sites coordinates were found. In the next step the structures of known compounds of N. jatamansi were collected. For identifying the protein-lagand interactions, a docking process was run in AutoDock and an output was received. To complete our study, the similarity between antidepressant conventional drugs and N. jatamansi compounds was analyzed. A SP map figured by Cytoscape Software, shows the relations between herbal compounds, molecular targets and depression. RESULTS: According to the docking results, we can suggest several important targets that we have no drugs for, or several natural compounds that play an important role in depression process. According to the similarity results we can suggest several molecules for extraction or synthesis that need more researches for their therapeutic effects. This study shows that how N. jatamansi can effect on depression by multiple molecular targeting with multiple compounds. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1389200219666180305151011 PMID: 29512454 [Indexed for MEDLINE] 282. Clin Pharmacol Ther. 2013 May;93(5):413-24. doi: 10.1038/clpt.2013.29. Epub 2013 Feb 11. Systems approaches in risk assessment. Lesko LJ(1), Zheng S, Schmidt S. Author information: (1)Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, University of Florida at Lake Nona, Orlando, Florida, USA. llesko@cop.ufl.edu Adverse drug events (ADEs) remain a universal problem in drug development, regulatory review, and clinical practice with a substantial financial burden on the global health-care system. Recent advances in molecular and "omics" technologies, along with online databases and bioinformatics, have enabled a more integrative approach to understanding drug-target (protein) interactions, both desirable and undesirable, within a biological system. This has led to the development of systems approaches to risk assessment in an attempt to complement and improve on contemporary observational and predictive strategies for assessing risk. Although still in an evolutionary phase, systems approaches have the potential to markedly advance our understanding of ADEs and ability to predict them. Systems approaches will also move personalized medicine forward by enabling better identification of individual and subgroup risk factors. DOI: 10.1038/clpt.2013.29 PMID: 23531724 [Indexed for MEDLINE] 283. Cardiovasc Hematol Agents Med Chem. 2013 Mar;11(1):14-24. Using literature-based discovery to identify novel therapeutic approaches. Hristovski D(1), Rindflesch T, Peterlin B. Author information: (1)University of Ljubljana, Medical faculty, Institute for Biostatistics and Medical Informatics, Ljubljana, Slovenia. dimitar.hristovski@mf.uni-lj.si We present a promising in silico paradigm called literature-based discovery (LBD) and describe its potential to identify novel pharmacologic approaches to treating diseases. The goal of LBD is to generate novel hypotheses by analyzing the vast biomedical literature. Additional knowledge resources, such as ontologies and specialized databases, are often used to supplement the published literature. MEDLINE, the largest and most important biomedical bibliographic database, is the most common source for exploiting LBD. There are two variants of LBD, open discovery and closed discovery. With open discovery we can, for example, try to find a novel therapeutic approach for a given disease, or find new therapeutic applications for an existing drug. With closed discovery we can find an explanation for a relationship between two concepts. For example, if we already have a hypothesis that a particular drug is useful for a particular disease, with closed discovery we can identify the mechanisms through which the drug could have a therapeutic effect on the disease. We briefly describe the methodology behind LBD and then discuss in more detail currently available LBD tools; we also mention in passing some of those no longer available. Next we present several examples in which LBD has been exploited for identifying novel therapeutic approaches. In conclusion, LBD is a powerful paradigm with considerable potential to complement more traditional drug discovery methods, especially for drug target discovery and for existing drug relabeling. PMID: 22845900 [Indexed for MEDLINE] 284. Molecules. 2013 Jan 8;18(1):735-56. doi: 10.3390/molecules18010735. Benchmarking ligand-based virtual High-Throughput Screening with the PubChem database. Butkiewicz M(1), Lowe EW Jr, Mueller R, Mendenhall JL, Teixeira PL, Weaver CD, Meiler J. Author information: (1)Department of Chemistry, Pharmacology, and Biomedical Informatics, Center for Structural Biology, Institute of Chemical Biology, Vanderbilt University, Nashville, TN 37232, USA. With the rapidly increasing availability of High-Throughput Screening (HTS) data in the public domain, such as the PubChem database, methods for ligand-based computer-aided drug discovery (LB-CADD) have the potential to accelerate and reduce the cost of probe development and drug discovery efforts in academia. We assemble nine data sets from realistic HTS campaigns representing major families of drug target proteins for benchmarking LB-CADD methods. Each data set is public domain through PubChem and carefully collated through confirmation screens validating active compounds. These data sets provide the foundation for benchmarking a new cheminformatics framework BCL::ChemInfo, which is freely available for non-commercial use. Quantitative structure activity relationship (QSAR) models are built using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), and Kohonen networks (KNs). Problem-specific descriptor optimization protocols are assessed including Sequential Feature Forward Selection (SFFS) and various information content measures. Measures of predictive power and confidence are evaluated through cross-validation, and a consensus prediction scheme is tested that combines orthogonal machine learning algorithms into a single predictor. Enrichments ranging from 15 to 101 for a TPR cutoff of 25% are observed. DOI: 10.3390/molecules18010735 PMCID: PMC3759399 PMID: 23299552 [Indexed for MEDLINE] 285. Parasitology. 2018 Dec;145(14):1907-1916. doi: 10.1017/S0031182018000677. Epub 2018 Apr 25. Identification of novel therapeutic candidates in Cryptosporidium parvum: an in silico approach. Panda C(1), Mahapatra RK(2). Author information: (1)Department of Computer Science and Engineering,National Institute of Technology Patna,Patna-800005,India. (2)School of Biotechnology,KIIT University,Bhubaneswar-751024,Odisha,India. Unavailability of vaccines and effective drugs are primarily responsible for the growing menace of cryptosporidiosis. This study has incorporated a bioinformatics-based screening approach to explore potential vaccine candidates and novel drug targets in Cryptosporidium parvum proteome. A systematic strategy was defined for comparative genomics, orthology with related Cryptosporidium species, prioritization parameters and MHC class I and II binding promiscuity. The approach reported cytoplasmic protein cgd7_1830, a signal peptide protein, as a novel drug target. SWISS-MODEL online server was used to generate the 3D model of the protein and was validated by PROCHECK. The model has been subjected to in silico docking study with screened potent lead compounds from the ZINC database, PubChem and ChEMBL database using Flare software package of Cresset®. Furthermore, the approach reported protein cgd3_1400, as a vaccine candidate. The predicted B- and T-cell epitopes on the proposed vaccine candidate with highest scores were also subjected to docking study with MHC class I and II alleles using ClusPro web server. Results from this study could facilitate selection of proteins which could serve as drug targets and vaccine candidates to efficiently tackle the growing threat of cryptosporidiosis. DOI: 10.1017/S0031182018000677 PMID: 29692282 286. PLoS One. 2008;3(11):e3685. doi: 10.1371/journal.pone.0003685. Epub 2008 Nov 10. Evolutionary patterning: a novel approach to the identification of potential drug target sites in Plasmodium falciparum. Durand PM(1), Naidoo K, Coetzer TL. Author information: (1)Department of Molecular Medicine and Haematology, University of the Witwatersrand and National Health Laboratory Service, Johannesburg, South Africa. pierre.durand@wits.ac.za Malaria continues to be the most lethal protozoan disease of humans. Drug development programs exhibit a high attrition rate and parasite resistance to chemotherapeutic drugs exacerbates the problem. Strategies that limit the development of resistance and minimize host side-effects are therefore of major importance. In this study, a novel approach, termed evolutionary patterning (EP), was used to identify suitable drug target sites that would minimize the emergence of parasite resistance. EP uses the ratio of non-synonymous to synonymous substitutions (omega) to assess the patterns of evolutionary change at individual codons in a gene and to identify codons under the most intense purifying selection (omega < or = 0.1). The extreme evolutionary pressure to maintain these residues implies that resistance mutations are highly unlikely to develop, which makes them attractive chemotherapeutic targets. Method validation included a demonstration that none of the residues providing pyrimethamine resistance in the Plasmodium falciparum dihydrofolate reductase enzyme were under extreme purifying selection. To illustrate the EP approach, the putative P. falciparum glycerol kinase (PfGK) was used as an example. The gene was cloned and the recombinant protein was active in vitro, verifying the database annotation. Parasite and human GK gene sequences were analyzed separately as part of protozoan and metazoan clades, respectively, and key differences in the evolutionary patterns of the two molecules were identified. Potential drug target sites containing residues under extreme evolutionary constraints were selected. Structural modeling was used to evaluate the functional importance and drug accessibility of these sites, which narrowed down the number of candidates. The strategy of evolutionary patterning and refinement with structural modeling addresses the problem of targeting sites to minimize the development of drug resistance. This represents a significant advance for drug discovery programs in malaria and other infectious diseases. DOI: 10.1371/journal.pone.0003685 PMCID: PMC2577034 PMID: 18997863 [Indexed for MEDLINE] 287. BMC Bioinformatics. 2015 Feb 21;16:55. doi: 10.1186/s12859-015-0472-9. Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research. Bravo À(1), Piñero J(2), Queralt-Rosinach N(3), Rautschka M(4), Furlong LI(5). Author information: (1)Research Programme on Biomedical Informatics (GRIB), IMIM, DCEXS, Universitat Pompeu Fabra, Barcelona, Spain. abravo@imim.es. (2)Research Programme on Biomedical Informatics (GRIB), IMIM, DCEXS, Universitat Pompeu Fabra, Barcelona, Spain. jpinero@imim.es. (3)Research Programme on Biomedical Informatics (GRIB), IMIM, DCEXS, Universitat Pompeu Fabra, Barcelona, Spain. nqueralt@imim.es. (4)Research Programme on Biomedical Informatics (GRIB), IMIM, DCEXS, Universitat Pompeu Fabra, Barcelona, Spain. rautschy@gmail.com. (5)Research Programme on Biomedical Informatics (GRIB), IMIM, DCEXS, Universitat Pompeu Fabra, Barcelona, Spain. lfurlong@imim.es. BACKGROUND: Current biomedical research needs to leverage and exploit the large amount of information reported in scientific publications. Automated text mining approaches, in particular those aimed at finding relationships between entities, are key for identification of actionable knowledge from free text repositories. We present the BeFree system aimed at identifying relationships between biomedical entities with a special focus on genes and their associated diseases. RESULTS: By exploiting morpho-syntactic information of the text, BeFree is able to identify gene-disease, drug-disease and drug-target associations with state-of-the-art performance. The application of BeFree to real-case scenarios shows its effectiveness in extracting information relevant for translational research. We show the value of the gene-disease associations extracted by BeFree through a number of analyses and integration with other data sources. BeFree succeeds in identifying genes associated to a major cause of morbidity worldwide, depression, which are not present in other public resources. Moreover, large-scale extraction and analysis of gene-disease associations, and integration with current biomedical knowledge, provided interesting insights on the kind of information that can be found in the literature, and raised challenges regarding data prioritization and curation. We found that only a small proportion of the gene-disease associations discovered by using BeFree is collected in expert-curated databases. Thus, there is a pressing need to find alternative strategies to manual curation, in order to review, prioritize and curate text-mining data and incorporate it into domain-specific databases. We present our strategy for data prioritization and discuss its implications for supporting biomedical research and applications. CONCLUSIONS: BeFree is a novel text mining system that performs competitively for the identification of gene-disease, drug-disease and drug-target associations. Our analyses show that mining only a small fraction of MEDLINE results in a large dataset of gene-disease associations, and only a small proportion of this dataset is actually recorded in curated resources (2%), raising several issues on data prioritization and curation. We propose that joint analysis of text mined data with data curated by experts appears as a suitable approach to both assess data quality and highlight novel and interesting information. DOI: 10.1186/s12859-015-0472-9 PMCID: PMC4466840 PMID: 25886734 [Indexed for MEDLINE] 288. PLoS One. 2016 Aug 30;11(8):e0161913. doi: 10.1371/journal.pone.0161913. eCollection 2016. A Computational Model for Predicting RNase H Domain of Retrovirus. Wu S(1), Zhang X(1), Han J(1). Author information: (1)School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China. Erratum in PLoS One. 2016 Oct 18;11(10 ):e0165216. RNase H (RNH) is a pivotal domain in retrovirus to cleave the DNA-RNA hybrid for continuing retroviral replication. The crucial role indicates that RNH is a promising drug target for therapeutic intervention. However, annotated RNHs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. In this work, a computational RNH model was proposed to annotate new putative RNHs (np-RNHs) in the retroviruses. It basically predicts RNH domains through recognizing their start and end sites separately with SVM method. The classification accuracy rates are 100%, 99.01% and 97.52% respectively corresponding to jack-knife, 10-fold cross-validation and 5-fold cross-validation test. Subsequently, this model discovered 14,033 np-RNHs after scanning sequences without RNH annotations. All these predicted np-RNHs and annotated RNHs were employed to analyze the length, hydrophobicity and evolutionary relationship of RNH domains. They are all related to retroviral genera, which validates the classification of retroviruses to a certain degree. In the end, a software tool was designed for the application of our prediction model. The software together with datasets involved in this paper can be available for free download at https://sourceforge.net/projects/rhtool/files/?source=navbar. DOI: 10.1371/journal.pone.0161913 PMCID: PMC5019361 PMID: 27574780 [Indexed for MEDLINE] Conflict of interest statement: The authors have declared that no competing interests exist. 289. Res Pharm Sci. 2016 May-Jun;11(3):250-8. Identification of novel bacterial DNA gyrase inhibitors: An in silico study. Rahimi H(1), Najafi A(2), Eslami H(3), Negahdari B(4), Moghaddam MM(5). Author information: (1)Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran. (2)Molecular Biology Research Center, Baqiyatallah University of Medical Sciences, Tehran, I.R. Iran. (3)Department of Pharmacology, Molecular Medicine Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, I.R. Iran. (4)Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, I.R. Iran. (5)Applied Biotechnology Research Center, Baqiyatallah University of Medical Sciences, Tehran, I.R. Iran. Owing to essential role in bacterial survival, DNA gyrase has been exploited as a validated drug target. However, rapidly emerging resistance to gyrase-targeted drugs such as widely utilized fluoroquinolones reveals the necessity to develop novel compounds with new mechanism of actions against this enzyme. Here, an attempt has been made to identify new drug-like molecules for Shigella flexneri DNA gyrase inhibition through in silico approaches. The structural similarity search was carried out using the natural product simocyclinone D8, a unique gyrase inhibitor, to virtually screen ZINC database. A total of 11830 retrieved hits were further screened for selection of high-affinity compounds by implementing molecular docking followed by investigation of druggability according to Lipinski's rule, biological activity and physiochemical properties. Among the hits initially identified, three molecules were then confirmed to have reasonable gyrase-binding affinity and to follow Lipinski's rule. Based on these in silico findings, three compounds with different chemical structures from previously identified gyrase inhibitors were proposed as potential candidates for the treatment of fluoroquinolone-resistant strains and deserve further investigations. PMCID: PMC4962306 PMID: 27499795 290. In Silico Biol. 2006;6(1-2):43-7. In silico identification of potential therapeutic targets in the human pathogen Helicobacter pylori. Dutta A(1), Singh SK, Ghosh P, Mukherjee R, Mitter S, Bandyopadhyay D. Author information: (1)Department of Biotechnology, Bengal College of Engineering & Technology, Durgapur 713 212, India. Availability of genome sequences of pathogens has provided a tremendous amount of information that can be useful in drug target and vaccine target identification. One of the recently adopted strategies is based on a subtractive genomics approach, in which the subtraction dataset between the host and pathogen genome provides information for a set of genes that are likely to be essential to the pathogen but absent in the host. This approach has been used successfully in recent times to identify essential genes in Pseudomonas aeruginosa. We have used the same methodology to analyse the whole genome sequence of the human gastric pathogen Helicobacter pylori. Our analysis revealed that out of the 1590 coding sequences of the pathogen, 40 represent essential genes that have no human homolog. We have further analysed these 40 genes by the protein sequence databases to list some 10 genes whose products are possibly exposed on the pathogen surface. This preliminary work reported here identifies a small subset of the Helicobacter proteome that might be investigated further for identifying potential drug and vaccine targets in this pathogen. PMID: 16789912 [Indexed for MEDLINE] 291. PLoS One. 2012;7(12):e50819. doi: 10.1371/journal.pone.0050819. Epub 2012 Dec 5. Systemic analysis of gene expression profiles identifies ErbB3 as a potential drug target in pediatric alveolar rhabdomyosarcoma. Nordberg J(1), Mpindi JP, Iljin K, Pulliainen AT, Kallajoki M, Kallioniemi O, Elenius K, Elenius V. Author information: (1)Department of Medical Biochemistry and Genetics, University of Turku, Turku, Finland. Pediatric sarcomas, including rhabdomyosarcomas, Ewing's sarcoma, and osteosarcoma, are aggressive tumors with poor survival rates. To overcome problems associated with nonselectivity of the current therapeutic approaches, targeted therapeutics have been developed. Currently, an increasing number of such drugs are used for treating malignancies of adult patients but little is known about their effects in pediatric patients. We analyzed expression of 24 clinically approved target genes in a wide variety of pediatric normal and malignant tissues using a novel high-throughput systems biology approach. Analysis of the Genesapiens database of human transcriptomes demonstrated statistically significant up-regulation of VEGFC and EPHA2 in Ewing's sarcoma, and ERBB3 in alveolar rhabdomyosarcomas. In silico data for ERBB3 was validated by demonstrating ErbB3 protein expression in pediatric rhabdomyosarcoma in vitro and in vivo. ERBB3 overexpression promoted whereas ERBB3-targeted siRNA suppressed rhabdomyosarcoma cell gowth, indicating a functional role for ErbB3 signaling in rhabdomyosarcoma. These data suggest that drugs targeting ErbB3, EphA2 or VEGF-C could be further tested as therapeutic targets for pediatric sarcomas. DOI: 10.1371/journal.pone.0050819 PMCID: PMC3515522 PMID: 23227212 [Indexed for MEDLINE] 292. Drug Discov Today. 2002 Mar 1;7(5):315-23. The evolving role of information technology in the drug discovery process. Augen J(1). Author information: (1)Strategy, IBM Life Sciences, Route 100, Somers, NY 10589, USA. jaugen@us.ibm.com Comment in Drug Discov Today. 2002 Apr 1;7(7):406. Information technologies for chemical structure prediction, heterogeneous database access, pattern discovery, and systems and molecular modeling have evolved to become core components of the modern drug discovery process. As this evolution continues, the balance between in silico modeling and 'wet' chemistry will continue to shift and it might eventually be possible to step through the discovery pipeline without the aid of traditional laboratory techniques. Rapid advances in the industrialization of gene sequencing combined with databases of protein sequence and structure have created a target-rich but lead-poor environment. During the next decade, newer information technologies that facilitate the molecular modeling of drug-target interactions are likely to shift this balance towards molecular-based personalized medicine -- the ultimate goal of the drug discovery process. PMID: 11854055 [Indexed for MEDLINE] 293. Int J Mycobacteriol. 2018 Jan-Mar;7(1):61-68. doi: 10.4103/ijmy.ijmy_174_17. Identification of potential inhibitors for mycobacterial uridine diphosphogalactofuranose-galactopyranose mutase enzyme: A novel drug target through in silico approach. Nayak T(1), Jena L(1), Waghmare P(2), Harinath BC(3). Author information: (1)Bioinformatics Centre, Mahatma Gandhi Institute of Medical Sciences, Sevagram, Wardha, Maharashtra, India. (2)Department of Biochemistry, Mahatma Gandhi Institute of Medical Sciences, Sevagram, Wardha, Maharashtra, India. (3)JB Tropical Disease Research Centre, Mahatma Gandhi Institute of Medical Sciences, Sevagram, Wardha, Maharashtra, India. Background: The Mycobacterium tuberculosis (MTB) uridine diphosphogalactofuranose (UDP)-galactopyranose mutase (UGM) is an essential flavoenzyme for mycobacterial viability and an important component of cell wall. It catalyzes the interconversion of UDP-galactopyranose into UDP-galactofuranose, a key building block for cell wall construction, essential for linking the peptidoglycan and mycolic acid cell wall layers in MTB through a 2-keto intermediate. Further, as this enzyme is not present in humans, it is an excellent therapeutic target for MTB. Thus, inhibition of this UGM enzyme is a good approach to explore new anti-TB drug. This study aims to find novel and effective inhibitors against UGM from reported natural phytochemicals and ZINC database using virtual screening approach. Methods: In this study, 148 phytochemicals with reported antitubercular activity and 5280 ZINC compounds with 70% structural similarity with the natural substrate of UGM (UDP-galactopyranose and UDP-galactofuranose) were screened against UGM. Results: In virtual screening, 19 phytochemicals and 477 ZINC compounds showed comparatively better binding affinity than natural substrates. Among them, best 10 compounds from each group were proposed as potential inhibitors for UGM based on the binding energy and protein-ligand interaction analysis. Among phytochemicals, three compounds, namely, tiliacorine, amentoflavone, and 2'-nortiliacorinine showed highest binding affinity (binding energy of -10.5, -10.4, and -10.3 Kcal/mol, respectively), while among ZINC compounds, ZINC08219848 and ZINC08217649, showing highest binding affinity (binding energy of -10.0 and -9.7 Kcal/mol, respectively) toward UGM as compared to its substrates. Conclusion: These selected compounds may be proposed as potential inhibitors of UGM and need to be tested in TB culture studies in vitro to assess their anti-TB activity. DOI: 10.4103/ijmy.ijmy_174_17 PMID: 29516888 Conflict of interest statement: There are no conflicts of interest. 294. J Chem Inf Model. 2011 Aug 22;51(8):1882-96. doi: 10.1021/ci200216z. Epub 2011 Aug 8. In silico carborane docking to proteins and potential drug targets. Calvaresi M(1), Zerbetto F. Author information: (1)Dipartimento di Chimica G. Ciamician, Università di Bologna, Bologna, Italy. matteo.calvaresi@studio.unibo.it The presence of boron atoms has made carboranes, C(2)B(10)H(12), attractive candidates for boron neutron capture therapy. Because of their chemistry and possible conjugation with proteins, they can also be used to enhance interactions between pharmaceuticals and their targets and to increase the in vivo stability and bioavailability of compounds that are normally metabolized rapidly. Carboranes are isosteric to a rotating phenyl group, which they can substitute successfully in biologically active systems. A reverse ligand-protein docking approach was used in this work to identify binding proteins for carboranes. The screening was carried out on the drug target database PDTD that contains 1207 entries covering 841 known potential drug targets with structures taken from the Protein Data Bank. First, for validation, the protocol was applied to three crystal structures of proteins in which carborane derivatives are present. Then, the model was applied to systems for which the protein structure is available, but the binding site of carborane has not been reported. These systems were used for further validation of the protocol, while simultaneously providing new insight into the interactions between cage and protein. Finally, the screening was carried out on the database to reveal potential carborane binding targets of interest for biological and pharmacological activity. Carboranes are predicted to bind well to protease and metalloprotease enzymes. Other carborane pharmaceutical targets are also discussed, together with possible protein carriers. DOI: 10.1021/ci200216z PMID: 21774557 [Indexed for MEDLINE] 295. Exp Ther Med. 2013 Jul;6(1):125-132. Epub 2013 May 9. Network pharmacology-based prediction of the multi-target capabilities of the compounds in Taohong Siwu decoction, and their application in osteoarthritis. Zheng CS(1), Xu XJ, Ye HZ, Wu GW, Li XH, Xu HF, Liu XX. Author information: (1)Fujian Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122; ; Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122; Taohong Siwu decoction (THSWD), a formulation prescribed in traditional Chinese medicine (TCM), has been widely used in the treatment of osteoarthritis (OA). TCM has the potential to prevent diseases, such as OA, in an integrative and holistic manner. However, the system-level characterization of the drug-target interactions of THSWD has not been elucidated. In the present study, we constructed a novel modeling system, by integrating chemical space, virtual screening and network pharmacology, to investigate the molecular mechanism of action of THSWD. The chemical distribution of the ligand database and the potential compound prediction demonstrated that THSWD, as a natural combinatorial chemical library, comprises abundant drug-like and lead-like compounds that may act as potential inhibitors for a number of important target proteins associated with OA. Moreover, the results of the 'compound-target network' analysis demonstrated that 19 compounds within THSWD were correlated with more than one target, whilst the maximum degree of correlation for the compounds was seven. Furthermore, the 'target-disease network' indicated that THSWD may potentially be effective against 69 diseases. These results may aid in the understanding of the use of THSWD as a multi-target therapy in OA. Moreover, they may be useful in establishing other pharmacological effects that may be brought about by THSWD. The in silico method used in this study has the potential to advance the understanding of the molecular mechanisms of TCM. DOI: 10.3892/etm.2013.1106 PMCID: PMC3735841 PMID: 23935733 296. Front Microbiol. 2015 Apr 9;6:235. doi: 10.3389/fmicb.2015.00235. eCollection 2015. A review on computational systems biology of pathogen-host interactions. Durmuş S(1), Çakır T(1), Özgür A(2), Guthke R(3). Author information: (1)Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Kocaeli Turkey. (2)Department of Computer Engineering, Boǧaziçi University, Istanbul Turkey. (3)Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, Jena Germany. Pathogens manipulate the cellular mechanisms of host organisms via pathogen-host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein-protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature. DOI: 10.3389/fmicb.2015.00235 PMCID: PMC4391036 PMID: 25914674 297. Front Chem. 2017 Oct 31;5:88. doi: 10.3389/fchem.2017.00088. eCollection 2017. Targeting Dengue Virus NS-3 Helicase by Ligand based Pharmacophore Modeling and Structure based Virtual Screening. Halim SA(1), Khan S(1), Khan A(2)(3), Wadood A(4), Mabood F(5), Hussain J(5), Al-Harrasi A(3). Author information: (1)Department of Biochemistry, Kinnaird College for Women, Lahore, Pakistan. (2)Department of Chemistry, COMSATS Institute of Information Technology, Abbottabad, Pakistan. (3)UoN Chair of Oman Medicinal Plants and Marine Products, University of Nizwa, Nizwa, Oman. (4)Department of Biochemistry, Shankar Campus, Abdul Wali Khan University Mardan, Mardan, Pakistan. (5)Department of Biological Sciences and Chemistry, College of Arts and Sciences, University of Nizwa, Nizwa, Oman. Dengue fever is an emerging public health concern, with several million viral infections occur annually, for which no effective therapy currently exist. Non-structural protein 3 (NS-3) Helicase encoded by the dengue virus (DENV) is considered as a potential drug target to design new and effective drugs against dengue. Helicase is involved in unwinding of dengue RNA. This study was conducted to design new NS-3 Helicase inhibitor by in silico ligand- and structure based approaches. Initially ligand-based pharmacophore model was generated that was used to screen a set of 1201474 compounds collected from ZINC Database. The compounds matched with the pharmacophore model were docked into the active site of NS-3 helicase. Based on docking scores and binding interactions, 25 compounds are suggested to be potential inhibitors of NS3 Helicase. The pharmacokinetic properties of these hits were predicted. The selected hits revealed acceptable ADMET properties. This study identified potential inhibitors of NS-3 Helicase in silico, and can be helpful in the treatment of Dengue. DOI: 10.3389/fchem.2017.00088 PMCID: PMC5671650 PMID: 29164104 298. Bioinformation. 2007 Oct 15;2(2):68-72. Choke point analysis of metabolic pathways in E.histolytica: a computational approach for drug target identification. Singh S(1), Malik BK, Sharma DK. Author information: (1)Center for Energy Studies, Indian Institute of Technology Delhi, Hauz Khas, New Delhi-110016, India. shailza_iitd@yahoo.com With the Entamoeba genome essentially complete, the organism can be studied from a whole genome standpoint. The understanding of cellular mechanisms and interactions between cellular components is instrumental to the development of new effective drugs and vaccines. Metabolic pathway analysis is becoming increasingly important for assessing inherent network properties in reconstructed biochemical reaction networks. Metabolic pathways illustrate how proteins work in concert to produce cellular compounds or to transmit information at different levels. Identification of drug targets in E. histolytica through metabolic pathway analysis promises to be a novel approach in this direction. This article focuses on the identification of drug targets by subjecting the Entamoeba genome to BLAST with the e-value inclusion threshold set to 0.005 and choke point analysis. A total of 86.9 percent of proposed drug targets with biological evidence are chokepoint reactions in Entamoeba genome database. PMCID: PMC2174424 PMID: 18188424 299. Int J Oncol. 2018 Nov;53(5):1869-1880. doi: 10.3892/ijo.2018.4536. Epub 2018 Aug 22. COL1A1: A potential therapeutic target for colorectal cancer expressing wild-type or mutant KRAS. Zhang Z(1), Fang C(2), Wang Y(1), Zhang J(3), Yu J(3), Zhang Y(4), Wang X(5), Zhong J(1). Author information: (1)Department of Pathology, Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China. (2)Department of Anesthesiology, The Third Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453100, P.R. China. (3)Department of Pathology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China. (4)Department of Oncology, The Third Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453100, P.R. China. (5)Henan Key Laboratory of Medical Tissue Regeneration, Xinxiang Medical University, Xinxiang, Henan 453003, P.R. China. Colorectal cancer (CRC) treatment primarily relies on chemotherapy along with surgery, radiotherapy and, more recently, targeted therapy at the late stages. However, chemotherapeutic drugs have high cytotoxicity, and the similarity between the effects of these drugs on cancerous and healthy cells limits their wider use in clinical settings. Targeted monoclonal antibody treatment may compensate for this deficiency. Epidermal growth factor receptor (EGFR)‑targeted drugs have a positive effect on CRC with intact KRAS proto-oncogene GTPase (KRAS or KRASWT), but may be ineffective or harmful in patients with KRAS mutations (KRASMUT). Therefore, it is important to identify drug target genes that are uniformly effective with regards to KRASWT and KRASMUT CRC. The present study performed gene expression analysis, and identified 294 genes upregulated in KRASWT and KRASMUT CRC samples. Collagen type I α 1 (COL1A1) was identified as the hub gene through STRING and Cytoscape analyses. Consistent with results obtained from Oncomine, a cancer microarray database and web-based data-mining platform, it was demonstrated that the expression of COL1A1 was significantly upregulated in CRC tissues and cell lines regardless of KRAS status. Inhibition of COL1A1 in KRASWT and KRASMUT CRC cell lines significantly decreased cell proliferation and invasion. In addition, increased COL1A1 expression in CRC was significantly associated with serosal invasion, lymph metastases and hematogenous metastases. Taken together, the findings of the present study indicated that COL1A1 may serve as a candidate diagnostic biomarker and a promising therapeutic target for CRC. DOI: 10.3892/ijo.2018.4536 PMCID: PMC6192778 PMID: 30132520 300. Oncol Rep. 2019 Feb 14. doi: 10.3892/or.2019.7014. [Epub ahead of print] The underlying molecular mechanism and potential drugs for treatment in papillary renal cell carcinoma: A study based on TCGA and Cmap datasets. Pang JS(1), Li ZK(1), Lin P(2), Wang XD(2), Chen G(1), Yan HB(3), Li SH(3). Author information: (1)Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China. (2)Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China. (3)Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China. Papillary renal cell carcinoma (PRCC) accounts for 15‑20% of all kidney neoplasms and continually attracts attention due to the increase in the incidents in which it occurs. The molecular mechanism of PRCC remains unclear and the efficacy of drugs that treat PRCC lacks sufficient evidence in clinical trials. Therefore, it is necessary to investigate the underlying mechanism in the development of PRCC and identify additional potential anti‑PRCC drugs for its treatment. The differently expressed genes (DEGs) of PRCC were identified, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses for functional annotation. Then, potential drugs for PRCC treatment were predicted by Connectivity Map (Cmap) based on DEGs. Furthermore, the latent function of query drugs in PRCC was explored by integrating drug‑target, drug‑pathway and drug‑protein interactions. In total, 627 genes were screened as DEGs, and these DEGs were annotated using KEGG pathway analyses and were clearly associated with the complement and coagulation cascades, amongst others. Then, 60 candidate drugs, as predicted based on DEGs, were obtained from the Cmap database. Vorinostat was considered as the most promising drug for detailed discussion. Following protein‑protein interaction (PPI) analysis and molecular docking, vorinostat was observed to interact with C3 and ANXN1 proteins, which are the upregulated hub genes and may serve as oncologic therapeutic targets in PRCC. Among the top 20 metabolic pathways, several significant pathways, such as complement and coagulation cascades and cell adhesion molecules, may greatly contribute to the development and progression of PRCC. Following the performance of the PPI network and molecular docking tests, vorinostat exhibited a considerable and promising application in PRCC treatment by targeting C3 and ANXN1. DOI: 10.3892/or.2019.7014 PMID: 30816528 301. Curr Drug Metab. 2008 Mar;9(3):213-20. Proteomics: technologies for protein analysis. Gomase VS(1), Kale KV, Tagore S, Hatture SR. Author information: (1)Department of Bioinformatics, Dr. D. Y. Patil Institute for Biotechnology and Bioinformatics, Padmashree Dr. D. Y. Patil University, Plot No-50, Sector-15, CBD Belapur, Navi Mumbai, 400614, India. virusgene1@yahoo.co.in Proteomics technologies have produced an abundance of drug targets, which is creating a bottleneck in drug development process. There is an increasing need for better target validation for new drug development and proteomic technologies are contributing to it. Identifying a potential protein drug target within a cell is a major challenge in modern drug discovery; techniques for screening the proteome are, therefore, an important tool. Major difficulties for target identification include the separation of proteins and their detection. These technologies are compared to enable the selection of the one by matching the needs of a particular project. There are prospects for further improvement, and proteomics technologies will form an important addition to the existing genomic and chemical technologies for new target validation. Proteomics is applicable for protein analysis and bioinformatics based analysis gives the comprehensive molecular description of the actual protein component. Bioinformatics is being increasingly used to support target validation by providing functionally predictive information mined from databases and experimental datasets using a variety of computational tools. This review is focused on key technologies for proteomics strategy and their application in protein analysis. PMID: 18336224 [Indexed for MEDLINE] 302. Headache. 2016 Apr;56(4):622-44. doi: 10.1111/head.12788. Epub 2016 Mar 25. Adipokines and Migraine: A Systematic Review. Peterlin BL(1), Sacco S(2), Bernecker C(3)(4), Scher AI(5). Author information: (1)Johns Hopkins University School of Medicine, Department of Neurology, Baltimore, MD, USA. (2)University of L'Aquila, Department of Applied Clinical Sciences and Biotechnology, Institute of Neurology, L'Aquila, Italy. (3)Medical University of Graz, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Graz, Austria. (4)Medical University of Graz, Department of Blood Group Serology and Transfusion Medicine, Graz, Austria. (5)Uniformed Services University, Bethesda, MD, USA. BACKGROUND: Migraine is comorbid with obesity. Recent research suggests an association between migraine and adipocytokines, proteins that are predominantly secreted from adipose tissue and which participate in energy homeostasis and inflammatory processes. OBJECTIVES: In this review, we first briefly discuss the association between migraine and obesity and the importance of adipose tissue as a neuroendocrine organ. We then present a systematic review of the extant literature evaluating circulating levels of adiponectin and leptin in those with migraine. METHODS: A search of the PubMed database was conducted using the keywords "migraine," "adiponectin," and "leptin." In addition reference lists of relevant articles were reviewed for possible inclusion. English language studies published between 2005 and 2015 evaluating circulating blood concentration of adiponectin or leptin in those with migraine were included. CONCLUSIONS: While the existing data are suggestive that adipokines may be associated with migraine, substantial study design differences and conflicting results limit definitive conclusions. Future research utilizing carefully considered designs and methodology is warranted. In particular careful and systematic characterization of pain states at the time of samples, as well as systematic consideration of demographic (e.g., age, sex) and other vital covariates (e.g., obesity status, lipids) are needed to determine if adipokines play a role in migraine pathophysiology and if any adipokine represents a viable, novel migraine biomarker, or drug target. © 2016 American Headache Society. DOI: 10.1111/head.12788 PMCID: PMC4836978 PMID: 27012149 [Indexed for MEDLINE] 303. Nucleic Acids Res. 2010 Jan;38(Database issue):D552-6. doi: 10.1093/nar/gkp937. Epub 2009 Nov 6. STITCH 2: an interaction network database for small molecules and proteins. Kuhn M(1), Szklarczyk D, Franceschini A, Campillos M, von Mering C, Jensen LJ, Beyer A, Bork P. Author information: (1)Biotechnology Center, TU Dresden, 01062 Dresden, Germany. Over the last years, the publicly available knowledge on interactions between small molecules and proteins has been steadily increasing. To create a network of interactions, STITCH aims to integrate the data dispersed over the literature and various databases of biological pathways, drug-target relationships and binding affinities. In STITCH 2, the number of relevant interactions is increased by incorporation of BindingDB, PharmGKB and the Comparative Toxicogenomics Database. The resulting network can be explored interactively or used as the basis for large-scale analyses. To facilitate links to other chemical databases, we adopt InChIKeys that allow identification of chemicals with a short, checksum-like string. STITCH 2.0 connects proteins from 630 organisms to over 74,000 different chemicals, including 2200 drugs. STITCH can be accessed at http://stitch.embl.de/. DOI: 10.1093/nar/gkp937 PMCID: PMC2808890 PMID: 19897548 [Indexed for MEDLINE] 304. Microb Pathog. 2017 Feb;103:94-106. doi: 10.1016/j.micpath.2016.12.015. Epub 2016 Dec 16. Computational identification of potent inhibitors for Streptomycin 3″-adenylyltransferase of Serratia marcescens. Prabhu D(1), Vidhyavathi R(1), Jeyakanthan J(2). Author information: (1)Department of Bioinformatics, Alagappa University, Science Campus, Karaikudi 630004, Tamil Nadu, India. (2)Department of Bioinformatics, Alagappa University, Science Campus, Karaikudi 630004, Tamil Nadu, India. Electronic address: jjkanthan@gmail.com. Serratia marcescens is an opportunistic pathogen responsible for the respiratory and urinary tract infections in humans. The antibiotic resistance mechanism of S. marcescens is mediated through aminoglycoside modification enzyme that transfer adenyl group from substrate to antibiotic through regiospecific transfers for the inactivation of antibiotics. Streptomycin 3″-adenylyltransferase acts on the 3' position of the antibiotic and considered as a novel drug target to overcome bacterial antibiotic resistance. Till now, there is no experimentally solved crystal structure of Streptomycin 3″-adenylyltransferase in S. marcescens. Hence, the present study was initiated to construct the three dimensional structure of Streptomycin 3″-adenylyltransferase in order to understand the binding mechanism. The modeled structure was subjected to structure-based virtual screening to identify potent compounds from the five chemical structure databases. Furthermore, different computational methods such as molecular docking, molecular dynamics simulations, ADME toxicity assessment, free energy and density functional theory calculations predicted the structural, binding and pharmacokinetic properties of the best five compounds. Overall, the results suggested that stable binding confirmation of the five potent compounds were mediated through hydrophobic, π-π stacking, salt bridges and hydrogen bond interactions. The identified compounds could pave way for the development of anti-pathogenic agents as potential drug entities. Copyright © 2016 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.micpath.2016.12.015 PMID: 27993702 [Indexed for MEDLINE] 305. Curr Top Med Chem. 2018 Nov 20. doi: 10.2174/1568026619666181120150633. [Epub ahead of print] In silico protein interaction network analysis of virulence proteins associated with invasive aspergillosis for drug discovery. Chaudhary R(1), Balhara M(1), Jangir DK(1), Dangi M(2), Dangi M(1), Chhillar AK(1). Author information: (1)Centre for Biotechnology, Maharshi Dayanand University, Rohtak-124001, Haryana. India. (2)Centre for Bioinformatics, Maharshi Dayanand University, Rohtak-124001, Haryana. India. Protein-Protein interaction (PPI) network analysis of virulence proteins of Aspergillus fumigatus is prevailing strategy to understand the mechanism behind the virulence of A. fumigatus. The identification of major hub proteins and targeting the hub protein as a new antifungal drug target will help in treating the invasive aspergillosis. In the present study, the PPI network of 96 virulence (drug target) proteins of A. fumigatus were investigated which resulted in 103 nodes and 430 edges. Topological enrichment analysis of the PPI network was also carried out by using STRING database and Network analyzer a cytoscape plug inn app. The key enriched KEGG pathway and protein domains were analyzed by STRING. Manual curation of PPI data identified three proteins (PyrABCN-43, AroM-34, and Glt1-34) of A. fumigatus possess the highest interacting partners. Top 10% hub proteins were also identified from the network using cytohubba on the basis of seven algorithms, i.e. betweenness, radiality, closeness, degree, bottleneck, MCC and EPC. Homology model and the active pocket of top three hub proteins were also predicted. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1568026619666181120150633 PMID: 30465504 306. Inflamm Res. 2015 Jan;64(1):9-20. doi: 10.1007/s00011-014-0780-y. Epub 2014 Nov 7. Role of farnesoid X receptor in inflammation and resolution. Shaik FB(1), Prasad DV, Narala VR. Author information: (1)Department of Zoology, Yogi Vemana University, Kadapa, 516 003, AP, India. OBJECTIVE: The aim of this paper is to review the developments of farnesoid X receptor (FXR) biology, its ligands, and various functions, in particular we discuss the anti-inflammatory and anti-fibrotic role in chronic inflammatory diseases. INTRODUCTION: FXR is a ligand-dependent transcription factor belonging to the nuclear hormone receptor superfamily. The accrued data have shown that the FXR plays important roles not only in bile acid, lipid metabolism, and carbohydrate homeostasis, but also in inflammatory responses. The anti-inflammatory and anti-fibrotic effects of FXR on chronic inflammatory diseases are not well documented. METHODS: A literature survey was performed using PubMed database search to gather complete information regarding FXR and its role in inflammation. RESULTS AND DISCUSSION: FXR is highly expressed in liver, intestine, kidney and adrenals, but with lower expression in fat tissue, heart and recently it has been found to express in lungs too. Primary bile acids, cholic acid and chenodeoxycholic acid are the natural endogenous ligands for FXR. GW4064 and 6α-ethyl-chenodeoxycholic acid are the synthetic high-affinity agonists. An exhaustive literature survey revealed that FXR acts as a key metabolic regulator and potential drug target for many metabolic syndromes that include chronic inflammatory diseases. DOI: 10.1007/s00011-014-0780-y PMID: 25376338 [Indexed for MEDLINE] 307. Bioinformation. 2011;7(8):379-83. Epub 2011 Dec 21. Identification of potential apicoplast associated therapeutic targets in human and animal pathogen Toxoplasma gondii ME49. Saremy S, Boroujeni ME, Bhattacharjee B, Mittal V, Chatterjee J. Toxoplasma gondii ME49 is an obligatory intracellular apicomplexa parasite that causes toxoplasmosis in humans, domesticated and wild animals. Waterborne outbreaks of acute toxoplasmosis worldwide reinforce the transmission of Toxoplasma gondii ME49 to humans through contaminated water and may have a greater epidemiological impact than previously believed. In the quest for drug and vaccine target identification subtractive genomics involving subtraction between the host and pathogen genome has been implemented for enlisting essential pathogen specific proteins. Using this approach, our analysis on both human and Toxoplasma gondii ME49 reveals that out of 7987 protein coding sequences of the pathogen, 950 represent essential non human-homologous proteins. Subcellular localization prediction & comparative-biochemical pathway analysis of these essential proteins gives a list of apicoplast-associated proteins having unique pathogen-specific metabolic pathway. These apicoplast-associated enzymes involved in fatty acid biosynthesis pathway of Toxoplasma gondii ME49, may be used as potential drug targets, as the pathway is vital for the protozoan's survival. Structure prediction of drug target proteins was done using fold based recognition method. Screening of the functional inhibitors against these novel targets may result in discovery of novel therapeutic compounds that can be effective against Toxoplasma gondii ME49.ABBREVIATIONS: DEG - Database of Essential Gene, KEGG - Kyoto Encyclopaedia of Genes and Genomes, KAAS - KEGG Automated Annotation Server, PFP - Protein Function Prediction, COG - Cluster of Orthologous Genes. PMCID: PMC3280436 PMID: 22347778 308. Methods Mol Biol. 2018;1824:33-47. doi: 10.1007/978-1-4939-8630-9_3. In Silico Drug Design: Non-peptide Mimetics for the Immunotherapy of Multiple Sclerosis. Tzoupis H(1), Tselios T(2). Author information: (1)Department of Chemistry, University of Patras, Patras, Greece. (2)Department of Chemistry, University of Patras, Patras, Greece. ttselios@upatras.gr. Advances in theoretical chemistry have led to the development of various robust computational techniques employed in drug design. Pharmacophore modeling, molecular docking, and molecular dynamics (MD) simulations have been extensively applied, separately or in combination, in the design of potent molecules. The techniques involve the identification of a potential drug target (e.g., protein) and its subsequent characterization. The next step in the process comprises the development of a map describing the interaction patterns between the target molecule and its natural substrate. Once these key features are identified, it is possible to explore the map and screen large databases of molecules to identify potential drug candidates for further refinement.Multiple sclerosis (MS) is an autoimmune disease where the immune system attacks the myelin sheath of nerve cells. The process involves the activation of encephalitogenic T cells via the formation of the trimolecular complex between the human leukocyte antigen (HLA), an immunodominant epitope of myelin proteins, and the T-cell receptor (TCR). Herein, the process for rational design and development of altered peptide ligands (APLs) and non-peptide mimetics against MS is described through the utilization of computational methods. DOI: 10.1007/978-1-4939-8630-9_3 PMID: 30039400 309. J Comput Biol. 2010 May;17(5):669-84. doi: 10.1089/cmb.2009.0032. Protein-protein interaction network evaluation for identifying potential drug targets. Hormozdiari F(1), Salari R, Bafna V, Sahinalp SC. Author information: (1)School of Computing Science, Simon Fraser University, Burnaby, Canada. As pathogens evolve effective schemes to overcome the effect of antibiotics, the prevalent "one drug and one drug target" approach is falling behind. We propose novel strategies for identifying potential multiple-drug targets in pathogenic protein-protein interaction (PPI) networks with the goal of disrupting known pathways/complexes. Given a set S of pathogenic pathways/complexes, we first consider computing the minimum number of proteins (with no human orthologs) whose removal from the PPI network disrupts all pathways/complexes. Unfortunately, even the best approximation algorithms for this (NP-hard) problem return too many targets to be practical. Thus, we focus on computing the optimal tradeoff (i.e., maximum ratio) between the number of disrupted essential pathways/complexes and the protein targets. For this "sparsest cut" problem, we describe two polynomial time algorithms with respective approximation factors of |S| and O(√ n) (n: number of nodes). On the Escherichia coli PPI network with nine essential (signaling) paths from the KEGG database, our algorithms show how to disrupt three of them by targeting only three proteins (two of them essential proteins). We also consider the case where there are no available essential pathways/complexes to guide us. In order to maximize the number of disrupted "potential" pathways/complexes, we show how to compute the smallest set of proteins whose removal partitions the PPI network into two almost-equal sized subnetworks so as to maximize the number of potential pathways/complexes disrupted. This approach yields 28 potential targets (four of them known drug targets) on the E. coli PPI network whose removal partitions it to two subnetworks with relative sizes of 1-5. DOI: 10.1089/cmb.2009.0032 PMID: 20500021 [Indexed for MEDLINE] 310. J Cell Biochem. 2013 May;114(5):1145-52. doi: 10.1002/jcb.24457. Target network analysis of adiponectin, a multifaceted adipokine. Chen X(1). Author information: (1)State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China. xpchen@umac.mo Application of network analysis to dissect the potential molecular mechanisms of biological processes and complicated diseases has been the new trend in biology and medicine in recent years. Among which, the protein-protein interactions (PPI) networks attract interests of most researchers. Adiponectin, a cytokine secreted from adipose tissue, participates in a number of metabolic processes, including glucose regulation and fatty acid metabolism and involves in a series of complicated diseases from head to toe. Hundreds of proteins including many identified and potential drug targets have been reported to be involved in adiponectin related signaling pathways, which comprised a complicated regulation network. Therapeutic target database (TTD) provides extensive information about the known and explored therapeutic protein targets and the signaling pathway information. In this study, adiponectin associated drug targets based PPI was constructed and its topological properties were analyzed, which might provide some insight into the dissection of adiponectin action mechanisms and promote adiponectin signaling based drug target identification and drug discovery. Copyright © 2012 Wiley Periodicals, Inc. DOI: 10.1002/jcb.24457 PMID: 23192392 [Indexed for MEDLINE] 311. Curr Top Med Chem. 2011;11(10):1292-300. Rapid analysis of pharmacology for infectious diseases. Hopkins AL(1), Bickerton GR, Carruthers IM, Boyer SK, Rubin H, Overington JP. Author information: (1)Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, UK. a.hopkins@dundee.ac.uk Pandemic, epidemic and endemic infectious diseases are united by a common problem: how do we rapidly and cost-effectively identify potential pharmacological interventions to treat infections? Given the large number of emerging and neglected infectious diseases and the fact that they disproportionately afflict the poorest members of the global society, new ways of thinking are required to developed high productivity discovery systems that can be applied to a larger number of pathogens. The growing availability of parasite genome data provides the basis for developing methods to prioritize, a priori, the potential drug target and pharmacological landscape of an infectious disease. Thus the overall objective of infectious disease informatics is to enable the rapid generation of plausible, novel medical hypotheses of testable pharmacological experiments, by uncovering undiscovered relationships in the wealth of biomedical literature and databases that were collected for other purposes. In particular our goal is to identify potential drug targets present in a pathogen genome and prioritize which pharmacological experiments are most likely to discover drug-like lead compounds rapidly against a pathogen (i.e. which specific compounds and drug targets should be screened, in which assays and where they can be sourced). An integral part of the challenge is the development and integration of methods to predict druggability, essentiality, synthetic lethality and polypharmacology in pathogen genomes, while simultaneously integrating the inevitable issues of chemical tractability and the potential for acquired drug resistance from the start. PMCID: PMC3182413 PMID: 21401504 [Indexed for MEDLINE] 312. 3 Biotech. 2017 May;7(1):46. doi: 10.1007/s13205-017-0713-x. Epub 2017 Apr 25. Unveiling differentially expressed genes upon regulation of transcription factors in sepsis. Zhang J(1), Cheng Y(1), Duan M(2), Qi N(2), Liu J(3). Author information: (1)Department of Emergency Medicine, Affiliated Hospital of Taishan Medical University of Shandong Province, No.706 TaiShan Street, Taishan District, Taian, 271000, China. (2)Department of Pediatrics, Affiliated Hospital of Taishan Medical University of Shandong Province, Taian, China. (3)Department of Emergency Medicine, Affiliated Hospital of Taishan Medical University of Shandong Province, No.706 TaiShan Street, Taishan District, Taian, 271000, China. LiuJiande54321@163.com. In this study, we integrated the gene expression data of sepsis to reveal more precise genome-wide expression signature to shed light on the pathological mechanism of sepsis. Differentially expressed genes via integrating five microarray datasets from the Gene Expression Omnibus database were obtained. The gene function and involved pathways of differentially expressed genes (DEGs) were detected by GeneCodis3. Transcription factors (TFs) targeting top 20 dysregulated DEGs (including up- and downregulated genes) were found based on the TRANSFAC. A total of 1339 DEGs were detected including 788 upregulated and 551 downregulated genes. These genes were mostly involved in DNA-dependent transcription regulation, blood coagulation, and innate immune response, pathogenic escherichia coli infection, epithelial cell signaling in helicobacter pylori infection, and chemokine signaling pathway. TFs bioinformatic analysis of 20 DEGs generated 374 pairs of TF-target gene involving 47 TFs. At last, we found that five top ten upregulated DEGs (S100A8, S100A9, S100A12, PGLYRP1 and MMP9) and three downregulated DEGs (ZNF84, CYB561A3 and BST1) were under the regulation of three hub TFs of Pax-4, POU2F1, and Nkx2-5. The identified eight DEGs may be regarded as the diagnosis marker and drug target for sepsis. DOI: 10.1007/s13205-017-0713-x PMCID: PMC5428098 PMID: 28444588 313. J Cheminform. 2015 Jun 19;7:30. doi: 10.1186/s13321-015-0072-8. eCollection 2015. The Chemical Validation and Standardization Platform (CVSP): large-scale automated validation of chemical structure datasets. Karapetyan K(1), Batchelor C(2), Sharpe D(2), Tkachenko V(1), Williams AJ(3). Author information: (1)Royal Society of Chemistry, US Office, 904 Tamaras Circle, Wake Forest, NC 27587 USA. (2)Thomas Graham House, Science Park, 290 Milton Road, Cambridge, UK. (3)Royal Society of Chemistry, US Office, 904 Tamaras Circle, Wake Forest, NC 27587 USA ; Environmental Protection Agency, Research Triangle Park, NC USA. BACKGROUND: There are presently hundreds of online databases hosting millions of chemical compounds and associated data. As a result of the number of cheminformatics software tools that can be used to produce the data, subtle differences between the various cheminformatics platforms, as well as the naivety of the software users, there are a myriad of issues that can exist with chemical structure representations online. In order to help facilitate validation and standardization of chemical structure datasets from various sources we have delivered a freely available internet-based platform to the community for the processing of chemical compound datasets. RESULTS: The chemical validation and standardization platform (CVSP) both validates and standardizes chemical structure representations according to sets of systematic rules. The chemical validation algorithms detect issues with submitted molecular representations using pre-defined or user-defined dictionary-based molecular patterns that are chemically suspicious or potentially requiring manual review. Each identified issue is assigned one of three levels of severity - Information, Warning, and Error - in order to conveniently inform the user of the need to browse and review subsets of their data. The validation process includes validation of atoms and bonds (e.g., making aware of query atoms and bonds), valences, and stereo. The standard form of submission of collections of data, the SDF file, allows the user to map the data fields to predefined CVSP fields for the purpose of cross-validating associated SMILES and InChIs with the connection tables contained within the SDF file. This platform has been applied to the analysis of a large number of data sets prepared for deposition to our ChemSpider database and in preparation of data for the Open PHACTS project. In this work we review the results of the automated validation of the DrugBank dataset, a popular drug and drug target database utilized by the community, and ChEMBL 17 data set. CVSP web site is located at http://cvsp.chemspider.com/. CONCLUSION: A platform for the validation and standardization of chemical structure representations of various formats has been developed and made available to the community to assist and encourage the processing of chemical structure files to produce more homogeneous compound representations for exchange and interchange between online databases. While the CVSP platform is designed with flexibility inherent to the rules that can be used for processing the data we have produced a recommended rule set based on our own experiences with the large data sets such as DrugBank, ChEMBL, and data sets from ChemSpider. DOI: 10.1186/s13321-015-0072-8 PMCID: PMC4494041 PMID: 26155308 314. Clin Microbiol Infect. 2016 Jul;22(7):600-6. doi: 10.1016/j.cmi.2016.04.014. Epub 2016 Apr 22. Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers. Dix A(1), Vlaic S(2), Guthke R(1), Linde J(3). Author information: (1)Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute, Germany. (2)Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute, Germany; Department of Bioinformatics, Friedrich-Schiller-University, Jena, Germany. (3)Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute, Germany. Electronic address: joerg.linde@leibniz-hki.de. In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host-pathogen interaction models. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved. DOI: 10.1016/j.cmi.2016.04.014 PMID: 27113568 [Indexed for MEDLINE] 315. FEBS J. 2015 Apr;282(8):1481-511. doi: 10.1111/febs.13237. Epub 2015 Mar 23. Construction and validation of a detailed kinetic model of glycolysis in Plasmodium falciparum. Penkler G(1), du Toit F, Adams W, Rautenbach M, Palm DC, van Niekerk DD, Snoep JL. Author information: (1)Department of Biochemistry, Stellenbosch University, Matieland, South Africa; Molecular Cell Physiology, Vrije Universiteit Amsterdam, The Netherlands. The enzymes in the Embden-Meyerhof-Parnas pathway of Plasmodium falciparum trophozoites were kinetically characterized and their integrated activities analyzed in a mathematical model. For validation of the model, we compared model predictions for steady-state fluxes and metabolite concentrations of the hexose phosphates with experimental values for intact parasites. The model, which is completely based on kinetic parameters that were measured for the individual enzymes, gives an accurate prediction of the steady-state fluxes and intermediate concentrations. This is the first detailed kinetic model for glucose metabolism in P. falciparum, one of the most prolific malaria-causing protozoa, and the high predictive power of the model makes it a strong tool for future drug target identification studies. The modelling workflow is transparent and reproducible, and completely documented in the SEEK platform, where all experimental data and model files are available for download.DATABASE: The mathematical models described in the present study have been submitted to the JWS Online Cellular Systems Modelling Database (http://jjj.bio.vu.nl/database/penkler). The investigation and complete experimental data set is available on SEEK (10.15490/seek.1. INVESTIGATION: 56). © 2015 FEBS. DOI: 10.1111/febs.13237 PMID: 25693925 [Indexed for MEDLINE] 316. Oncotarget. 2017 Mar 28;8(13):22166-22174. doi: 10.18632/oncotarget.13125. An early biomarker and potential therapeutic target of RUNX 3 hypermethylation in breast cancer, a system review and meta-analysis. Lu DG(1), Ma YM(2), Zhu AJ(3), Han YW(4). Author information: (1)Clinical Laboratory, Linyi People's Hospital, Linyi, Shandong, P.R. China. (2)Clinical Laboratory, Linyi Chest Hospital, Linyi, Shandong, P.R. China. (3)Department of ophtalmology, Linyi People's Hospital, Linyi, Shandong, P.R. China. (4)Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China. Runt-related transcription factor 3 (RUNX3) methylation plays an important role in the carcinogenesis of breast cancer (BC). However, the association between RUNX3 hypermethylation and significance of BC remains under investigation. The purpose of this study is to perform a meta-analysis and literature review to evaluate the clinicopathological significance of RUNX3 hypermethylation in BC. A comprehensive literature search was performed in Medline, Web of Science, EMBASE, Cochrane Library Database, CNKI and Google scholar. A total of 10 studies and 747 patients were included for the meta-analysis. Pooled odds ratios (ORs) with corresponding confidence intervals (CIs) were evaluated and summarized respectively. RUNX3 hypermethylation was significantly correlated with the risk of ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC), OR was 50.37, p < 0.00001 and 22.66, p < 0.00001 respectively. Interestingly, the frequency of RUNX3 hypermethylation increased in estrogen receptor (ER) positive BC, OR was 12.12, p = 0.005. High RUNX3 mRNA expression was strongly associated with better relapse-free survival (RFS) in BC patients. In summary, RUNX3 methylation could be a promising early biomarker for the diagnosis of BC. High RUNX3 mRNA expression is correlated to better RFS in BC patients. RUNX3 could be a potential therapeutic target for the development of personalized therapy. DOI: 10.18632/oncotarget.13125 PMCID: PMC5400655 PMID: 27825140 [Indexed for MEDLINE] 317. Genomics Inform. 2016 Sep;14(3):125-135. Epub 2016 Sep 30. Sequence Analysis of Hypothetical Proteins from Helicobacter pylori 26695 to Identify Potential Virulence Factors. Naqvi AA(1), Anjum F(2), Khan FI(3), Islam A(1), Ahmad F(1), Hassan MI(1). Author information: (1)Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India. (2)Female College of Applied Medical Science, Taif University, Al-Taif 21974, Kingdom of Saudi Arabia. (3)School of Chemistry and Chemical Engineering, Henan University of Technology, Henan 450001, China. Helicobacter pylori is a Gram-negative bacteria that is responsible for gastritis in human. Its spiral flagellated body helps in locomotion and colonization in the host environment. It is capable of living in the highly acidic environment of the stomach with the help of acid adaptive genes. The genome of H. pylori 26695 strain contains 1,555 coding genes that encode 1,445 proteins. Out of these, 340 proteins are characterized as hypothetical proteins (HP). This study involves extensive analysis of the HPs using an established pipeline which comprises various bioinformatics tools and databases to find out probable functions of the HPs and identification of virulence factors. After extensive analysis of all the 340 HPs, we found that 104 HPs are showing characteristic similarities with the proteins with known functions. Thus, on the basis of such similarities, we assigned probable functions to 104 HPs with high confidence and precision. All the predicted HPs contain representative members of diverse functional classes of proteins such as enzymes, transporters, binding proteins, regulatory proteins, proteins involved in cellular processes and other proteins with miscellaneous functions. Therefore, we classified 104 HPs into aforementioned functional groups. During the virulence factors analysis of the HPs, we found 11 HPs are showing significant virulence. The identification of virulence proteins with the help their predicted functions may pave the way for drug target estimation and development of effective drug to counter the activity of that protein. DOI: 10.5808/GI.2016.14.3.125 PMCID: PMC5056897 PMID: 27729842 318. Expert Rev Anti Infect Ther. 2016;14(3):285-97. doi: 10.1586/14787210.2016.1141676. Epub 2016 Feb 3. Novel insights into human respiratory syncytial virus-host factor interactions through integrated proteomics and transcriptomics analysis. Dapat C(1), Oshitani H(1). Author information: (1)a Department of Virology , Tohoku University Graduate School of Medicine , Sendai , Miyagi Prefecture , Japan. The lack of vaccine and limited antiviral options against respiratory syncytial virus (RSV) highlights the need for novel therapeutic strategies. One alternative is to develop drugs that target host factors required for viral replication. Several microarray and proteomics studies had been published to identify possible host factors that are affected during RSV replication. In order to obtain a comprehensive understanding of RSV-host interaction, we integrated available proteome and transcriptome datasets and used it to construct a virus-host interaction network. Then, we interrogated the network to identify host factors that are targeted by the virus and we searched for drugs from the DrugBank database that interact with these host factors, which may have potential applications in repositioning for future treatment options of RSV infection. DOI: 10.1586/14787210.2016.1141676 PMCID: PMC4819838 PMID: 26760927 [Indexed for MEDLINE] 319. CPT Pharmacometrics Syst Pharmacol. 2014 Jan 29;3:e97. doi: 10.1038/psp.2013.75. Identifying tinnitus-related genes based on a side-effect network analysis. Elgoyhen AB(1), Langguth B(2), Nowak W(3), Schecklmann M(2), De Ridder D(4), Vanneste S(5). Author information: (1)1] Instituto de Investigaciones en Ingeniería Genética y Biología Molecular, Dr. Héctor N Torres, Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina [2] Departamento de Farmacología, Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires, Argentina. (2)Department of Psychiatry and Psychotherapy, Interdisciplinary Tinnitus Clinic, University of Regensburg, Regensburg, Germany. (3)Departamento de Farmacología, Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires, Argentina. (4)Department of Surgical Sciences, Unit of Neurosurgery, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand. (5)Laboratory for Auditory & Integrative Neuroscience, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas, USA. Tinnitus, phantom sound perception, is a worldwide highly prevalent disorder for which no clear underlying pathology has been established and for which no approved drug is on the market. Thus, there is an urgent need for new approaches to understand this condition. We used a network pharmacology side-effect analysis to search for genes that are involved in tinnitus generation. We analyzed a network of 1,313 drug-target pairs, based on 275 compounds that elicit tinnitus as side effect and their targets reported in databases, and used a quantitative score to identify emergent significant targets that were more common than expected at random. Cyclooxigenase 1 and 2 were significant, which validates our approach, since salicylate is a known tinnitus generator. More importantly, we predict previously unknown tinnitus-related targets. The present results have important implications toward understanding tinnitus pathophysiology and might pave the way toward the design of novel pharmacotherapies.CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e97; doi:10.1038/psp.2013.75; published online 29 January 2014. DOI: 10.1038/psp.2013.75 PMCID: PMC3910011 PMID: 24477090 320. Zhongguo Zhong Yao Za Zhi. 2014 Jun;39(12):2336-40. [Study on prediction of compound-target-disease network of chuanxiong rhizoma based on random forest algorithm]. [Article in Chinese] Yuan J, Li XJ, Chen C, Song XG, Wang SM. To collect small molecule drugs and their drug target data such as enzymes, ion channels, G-protein-coupled receptors and nuclear receptors from KEGG database as the training sets, in order to establish drug-target interaction models based on the random forest algorithm. The accuracies of the models were evaluated by the 10-fold cross-validation test, showing that the predicted success rates of the four drug target models were 71.34%, 67.08%, 73.17% and 67.83%, respectively. The models were adopted to predict the targets of 26 chemical components and establish the compound-target-disease network. The results were well verified by literatures. The models established in this paper are highly accurate, and can be used to discover potential targets in other traditional Chinese medicine ingredients. PMID: 25244771 [Indexed for MEDLINE] 321. J Comput Chem. 2015 Jun 5;36(15):1132-56. doi: 10.1002/jcc.23905. DOCK 6: Impact of new features and current docking performance. Allen WJ(1), Balius TE, Mukherjee S, Brozell SR, Moustakas DT, Lang PT, Case DA, Kuntz ID, Rizzo RC. Author information: (1)Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York, 11794. This manuscript presents the latest algorithmic and methodological developments to the structure-based design program DOCK 6.7 focused on an updated internal energy function, new anchor selection control, enhanced minimization options, a footprint similarity scoring function, a symmetry-corrected root-mean-square deviation algorithm, a database filter, and docking forensic tools. An important strategy during development involved use of three orthogonal metrics for assessment and validation: pose reproduction over a large database of 1043 protein-ligand complexes (SB2012 test set), cross-docking to 24 drug-target protein families, and database enrichment using large active and decoy datasets (Directory of Useful Decoys [DUD]-E test set) for five important proteins including HIV protease and IGF-1R. Relative to earlier versions, a key outcome of the work is a significant increase in pose reproduction success in going from DOCK 4.0.2 (51.4%) → 5.4 (65.2%) → 6.7 (73.3%) as a result of significant decreases in failure arising from both sampling 24.1% → 13.6% → 9.1% and scoring 24.4% → 21.1% → 17.5%. Companion cross-docking and enrichment studies with the new version highlight other strengths and remaining areas for improvement, especially for systems containing metal ions. The source code for DOCK 6.7 is available for download and free for academic users at http://dock.compbio.ucsf.edu/. © 2015 Wiley Periodicals, Inc. DOI: 10.1002/jcc.23905 PMCID: PMC4469538 PMID: 25914306 [Indexed for MEDLINE] 322. BMC Bioinformatics. 2014 Jan 24;15:26. doi: 10.1186/1471-2105-15-26. Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach. Mukhopadhyay A, Ray S(1), Maulik U. Author information: (1)Department of Computer Science and Engineering, Aliah University, Kolkata-700091, West Bengal, India. sumantababai86@gmail.com. BACKGROUND: Discovering novel interactions between HIV-1 and human proteins would greatly contribute to different areas of HIV research. Identification of such interactions leads to a greater insight into drug target prediction. Some recent studies have been conducted for computational prediction of new interactions based on the experimentally validated information stored in a HIV-1-human protein-protein interaction database. However, these techniques do not predict any regulatory mechanism between HIV-1 and human proteins by considering interaction types and direction of regulation of interactions. RESULTS: Here we present an association rule mining technique based on biclustering for discovering a set of rules among human and HIV-1 proteins using the publicly available HIV-1-human PPI database. These rules are subsequently utilized to predict some novel interactions among HIV-1 and human proteins. For prediction purpose both the interaction types and direction of regulation of interactions, (i.e., virus-to-host or host-to-virus) are considered here to provide important additional information about the regulation pattern of interactions. We have also studied the biclusters and analyzed the significant GO terms and KEGG pathways in which the human proteins of the biclusters participate. Moreover the predicted rules have also been analyzed to discover regulatory relationship between some human proteins in course of HIV-1 infection. Some experimental evidences of our predicted interactions have been found by searching the recent literatures in PUBMED. We have also highlighted some human proteins that are likely to act against the HIV-1 attack. CONCLUSIONS: We pose the problem of identifying new regulatory interactions between HIV-1 and human proteins based on the existing PPI database as an association rule mining problem based on biclustering algorithm. We discover some novel regulatory interactions between HIV-1 and human proteins. Significant number of predicted interactions has been found to be supported by recent literature. DOI: 10.1186/1471-2105-15-26 PMCID: PMC3922888 PMID: 24460683 [Indexed for MEDLINE] 323. FEBS J. 2016 Feb;283(4):634-46. doi: 10.1111/febs.13615. Epub 2016 Jan 4. Targeting glycolysis in the malaria parasite Plasmodium falciparum. van Niekerk DD(1), Penkler GP(1)(2), du Toit F(1), Snoep JL(1)(2)(3). Author information: (1)Department of Biochemistry, Stellenbosch University, Matieland, South Africa. (2)Molecular Cell Physiology, Vrije Universiteit Amsterdam, The Netherlands. (3)MIB, University of Manchester, UK. Glycolysis is the main pathway for ATP production in the malaria parasite Plasmodium falciparum and essential for its survival. Following a sensitivity analysis of a detailed kinetic model for glycolysis in the parasite, the glucose transport reaction was identified as the step whose activity needed to be inhibited to the least extent to result in a 50% reduction in glycolytic flux. In a subsequent inhibitor titration with cytochalasin B, we confirmed the model analysis experimentally and measured a flux control coefficient of 0.3 for the glucose transporter. In addition to the glucose transporter, the glucokinase and phosphofructokinase had high flux control coefficients, while for the ATPase a small negative flux control coefficient was predicted. In a broader comparative analysis of glycolytic models, we identified a weakness in the P. falciparum pathway design with respect to stability towards perturbations in the ATP demand.DATABASE: The mathematical model described here has been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at http://jjj.bio.vu.nl/database/vanniekerk1. The SEEK-study including the experimental data set is available at DOI 10.15490/seek.1. INVESTIGATION: 56 (http://dx.doi.org/10.15490/seek.1. INVESTIGATION: 56). © 2015 FEBS. DOI: 10.1111/febs.13615 PMID: 26648082 [Indexed for MEDLINE] 324. RNA. 2017 May;23(5):770-781. doi: 10.1261/rna.059865.116. Epub 2017 Feb 17. Advancing viral RNA structure prediction: measuring the thermodynamics of pyrimidine-rich internal loops. Phan A(1), Mailey K(1), Saeki J(1), Gu X(1)(2), Schroeder SJ(3)(2). Author information: (1)Department of Chemistry and Biochemistry. (2)Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma 73019, USA. (3)Department of Chemistry and Biochemistry susan.schroeder@ou.edu. Accurate thermodynamic parameters improve RNA structure predictions and thus accelerate understanding of RNA function and the identification of RNA drug binding sites. Many viral RNA structures, such as internal ribosome entry sites, have internal loops and bulges that are potential drug target sites. Current models used to predict internal loops are biased toward small, symmetric purine loops, and thus poorly predict asymmetric, pyrimidine-rich loops with >6 nucleotides (nt) that occur frequently in viral RNA. This article presents new thermodynamic data for 40 pyrimidine loops, many of which can form UU or protonated CC base pairs. Uracil and protonated cytosine base pairs stabilize asymmetric internal loops. Accurate prediction rules are presented that account for all thermodynamic measurements of RNA asymmetric internal loops. New loop initiation terms for loops with >6 nt are presented that do not follow previous assumptions that increasing asymmetry destabilizes loops. Since the last 2004 update, 126 new loops with asymmetry or sizes greater than 2 × 2 have been measured. These new measurements significantly deepen and diversify the thermodynamic database for RNA. These results will help better predict internal loops that are larger, pyrimidine-rich, and occur within viral structures such as internal ribosome entry sites. © 2017 Phan et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society. DOI: 10.1261/rna.059865.116 PMCID: PMC5393185 PMID: 28213527 [Indexed for MEDLINE] 325. Int J Mol Sci. 2018 Dec 29;20(1). pii: E115. doi: 10.3390/ijms20010115. Discovery of High Affinity Receptors for Dityrosine through Inverse Virtual Screening and Docking and Molecular Dynamics. Wang F(1), Yang W(2)(3), Hu X(4). Author information: (1)School of Life Science, Linyi University, Linyi 276000, China. wangfangfang@lyu.edu.cn. (2)Department of Microbiology, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia. zhuxiaoqing88@163.com. (3)Arieh Warshel Institute of Computational Biology, the Chinese University of Hong Kong, 2001 Longxiang Road, Longgang District, Shenzhen 518000, China. zhuxiaoqing88@163.com. (4)School of Life Science, Linyi University, Linyi 276000, China. huxiaojun@lyu.edu.cn. Dityrosine is the product of oxidation that has been linked to a number of serious pathological conditions. Evidence indicates that high amounts of dityrosine exist in oxidized milk powders and some milk related foodstuffs, further reducing the nutritional value of oxidized proteins. Therefore, we hypothesize that some receptors related to special diseases would be targets for dityrosine. However, the mechanisms of the interaction of dityrosine with probable targets are still unknown. In the present work, an inverse virtual screening approach was performed to screen possible novel targets for dityrosine. Molecular docking studies were performed on a panel of targets extracted from the potential drug target database (PDTD) to optimize and validate the screening results. Firstly, two different conformations cis- and trans- were found for dityrosine during minimization. Moreover, Tubulin (αT) (-11.0 kcal/mol) was identified as a target for cis-dityrosine (CDT), targets including αT (-11.2 kcal/mol) and thyroid hormone receptor beta-1 (-10.7 kcal/mol) presented high binding affinities for trans-dityrosine (TDT). Furthermore, in order to provide binding complexes with higher precision, the three docked systems were further refined by performing thermo dynamic simulations. A series of techniques for searching for the most stable binding pose and the calculation of binding free energy are elaborately provided in this work. The major interactions between these targets and dityrosine were hydrophobic, electrostatic and hydrogen bonding. The application of inverse virtual screening method may facilitate the prediction of unknown targets for known ligands, and direct future experimental assays. DOI: 10.3390/ijms20010115 PMCID: PMC6337580 PMID: 30597963 326. Crit Rev Microbiol. 2018 Dec 6:1-14. doi: 10.1080/1040841X.2018.1538934. [Epub ahead of print] Underscoring interstrain variability and the impact of growth conditions on associated antimicrobial susceptibilities in preclinical testing of novel antimicrobial drugs. Sanchez DA(1)(2), Martinez LR(3). Author information: (1)a Howard University College of Medicine , Washington , DC , USA. (2)b Brigham and Women's Hospital , Boston , MA , USA. (3)c Department of Biological Sciences , The Border Biomedical Research Center, University of Texas at El Paso , El Paso , TX , USA. In the era of multidrug resistant (MDR) organisms, reliable efficacy testing of novel antimicrobials during developmental stages is of paramount concern prior to introduction in clinical trials. Unfortunately, interstrain variability is often underappreciated when appraising the efficacy of innovative antimicrobials as preclinical testing of a limited number of standardized strains in unvarying conditions does not account for the vastness and potential for hyperdiversity among and within microbial populations. In this review, the importance of accounting for interstrain variability's potential to impact breadth of novel drug efficacy evaluation in the early stages of drug development will be discussed. Additionally, testing under varying microenvironmental conditions that may influence drug efficacy will be discussed. Biofilm growth, the influence of polymicrobial growth, mechanisms of antimicrobial resistance, pH, anaerobic conditions, and other virulence factors are some of critical issues that require more attention and standardization during preclinical drug efficacy evaluation. Furthermore, potential solutions for addressing this issue in pre-clinical antimicrobial development are proposed via centralization of microbial characterization and drug target databases, testing of a large number of clinical strains, inclusion of mutator strains in testing and the use of growth parameter mathematical models for testing. DOI: 10.1080/1040841X.2018.1538934 PMID: 30522365 327. Bioinformation. 2014 Jun 30;10(6):358-64. doi: 10.6026/97320630010358. eCollection 2014. Virtual Screening of compounds to 1-deoxy-Dxylulose 5-phosphate reductoisomerase (DXR) from Plasmodium falciparum. Chaudhary KK(1), Prasad CV(1). Author information: (1)Division of Applied Sciences & IRCB, Systems Biology lab, Indian Institute of Information Technology Allahabad, Deoghat, Jhalwa, Allahabad 211012, India. The 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) protein (Gen Bank ID AAN37254.1) from Plasmodium falciparum is a potential drug target. Therefore, it is of interest to screen DXR against a virtual library of compounds (at the ZINC database) for potential binders as possible inhibitors. This exercise helped to choose 10 top ranking molecules with ZINC00200163 [N-(2,2di methoxy ethyl)-6-methyl-2, 3, 4, 9-tetrahydro-1H-carbazol-1-amine] a having good fit (-6.43 KJ/mol binding energy) with the target protein. Thus, ZINC00200163 is identified as a potential molecule for further comprehensive characterization and in-depth analysis. DOI: 10.6026/97320630010358 PMCID: PMC4110427 PMID: 25097379 328. J Biomol Struct Dyn. 2018 Jun;36(8):2045-2057. doi: 10.1080/07391102.2017.1341337. Epub 2017 Jun 26. Structure-based screening and molecular dynamics simulations offer novel natural compounds as potential inhibitors of Mycobacterium tuberculosis isocitrate lyase. Shukla R(1), Shukla H(1), Sonkar A(1), Pandey T(1), Tripathi T(1). Author information: (1)a Molecular and Structural Biophysics Laboratory, Department of Biochemistry , North-Eastern Hill University , Shillong 793022 , India. Mycobacterium tuberculosis is the etiological agent of tuberculosis in humans and is responsible for more than two million deaths annually. M. tuberculosis isocitrate lyase (MtbICL) catalyzes the first step in the glyoxylate cycle, plays a pivotal role in the persistence of M. tuberculosis, which acts as a potential target for an anti-tubercular drug. To identify the potential anti-tuberculosis compound, we conducted a structure-based virtual screening of natural compounds from the ZINC database (n = 1,67,748) against the MtbICL structure. The ligands were docked against MtbICL in three sequential docking modes that resulted in 340 ligands having better docking score. These compounds were evaluated for Lipinski and ADMET prediction, and 27 compounds were found to fit well with re-docking studies. After refinement by molecular docking and drug-likeness analyses, three potential inhibitors (ZINC1306071, ZINC2111081, and ZINC2134917) were identified. These three ligands and the reference compounds were further subjected to molecular dynamics simulation and binding energy analyses to compare the dynamic structure of protein after ligand binding and the stability of the MtbICL and bound complexes. The binding free energy analyses were calculated to validate and capture the intermolecular interactions. The results suggested that the three compounds had a negative binding energy with -96.462, -143.549, and -122.526 kJ mol-1 for compounds with IDs ZINC1306071, ZINC2111081, and ZINC2134917, respectively. These lead compounds displayed substantial pharmacological and structural properties to be drug candidates. We concluded that ZINC2111081 has a great potential to inhibit MtbICL and would add to the drug discovery process against tuberculosis. DOI: 10.1080/07391102.2017.1341337 PMID: 28605994 [Indexed for MEDLINE] 329. Oncol Lett. 2017 Dec;14(6):8014-8020. doi: 10.3892/ol.2017.7211. Epub 2017 Oct 18. Identification and validation of PSAT1 as a potential prognostic factor for predicting clinical outcomes in patients with colorectal carcinoma. Qian C(1), Xia Y(1), Ren Y(1), Yin Y(1), Deng A(2). Author information: (1)Department of General Surgery, Huzhou Maternity and Child Care Hospital, Huzhou, Zhejiang 313000, P.R. China. (2)Department of Laboratory Diagnosis, Changhai Hospital, Second Military Medical University, Shanghai 200433, P.R. China. The aim of the present study was to explore the existence of known or candidate drug-target genes that are upregulated in colorectal cancer (CRC) and may serve as novel prognostic factors or therapeutic targets for this type of malignancy. An in silico analysis was conducted using the Oncomine tool to compare the expression levels of a list of drug-target genes between cancerous and normal tissues in 6 independent CRC cohorts retrieved from the Oncomine database. Phosphoserine aminotransferase 1 (PSAT1) was identified as the top-ranked upregulated gene in CRC tumors, and was highly expressed in patients with chemoresistant disease. Subsequently, the expression of PSAT1 was further experimentally validated using immunohistochemistry in an independent cohort of CRC specimens. The immunohistochemistry results demonstrated that PSAT1 was overexpressed in the CRC tissues compared with the normal colorectal tissues, which was consistent with the previous in silico analysis. Furthermore, PSAT1 overexpression was associated with response to irinotecan, 5-fluorouracil and leucovorin chemotherapy, and with shorter survival time, and retained significance as an independent prognostic factor for CRC when subjected to the multivariate analysis with a Cox's proportional hazards model. Therefore, the present results implicate PSAT1 as a potential prognostic biomarker and a promising therapeutic target for CRC. Targeted PSAT1 inhibition in the treatment of CRC warrants further investigation. DOI: 10.3892/ol.2017.7211 PMCID: PMC5755227 PMID: 29344244 330. Bioinformatics. 2011 Aug 1;27(15):2083-8. doi: 10.1093/bioinformatics/btr331. Epub 2011 Jun 2. Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction. Zhang Z(1), Li Y, Lin B, Schroeder M, Huang B. Author information: (1)Systems Biology Division, Zhejiang-California International NanoSystems Institute, Zhejiang University, 310029 Hangzhou, China. MOTIVATION: Protein-ligand binding sites are the active sites on protein surface that perform protein functions. Thus, the identification of those binding sites is often the first step to study protein functions and structure-based drug design. There are many computational algorithms and tools developed in recent decades, such as LIGSITE(cs/c), PASS, Q-SiteFinder, SURFNET, and so on. In our previous work, MetaPocket, we have proved that it is possible to combine the results of many methods together to improve the prediction result. RESULTS: Here, we continue our previous work by adding four more methods Fpocket, GHECOM, ConCavity and POCASA to further improve the prediction success rate. The new method MetaPocket 2.0 and the individual approaches are all tested on two datasets of 48 unbound/bound and 210 bound structures as used before. The results show that the average success rate has been raised 5% at the top 1 prediction compared with previous work. Moreover, we construct a non-redundant dataset of drug-target complexes with known structure from DrugBank, DrugPort and PDB database and apply MetaPocket 2.0 to this dataset to predict drug binding sites. As a result, >74% drug binding sites on protein target are correctly identified at the top 3 prediction, and it is 12% better than the best individual approach. AVAILABILITY: The web service of MetaPocket 2.0 and all the test datasets are freely available at http://projects.biotec.tu-dresden.de/metapocket/ and http://sysbio.zju.edu.cn/metapocket. DOI: 10.1093/bioinformatics/btr331 PMID: 21636590 [Indexed for MEDLINE] 331. Cell Biochem Biophys. 2017 Mar;75(1):35-48. doi: 10.1007/s12013-016-0772-3. Epub 2016 Dec 2. Hybrid Receptor-Bound/MM-GBSA-Per-residue Energy-Based Pharmacophore Modelling: Enhanced Approach for Identification of Selective LTA4H Inhibitors as Potential Anti-inflammatory Drugs. Appiah-Kubi P(1), Soliman M(2). Author information: (1)University of Kwazulu-Natal, Durban, Kwazulu-Natal, South Africa. (2)University of Kwazulu-Natal, Durban, Kwazulu-Natal, South Africa. soliman@ukzn.ac.za. Leukotriene A4 hydrolase has been identified as an enzyme with dual anti- and pro-inflammatory role, thus, the conversion of leukotriene to leukotriene B4 in the initiation stage of inflammation and the removal of the chemotactic Pro-Gly-Pro tripeptide. These findings make leukotriene A4 hydrolase an attractive drug target: suggesting an innovative approach towards the identification and design of novel class of compounds that can selectively inhibit leukotriene B4 synthesis while sparing the aminopeptidase activity. Previous inhibitors block the dual activity of the enzyme. Recently, a small lead molecule inhibitor denoted as ARM1 has been identified to block the hydrolase activity of leukotriene A4 hydrolase whilst sparing the aminopeptidase activity. In this study, a hybrid receptor-bound/MM-GBSA-per-residue energy based pharmacophore modeling approach was implemented to identify potential selective hydrolase inhibitors of leukotriene A4 hydrolase. In this approach, active site residues that favorably contributed to the binding of the bound conformation of ARM1 were derived from MD ensembles and MM/GBSA thermodynamic calculations. These residues were then mapped to key pharmacophore features of ARM1. The generated pharmacophore model was used to search the ZINC database for 3D structures that match the pharmacophore. Five new compounds have been identified and proposed as potential epoxide hydrolase selective inhibitors of leukotriene A4 hydrolase. Molecular docking and MM/GBSA analyses revealed that, these top five lead-like compounds ZINC00142747, ZINC94260794, ZINC01382396, ZINC02508448, and ZINC53994447 showed better binding affinities to the hydrolase active site pocket compared to ARM1. Per-residue energy decomposition analysis revealed that amino acid residues Phe314, Tyr378, Pro382, Trp311, Val367, and Ala377 are key residues critical in the selective inhibition of these hits. Information highlighted in this study may guide the the design the next generation of novel and potent epoxide hydrolase selective inhibitors of leukotriene A4 hydrolase. DOI: 10.1007/s12013-016-0772-3 PMID: 27914004 [Indexed for MEDLINE] 332. 3 Biotech. 2019 Feb;9(2):40. doi: 10.1007/s13205-019-1567-1. Epub 2019 Jan 10. Designing quorum sensing inhibitors of Pseudomonas aeruginosa utilizing FabI: an enzymic drug target from fatty acid synthesis pathway. Kalia M(1), Yadav VK(1), Singh PK(1), Dohare S(1), Sharma D(2), Narvi SS(3), Agarwal V(1). Author information: (1)1Department of Biotechnology, Motilal Nehru National Institute of Technology Allahabad, Allahabad, India. (2)2Department of Mathematics, National Institute of Technology Raipur, Raipur, India. (3)3Department of Chemistry, Motilal Nehru National Institute of Technology Allahabad, Allahabad, India. Pseudomonas aeruginosa infections are a leading cause of death in patients suffering from respiratory diseases. The multidrug-resistant nature of Pseudomonas is potentiated by a process known as quorum sensing. The aim of this study was to reveal new inhibitors of a well-validated but quite unexplored target, enoyl-ACP reductase, which contributes acyl chain lengths of N-acyl homoserine lactones that are major signaling molecules in gram-negative bacteria. In the present study, the crystal structure of FabI (PDB, ID 4NR0) was used for the structure-based identification of quorum sensing inhibitors of Pseudomonas aeruginosa. Active site residues of FabI were identified from the complex of FabI with triclosan and these active site residues were further used to screen for potential inhibitors from natural database. Three-dimensional structures of the 75 natural compounds were retrieved from the ZINC database and screened using PyRX software against FabI. Thirty-eight molecules from the initial screening were sorted on the basis of binding energy, using the known inhibitor triclosan as a standard. These molecules were subjected to various secondary filters, such as Lipinski's Rule of Five, ADME, and toxicity. Finally, eight lead-like molecules were obtained after their evaluation for drug-like characteristics. The present study will open a new window for designing QS inhibitors against P. aeruginosa. DOI: 10.1007/s13205-019-1567-1 PMCID: PMC6328400 [Available on 2020-02-01] PMID: 30675450 Conflict of interest statement: Compliance with ethical standardsThe authors have no conflicts of interest. 333. Drug Dev Res. 2018 Sep;79(6):260-274. doi: 10.1002/ddr.21460. Epub 2018 Sep 23. Design, graph theoretical analysis, density functionality theories, Insilico modeling, synthesis, characterization and biological activities of novel thiazole fused quinazolinone derivatives. Saravanan G(1), Panneerselvam T(2), Alagarsamy V(1), Kunjiappan S(3), Parasuraman P(4), Murugan I(5), Dinesh Kumar P(6). Author information: (1)Department of Pharmaceutical Chemistry, MNR College of Pharmacy, Sangareddy, Telangana, India. (2)Department of Pharmaceutical Chemistry, Karavali College of Pharmacy, Mangalore, Karnataka, India. (3)International Research Center, Kalasalingam University, Krishnan Koil, Tamil Nadu, India. (4)Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bengaluru, Karnataka, India. (5)Department of Biotechnology, P.S.R Engineering College, Sivakasi, Tamilnadu, India. (6)Hindu College of Pharmacy, Guntur, Andhra Pradesh, India. Hit, Lead & Candidate Discovery A series of 2-(2-substituted benzylidenehydrazinyl-2-oxopropyl)-3-(4-[4-oxo-2-phenylthiazolo din-3-yl]phenyl)quinazolin-4(3H)-one 7a-7l were synthesized and characterized by IR, 1 H-NMR, 13 C-NMR, mass spectroscopy and elemental analyses. In this present study, the density functionality theory was performed to identify drug stability. Further we introduced graph theoretical analysis by utilised Kyoto Encyclopedia of Genes and Genomes (KEGG) database and Cytoscape software to identify drug target. Based on the observed drug target insilico modeling was executed to know effective drug. The antiepileptic effects of title compounds were evaluated by using MES and subcutaneous pentylenetetrazole (scPTZ) test. Acute neurological toxicity of title compounds was studied by using standardized rotorod test. After 0.5 hr of period many of the compounds showed anticonvulsant activity at MES or scPTZ test. Comparison of the biological activity of test compounds with its chemical structures indicates that, compounds possessing electron donating group exhibited superior activity than the analogs having electron withdrawing moieties. Among the electron donating group tested, amino derivative exhibited good activity than rest of derivatives. From the study it was concluded that, the compound 7j was established as very potent compared with rest of the compounds and standard drugs subjected to biological studies. Thus the compound 2-(2-[4-aminobenzylidene]hydrazinyl-2-oxopropyl)-3-(4-[4-oxo-2-phenylthiazolidin- 3-yl]phenyl) quinazolin-4(3H)-one (7j) came out as pilot derivative without any neurotoxicity with a wide spectrum of antiepileptic activity. HIGHLIGHTS: The performed work is having great significance in terms of Graph theoretical analysis used to identify drug target In silico modeling used to identify designed drug interaction with identify target Density functionality studies used to identify synthesized compound energy band gap which is correlate with enhancement of its biological activity Antiepileptic effects of entire synthesized quinazolinone scaffolds were evaluated by MES and scPTZ test 2-(2-[4-aminobenzylidene]hydrazinyl-2-oxopropyl)-3-(4-[4-oxo-2-phenylthiazolidin- 3-yl]phenyl) quinazolin-4(3H)-one (7j) was established as very potent compared to the rest of the compounds and standard drugs which were subjected to biological studies. © 2018 Wiley Periodicals, Inc. DOI: 10.1002/ddr.21460 PMID: 30244475 [Indexed for MEDLINE] 334. Oncotarget. 2016 Jun 21;7(25):38670-38680. doi: 10.18632/oncotarget.9578. Functional/activity network (FAN) analysis of gene-phenotype connectivity liaised by grape polyphenol resveratrol. Hsieh TC(1), Wu ST(2), Bennett DJ(1), Doonan BB(1), Wu E(3)(4)(5), Wu JM(1). Author information: (1)Department of Biochemistry and Molecular Biology, New York Medical College, Valhalla, New York 10595, U.S.A. (2)Division of Urology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC. (3)Department of Neurosurgery, Baylor Scott and White Health, Temple, Texas, 76508, U.S.A. (4)Department of Surgery, Texas A&M College of Medicine, Temple, Texas 76504, U.S.A. (5)Department of Pharmaceutical Sciences, Texas A&M Health Science Center, College Station, Texas 77843, U.S.A. Resveratrol is a polyphenol that has witnessed an unprecedented yearly growth in PubMed citations since the late 1990s. Based on the diversity of cellular processes and diseases resveratrol reportedly affects and benefits, it is likely that the interest in resveratrol will continue, although uncertainty regarding its mechanism in different biological systems remains.We hypothesize that insights on disease-modulatory activities of resveratrol might be gleaned by systematically dissecting the publicly available published data on chemicals and drugs. In this study, we tested our hypothesis by querying DTome (Drug-Target Interactome), a web-based tool containing data compiled from open-source databases including DrugBank, PharmGSK, and Protein Interaction Network Analysis (PINA). Four direct protein targets (DPT) and 219 DPT-associated genes were identified for resveratrol. The DPT-associated genes were scrutinized by WebGestalt (WEB-based Gene SeT Analysis Toolkit). This enrichment analysis resulted in 10 identified KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Refined analysis of KEGG pathways showed that 2 - one linked to p53 and a second to prostate cancer - have functional connectivity to resveratrol and its four direct protein targets. These results suggest that a functional activity network (FAN) approach may be considered as a new paradigm for guiding future studies of resveratrol. FAN analysis resembles a BioGPS, with capability for mapping a Web-based scientific track that can productively and cost effectively connect resveratrol to its primary and secondary target proteins and to its biological functions. DOI: 10.18632/oncotarget.9578 PMCID: PMC5122419 PMID: 27232943 [Indexed for MEDLINE] Conflict of interest statement: The authors declare no conflicts of interest. 335. Sci Rep. 2017 Jul 5;7(1):4653. doi: 10.1038/s41598-017-04748-9. Virtual Screening, pharmacophore development and structure based similarity search to identify inhibitors against IdeR, a transcription factor of Mycobacterium tuberculosis. Rohilla A(1), Khare G(2), Tyagi AK(3)(4). Author information: (1)Department of Biochemistry, University of Delhi South Campus, Benito Juarez road, New Delhi, 110021, India. (2)Department of Biochemistry, University of Delhi South Campus, Benito Juarez road, New Delhi, 110021, India. garima1822@yahoo.co.in. (3)Department of Biochemistry, University of Delhi South Campus, Benito Juarez road, New Delhi, 110021, India. aniltyagi@south.du.ac.in. (4)Vice Chancellor, Guru Gobind Singh Indraprastha University, Sector 16-C, Dwarka, New Delhi, India. aniltyagi@south.du.ac.in. ideR, an essential gene of Mycobacterium tuberculosis, is an attractive drug target as its conditional knockout displayed attenuated growth phenotype in vitro and in vivo. To the best of our knowledge, no inhibitors of IdeR are identified. We carried out virtual screening of NCI database against the IdeR DNA binding domain followed by inhibition studies using EMSA. Nine compounds exhibited potent inhibition with NSC 281033 (I-20) and NSC 12453 (I-42) exhibiting IC50 values of 2 µg/ml and 1 µg/ml, respectively. We then attempted to optimize the leads firstly by structure based similarity search resulting in a class of inhibitors based on I-42 containing benzene sulfonic acid, 4-hydroxy-3-[(2-hydroxy-1-naphthalenyl) azo] scaffold with 4 molecules exhibiting IC50 ≤ 10 µg/ml. Secondly, optimization included development of energy based pharmacophore and screening of ZINC database followed by docking studies, yielding a molecule with IC50 of 60 µg/ml. More importantly, a five-point pharmacophore model provided insight into the features essential for IdeR inhibition. Five molecules with promising IC50 values also inhibited M. tuberculosis growth in broth culture with MIC90 ranging from 17.5 µg/ml to 100 µg/ml and negligible cytotoxicity in various cell lines. We believe our work opens up avenues for further optimization studies. DOI: 10.1038/s41598-017-04748-9 PMCID: PMC5498548 PMID: 28680150 [Indexed for MEDLINE] 336. Tumour Biol. 2017 Apr;39(4):1010428317695926. doi: 10.1177/1010428317695926. Distinct prognostic values of YAP1 in gastric cancer. Yu L(1), Gao C(1), Feng B(1), Wang L(1), Tian X(1), Wang H(2), Ma D(2). Author information: (1)1 Key Laboratory of Cancer Invasion and Metastasis of the Ministry of Education, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. (2)2 Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. The Hippo pathway regulates intrinsic organ sizes by regulating apoptosis and cell proliferation. YAP1 (yes-associated protein 1) is a transcriptional effector of the Hippo pathway. YAP1 expression is reported to be associated with gastric cancer carcinogenesis and malignancy. In this study, we compared the expression of YAP1 in gastric cancer and normal stomach tissues. Tissue microarray analysis was performed in 156 gastric cancer samples, 8 adjacent normal stomach tissues, and 4 normal stomach tissues. We also analyzed the association between YAP1 protein expression and clinicopathological features, such as age, gender, histological differentiation, and clinical stages. We used the ONCOMINE database and the Kaplan-Meier plotter to analyze YAP1 expression status in different clinicopathological parameters of gastric cancer. We also used the Kaplan-Meier plotter to summarize the survival information of YAP1 from a total of 631 gastric cancer patients. YAP1 expression was found to be elevated in gastric cancer tissues compared to normal stomach tissues. YAP1 messenger RNA was found to be upregulated in gastric intestinal-type adenocarcinoma and gastric mixed adenocarcinoma compared to gastric mucosa. YAP1 high expression was found to be correlated to worse overall survival for all gastric cancer patients followed for 20 years. These results indicate that YAP1 can be used to predict the prognosis of gastric cancer. And YAP1 maybe a potential drug target for gastric cancer patients. DOI: 10.1177/1010428317695926 PMID: 28381174 [Indexed for MEDLINE] 337. Chem Biol Drug Des. 2017 Oct;90(4):545-553. doi: 10.1111/cbdd.12976. Epub 2017 May 22. Deleterious effects of non-synonymous single nucleotide variants of human IL-1β gene. Zhang YH(1), Song J(1), Zhang J(1), Shao J(1). Author information: (1)Department of Orthopedic Surgery, Xinhua Hospital Affiliated to Shanghai JiaoTong University School of Medicine, Shanghai, China. The IL-1β gene is currently topic of interest for its important role in the pathogenesis of intervertebral disk degeneration. The new sequencing technology makes it crucial to study the effects of variants in IL-1β. Thus, 714 IL-1β variants with evidence supporting were collected from the EMBL database. Among them, 62 were non-synonymous single nucleotide variants (nsSNVs). Furthermore, six common nsSNVs were predicted to have damaging effects by SIFT, PolyPhen, PROVEAN and SNPs&GO. Based on the constructed three-dimensional structure of pro-IL-1β, rs375479974 with a mutation of Phe to Ser was proposed to reduce the stability of the pro-IL-1β protein. The rs375479974 variant was found to cause least common stabilizing amino acid residues, decrease hydrophilic and increase hydrophobic surface areas in the greatest degree, and have the lowest free energy alterations in I-Mutant 2.0 sequence analysis. When analyzing the interaction between the experimental 3D structure of mature IL-1β and its neutralizing McAb canakinumab complex, the rs775174784 substitution of Leu with Phe was found to attenuate this interaction by reducing binding energy, while rs375479974 not. Molecular dynamics simulation results in intervertebral disk environment supported rs775174784's effects. These results suggest that both rs375479974 and rs775174784 may have potential clinical and drug target implications. © 2017 John Wiley & Sons A/S. DOI: 10.1111/cbdd.12976 PMID: 28296211 [Indexed for MEDLINE] 338. J Am Stat Assoc. 2016;111(513):73-92. Epub 2016 May 5. Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach. Pham LM(1), Carvalho L(1), Schaus S(1), Kolaczyk ED(1). Author information: (1)Boston University, Boston, MA 02215. Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression and biological pathways using confirmatory factor analysis, (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation using a conditional autoregressive model, and (iii) the third level is a spike-and-slab prior on the perturbations. We then identify perturbations through posterior-based variable selection. We illustrate our approach using gene transcription drug perturbation profiles from the DREAM7 drug sensitivity predication challenge data set. Our proposed method identified regulatory pathways that are known to play a causative role and that were not readily resolved using gene set enrichment analysis or exploratory factor models. Simulation results are presented assessing the performance of this model relative to a network-free variant and its robustness to inaccuracies in biological databases. DOI: 10.1080/01621459.2015.1110523 PMCID: PMC5026418 PMID: 27647944 339. Appl Biochem Biotechnol. 2012 Dec;168(7):1792-805. doi: 10.1007/s12010-012-9897-z. Epub 2012 Oct 6. In silico study and validation of phosphotransacetylase (PTA) as a putative drug target for Staphylococcus aureus by homology-based modelling and virtual screening. Morya VK(1), Dewaker V, Kim EK. Author information: (1)National Research Laboratory of Bioactive Materials, Department of Biological Engineering, Inha University, Incheon 42-751, Korea. moryavviek@gmail.com Staphylococcus aureus, a Gram-positive bacterium, can cause a range of illnesses from minor skin infections to life-threatening diseases, such as bacteraemia, endocarditis, meningitis, osteomyelitis, pneumonia, toxic shock syndrome and sepsis. Due to the emergence of antibiotic resistance strains, there is a need to develop of new class of antibiotics or drug for this pathogen. The phosphotransacetylase enzyme plays an important role in the acetate metabolism and found to be essential for the survival of the S. aureus. This enzyme was evaluated as a putative drug target for S. aureus by in silico analysis. The 3D structure of the phosphotransacetylase from S. aureus was modelled, using the 1TD9 chain 'A' from Bacillus subtilis as a template at the resolution of 2.75 Å. The generated model has been validated by PROCHECK, WHAT IF and SuperPose. The docking was performed by the Molegro virtual docker using the ZINC database generated ligand library. The ligand library was generated within the limitation of the Lipinski rule of five. Based on the dock-score, five molecules have been subjected to ADME/TOX analysis and subjected for pharmacophore model generation. The zinc IDs of the potential inhibitors are ZINC08442078, ZINC8442200, ZINC 8442087 and ZINC 8442184 and found to be pharmacologically active antagonist of phosphotransacetylase. The molecules were evaluated as no-carcinogenic and persistent molecule by START programme. DOI: 10.1007/s12010-012-9897-z PMID: 23054816 [Indexed for MEDLINE] 340. Annu Rev Genomics Hum Genet. 2001;2:259-69. The impact of microbial genomics on antimicrobial drug development. Tang CM(1), Moxon ER. Author information: (1)University Department of Paediatrics, John Radcliffe Hosptial, Oxford OX3 9DU, United Kingdom. christoph.tang@paediatrics.oxford.ac.uk There is an urgent need to develop novel classes of antibiotics to counter the threat of the spread of multiply resistant bacterial pathogens. The availability of the complete genome sequence of many pathogenic microbes provides information on every potential drug target and is an invaluable resource in the search for novel compounds. Here, we review the approaches being taken to exploit the genome databases through a combination of bioinformatics, transcriptional analysis, and a further understanding of the molecular basis of the disease process. The emphasis is changing from compound screening to target hunting, as the latter offers flexible ways to design and optimize the next generation of broad-spectrum antibiotics. DOI: 10.1146/annurev.genom.2.1.259 PMID: 11701651 [Indexed for MEDLINE] 341. J Biomol Struct Dyn. 2018 Jun;36(8):2147-2162. doi: 10.1080/07391102.2017.1344141. Epub 2017 Jul 4. Identification of potential inhibitors of Fasciola gigantica thioredoxin1: computational screening, molecular dynamics simulation, and binding free energy studies. Shukla R(1), Shukla H(1), Kalita P(1), Sonkar A(1), Pandey T(1), Singh DB(2), Kumar A(3), Tripathi T(1). Author information: (1)a Molecular and Structural Biophysics Laboratory, Department of Biochemistry , North-Eastern Hill University , Shillong 793022 , India. (2)b Department of Biotechnology, Institute of Biosciences and Biotechnology , Chhatrapati Shahu Ji Maharaj University , Kanpur 208024 , India. (3)c Department of Biotechnology , National Institute of Technology , Raipur 492010 , India. Fasciola gigantica is the causative organism of fascioliasis and is responsible for major economic losses in livestock production globally. F. gigantica thioredoxin1 (FgTrx1) is an important redox-active enzyme involved in maintaining the redox homeostasis in the cell. To identify a potential anti-fasciolid compound, we conducted a structure-based virtual screening of natural compounds from the ZINC database (n = 1,67,740) against the FgTrx1 structure. The ligands were docked against FgTrx1 and 309 ligands were found to have better docking score. These compounds were evaluated for Lipinski and ADMET prediction, and 30 compounds were found to fit well for re-docking studies. After refinement by molecular docking and drug-likeness analysis, three potential inhibitors (ZINC15970091, ZINC9312362, and ZINC9312661) were identified. These three ligands were further subjected to molecular dynamics simulation (MDS) to compare the dynamics and stability of the protein structure after binding of the ligands. The binding free energy analyses were calculated to determine the intermolecular interactions. The results suggested that the two compounds had a binding free energy of -82.237, and -109.52 kJ.mol-1 for compounds with IDs ZINC9312362 and ZINC9312661, respectively. These predicted compounds displayed considerable pharmacological and structural properties to be drug candidates. We concluded that these two compounds could be potential drug candidates to fight against F. gigantica parasites. DOI: 10.1080/07391102.2017.1344141 PMID: 28627969 [Indexed for MEDLINE] 342. Interdiscip Sci. 2016 Dec;8(4):403-411. Epub 2015 Aug 15. Computational Analysis and Binding Site Identification of Type III Secretion System ATPase from Pseudomonas aeruginosa. Dash R(1), Hosen SM(2), Sultana T(1), Junaid M(3), Majumder M(1), Ishat IA(1), Uddin MM(4). Author information: (1)Department of Pharmacy, BGC Trust University Bangladesh, Chittagong, 4000, Bangladesh. (2)Drugs and Toxins Research Division, Bangladesh Council of Scientific and Industrial Research (BCSIR), Chittagong, 4220, Bangladesh. (3)Department of Pharmaceutical Sciences, North South University, Dhaka, 1229, Bangladesh. (4)Department of Pharmacy, University of Chittagong, Chittagong, 4331, Bangladesh. nasirmir@cu.ac.bd. In many gram-negative bacteria, the type III secretion system (T3SS), as a virulence factor, is an attractive target for developing novel antibacterial. Regarding this, in our study, we aimed to identify the putative drug target for Pseudomonas aeruginosa, considering ATPase enzyme involved in the type III secretion system. Selective protein sequence of P. aeruginosa involved in the T3SS was retrieved from NCBI databases, and its homologues were subjected to phylogenetic analysis. Its association in T3SS was analyzed via STRING, and the 3D structure was determined by means of homology modeling followed by intensive optimization and validation. The binding site was predicted by 3DLigandSite and examined through molecular docking simulation by Autodock Vina with salicylidene acylhydrazide class of virulence-blocking compounds. PROCHECK analysis showed that 96.7 % of the residues were in the most favored regions, 1.9 % were in the additional allowed region, and 1.4 % were in the generously allowed region of the Ramachandran plot. The refined model yielded ERRAT scores of 88.124 and Verify3D value of 0.2, which indicates that the environmental profile of the model is good. The best binding affinity was observed by ME0055 compound, and ALA160, ALA161, GlY162, GLY163, GLY164, GLY165, SER166, THR167, TYR338, and PRO339 residues were found to be having complementary in the ligand-binding site. However, these findings should be further confirmed by wet lab studies for design a targeted therapeutic agent. DOI: 10.1007/s12539-015-0121-z PMID: 26275670 [Indexed for MEDLINE] 343. J Med Chem. 2000 Feb 10;43(3):401-8. Successful virtual screening of a chemical database for farnesyltransferase inhibitor leads. Perola E(1), Xu K, Kollmeyer TM, Kaufmann SH, Prendergast FG, Pang YP. Author information: (1)Mayo Clinic Cancer Center, Tumor Biology Program, Department of Molecular Pharmacology, Molecular Neuroscience Program, Mayo Medical School and Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55905, USA. Virtual screening of chemical databases is an emerging approach in drug discovery that uses computers to dock chemicals into the active site of a drug target to identify leads through evaluation of binding affinities of the chemicals. However, there are concerns about the validity and scope of the reported virtual screens due to lack of studies to show that randomly selected chemicals are not equally active and due to the fact that metalloproteins were rarely used as drug targets. We have performed a virtual screening of a chemical database to identify prototypic inhibitors of farnesyltransferase (FT) with zinc present in the active site. Among the 21 compounds identified by computers, four inhibited FT in vitro with IC(50) values in the range from 25 to 100 microM. The most potent inhibitor also inhibited FT in human lung cancer cells. In contrast, none of 21 randomly selected compounds have an IC(50) lower than 100 microM. The results demonstrate the validity of virtual screening and the feasibility of applications of this approach to metalloprotein drug targets, such as matrix metalloproteinases, farnesyltransferase, and HIV-1 integrase, for the treatments of cardiovascular diseases, cancers, and AIDS. PMID: 10669567 [Indexed for MEDLINE] 344. PLoS Comput Biol. 2012;8(4):e1002457. doi: 10.1371/journal.pcbi.1002457. Epub 2012 Apr 5. Automatic filtering and substantiation of drug safety signals. Bauer-Mehren A(1), van Mullingen EM, Avillach P, Carrascosa Mdel C, Garcia-Serna R, Piñero J, Singh B, Lopes P, Oliveira JL, Diallo G, Ahlberg Helgee E, Boyer S, Mestres J, Sanz F, Kors JA, Furlong LI. Author information: (1)Research Programme on Biomedical Informatics-GRIB, IMIM-Hospital del Mar Research Institute, DCEX, Universitat Pompeu Fabra, Barcelona, Spain. Erratum in PLoS Comput Biol. 2012 May;8(5): doi/10.1371/annotation/695450aa-95a0-491d-804d-470cbfa861e8. Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions. DOI: 10.1371/journal.pcbi.1002457 PMCID: PMC3320573 PMID: 22496632 [Indexed for MEDLINE] 345. Cancer Manag Res. 2018 Jan 24;10:153-166. doi: 10.2147/CMAR.S152951. eCollection 2018. Solute carrier transporters: potential targets for digestive system neoplasms. Xie J(1)(2), Zhu XY(1)(2), Liu LM(1)(2), Meng ZQ(1)(2). Author information: (1)Department of Integrative Oncology, Fudan University Shanghai Cancer Center. (2)Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China. Digestive system neoplasms are the leading causes of cancer-related death all over the world. Solute carrier (SLC) superfamily is composed of a series of transporters that are ubiquitously expressed in organs and tissues of digestive systems and mediate specific uptake of small molecule substrates in facilitative manner. Given the important role of SLC proteins in maintaining normal functions of digestive system, dysregulation of these protein in digestive system neoplasms may deliver biological and clinical significance that deserves systemic studies. In this review, we critically summarized the recent advances in understanding the role of SLC proteins in digestive system neoplasms. We highlighted that several SLC subfamilies, including metal ion transporters, transporters of glucose and other sugars, transporters of urea, neurotransmitters and biogenic amines, ammonium and choline, inorganic cation/anion transporters, transporters of nucleotide, amino acid and oligopeptide organic anion transporters, transporters of vitamins and cofactors and mitochondrial carrier, may play important roles in mediating the initiation, progression, metastasis, and chemoresistance of digestive system neoplasms. Proteins in these SLC subfamilies may also have diagnostic and prognostic values to particular cancer types. Differential expression of SLC proteins in tumors of digestive system was analyzed by extracting data from human cancer database, which revealed that the roles of SLC proteins may either be dependent on the substrates they transport or be tissue specific. In addition, small molecule modulators that pharmacologically regulate the functions of SLC proteins were discussed for their possible application in the treatment of digestive system neoplasms. This review highlighted the potential of SLC family proteins as drug target for the treatment of digestive system neoplasms. DOI: 10.2147/CMAR.S152951 PMCID: PMC5788932 PMID: 29416375 Conflict of interest statement: Disclosure The authors report no conflicts of interest in this work. 346. PLoS One. 2013 Dec 5;8(12):e82160. doi: 10.1371/journal.pone.0082160. eCollection 2013. CyTargetLinker: a cytoscape app to integrate regulatory interactions in network analysis. Kutmon M(1), Kelder T, Mandaviya P, Evelo CT, Coort SL. Author information: (1)Department of Bioinformatics - BiGCaT, NUTRIM School for Nutrition, Toxicology and Metabolism, University of Maastricht, Maastricht, The Netherlands ; Netherlands Consortium for Systems Biology (NCSB), Amsterdam, The Netherlands. INTRODUCTION: The high complexity and dynamic nature of the regulation of gene expression, protein synthesis, and protein activity pose a challenge to fully understand the cellular machinery. By deciphering the role of important players, including transcription factors, microRNAs, or small molecules, a better understanding of key regulatory processes can be obtained. Various databases contain information on the interactions of regulators with their targets for different organisms, data recently being extended with the results of the ENCODE (Encyclopedia of DNA Elements) project. A systems biology approach integrating our understanding on different regulators is essential in interpreting the regulation of molecular biological processes. IMPLEMENTATION: We developed CyTargetLinker (http://projects.bigcat.unimaas.nl/cytargetlinker), a Cytoscape app, for integrating regulatory interactions in network analysis. Recently we released CyTargetLinker as one of the first apps for Cytoscape 3. It provides a user-friendly and flexible interface to extend biological networks with regulatory interactions, such as microRNA-target, transcription factor-target and/or drug-target. Importantly, CyTargetLinker employs identifier mapping to combine various interaction data resources that use different types of identifiers. RESULTS: Three case studies demonstrate the strength and broad applicability of CyTargetLinker, (i) extending a mouse molecular interaction network, containing genes linked to diabetes mellitus, with validated and predicted microRNAs, (ii) enriching a molecular interaction network, containing DNA repair genes, with ENCODE transcription factor and (iii) building a regulatory meta-network in which a biological process is extended with information on transcription factor, microRNA and drug regulation. CONCLUSIONS: CyTargetLinker provides a simple and extensible framework for biologists and bioinformaticians to integrate different regulatory interactions into their network analysis approaches. Visualization options enable biological interpretation of complex regulatory networks in a graphical way. Importantly the incorporation of our tool into the Cytoscape framework allows the application of CyTargetLinker in combination with a wide variety of other apps for state-of-the-art network analysis. DOI: 10.1371/journal.pone.0082160 PMCID: PMC3855388 PMID: 24340000 [Indexed for MEDLINE] 347. J Pharm Bioallied Sci. 2015 Jul-Sep;7(3):212-7. doi: 10.4103/0975-7406.160023. In-silico gene co-expression network analysis in Paracoccidioides brasiliensis with reference to haloacid dehalogenase superfamily hydrolase gene. Satpathy R(1), Konkimalla VB(2), Ratha J(1). Author information: (1)School of Life Science, Sambalpur University, Jyoti Vihar, Burla, Odisha, India. (2)School of Biological Sciences, National Institute of Science Education and Research, Bhubaneswar, Odisha, India. CONTEXT: Paracoccidioides brasiliensis, a dimorphic fungus is the causative agent of paracoccidioidomycosis, a disease globally affecting millions of people. The haloacid dehalogenase (HAD) superfamily hydrolases enzyme in the fungi, in particular, is known to be responsible in the pathogenesis by adhering to the tissue. Hence, identification of novel drug targets is essential. AIMS: In-silico based identification of co-expressed genes along with HAD superfamily hydrolase in P. brasiliensis during the morphogenesis from mycelium to yeast to identify possible genes as drug targets. MATERIALS AND METHODS: In total, four datasets were retrieved from the NCBI-gene expression omnibus (GEO) database, each containing 4340 genes, followed by gene filtration expression of the data set. Further co-expression (CE) study was performed individually and then a combination these genes were visualized in the Cytoscape 2. 8.3. STATISTICAL ANALYSIS USED: Mean and standard deviation value of the HAD superfamily hydrolase gene was obtained from the expression data and this value was subsequently used for the CE calculation purpose by selecting specific correlation power and filtering threshold. RESULTS: The 23 genes that were thus obtained are common with respect to the HAD superfamily hydrolase gene. A significant network was selected from the Cytoscape network visualization that contains total 7 genes out of which 5 genes, which do not have significant protein hits, obtained from gene annotation of the expressed sequence tags by BLAST X. For all the protein PSI-BLAST was performed against human genome to find the homology. CONCLUSIONS: The gene co-expression network was obtained with respect to HAD superfamily dehalogenase gene in P. Brasiliensis. DOI: 10.4103/0975-7406.160023 PMCID: PMC4517324 PMID: 26229356 348. J Biomol Struct Dyn. 2017 Nov 29:1-18. doi: 10.1080/07391102.2017.1396255. [Epub ahead of print] Ligand- and structure-based in silico studies to identify kinesin spindle protein (KSP) inhibitors as potential anticancer agents. Balakumar C(1), Ramesh M(1), Tham CL(2), Khathi SP(1), Kozielski F(2), Srinivasulu C(1), Hampannavar GA(1), Sayyad N(1), Soliman ME(1), Karpoormath R(1). Author information: (1)a Discipline of Pharmaceutical Sciences, College of Health Sciences , University of KwaZulu-Natal (UKZN) , Westville , Durban 4001 , South Africa. (2)b Department of Pharmaceutical and Biological Chemistry , The School of Pharmacy, University College London , 29-39, Brunswick Square, London WC1N 1AX , UK. Kinesin spindle protein (KSP) belongs to the kinesin superfamily of microtubule-based motor proteins. KSP is responsible for the establishment of the bipolar mitotic spindle which mediates cell division. Inhibition of KSP expedites the blockade of the normal cell cycle during mitosis through the generation of monoastral MT arrays that finally cause apoptotic cell death. As KSP is highly expressed in proliferating/cancer cells, it has gained considerable attention as a potential drug target for cancer chemotherapy. Therefore, this study envisaged to design novel KSP inhibitors by employing computational techniques/tools such as pharmacophore modelling, virtual database screening, molecular docking and molecular dynamics. Initially, the pharmacophore models were generated from the data-set of highly potent KSP inhibitors and the pharmacophore models were validated against in house test set ligands. The validated pharmacophore model was then taken for database screening (Maybridge and ChemBridge) to yield hits, which were further filtered for their drug-likeliness. The potential hits retrieved from virtual database screening were docked using CDOCKER to identify the ligand binding landscape. The top-ranked hits obtained from molecular docking were progressed to molecular dynamics (AMBER) simulations to deduce the ligand binding affinity. This study identified MB-41570 and CB-10358 as potential hits and evaluated these experimentally using in vitro KSP ATPase inhibition assays. DOI: 10.1080/07391102.2017.1396255 PMID: 29064326 349. J Pharm Pharm Sci. 2013;16(2):331-41. Drug discovery inspired by mother nature: seeking natural biochemotypes and the natural assembly rules of the biochemome. Gu Q(1), Yan X, Xu J. Author information: (1)Research Center for Drug Discovery, School of Pharmaceutical Sciences, and Institute of Human Virology, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou, China. PURPOSE: The Human Genome Project is producing a new biological 'periodic table', which defines all genes for making macromolecules (proteins, DNA, RNA, etc) and the relations between genes and their biological functions. We now need to consider whether to initiate a biochemome project aimed at discovering biochemistry's 'periodic table', which would define all molecular parts for making small molecules (natural products) and the relations between the parts and their functions to regulate genes. By understanding the Biochemome, we might be able to design biofunctional molecules based upon a set of molecular parts for drug innovation. METHODS: A number of algorithms for processing chemical structures are used to systematically derive chemoyls (natural building blocks) from a database of compounds identified in Traditional Chinese Medicine (TCM). The rules to combine chemoyls for biological activities are then deduced by mining an annotated TCM structure-activity database (ATCMD). Based upon the rules and the basic chemoyls, a chemical library can be biochemically profiled, virtual synthetic routes can be planned, and lead compounds can be identified for a specific drug target. CONCLUSIONS: The Biochemome is the complete set of molecular components (chemoyls) in an organism and Biochemomics studies the rules governing their assembly and their evolution, together with the relations between the Biochemome and drug targets. This approach provides a new paradigm for drug discovery that is based on a comprehensive knowledge of the synthetic origins of biochemical diversity, and helps to direct biomimetic syntheses aimed at assembling quasi-natural product libraries for drug screening. PMID: 23958202 [Indexed for MEDLINE] 350. Eur J Med Chem. 2001 May;36(5):395-405. Novel inhibitors of Trypanosoma cruzi dihydrofolate reductase. Zuccotto F(1), Zvelebil M, Brun R, Chowdhury SF, Di Lucrezia R, Leal I, Maes L, Ruiz-Perez LM, Gonzalez Pacanowska D, Gilbert IH. Author information: (1)Welsh School of Pharmacy, Cardiff University, Redwood Building, King Edward VII Avenue, Cardiff CF10 3XF, UK. There is an urgent need for the development of new drugs to treat Chagas' disease, which is caused by the protozoan parasite Trypanosoma cruzi. The enzyme dihydrofolate reductase (DHFR) has been a very successful drug target in a number of diseases and we decided to investigate it as a potential drug target for Chagas' disease. A homology model of the enzyme was used to search the Cambridge Structural Database using the program DOCK 3.5. Compounds were then tested against the enzyme and the whole parasite. Compounds were also screened against the related parasite, Trypanosoma brucei. PMID: 11451529 [Indexed for MEDLINE] 351. J Mol Graph Model. 2014 Apr;49:68-79. doi: 10.1016/j.jmgm.2014.01.007. Epub 2014 Jan 29. Discovery of novel anti-leishmanial agents targeting LdLip3 lipase. Parameswaran S(1), Saudagar P(1), Dubey VK(1), Patra S(2). Author information: (1)Department of Biotechnology, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India. (2)Department of Biotechnology, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India. Electronic address: sanjukta@iitg.ernet.in. Leishmaniasis is a neglected tropical disease, caused by several species of Leishmania. Being an opportunistic lipid-scavenging pathogen, Leishmania relies extensively on lipid metabolism especially for host-pathogen interaction, utilizing host lipids for energy and virulence. The rational approach is to target lipid metabolism of the pathogen focusing lipid-catabolizing lipases. The LdLip3 lipase is considered as drug target as it is constitutively expressed in both promastigote and amastigote forms. Since the LdLip3 structure is not known, we modeled its three-dimensional structure to implement structure-based drug discovery approach. Similarity-based virtual screening was carried out to identify potential inhibitors utilizing NCI diversity set on ZINC database including natural products. Implementing computational and experimental approaches, four anti-leishmanial agents were discovered. The screened molecules ZINC01821375, ZINC04008765, ZINC06117316 and ZINC12653571 had anti-leishmanial activity with IC50 (% viable promastigotes vs. concentration) of 5.2±1.8μM, 13.1±2.6μM, 9.4±2.6μM and 17.3±3.1μM, respectively. The molecules showed negligible toxicity toward mouse macrophages. Based on the contact footprinting analysis, new molecules were designed with better predicted free energy of binding than discovered anti-leishmanial agents. Further validation for the therapeutic utility of discovered molecules can be carried out by the research community to combat leishmaniasis. Copyright © 2014 Elsevier Inc. All rights reserved. DOI: 10.1016/j.jmgm.2014.01.007 PMID: 24530543 [Indexed for MEDLINE] 352. Interdiscip Sci. 2016 Sep;8(3):284-93. doi: 10.1007/s12539-015-0282-9. Epub 2015 Aug 23. Computational Analysis of the Domain Architecture and Substrate-Gating Mechanism of Prolyl Oligopeptidases from Shewanella woodyi and Identification of Probable Lead Molecules. Patil P(1), Skariyachan S(2)(3), Mutt E(4), Kaushik S(5). Author information: (1)R&D Centre, Department of Biotechnology, Dayananda Sagar College of Engineering, Bangalore, 560 078, India. (2)R&D Centre, Department of Biotechnology, Dayananda Sagar College of Engineering, Bangalore, 560 078, India. sinoshskariya@gmail.com. (3)Visvesvaraya Technological University, Belgaum, Karnataka, India. sinoshskariya@gmail.com. (4)National Centre for Biological Sciences, GKVK campus, Bangalore, Karnataka, 560065, India. (5)Department of Bioengineering and Therapeutic Sciences, Helen Diller Family Comprehensive Cancer, University of California, San Francisco, 1450 3rd St., San Francisco, CA, 94158, USA. Prolyl oligopeptidases (POPs) are serine proteases found in prokaryotes and eukaryotes which hydrolyze the peptide bond containing proline. The current study focuses on the analysis of POP sequences, their distribution and domain architecture in Shewanella woodyi, a Gram-negative, luminous bacterium which causes celiac sprue and similar infections in marine organisms. The POP undergoes huge interdomain movement, which allows possible route for the entry of any substrate. Hence, it offers an opportunity to understand the mechanism of substrate gating by studying the domain architecture and possibility to identify a probable drug target. In the present study, the POP sequence was retrieved from GenBank database and the best homologous templates were identified by PSI-BLAST search. The three-dimensional structures of the closed and open forms of POP from S. woodyi, which are not available in native form, were generated by homology modeling. The ideal lead molecules were screened by computer-aided virtual screening, and the binding potential of the best leads toward the target was studied by molecular docking. The domain architecture of the POP revealed that it has a propeller domain consists of [Formula: see text]-sheets, surrounded by [Formula: see text]-helices and [Formula: see text] hydrolase domain with catalytic triad containing Ser-564, Asp-646 and His-681. The hypothetical models of open and closed POP showed backbone RMSD value of 0.56 and 0.65 Å, respectively. Ramachandran plot of the open and closed POP conformations accounts for 99.4 and 98.7 % residues in the favoured region, respectively. Our study revealed that propeller domain comes as an insert between N-terminal and C-terminal [Formula: see text] hydrolase domain. Molecular docking, drug likeness properties and ADME prediction suggested that KUC-103481N and Pramiracetum can be used as probable lead molecules toward the POP from S. woodyi. DOI: 10.1007/s12539-015-0282-9 PMID: 26298583 [Indexed for MEDLINE] 353. Oncotarget. 2015 Sep 29;6(29):28164-72. doi: 10.18632/oncotarget.4461. Functional genomic mRNA profiling of a large cancer data base demonstrates mesothelin overexpression in a broad range of tumor types. Lamberts LE(1), de Groot DJ(1), Bense RD(1), de Vries EG(1), Fehrmann RS(1). Author information: (1)University of Groningen, University Medical Center Groningen, Department of Medical Oncology, Groningen, The Netherlands. The membrane bound glycoprotein mesothelin (MSLN) is a highly specific tumor marker, which is currently exploited as target for drugs. There are only limited data available on MSLN expression by human tumors. Therefore we determined overexpression of MSLN across different tumor types with Functional Genomic mRNA (FGM) profiling of a large cancer database. Results were compared with data in articles reporting immunohistochemical (IHC) MSLN tumor expression. FGM profiling is a technique that allows prediction of biologically relevant overexpression of proteins from a robust data set of mRNA microarrays. This technique was used in a database comprising 19,746 tumors to identify for 41 tumor types the percentage of samples with an overexpression of MSLN compared to a normal background. A literature search was performed to compare the FGM profiling data with studies reporting IHC MSLN tumor expression. FGM profiling showed MSLN overexpression in gastrointestinal (12-36%) and gynecological tumors (20-66%), non-small cell lung cancer (21%) and synovial sarcomas (30%). The overexpression found in thyroid cancers (5%) and renal cell cancers (10%) was not yet reported with IHC analyses. We observed that MSLN amplification rate within esophageal cancer depends on the histotype (31% for adenocarcinomas versus 3% for squamous-cell carcinomas). Subset analysis in breast cancer showed MSLN amplification rates of 28% in triple-negative breast cancer (TNBC) and 33% in basal-like breast cancer. Further subtype analysis of TNBCs showed the highest amplification rate (42%) in the basal-like 1 subtype and the lowest amplification rate (9%) in the luminal androgen receptor subtype. DOI: 10.18632/oncotarget.4461 PMCID: PMC4695051 PMID: 26172299 [Indexed for MEDLINE] 354. BioData Min. 2013 Nov 15;6(1):20. doi: 10.1186/1756-0381-6-20. Structural updates of alignment of protein domains and consequences on evolutionary models of domain superfamilies. Mutt E(1), Rani SS, Sowdhamini R. Author information: (1)National Centre for Biological Sciences (TIFR), UAS-GKVK Campus, Bellary Road, 560 065 Bangalore, India. mini@ncbs.res.in. BACKGROUND: Influx of newly determined crystal structures into primary structural databases is increasing at a rapid pace. This leads to updation of primary and their dependent secondary databases which makes large scale analysis of structures even more challenging. Hence, it becomes essential to compare and appreciate replacement of data and inclusion of new data that is critical between two updates. PASS2 is a database that retains structure-based sequence alignments of protein domain superfamilies and relies on SCOP database for its hierarchy and definition of superfamily members. Since, accurate alignments of distantly related proteins are useful evolutionary models for depicting variations within protein superfamilies, this study aims to trace the changes in data in between PASS2 updates. RESULTS: In this study, differences in superfamily compositions, family constituents and length variations between different versions of PASS2 have been tracked. Studying length variations in protein domains, which have been introduced by indels (insertions/deletions), are important because theses indels act as evolutionary signatures in introducing variations in substrate specificity, domain interactions and sometimes even regulating protein stability. With this objective of classifying the nature and source of variations in the superfamilies during transitions (between the different versions of PASS2), increasing length-rigidity of the superfamilies in the recent version is observed. In order to study such length-variant superfamilies in detail, an improved classification approach is also presented, which divides the superfamilies into distinct groups based on their extent of length variation. CONCLUSIONS: An objective study in terms of transition between the database updates, detailed investigation of the new/old members and examination of their structural alignments is non-trivial and will help researchers in designing experiments on specific superfamilies, in various modelling studies, in linking representative superfamily members to rapidly expanding sequence space and in evaluating the effects of length variations of new members in drug target proteins. The improved objective classification scheme developed here would be useful in future for automatic analysis of length variation in cases of updates of databases or even within different secondary databases. DOI: 10.1186/1756-0381-6-20 PMCID: PMC4175504 PMID: 24237883 355. Oncol Lett. 2018 May;15(5):7783-7793. doi: 10.3892/ol.2018.8271. Epub 2018 Mar 15. Bioinformatic analysis and prediction of the function and regulatory network of long non-coding RNAs in hepatocellular carcinoma. Cao MR(1), Han ZP(2), Liu JM(1), Li YG(2), Lv YB(2), Zhou JB(2), He JH(2). Author information: (1)Department of General Surgery, The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong 510630, P.R. China. (2)Department of Laboratory, Central Hospital of Panyu, Guangzhou, Guangdong 511400, P.R. China. Computational analysis and bioinformatics have significantly advanced the ability of researchers to process and analyze biological data. Molecular data from human and model organisms may facilitate drug target validation and identification of biomarkers with increased predictive accuracy. The aim of the present study was to investigate the function of long non-coding RNAs (lncRNAs) in hepatocellular carcinoma (HCC) using online databases, and to predict their regulatory mechanism. HCC-associated lncRNAs, their downstream transcription factors and microRNAs (miRNAs/miRs), as well as the HCC-associated target genes, were identified using online databases. HCC-associated lncRNAs, including HOX antisense intergenic RNA (HOTAIR) and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) were selected based on established databases of lncRNAs. The interaction between the HCC-associated lncRNAs and miRNAs (hsa-miR-1, hsa-miR-20a-5p) was predicted using starBase2.0. Signal transducer and activator of transcription 1, hepatocyte nuclear factor 4α (HNF4A), octamer-binding transcription factor 4, Nanog homeobox (NANOG), caudal type homeobox 2 (CDX2), DEAD-box helicase 5, brahma-related gene 1, MYC-associated factor X and MYC proto-oncogene, bHLH transcription factor have been identified as the transcription factors for HOTAIR and MALAT1 using ChIPBase. Additionally, CDX2, HNF4A, NANOG, ETS transcription factor, Jun proto-oncogene and forkhead box protein A1 were identified as the transcription factors for hsa-miR-1 and hsa-miR-20a-5p. CDX2, HNF4A and NANOG were the transcriptional factors in common between the lncRNAs and miRNAs. Cyclin D1, E2F transcription factor 1, epithelial growth factor receptor, MYC, MET proto-oncogene, receptor tyrosine kinase and vascular endothelial growth factor A were identified as target genes for the HCC progression, two of which were also the target genes of hsa-miR-1 and hsa-miR-20a-5p using the miRwalk and OncoDN. HCC databases. Additionally, these target genes may be involved in biological functions, including the regulation of cell growth, cell cycle progression and mitosis, and in disease progression, as demonstrated using DAVID clustering analysis. The present study aimed to predict a regulatory network of lncRNAs in HCC progression using bioinformatics analysis. DOI: 10.3892/ol.2018.8271 PMCID: PMC5934726 PMID: 29740493 356. Evid Based Complement Alternat Med. 2014;2014:436863. doi: 10.1155/2014/436863. Epub 2014 Apr 29. Potential Protein Phosphatase 2A Agents from Traditional Chinese Medicine against Cancer. Chen KC(1), Chen HY(2), Chen CY(3). Author information: (1)School of Pharmacy, China Medical University, Taichung 40402, Taiwan. (2)Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan. (3)Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan ; School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan ; Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Protein phosphatase 2A (PP2A) is an important phosphatase which regulates various cellular processes, such as protein synthesis, cell growth, cellular signaling, apoptosis, metabolism, and stress responses. It is a holoenzyme composed of the structural A and catalytic C subunits and a regulatory B subunit. As an environmental toxin, okadaic acid, is a tumor promoter and binds to PP2A catalytic C subunit and the cancer-associated mutations in PP2A structural A subunit in human tumor tissue; PP2A may have tumor-suppressing function. It is a potential drug target in the treatment of cancer. In this study, we screen the TCM compounds in TCM Database@Taiwan to investigate the potent lead compounds as PP2A agent. The results of docking simulation are optimized under dynamic conditions by MD simulations after virtual screening to validate the stability of H-bonds between PP2A- α protein and each ligand. The top TCM candidates, trichosanatine and squamosamide, have potential binding affinities and interactions with key residues Arg89 and Arg214 in the docking simulation. In addition, these interactions were stable under dynamic conditions. Hence, we propose the TCM compounds, trichosanatine and squamosamide, as potential candidates as lead compounds for further study in drug development process with the PP2A- α protein. DOI: 10.1155/2014/436863 PMCID: PMC4020536 PMID: 24868239 357. Oncotarget. 2017 Jul 20;8(51):89142-89148. doi: 10.18632/oncotarget.19408. eCollection 2017 Oct 24. The diagnostic and prognostic value of CHFR hypermethylation in colorectal cancer, a meta-analysis and literature review. Sun Z(1), Liu J(2), Jing H(1), Dong SX(3), Wu J(1). Author information: (1)Department of Pathology, Huaihe Hospital, Henan University, 8 Bao Bei Lu, GuLou Qu, Kaifeng 475000, China. (2)Department of Radiotherapy, Huaihe Hospital, Henan University, Kaifeng 475000, China. (3)Department of Gastrointestinal Surgery, Linyi People's Hospital, Linyi 276001, Shandong, China. The Checkpoint with Forkhead-associated and Ring finger domains (CHFR) is a mitotic checkpoint and tumor-suppressor gene, its loss contributes tumorigenesis of epithelial cancers including colorectal carcinoma (CRC). The diagnostic and prognostic value of CHFR promoter hypermethylation in CRC remains unclear. This study aimed to conduct a meta-analysis and literature review and investigate clinicopathological significance of CHFR promoter hypermethylation in CRC. The following online database were used: PubMed, EMBASE, and Web of Science up to March 2017. Odds Ratios (OR) and Hazard Ratios (HR) with 95% corresponding confidence intervals (CIs) were calculated. A total of seven relevant articles were available for meta-analysis which included 966 patients. The frequency of CHFR promoter hypermethylation significantly increased in CRC compared to normal colorectal mucosa tissue, pooled OR was 8.35, p < 0.00001. CHFR promoter hypermethylation was not significantly correlated to stage, OR was 1.16, p = 0.63. However, CHFR promoter hypermethylation was more frequently observed in CRC with positive lymph nodes metastasis than CRC with negative lymph nodes metastasis, OR was 0.46, p = 0.03. Additionally CHFR promoter hypermethylation was significantly related to poor overall survival in patients with CRC, HR was 0.62, p = 0.008. Based on these results, tumor CHFR promoter hypermethylation is not only a diagnostic biomarker for CRC, but also a prognostic marker. CHFR promoter hypermethylation is significantly associated with worse overall survival in patients with CRC. Our data suggested that CHFR could be a potential drug target for development of demethylation treatment for patients with CRC. DOI: 10.18632/oncotarget.19408 PMCID: PMC5687676 PMID: 29179506 Conflict of interest statement: CONFLICTS OF INTEREST The authors declare that they have no competing interests, and have no any financial disclosures. 358. Int J Biochem Cell Biol. 2007;39(6):1156-64. Epub 2007 Mar 7. Druggability of human disease genes. Sakharkar MK(1), Sakharkar KR, Pervaiz S. Author information: (1)Nanyang Centre for Supercomputing and Visualization, School of Mechanical and Aerospace Engineering (MAE), Nanyang Technological University, Singapore. The availability of complete genome sequences and the wealth of large-scale biological datasets provide an unprecedented opportunity to elucidate the genetic basis of human diseases. Here we use integrative in silico approaches to provide an accurate description of gene functions to a set of 1737 highly curated disease genes in the human genome. This analysis is the first attempt on in silico identification of druggable domains within disease genes. We provide information on gene architecture and function, druggability in the context of available drugs, and evolutionary conservation across 38 model eukaryotic genomes. These data could serve as a useful compendium for integrated information on disease genes with the potential for exploring pharmaceutically exploitable targets. Our analyses underscore the utility of large genomic databases for in silico systematic drug target identification in the post-genomic era. DOI: 10.1016/j.biocel.2007.02.018 PMID: 17446117 [Indexed for MEDLINE] 359. J Cell Commun Signal. 2018 Sep;12(3):513-527. doi: 10.1007/s12079-017-0441-3. Epub 2018 Jan 12. Mechanistic regulation of epithelial-to-mesenchymal transition through RAS signaling pathway and therapeutic implications in human cancer. Tripathi K(1), Garg M(2). Author information: (1)Department of Biochemistry, University of Lucknow, Lucknow, 226007, India. (2)Department of Biochemistry, University of Lucknow, Lucknow, 226007, India. minal14@yahoo.com. RAS effector signaling instead of being simple, unidirectional and linear cascade, is actually recognized as highly complex and dynamic signaling network. RAF-MEK-ERK cascade, being at the center of complex signaling network, links to multiple scaffold proteins through feed forward and feedback mechanisms and dynamically regulate tumor initiation and progression. Three isoforms of Ras harbor mutations in a cell and tissue specific manner. Besides mutations, their epigenetic silencing also attributes them to exhibit oncogenic activities. Recent evidences support the functions of RAS oncoproteins in the acquisition of tumor cells with Epithelial-to-mesenchymal transition (EMT) features/ epithelial plasticity, enhanced metastatic potential and poor patient survival. Google Scholar electronic databases and PubMed were searched for original papers and reviews available till date to collect information on stimulation of EMT core inducers in a Ras driven cancer and their regulation in metastatic spread. Improved understanding of the mechanistic basis of regulatory interactions of microRNAs (miRs) and EMT by reprogramming the expression of targets in Ras activated cancer, may help in designing effective anticancer therapies. Apparent lack of adverse events associated with the delivery of miRs and tissue response make 'drug target miRNA' an ideal therapeutic tool to achieve progression free clinical response. DOI: 10.1007/s12079-017-0441-3 PMCID: PMC6039341 PMID: 29330773 360. Brief Bioinform. 2015 Sep;16(5):865-72. doi: 10.1093/bib/bbu053. Epub 2015 Jan 21. The complexity, challenges and benefits of comparing two transporter classification systems in TCDB and Pfam. Chiang Z, Vastermark A, Punta M, Coggill PC, Mistry J, Finn RD, Saier MH Jr. Transport systems comprise roughly 10% of all proteins in a cell, playing critical roles in many processes. Improving and expanding their classification is an important goal that can affect studies ranging from comparative genomics to potential drug target searches. It is not surprising that different classification systems for transport proteins have arisen, be it within a specialized database, focused on this functional class of proteins, or as part of a broader classification system for all proteins. Two such databases are the Transporter Classification Database (TCDB) and the Protein family (Pfam) database. As part of a long-term endeavor to improve consistency between the two classification systems, we have compared transporter annotations in the two databases to understand the rationale for differences and to improve both systems. Differences sometimes reflect the fact that one database has a particular transporter family while the other does not. Differing family definitions and hierarchical organizations were reconciled, resulting in recognition of 69 Pfam 'Domains of Unknown Function', which proved to be transport protein families to be renamed using TCDB annotations. Of over 400 potential new Pfam families identified from TCDB, 10% have already been added to Pfam, and TCDB has created 60 new entries based on Pfam data. This work, for the first time, reveals the benefits of comprehensive database comparisons and explains the differences between Pfam and TCDB. © The Author 2015. Published by Oxford University Press. DOI: 10.1093/bib/bbu053 PMCID: PMC4570203 PMID: 25614388 [Indexed for MEDLINE] 361. PLoS Comput Biol. 2009 Jul;5(7):e1000450. doi: 10.1371/journal.pcbi.1000450. Epub 2009 Jul 31. Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts. Li J(1), Zhu X, Chen JY. Author information: (1)State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, China. The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin. DOI: 10.1371/journal.pcbi.1000450 PMCID: PMC2709445 PMID: 19649302 [Indexed for MEDLINE] 362. Parasit Vectors. 2016 Nov 4;9(1):570. Preliminary analysis of Psoroptes ovis transcriptome in different developmental stages. He ML(1), Xu J(1), He R(1), Shen NX(1), Gu XB(1), Peng XR(2), Yang GY(3). Author information: (1)Department of Parasitology, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, 611130, China. (2)Department of Chemistry, College of Life and Basic Science, Sichuan Agricultural University, Chengdu, 611130, China. (3)Department of Parasitology, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, 611130, China. guangyou1963@aliyun.com. BACKGROUND: Psoroptic mange is a chronic, refractory, contagious and infectious disease mainly caused by the mange mite Psoroptes ovis, which can infect horses, sheep, buffaloes, rabbits, other domestic animals, deer, wild camels, foxes, minks, lemurs, alpacas, elks and other wild animals. Features of the disease include intense pruritus and dermatitis, depilation and hyperkeratosis, which ultimately result in emaciation or death caused by secondary bacterial infections. The infestation is usually transmitted by close contact between animals. Psoroptic mange is widespread in the world. In this paper, the transcriptome of P. ovis is described following sequencing and analysis of transcripts from samples of larvae (i.e. the Pso_L group) and nymphs and adults (i.e. the Pso_N_A group). The study describes differentially expressed genes (DEGs) and genes encoding allergens, which help understanding the biology of P. ovis and lay foundations for the development of vaccine antigens and drug target screening. METHODS: The transcriptome of P. ovis was assembled and analyzed using bioinformatic tools. The unigenes of P. ovis from each developmental stage and the unigenes differentially between developmental stages were compared with allergen protein sequences contained in the allergen database website to predict potential allergens. RESULTS: We identified 38,836 unigenes, whose mean length was 825 bp. On the basis of sequence similarity with seven databases, a total of 17,366 unigenes were annotated. A total of 1,316 DEGs were identified, including 496 upregulated and 820 downregulated in the Pso_L group compared with the Pso_N_A group. We predicted 205 allergens genes in the two developmental stages similar to genes from other mites and ticks, of these, 14 were among the upregulated DEGs and 26 among the downregulated DEGs. CONCLUSION: This study provides a reference transcriptome of P. ovis in absence of a reference genome. The analysis of DEGs and putative allergen genes may lay the foundation for studies of functional genomics, immunity and gene expression profiles of this parasitic mite species. DOI: 10.1186/s13071-016-1856-z PMCID: PMC5096302 PMID: 27809902 [Indexed for MEDLINE] 363. Interdiscip Sci. 2011 Sep;3(3):217-31. doi: 10.1007/s12539-011-0101-x. Epub 2011 Sep 29. Screening natural products database for identification of potential antileishmanial chemotherapeutic agents. Venkatesan SK(1), Saudagar P, Shukla AK, Dubey VK. Author information: (1)Department of Biotechnology, Indian Institute of Technology Guwahati, Assam, 781039, India. Leishmaniasis is a parasitic infection caused by unicellular protozoan organism belonging to the family Trypanosomatidae. Among various forms of the disease, visceral leishmaniasis is the most lethal and caused by Leishmania infantum or Leishmania donovani. The redox metabolism of parasite requires a key enzyme, trypanothione reductase which is a validated drug target. In the past decade, it was observed that these protozoan parasites had developed resistance against many of available drugs. Importantly in the case of visceral leishmaniasis drug resistance is very high in the Indian subcontinent, a major endemic region of Leishmania donovani infection. In search for new drugs, we aim to identify potential natural product inhibitors of trypanothione reductase which can be further developed as anti-leishmanial drug. We have performed in silico virtual screening of a natural product data set of 800 diverse chemical entities. Leishmania infantum trypanothione reductase crystal structure (PDB ID: 2JK6) was used in the virtual screening process, docking studies to identify potential lead compounds. The compounds were sorted based upon their binding energy and the top 50 ranked protein-inhibitor complexes were clustered using AuPosSOM to ligand foot print the interactions. We report a few alkaloids and sterols for the first time, which could be potential trypanothione reductase inhibitors. The footprinting of protein-inhibitor interactions into clusters has also provided clues on various possible orientations that inhibitors can attain at the active site of Trypanothione reductase. Moreover, biological significance of the interactions has also been discussed. DOI: 10.1007/s12539-011-0101-x PMID: 21956744 [Indexed for MEDLINE] 364. Tumour Biol. 2016 May;37(5):6979-85. doi: 10.1007/s13277-015-4594-5. Epub 2015 Dec 12. Distinct prognostic values of four-Notch-receptor mRNA expression in ovarian cancer. Zhou X(1), Teng L(2), Wang M(3). Author information: (1)Department of Obstetrics and Gynecology, The Second People's Hospital of Liaocheng Affiliated to Taishan Medical College, No. 306 Jiankang Rd, Linqing, 252601, Shandong Province, People's Republic of China. Xinlingzhou01@163.com. (2)Department of Obstetrics and Gynecology, The Second People's Hospital of Liaocheng Affiliated to Taishan Medical College, No. 306 Jiankang Rd, Linqing, 252601, Shandong Province, People's Republic of China. (3)Department of Pathology, The Second People's Hospital of Liaocheng Affiliated to Taishan Medical College, Linqing, 252601, Shandong Province, People's Republic of China. Notch signaling pathway includes ligands and Notch receptors, which are frequently deregulated in several human malignancies including ovarian cancer. Aberrant activation of Notch signaling has been linked to ovarian carcinogenesis and progression. In the current study, we used the "Kaplan-Meier plotter" (KM plotter) database, in which updated gene expression data and survival information from a total of 1306 ovarian cancer patients were used to access the prognostic value of four Notch receptors in ovarian cancer patients. Hazard ratio (HR), 95 % confidence intervals, and log-rank P were calculated. Notch1 messenger RNA (mRNA) high expression was not found to be correlated to overall survival (OS) for all ovarian cancer, as well as in serous and endometrioid cancer patients followed for 20 years. However, Notch1 mRNA high expression is significantly associated with worsen OS in TP53 wild-type ovarian cancer patients, while it is significantly associated with better OS in TP53 mutation-type ovarian cancer patients. Notch2 mRNA high expression was found to be significantly correlated to worsen OS for all ovarian cancer patients, as well as in grade II ovarian cancer patients. Notch3 mRNA high expression was found to be significantly correlated to better OS for all ovarian cancer patients, but not in serous cancer patients and endometrioid cancer patients. Notch4 mRNA high expression was not found to be significantly correlated to OS for all ovarian cancer patients, serous cancer patients, and endometrioid cancer patients. These results indicate that there are distinct prognostic values of four Notch receptors in ovarian cancer. This information will be useful for better understanding of the heterogeneity and complexity in the molecular biology of ovarian cancer and for developing tools to more accurately predict their prognosis. Based on our results, Notch1 could be a potential drug target of TP53 wild-type ovarian cancer and Notch2 could be a potential drug target of ovarian cancer. DOI: 10.1007/s13277-015-4594-5 PMID: 26662955 [Indexed for MEDLINE] 365. J Theor Biol. 2017 Jun 21;423:63-70. doi: 10.1016/j.jtbi.2017.04.020. Epub 2017 Apr 26. A computational model for predicting integrase catalytic domain of retrovirus. Wu S(1), Han J(1), Zhang X(2), Zhong D(1), Liu R(1). Author information: (1)School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China. (2)School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China. Electronic address: zhangxinman@mail.xjtu.edu.cn. Integrase catalytic domain (ICD) is an essential part in the retrovirus for integration reaction, which enables its newly synthesized DNA to be incorporated into the DNA of infected cells. Owing to the crucial role of ICD for the retroviral replication and the absence of an equivalent of integrase in host cells, it is comprehensible that ICD is a promising drug target for therapeutic intervention. However, annotated ICDs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. Accordingly, it is of great importance to put forward a computational ICD model in this work to annotate these domains in the retroviruses. The proposed model then discovered 11,660 new putative ICDs after scanning sequences without ICD annotations. Subsequently in order to provide much confidence in ICD prediction, it was tested under different cross-validation methods, compared with other database search tools, and verified on independent datasets. Furthermore, an evolutionary analysis performed on the annotated ICDs of retroviruses revealed a tight connection between ICD and retroviral classification. All the datasets involved in this paper and the application software tool of this model can be available for free download at https://sourceforge.net/projects/icdtool/files/?source=navbar. Copyright © 2017 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.jtbi.2017.04.020 PMID: 28454901 [Indexed for MEDLINE] 366. J Recept Signal Transduct Res. 2015;35(5):370-80. doi: 10.3109/10799893.2014.956756. Epub 2014 Nov 18. Assessment of dual inhibition property of newly discovered inhibitors against PCAF and GCN5 through in silico screening, molecular dynamics simulation and DFT approach. Suryanarayanan V(1), Singh SK(1). Author information: (1)a Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics , Alagappa University , Karaikudi , Tamil Nadu , India. p300/CBP-associated factor (PCAF) is one among the histone acetyltransferase (HAT) family enzymes. It is involved in the regulation of transcription by modifying the chromatin structure indirectly through the acetylation of histones. It has been emerged as a promising drug target for various types of cancer. A four-point pharmacophore with two hydrogen bond acceptor, one aromatic ring and one hydrophobic feature, was generated for six highly active isothiazolone derivatives as PCAF inhibitors in order to elucidate their anticancer activity. The generated pharmacophore was used for screening three different databases such as Maybridge, Life Chemicals and Chembridge databases. The screened compounds were further filtered through docking studies. Then the compounds were further carried for ADME prediction. The best three compounds BTB09406, F1418-0051 and F1880-1727 were docked to GCN5 to explore the dual inhibitory properties. The conformational stability of the protein-ligand complexes were analyzed through molecular dynamics simulation. Three best compounds were finally went through electronic structure analysis using density functional theory (DFT) at B3LYP/6-31**G level to understand their molecular reactivity. The results obtained from this study exploit that the three best compounds (BTB09406, F1418-0051 and F1880-1727) were found to have more potent and dual inhibitory properties. DOI: 10.3109/10799893.2014.956756 PMID: 25404235 [Indexed for MEDLINE] 367. Cochrane Database Syst Rev. 2007 Jul 18;(3):CD006731. Pharmaceutical policies: effects of financial incentives for prescribers. Sturm H(1), Austvoll-Dahlgren A, Aaserud M, Oxman AD, Ramsay C, Vernby A, Kösters JP. Author information: (1)University Medical Center Tübingen, Comprehensive Cancer Center, Herrenberger Str. 23, Tübingen, Germany, D 72070. h.sturm@med.umcg.nl Update in Cochrane Database Syst Rev. 2015;8:CD006731. BACKGROUND: Pharmaceuticals, while central to medical therapy, pose a significant burden to health care budgets. Therefore regulations to control prescribing costs and improve quality of care are implemented increasingly. These include the use of financial incentives for prescribers, namely increased financial accountability using budgets and performance based payments. OBJECTIVES: To determine the effects on drug use, healthcare utilisation, health outcomes and costs (expenditures) of policies, that intend to affect prescribers by means of financial incentives. SEARCH STRATEGY: We searched the following databases and web sites: Effective Practice and Organisation of Care Group Register (August 2003), Cochrane Central Register of Controlled Trials (October 2003), MEDLINE (October 2005), EMBASE (October 2005), and other databases. SELECTION CRITERIA: Policies were defined as laws, rules, financial and administrative orders made by governments, non-government organisations or private insurers. One of the following outcomes had to be reported: drug use, healthcare utilisation, health outcomes, and costs. The study had to be a randomised or non-randomised controlled trial, interrupted time series analysis, repeated measures study or controlled before-after study evaluating financial incentives for prescribers introduced for a jurisdiction or healthcare system. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data and assessed study limitations. MAIN RESULTS: Thirteen evaluations of budgetary policies and none of performance based payments met our inclusion criteria. Ten studies evaluated general practice fundholding in the UK, one the Irish Indicative Drug Target Savings Scheme (IDTSS) and two evaluated German drug budgets for physicians in private practice. The interrupted time series analyses had some limitations. All the controlled before-after studies (all from the UK) had serious limitations. Drug expenditure (per item and per patient) and prescribed drug volume decreased with budgets in all three countries. Evidence indicated increased use of generic drugs in the UK and Ireland, but was inconclusive on the use of new and expensive drugs. We found no clear evidence of increased health care utilisation and no studies reporting effects on health. Administration costs were not reported. No studies on the effects of performance-based payments or other policies met our inclusion criteria. AUTHORS' CONCLUSIONS: Based on the evidence in this review from three Western European countries, drug budgets for physicians in private practice can limit drug expenditure by limiting the volume of prescribed drugs, increasing the use of generic drugs or both. Since the majority of studies included were found to have serious limitations, these results should be interpreted with care. DOI: 10.1002/14651858.CD006731 PMID: 17636851 [Indexed for MEDLINE] 368. Neurol Res. 2018 Sep;40(9):744-751. doi: 10.1080/01616412.2018.1475126. Epub 2018 May 21. Gene expression microarray analysis reveals prognostic markers of survival in high grade astrocytomas. Yang J(1), Hou Z(1), Wang C(1), Wang H(1), Zhang H(1). Author information: (1)a Department of Neurosurgery, Beijing Luhe Hospital , Capital Medical University , Beijing , China. OBJECTIVE: High grade astrocytoma (HGA) as an aggressive brain tumor, is always correlated with poor prognosis. In this paper, we aimed to explore the genetic prognostic biomarkers for HGA. METHODS: The genome-wide expression profile of 26 brain tumor samples obtained from 26 patients with HGA was downloaded from Gene Expression Omnibus. The risk genes for prognosis of HGA were identified and verified by the data in TCGA database. Protein-protein interaction (PPI) network of risk factor genes was constructed and significant module was screened. Function and pathway annotations were performed for risk genes and drug target genes were further analyzed. RESULTS: A total of 598 genes were identified as significant risk genes for prognosis, such as checkpoint kinase 1, potassium inwardly-rectifying channel, subfamily J, member 6, leukocyte receptor tyrosine kinase and uncharacterized LOC283887. All risk genes for prognosis of HGA were significantly enriched in cell cycle, mitotic as well as mitotic anaphase. While the genes in the network module mainly participated in functions such as cell cycle, mitotic cell cycle and cell cycle process. Moreover, the genes in the network module mainly participated in the pathways such as cell cycle and cell cycle, mitotic. Drug target analysis showed that seven genes were recorded in Drugbank database, and there were as many as 32 drug records of CHEK1. CONCLUSION: The prognostic effect of CHEK1 was validated based on the expression profile data of 615 low-grade glioma and glioblastoma samples. We proposed CHEK1 as prognostic biomarker for HGA. Our work might provide the candidate target for HGA therapy. DOI: 10.1080/01616412.2018.1475126 PMID: 29781781 [Indexed for MEDLINE] 369. Sheng Wu Hua Xue Yu Sheng Wu Wu Li Xue Bao (Shanghai). 2003 Nov;35(11):965-75. Proteomic technology and its biomedical applications. Lau AT(1), He QY, Chiu JF. Author information: (1)Institute of Molecular Biology, The University of Hong Kong, Hong Kong, China. Proteomics has its origins in two-dimensional gel electrophoresis (2-DE), a technique developed more than twenty years ago. 2-DE has a high-resolution capacity, and was initially used primarily for separating and characterizing proteins in complex mixtures. 2-DE remains an important tool for protein identification, but is now normally coupled with mass spectrometry (MS), a technique which has advanced considerably in recent years. The recent completion of human genome project has produced a large DNA database which can be utilized through bioinformatics, and the next challenge for scientists is to uncover the entire proteome of a particular organism. The integration of genomic and proteomic data will help to elucidate the functions of proteins in the pathogenesis of diseases and the ageing process, and could lead to the discovery of novel drug target proteins and biomarkers of diseases. This review describes recent advances in proteomic technology and discusses the potential applications of proteomics in biomedical research. PMID: 14614532 [Indexed for MEDLINE] 370. Assay Drug Dev Technol. 2007 Jun;5(3):381-90. Capture compound mass spectrometry: a technology for the investigation of small molecule protein interactions. Köster H(1), Little DP, Luan P, Muller R, Siddiqi SM, Marappan S, Yip P. Author information: (1)Caprotec GmbH, Berlin, Germany. hubert.koester@caprotec.com One of the major hurdles in the post-genomic era is to understand the function of genes and the interplay of many different cellular proteins. This is especially important for drug development. Capture compound mass spectrometry (CCMS) addresses this challenge by selectively reducing the complexity of the proteome. Capture compounds are trifunctional molecules: a selectivity function reversibly interacts via affinity with proteins; a reactivity function irreversibly forms a covalent bond outside the affinity binding site; and a sorting/pullout function allows the captured protein(s) to be isolated from cellular lysate for mass spectrometric analysis and characterization by database queries. In the present study, we demonstrate the use of a CCMS capture compound with a sulfonamide drug analog as its selectivity function, isolating an expected target protein from cell lysates containing a large excess of other "non-target" proteins. A future application of CCMS is to define or confirm drug target proteins and their mechanisms of drug action, or to discover off-target proteins that cause side effects, enabling subsequent drug structure optimization. DOI: 10.1089/adt.2006.039 PMID: 17638538 [Indexed for MEDLINE] 371. J Biomol Struct Dyn. 2017 Oct 27:1-16. doi: 10.1080/07391102.2017.1392897. [Epub ahead of print] Identification of novel natural inhibitors of Opisthorchis felineus cytochrome P450 using structure-based screening and molecular dynamic simulation. Shukla R(1), Chetri PB(1), Sonkar A(1), Pakharukova MY(2), Mordvinov VA(2), Tripathi T(1). Author information: (1)a Molecular and Structural Biophysics Laboratory, Department of Biochemistry , North-Eastern Hill University , Shillong 793022 , India. (2)b Laboratory of Molecular Mechanisms of Pathological Processes, Institute of Cytology and Genetics , Siberian Branch of the Russian Academy of Sciences , 10 Lavrentiev ave., Novosibirsk 630090 , Russia. Opisthorchis felineus is the etiological agent of opisthorchiasis in humans. O. felineus cytochrome P450 (OfCYP450) is an important enzyme in the parasite xenobiotic metabolism. To identify the potential anti-opisthorchid compound, we conducted a structure-based virtual screening of natural compounds from the ZINC database (n = 1,65,869) against the OfCYP450. The ligands were screened against OfCYP450 in four sequential docking modes that resulted in 361 ligands having better docking score. These compounds were evaluated for Lipinski and ADMET prediction, and 10 compounds were found to fit well with re-docking studies. After refinement by docking and drug-likeness analyses, four potential inhibitors (ZINC2358298, ZINC8790946, ZINC70707116, and ZINC85878789) were identified. These ligands with reference compounds (itraconazole and fluconazole) were further subjected to molecular dynamics simulation (MDS) and binding energy analyses to compare the dynamic structure of protein after ligand binding and the stability of the OfCYP450 and bound complexes. The binding energy analyses were also calculated. The results suggested that the compounds had a negative binding energy with -259.41, -110.09, -188.25, -163.30, -202.10, and -158.79 kJ mol-1 for itraconazole, fluconazole, and compounds with IDs ZINC2358298, ZINC8790946, ZINC70707116, and ZINC85878789, respectively. These lead compounds displayed significant pharmacological and structural properties to be drug candidates. On the basis of MDS results and binding energy analyses, we concluded that ZINC8790946, ZINC70707116, and ZINC85878789 have excellent potential to inhibit OfCYP450. DOI: 10.1080/07391102.2017.1392897 PMID: 29029597 372. Curr Comput Aided Drug Des. 2016;12(3):229-240. 2D QSAR and Virtual Screening based on Pyridopyrimidine Analogs of Epidermal Growth Factor Receptor Tyrosine Kinase. Sugunakala S, Selvaraj S(1). Author information: (1)Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli-620 024, Tamilnadu, India. selvarajsamuel@gmail.com. BACKGROUND: Epidermal Growth Factor Receptor tyrosine kinase (EGFR) is an important anticancer drug target. Series of pyridopyrimidine analogs have been reported as EGFR inhibitors and they inhibit by binding to the ATP binding pocket of the tyrosine kinase domain. OBJECTIVE: To identify key properties of pyridopyrimidine analogs involved in the inhibition of the EGFR protein tyrosine kinase by developing 2D QSAR model. METHODS: Variable selection was performed by least absolute shrinkage and selection operator (LASSO) method and multiple linear regression (MLR) method was applied by using Build QSAR software to develop QSAR model. Model validation was done by Leave One Out method (LOO). Further, based on the bioactive and structural similarity, virtual screening was performed using Pubchem database. Using the developed QSAR model and Molinspiration server, PIC50 values and kinase inhibition activity were predicted for all the virtually screened compounds respectively. RESULTS: The best QSAR model consists of two descriptors namely Basak and MOE type descriptors, and has R2 = 0.8205, F= 57.129 & S = 0.308 and the validation results show significant statistics of R2/cv = 0.655, Average standard deviation = 0.416. 140 compounds were obtained from virtual screening and the predicted PIC50 of all these compounds are in the range of 4.73 - 6.78. All the compounds produce positive scores which suggest that the compounds may have good kinase inhibitory profile. CONCLUSION: This developed model may be useful to predict EGFR inhibition activity (PIC50) for the newly synthesized pyridopyrimidines analogs. PMID: 27264509 [Indexed for MEDLINE] 373. BMC Genomics. 2016 Sep 20;17(1):742. doi: 10.1186/s12864-016-3002-x. Genome-wide survey and phylogeny of S-Ribosylhomocysteinase (LuxS) enzyme in bacterial genomes. Rao RM(1)(2), Pasha SN(1), Sowdhamini R(3). Author information: (1)National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK campus, Bellary Road, Bangalore, 560065, India. (2)Division of Biological Sciences, School of Natural Sciences, Bangalore University, Bangalore, 560056, India. (3)National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK campus, Bellary Road, Bangalore, 560065, India. mini@ncbs.res.in. BACKGROUND: The study of survival and communication of pathogenic bacteria is important to combat diseases caused by such micro-organisms. Bacterial cells communicate with each other using a density-dependent cell-cell communication process called Quorum Sensing (QS). LuxS protein is an important member of interspecies quorum-sensing system, involved in the biosynthesis of Autoinducer-2 (AI-2), and has been identified as a drug target. Despite the above mentioned significance, their evolution has not been fully studied, particularly from a structural perspective. RESULTS: Search for LuxS in the non-redundant database of protein sequences yielded 3106 sequences. Phylogenetic analysis of these sequences revealed grouping of sequences into five distinct clusters belonging to different phyla and according to their habitat. A majority of the neighbouring genes of LuxS have been found to be hypothetical proteins. However, gene synteny analyses in different bacterial genomes reveal the presence of few interesting gene neighbours. Moreover, LuxS gene was found to be a component of an operon in only six out of 36 genomes. Analysis of conserved motifs in representative LuxS sequences of different clusters revealed the presence of conserved motifs common to sequences of all the clusters as well as motifs unique to each cluster. Homology modelling of LuxS protein sequences of each cluster revealed few structural features unique to protein of each cluster. Analyses of surface electrostatic potentials of the homology models of each cluster showed the interactions that are common to all the clusters, as well as cluster-specific potentials and therefore interacting partners, which may be unique to each cluster. CONCLUSIONS: LuxS protein evolved early during the course of bacterial evolution, but has diverged into five subtypes. Analysis of sequence motifs and homology models of representative members reveal cluster-specific structural properties of LuxS. Further, it is also shown that LuxS protein may be involved in various protein-protein or protein-RNA interactions, which may regulate the activity of LuxS proteins in bacteria. DOI: 10.1186/s12864-016-3002-x PMCID: PMC5029033 PMID: 27650568 [Indexed for MEDLINE] 374. Nucleic Acids Res. 2008 Jan;36(Database issue):D684-8. Epub 2007 Dec 15. STITCH: interaction networks of chemicals and proteins. Kuhn M(1), von Mering C, Campillos M, Jensen LJ, Bork P. Author information: (1)European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH ('search tool for interactions of chemicals') integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug-target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. Our database contains interaction information for over 68,000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database. STITCH is available at http://stitch.embl.de/. DOI: 10.1093/nar/gkm795 PMCID: PMC2238848 PMID: 18084021 [Indexed for MEDLINE] 375. Front Cell Infect Microbiol. 2016 Jan 11;5:102. doi: 10.3389/fcimb.2015.00102. eCollection 2015. A Systems Biology Approach to Reveal Putative Host-Derived Biomarkers of Periodontitis by Network Topology Characterization of MMP-REDOX/NO and Apoptosis Integrated Pathways. Zeidán-Chuliá F(1), Gürsoy M(2), Neves de Oliveira BH(3), Özdemir V(4), Könönen E(5), Gürsoy UK(2). Author information: (1)Programa de Pós-Graduação em Ciências Biológicas: Bioquímica, Departamento de Bioquímica, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do SulPorto Alegre, Brazil; Department of Periodontology, Institute of Dentistry, University of TurkuTurku, Finland. (2)Department of Periodontology, Institute of Dentistry, University of Turku Turku, Finland. (3)Programa de Pós-Graduação em Ciências Biológicas: Bioquímica, Departamento de Bioquímica, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul Porto Alegre, Brazil. (4)Faculty of Communications and Office of the President, International Technology and Innovation Policy, Gaziantep UniversityGaziantep, Turkey; Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham (Amrita University)Kollam, India. (5)Department of Periodontology, Institute of Dentistry, University of TurkuTurku, Finland; Oral Health Care, Welfare DivisionTurku, Finland. Periodontitis, a formidable global health burden, is a common chronic disease that destroys tooth-supporting tissues. Biomarkers of the early phase of this progressive disease are of utmost importance for global health. In this context, saliva represents a non-invasive biosample. By using systems biology tools, we aimed to (1) identify an integrated interactome between matrix metalloproteinase (MMP)-REDOX/nitric oxide (NO) and apoptosis upstream pathways of periodontal inflammation, and (2) characterize the attendant topological network properties to uncover putative biomarkers to be tested in saliva from patients with periodontitis. Hence, we first generated a protein-protein network model of interactions ("BIOMARK" interactome) by using the STRING 10 database, a search tool for the retrieval of interacting genes/proteins, with "Experiments" and "Databases" as input options and a confidence score of 0.400. Second, we determined the centrality values (closeness, stress, degree or connectivity, and betweenness) for the "BIOMARK" members by using the Cytoscape software. We found Ubiquitin C (UBC), Jun proto-oncogene (JUN), and matrix metalloproteinase-14 (MMP14) as the most central hub- and non-hub-bottlenecks among the 211 genes/proteins of the whole interactome. We conclude that UBC, JUN, and MMP14 are likely an optimal candidate group of host-derived biomarkers, in combination with oral pathogenic bacteria-derived proteins, for detecting periodontitis at its early phase by using salivary samples from patients. These findings therefore have broader relevance for systems medicine in global health as well. DOI: 10.3389/fcimb.2015.00102 PMCID: PMC4707239 PMID: 26793622 [Indexed for MEDLINE] 376. Int J Biol Sci. 2018 Jul 1;14(8):807-810. doi: 10.7150/ijbs.27554. eCollection 2018. Special issue on Computational Resources and Methods in Biological Sciences. Lin H(1)(2), Peng S(3), Huang J(1)(2). Author information: (1)Center for Informational Biology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China. (2)School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China. (3)School of Computer Science, National University of Defense Technology, Changsha 410073, China. This special issue covers a wide range of topics in computational biology, such as database construction, sequence analysis and function prediction with machine learning methods, disease-related diagnosis, drug-target and drug discovery, and electronic health record system construction. DOI: 10.7150/ijbs.27554 PMCID: PMC6036761 PMID: 29989106 377. J Proteomics. 2013 Jun 28;86:27-42. doi: 10.1016/j.jprot.2013.04.036. Epub 2013 May 9. Identification of new protein coding sequences and signal peptidase cleavage sites of Helicobacter pylori strain 26695 by proteogenomics. Müller SA(1), Findeiß S, Pernitzsch SR, Wissenbach DK, Stadler PF, Hofacker IL, von Bergen M, Kalkhof S. Author information: (1)Department of Proteomics, UFZ, Helmholtz-Centre for Environmental Research Leipzig, 04318 Leipzig, Germany. Correct annotation of protein coding genes is the basis of conventional data analysis in proteomic studies. Nevertheless, most protein sequence databases almost exclusively rely on gene finding software and inevitably also miss protein annotations or possess errors. Proteogenomics tries to overcome these issues by matching MS data directly against a genome sequence database. Here we report an in-depth proteogenomics study of Helicobacter pylori strain 26695. MS data was searched against a combined database of the NCBI annotations and a six-frame translation of the genome. Database searches with Mascot and X! Tandem revealed 1115 proteins identified by at least two peptides with a peptide false discovery rate below 1%. This represents 71% of the predicted proteome. So far this is the most extensive proteome study of Helicobacter pylori. Our proteogenomic approach unambiguously identified four previously missed annotations and furthermore allowed us to correct sequences of six annotated proteins. Since secreted proteins are often involved in pathogenic processes we further investigated signal peptidase cleavage sites. By applying a database search that accommodates the identification of semi-specific cleaved peptides, 63 previously unknown signal peptides were detected. The motif LXA showed to be the predominant recognition sequence for signal peptidases.BIOLOGICAL SIGNIFICANCE: The results of MS-based proteomic studies highly rely on correct annotation of protein coding genes which is the basis of conventional data analysis. However, the annotation of protein coding sequences in genomic data is usually based on gene finding software. These tools are limited in their prediction accuracy such as the problematic determination of exact gene boundaries. Thus, protein databases own partly erroneous or incomplete sequences. Additionally, some protein sequences might also be missing in the databases. Proteogenomics, a combination of proteomic and genomic data analyses, is well suited to detect previously not annotated proteins and to correct erroneous sequences. For this purpose, the existing database of the investigated species is typically supplemented with a six-frame translation of the genome. Here, we studied the proteome of the major human pathogen Helicobacter pylori that is responsible for many gastric diseases such as duodenal ulcers and gastric cancer. Our in-depth proteomic study highly reliably identified 1115 proteins (FDR<0.01%) by at least two peptides (FDR<1%) which represent 71% of the predicted proteome deposited at NCBI. The proteogenomic data analysis of our data set resulted in the unambiguous identification of four previously missed annotations, the correction of six annotated proteins as well as the detection of 63 previously unknown signal peptides. We have annotated proteins of particular biological interest like the ferrous iron transport protein A, the coiled-coil-rich protein HP0058 and the lipopolysaccharide biosynthesis protein HP0619. For instance, the protein HP0619 could be a drug target for the inhibition of the LPS synthesis pathway. Furthermore it has been proven that the motif "LXA" is the predominant recognition sequence for the signal peptidase I of H. pylori. Signal peptidases are essential enzymes for the viability of bacterial cells and are involved in pathogenesis. Therefore signal peptidases could be novel targets for antibiotics. The inclusion of the corrected and new annotated proteins as well as the information of signal peptide cleavage sites will help in the study of biological pathways involved in pathogenesis or drug response of H. pylori. Copyright © 2013 Elsevier B.V. All rights reserved. DOI: 10.1016/j.jprot.2013.04.036 PMID: 23665149 [Indexed for MEDLINE] 378. Comb Chem High Throughput Screen. 2018;21(4):292-297. doi: 10.2174/1386207321666180220124259. The Discovery of Antibacterial Natural Compound Based on Peptide Deformylase. Liang L(1), Zhou Q(1), Hao Z(1), Wang F(1), Zhu Y(1), Lin Q(1), Gao J(1). Author information: (1)Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China. BACKGROUND: In recent years, Staphylococcus aureus have developed resistance to medicines used for the treatment of human infections. Therefore, the search for antibacterial agents of high potency against Staphylococcus aureus is of great concern. Peptide deformylase (PDF), a metalloprotease catalyzing the removal of a formyl group from newly synthesized proteins, has been considered to be an important antibacterial drug target. OBJECTIVE: To discover novel antibacterial drugs based on Staphylococcus aureus peptide deformylase. METHOD: PDF-based virtual screening of compounds from Traditional Chinese Medicine Database@Taiwan was performed by Sybyl X2.1 Surflex dock software. Compounds which possess high docking score were used for the following antibacterial experiments to evaluate their antibacterial activities. Kanamycin was also used in the antibacterial experiment as a control substance in the assay. Furthermore, molecular docking studies was applied to elucidate binding interaction between some compounds and PDF. In silico pharmacokinetic and toxicity prediction was explored to explain the reasons why these compounds might stand good chance of providing some pharmaceutical benefits. RESULTS: Gentiopicroside, protosappanin B, dihydromyricetin and cryptochlorogenic acid with high docking score were used for our subsequent antibacterial assays. The Minimum Inhibitory Concentration (MIC) of kanamycin and gentiopicroside were 0.008 mg·mL-1 and 0.431 mg·mL-1, respectively, other three compounds, protosappanin B, dihydromyricetin and cryptochlorogenic acid have close MIC value of 0.50 mg·mL-1. CONCLUSION: Dihydromyricetin, with the MIC value of 0.50 mg·mL-1 and relatively high drug score of 0.82, may serve as a novel antibacterial lead compound. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1386207321666180220124259 PMID: 29468960 379. Prog Mol Biol Transl Sci. 2013;114:251-76. doi: 10.1016/B978-0-12-386933-3.00007-8. Free fatty acid receptor GPR120 and pathogenesis of obesity and type 2 diabetes mellitus. Mo XL(1), Wei HK, Peng J, Tao YX. Author information: (1)Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, Alabama, USA. G protein-coupled receptor 120 (GPR120) was initially identified as an orphan receptor through mining the human genome databases. In 2005, GPR120 was deorphanized and shown to be a receptor for long-chain free fatty acids. GPR120 regulates various physiological processes, including gut hormone secretion, islet function, food preference, osteoclastogenesis, anti-inflammation, adipogenesis, and appetite control. Recently, a human genetic study conducted in European populations identified a loss-of-function GPR120 mutation associated with obesity and insulin resistance. Therefore, GPR120, the sensing receptor for long-chain free fatty acids, represents a novel drug target for the treatment of obesity and diabetes. Copyright © 2013 Elsevier Inc. All rights reserved. DOI: 10.1016/B978-0-12-386933-3.00007-8 PMID: 23317787 [Indexed for MEDLINE] 380. Methods Mol Biol. 2009;563:75-95. doi: 10.1007/978-1-60761-175-2_5. Manual annotation of protein interactions. Bureeva S(1), Zvereva S, Romanov V, Serebryiskaya T. Author information: (1)Metalogic OOO, Moscow, Russia. Protein interactions are the basic building blocks for assembly of pathways and networks. Almost any biologically meaningful functionality (for instance, linear signaling pathways, chains of metabolic reactions, transcription factor dimmers, protein complexes of transcriptosome, gene-disease associations) can be represented as a combination of binary relationships between "network objects" (genes, proteins, RNA species, bioactive compounds). Naturally, the assembled pathways and networks are only as good as their "weakest" link (i.e., a wrongly assigned interaction), and the errors multiply in multi-step pathways. Therefore, the utility of "systems biology" is fundamentally dependent on quality and relevance of protein interactions. The second important parameter is the sheer number of interactions assembled in the database. One needs a "critical mass" of species-specific interactions in order to build cohesive networks for a gene list, not a constellation of non-connected proteins and protein pairs. The third issue is semantic consistency between interactions of different types. Transient physical signal transduction interactions, reactions of endogenous metabolism, transcription factor-promoter binding, and kinetic drug-target interactions are all very different in nature. Yet, they have to fit well into one database format and be consistent in order to be useful in reconstruction of cellular processes.High-quality protein interactions are available in peer-reviewed "small experiment" literature and, to a much smaller extent, patents. However, it is very challenging to find the interactions, annotate with searchable (and computable) parameters, catalogue in the database format in computer readable form, and assemble into a database. There are hundreds of thousands of mammalian interactions scattered in tens of thousands of papers in a few thousands of scientific journals. There are no widely used standards for reporting the interactions in scientific texts and, therefore, text-mining tools have only limited applicability. In order to generate a meaningful database of protein interactions, one needs a well-developed technology of manual curation, equipped with computational solutions, managerial procedures, quality control, and users' feedback. Here we describe our ever-evolving annotation approach, the important annotation issues and our solutions, and the mammalian protein interactions database MetaBase which we have been working on for over 8 years. DOI: 10.1007/978-1-60761-175-2_5 PMID: 19597781 [Indexed for MEDLINE] 381. PLoS One. 2014 Oct 16;9(10):e109340. doi: 10.1371/journal.pone.0109340. eCollection 2014. Sequential application of ligand and structure based modeling approaches to index chemicals for their hH4R antagonism. Pappalardo M(1), Shachaf N(2), Basile L(3), Milardi D(4), Zeidan M(2), Raiyn J(2), Guccione S(5), Rayan A(2). Author information: (1)Department of Chemical Sciences, University of Catania, Catania, Italy. (2)Drug Discovery Informatics Lab, QRC-Qasemi Research Center, Al-Qasemi Academic College, Baka El-Garbiah, Israel. (3)Etnalead s.r.l., Scuola Superiore di Catania, University of Catania, Catania, Italy. (4)National Research Council, Institute of Biostructures and Bioimaging, Catania, Italy. (5)Etnalead s.r.l., Scuola Superiore di Catania, University of Catania, Catania, Italy; Department of Pharmaceutical Sciences, University of Catania, Catania, Italy. The human histamine H4 receptor (hH4R), a member of the G-protein coupled receptors (GPCR) family, is an increasingly attractive drug target. It plays a key role in many cell pathways and many hH4R ligands are studied for the treatment of several inflammatory, allergic and autoimmune disorders, as well as for analgesic activity. Due to the challenging difficulties in the experimental elucidation of hH4R structure, virtual screening campaigns are normally run on homology based models. However, a wealth of information about the chemical properties of GPCR ligands has also accumulated over the last few years and an appropriate combination of these ligand-based knowledge with structure-based molecular modeling studies emerges as a promising strategy for computer-assisted drug design. Here, two chemoinformatics techniques, the Intelligent Learning Engine (ILE) and Iterative Stochastic Elimination (ISE) approach, were used to index chemicals for their hH4R bioactivity. An application of the prediction model on external test set composed of more than 160 hH4R antagonists picked from the chEMBL database gave enrichment factor of 16.4. A virtual high throughput screening on ZINC database was carried out, picking ∼ 4000 chemicals highly indexed as H4R antagonists' candidates. Next, a series of 3D models of hH4R were generated by molecular modeling and molecular dynamics simulations performed in fully atomistic lipid membranes. The efficacy of the hH4R 3D models in discrimination between actives and non-actives were checked and the 3D model with the best performance was chosen for further docking studies performed on the focused library. The output of these docking studies was a consensus library of 11 highly active scored drug candidates. Our findings suggest that a sequential combination of ligand-based chemoinformatics approaches with structure-based ones has the potential to improve the success rate in discovering new biologically active GPCR drugs and increase the enrichment factors in a synergistic manner. DOI: 10.1371/journal.pone.0109340 PMCID: PMC4199621 PMID: 25330207 [Indexed for MEDLINE] 382. PLoS One. 2012;7(7):e41064. doi: 10.1371/journal.pone.0041064. Epub 2012 Jul 16. Prediction of chemical-protein interactions network with weighted network-based inference method. Cheng F(1), Zhou Y, Li W, Liu G, Tang Y. Author information: (1)Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China. Chemical-protein interaction (CPI) is the central topic of target identification and drug discovery. However, large scale determination of CPI is a big challenge for in vitro or in vivo experiments, while in silico prediction shows great advantages due to low cost and high accuracy. On the basis of our previous drug-target interaction prediction via network-based inference (NBI) method, we further developed node- and edge-weighted NBI methods for CPI prediction here. Two comprehensive CPI bipartite networks extracted from ChEMBL database were used to evaluate the methods, one containing 17,111 CPI pairs between 4,741 compounds and 97 G protein-coupled receptors, the other including 13,648 CPI pairs between 2,827 compounds and 206 kinases. The range of the area under receiver operating characteristic curves was 0.73 to 0.83 for the external validation sets, which confirmed the reliability of the prediction. The weak-interaction hypothesis in CPI network was identified by the edge-weighted NBI method. Moreover, to validate the methods, several candidate targets were predicted for five approved drugs, namely imatinib, dasatinib, sertindole, olanzapine and ziprasidone. The molecular hypotheses and experimental evidence for these predictions were further provided. These results confirmed that our methods have potential values in understanding molecular basis of drug polypharmacology and would be helpful for drug repositioning. DOI: 10.1371/journal.pone.0041064 PMCID: PMC3397956 PMID: 22815915 [Indexed for MEDLINE] 383. J Bioinform Comput Biol. 2015 Jun;13(3):1541002. doi: 10.1142/S0219720015410024. Epub 2015 Jan 11. Predicting essential genes and synthetic lethality via influence propagation in signaling pathways of cancer cell fates. Zhang F(1), Wu M, Li XJ, Li XL, Kwoh CK, Zheng J. Author information: (1)School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. A major goal of personalized anti-cancer therapy is to increase the drug effects while reducing the side effects as much as possible. A novel therapeutic strategy called synthetic lethality (SL) provides a great opportunity to achieve this goal. SL arises if mutations of both genes lead to cell death while mutation of either single gene does not. Hence, the SL partner of a gene mutated only in cancer cells could be a promising drug target, and the identification of SL pairs of genes is of great significance in pharmaceutical industry. In this paper, we propose a hybridized method to predict SL pairs of genes. We combine a data-driven model with knowledge of signalling pathways to simulate the influence of single gene knock-down and double genes knock-down to cell death. A pair of genes is considered as an SL candidate when double knock-down increases the probability of cell death significantly, but single knock-down does not. The single gene knock-down is confirmed according to the human essential genes database. Our validation against literatures shows that the predicted SL candidates agree well with wet-lab experiments. A few novel reliable SL candidates are also predicted by our model. DOI: 10.1142/S0219720015410024 PMID: 25669329 [Indexed for MEDLINE] 384. Bioinformation. 2005 Oct 7;1(2):56-7. A database for medicinal and aromatic plants of JK (Jammu and Kashmir) in India. Masood A(1), Shafi M. Author information: (1)Bioinformatics Centre, The University of Kashmir, Hazratbal, Srinagar, Jammu and Kashmir, India 190006. akbar@bioinfoku.org High throughput screening of small molecules for a given drug target is achieved using plant materials of medicinal value. Therefore, it is important to document the availability and location of such medicinal plants in the form of a database. Here, we describe a web database containing information (botanical name, common name, local name, botany, chemistry, folklore medicinal use and medicinal uses) about the medicinal and aromatic plants available in JK (Jammu and Kashmir). The database is available for free in public domain.AVAILABILITY: http://www.bioinfoku.org/db/medsearch.php. PMCID: PMC1891631 PMID: 17597854 385. J Biomol Struct Dyn. 2014 Apr;32(4):591-601. doi: 10.1080/07391102.2013.782825. Epub 2013 May 13. Exploring the structural features of Aspartate Trans Carbamoylase (TtATCase) from Thermus thermophilus HB8 through in silico approaches: a potential drug target for inborn error of pyrimidine metabolism. Kanagarajan S(1), Mutharasappan N, Dhamodharan P, Jeyaraman M, Ramadas K, Jeyaraman J. Author information: (1)a Department of Bioinformatics , Alagappa University , Room No. 2, Fourth Floor, Science Block, Alagappa University, Karaikudi , 630004 , India . Enzymes involved in the pyrimidine biosynthesis pathway have become an important target for the pharmacological intervention. One among those enzymes, Aspartate Trans Carbamoylase (ATCase), catalyses the condensation of aspartate and carbamoyl phosphate to form N-carbamoyl-l-aspartate and inorganic phosphate. The present study provides the molecular insights into the enzyme ATCase. The three-dimensional structure of ATCase from Thermus thermophilus HB8 was modeled based on the crystal structure of ATCase in Pyrococcus abyssi (PDB ID:1ML4). Molecular dynamics simulation was performed to identify the conformational stability of TtATCase with and without its ligand complexes. Based on the pharmacokinetic properties and the glide-docking scores of ligands from four databases (Maybridge, Binding, Asinex and Technology for Organic Synthesis (TOS laboratory) for the screening of ligands, we identified four potential ligand molecules for TtATCase. From the molecular docking results, we proposed that the residues Thr53, Arg104, and Gln219 are consistently involved in strong hydrogen-bonding interactions and play a vital role in the TtATCase activity. From the results of molecular dynamics simulation, the ligand molecules are found to bind appropriately to the target enzyme. However, the structure of TtATCase needs to be determined experimentally to confirm this. DOI: 10.1080/07391102.2013.782825 PMID: 23663010 [Indexed for MEDLINE] 386. J Biomol Struct Dyn. 2017 Oct;35(13):2895-2909. doi: 10.1080/07391102.2016.1234416. Epub 2016 Sep 29. Quercetin derivatives as non-nucleoside inhibitors for dengue polymerase: molecular docking, molecular dynamics simulation, and binding free energy calculation. Anusuya S(1), Gromiha MM(1). Author information: (1)a Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences , Indian Institute of Technology Madras , Chennai 600036 , Tamilnadu , India. Dengue is an important public health problem in tropical and subtropical regions of the world. Neither vaccine nor an antiviral medication is available to treat dengue. This insists the need of drug discovery for dengue. In order to find a potent lead molecule, RNA-dependent RNA polymerase which is essential for dengue viral replication is chosen as a drug target. As Quercetin showed antiviral activity against several viruses, quercetin derivatives developed by combinatorial library synthesis and mined from PubChem databases were screened for a potent anti-dengue viral agent. Our study predicted Quercetin 3-(6″-(E)-p-coumaroylsophoroside)-7-rhamnoside as a dengue polymerase inhibitor. The results were validated by molecular dynamics simulation studies which reveal water bridges and hydrogen bonds as major contributors for the stability of the polymerase-lead complex. Interactions formed by this compound with residues Trp795, Arg792 and Glu351 are found to be essential for the stability of the polymerase-lead complex. Our study demonstrates Quercetin 3-(6″-(E)-p-coumaroylsophoroside)-7-rhamnoside as a potent non-nucleoside inhibitor for dengue polymerase. DOI: 10.1080/07391102.2016.1234416 PMID: 27608509 [Indexed for MEDLINE] 387. J Biomol Screen. 2014 Jun;19(5):749-57. doi: 10.1177/1087057114521463. Epub 2014 Feb 11. Mining Natural-Products Screening Data for Target-Class Chemical Motifs. Coma I(1), Bandyopadhyay D(2), Diez E(3), Ruiz EA(3), de los Frailes MT(3), Colmenarejo G(3). Author information: (1)Molecular Discovery Research, GlaxoSmithKline R&D Pharmaceuticals, Tres Cantos, Spain isabel.coma@gsk.com. (2)GlaxoSmithKline R&D Pharmaceuticals, Collegeville, PA, USA. (3)Molecular Discovery Research, GlaxoSmithKline R&D Pharmaceuticals, Tres Cantos, Spain. In this article, we describe two complementary data-mining approaches used to characterize the GlaxoSmithKline (GSK) natural-products set (NPS) based on information from the high-throughput screening (HTS) databases. Both methods rely on the aggregation and analysis of a large set of single-shot screening data for a number of biological assays, with the goal to reveal natural-product chemical motifs. One of them is an established method based on the data-driven clustering of compounds using a wide range of descriptors,(1)whereas the other method partitions and hierarchically clusters the data to identify chemical cores.(2,3)Both methods successfully find structural scaffolds that significantly hit different groups of discrete drug targets, compared with their relative frequency of demonstrating inhibitory activity in a large number of screens. We describe how these methods can be applied to unveil hidden information in large single-shot HTS data sets. Applied prospectively, this type of information could contribute to the design of new chemical templates for drug-target classes and guide synthetic efforts for lead optimization of tractable hits that are based on natural-product chemical motifs. Relevant findings for 7TM receptors (7TMRs), ion channels, class-7 transferases (protein kinases), hydrolases, and oxidoreductases will be discussed. © 2014 Society for Laboratory Automation and Screening. DOI: 10.1177/1087057114521463 PMID: 24518065 [Indexed for MEDLINE] 388. Onco Targets Ther. 2018 May 24;11:3065-3074. doi: 10.2147/OTT.S161287. eCollection 2018. Gene expression and prognosis of NOX family members in gastric cancer. You X(1), Ma M(2)(3), Hou G(4), Hu Y(2), Shi X(1). Author information: (1)The First Department of Chemotherapy, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China. (2)State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China. (3)Department of Gastric Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, China. (4)Department of Oncology, The First Affiliated Hospital of Jiaxing University, Jiaxing, China. Introduction: Nicotinamide adenine dinucleotide phosphate (NADPH) oxidases (NOX) are frequently deregulated in several human malignancies, including gastric cancer (GC). NOX-derived reactive oxygen species have been reported to contribute to gastric carcinogenesis and cancer progression. However, the expression and prognostic role of individual NOX in GC patients remain elusive. Methods and materials: We investigated genetic alteration and mRNA expression of NOX family in GC patients via the cBioPortal, Human Protein Atlas, and Oncomine databases. Furthermore, we evaluated prognostic value of distinct NOX in GC patients through "The Kaplan-Meier plotter" database. Results: Our analysis demonstrated that mRNA deregulation of NOX genes was common alteration in GC patients. Compared with normal tissues, NOX1/2/4 mRNA expression levels in GC tissues were higher, while NOX5 and DUOX1/2 expression levels were lower. Importantly, our results indicated that high mRNA expression of NOX2 was associated with better overall survival whereas NOX4 and DUOX1 were correlated with worse overall survival in all GC patients, particularly in intestinal-type GC patients. In addition, our data also shed light on the diverse roles of individual NOX members in GC patients with different clinicopathological features, including human epidermal growth factor receptor 2 status, clinical stages, pathological grades, and different choices of treatments of GC patients. Conclusion: These findings suggest that individual NOX family genes, especially NOX2/4, and DUOX1, are potential prognostic markers in GC and implicate that the use of NOX inhibitor targeting NOX4 and DUOX1 may be an effective strategy for GC therapy. DOI: 10.2147/OTT.S161287 PMCID: PMC5975617 PMID: 29872318 Conflict of interest statement: Disclosure The authors report no conflicts of interest in this work. 389. RETRACTED ARTICLE Int J Biochem Cell Biol. 2010 Feb;42(2):230-40. doi: 10.1016/j.biocel.2009.10.007. Epub 2009 Oct 29. Environmental toxicogenomics: a post-genomic approach to analysing biological responses to environmental toxins. Jayapal M(1), Bhattacharjee RN, Melendez AJ, Hande MP. Author information: (1)Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, 2 Medical Drive, Singapore 117597, Singapore. Retraction in Int J Biochem Cell Biol. 2013 Nov;45(11):2713. Environmental genomics has revolutionised how researchers can study the molecular basis of adverse effects of environmental toxicants. It is expected that the new discipline will afford efficient and high-throughput means to delineate mechanisms of action, risk assessment, identify and understand basic pathogenic mechanisms that are critical to disease progression, predict toxicity of unknown agents and to more precisely phenotype disease subtypes. Previously, we have demonstrated the potential of environmental genomics in a toxicant exposure model and, perhaps, this might become a crucial tool in biological response marker or biomarker discovery. To illustrate how toxicogenomics can be useful, we provide here an overview of some of the past and potential future aspects of environmental genomics. The present article also reviews the principles and technological concerns, and the standards and databases of toxicogenomics. In addition, applications of toxicogenomics in drug target identifications and validation strategies are also discussed. Copyright (c) 2009 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.biocel.2009.10.007 PMID: 19836462 [Indexed for MEDLINE] 390. Mol Divers. 2013 Aug;17(3):421-34. doi: 10.1007/s11030-013-9441-2. Epub 2013 Apr 24. Toward a general predictive QSAR model for gamma-secretase inhibitors. Ajmani S(1), Janardhan S, Viswanadhan VN. Author information: (1)Department of Computational Chemistry, Jubilant Biosys Limited, #96, Industrial Suburb, 2nd Stage, Yeshwanthpur, Bangalore, 560022, India. Gamma secretase (GS) is an appealing drug target for Alzheimer disease and cancer because of its central role in the processing of amyloid precursor protein and the notch family of proteins. In the absence of three-dimensional structure of GS, there is an urgent need for new methods for the prediction and screening of GS inhibitors, for facilitating discovery of novel GS inhibitors. The present study reports QSAR studies on diverse chemical classes comprising 233 compounds collected from the ChEMBL database. Herein, continuous [PLS regression and neural-network (NN)] and categorical QSAR models (NN and linear discriminant analysis) were developed to obtain pertinent descriptors responsible for variation of GS inhibitor potency. Also, SAR within various chemical classes of compounds is analyzed with respect to important QSAR descriptors, which revealed the significance of electronegative substitutions on aryl rings (PEOE3) in determining variation of GS inhibitor potency. Furthermore, substitution of acyclic amines with N-substituted cyclic amines appears to be favorable for enhancing GS inhibitor potency by increasing the values of sssN_Cnt and number of aliphatic rings. The models developed are statistically significant and improve our understanding of compounds contributing toward GS inhibitor potency and aid in the rational design of novel potent GS inhibitors. DOI: 10.1007/s11030-013-9441-2 PMID: 23612850 [Indexed for MEDLINE] 391. Drug Res (Stuttg). 2017 May;67(5):289-301. doi: 10.1055/s-0042-124515. Epub 2017 Mar 7. Design, Network Analysis and In Silico Modeling of Biologically Significant 4-(substituted benzyl)-2-Amino-6-HydroxyPyrimidine-5-Carboxamide Nanoparticles. Panneerselvam T(1), Sivakumar V(2), Arumugam S(3), Selvaraj K(1), Indhumathy M(2). Author information: (1)International Research Centre, Kalasalingam University, Anand Nagar, Krishnankoil, Tamilnadu, India. (2)Department of Pharmaceutics, Arulmigu Kalasalingam College of Pharmacy, Anand Nagar, Krishnankoil, Tamilnadu, India. (3)National Centre for Advanced Research in Discrete Mathematics, Kalasalingam University, Anand nagar, Krishnankoil, Tamilnadu, India. The synthesized 4-(4-hydroxy benzyl)-2-amino-6-hydroxy pyrimidine-5-carboxamide was chosen to perform in silico modeling with identified drug target AGT, TNF, F2 and BCL2L1. The identified human proteins are vital in the pain management and also an important target for the study of wound healing activity. The enzymes were identified by using BioGRID, string database and network analysis through Cytoscape software. The wound healing activity was evaluated by excision wound model. The observed results revealed that, the pyrimidine nanoparticles showed significant wound healing activity compared to standard and synthesized compound. The detailed synthesis of nanoparticles formulation spectral analysis and pharmacological screening data's were reported. The revealed reports of synthesized analogues and formulated nanoparticles will generate a very good impact to the chemists and research scholars for further investigations in wound healing and pain management. © Georg Thieme Verlag KG Stuttgart · New York. DOI: 10.1055/s-0042-124515 PMID: 28268236 [Indexed for MEDLINE] Conflict of interest statement: Conflict of Interest: The authors have no conflict of interest. 392. J Antibiot (Tokyo). 2013 Aug;66(8):453-8. doi: 10.1038/ja.2013.30. Epub 2013 May 1. Xanthone derivatives could be potential antibiotics: virtual screening for the inhibitors of enzyme I of bacterial phosphoenolpyruvate-dependent phosphotransferase system. Huang KJ(1), Lin SH, Lin MR, Ku H, Szkaradek N, Marona H, Hsu A, Shiuan D. Author information: (1)Department of Life Science and Institute of Biotechnology, Interdisciplinary Program of Bioinformatics, National Dong Hwa University, Hualien, Taiwan. The phosphoenolpyruvate phosphotransferase system (PTS) is ubiquitous in eubacteria and absent from eukaryotes. The system consists of two phosphoryl carriers, enzyme I (EI) and the histidine-containing phosphoryl carrier protein (HPr), and several PTS transporters, catalyzing the concomitant uptake and phosphorylation of several carbohydrates. Since a deficiency of EI in bacterial mutants lead to severe growth defects, EI could be a drug target to develop antimicrobial agents. We used the 3D structure PDB 1ZYM of Escherichia coli EI as the target to virtually screen the potential tight binders from NPPEDIA (Natural Product Encyclopedia), ZINC and Super Natural databases. These databases were screened using the docking tools of Discovery Studio 2.0 and the Integrated Drug Design System IDDS. Among the many interesting hits, xanthone derivatives with reasonably high Dock scores received more attentions. Two of the xanthone derivatives were obtained to examine their capabilities to inhibit cell growth of both Gram-positive and Gram-negative bacterial strains. The results indicate that they may exert the inhibition effects by blocking the EI activities. We have demonstrated for the first time that the xanthone derivatives have high potential to be developed as future antibiotics. DOI: 10.1038/ja.2013.30 PMID: 23632921 [Indexed for MEDLINE] 393. BMC Med Genomics. 2013;6 Suppl 1:S10. doi: 10.1186/1755-8794-6-S1-S10. Epub 2013 Jan 23. Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data. Teng S(1), Yang JY, Wang L. Author information: (1)Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA. BACKGROUND: Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. RESULTS: In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs) and Support Vector Machines (SVMs) were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. CONCLUSIONS: A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression. DOI: 10.1186/1755-8794-6-S1-S10 PMCID: PMC3552705 PMID: 23369200 [Indexed for MEDLINE] 394. Zhongguo Zhong Yao Za Zhi. 2018 Oct;43(20):4125-4131. doi: 10.19540/j.cnki.cjcmm.20180508.002. [Study on mechanism of Drynariae Rhizoma in treating osteoporosis with integrative pharmacology perspective]. [Article in Chinese] Zhang YL(1)(2), Tang B(2)(3), Jiang JJ(1), Shen H(4), Xie YM(1), Wei X(3). Author information: (1)Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China. (2)Beijing University of Chinese Medicine, Beijing 100029, China. (3)Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China. (4)Changxindian Community Health Service Center, Beijing 100072, China. Drynariae Rhizoma has great significance in the clinical practice of osteoporosis treatment. Based on the perspective of integrative pharmacology, the study explored the mechanism of action of Drynariae Rhizoma in the treatment of osteoporosis. Six active components in Drynariae Rhizoma were obtained, mainly including glycosides and sterols. Taking the median of 2 times of "node connectivity" as the card value, the core node of the Chinese medicine target disease gene interaction network was selected. Based on this, three topological structural eigenvalues, such as "node connectivity" "node tightness" and "node connectivity" were calculated, thereby screening out four core targets of Drynariae Rhizoma treatment for osteoporosis, including thyroid parathyroid hormone 1 receptor (PTH1R), parathyroid hormone 2 receptor (PTH2R), calcitonin receptor gene (CALCR), and SPTBN1 gene (SPTBN1). Based on the gene ontology database GO and KEGG pathway database, the molecular function, intracellular localization, and biological reactions and pathways of proteins encoded by drug target genes were determined. Combined with enrichment calculation, it is predicted that osteoporosis may play a role in biosynthetic processes, such as circulatory system, nervous system, energy metabolism, prolactin signal pathway, GnRH signaling pathway, neurotrophic factor signaling pathway and other pathway. The conclusion of this study is certain with the existing research results, and the new target and new pathway could also be used as a theoretical basis for the further verification of osteoporosis. Copyright© by the Chinese Pharmaceutical Association. DOI: 10.19540/j.cnki.cjcmm.20180508.002 PMID: 30486541 Conflict of interest statement: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose. 395. Methods Mol Biol. 2018;1824:299-316. doi: 10.1007/978-1-4939-8630-9_18. Lead Identification Through the Synergistic Action of Biomolecular NMR and In Silico Methodologies. Marousis KD(1), Tsika AC(1), Birkou M(1), Matsoukas MT(2), Spyroulias GA(3). Author information: (1)Department of Pharmacy, University of Patras, Patras, Greece. (2)Department of Pharmacy, University of Patras, Patras, Greece. minmatsoukas@gmail.com. (3)Department of Pharmacy, University of Patras, Patras, Greece. G.A.Spyroulias@upatras.gr. The combination of virtual screening with biomolecular NMR can be a powerful approach in the first steps toward drug discovery. Here, we describe how computational methodologies to screen large databases readily available for testing small molecules, in synergy with NMR techniques focused on protein-ligand interactions, can be used in the early lead compound identification process against a protein drug target. DOI: 10.1007/978-1-4939-8630-9_18 PMID: 30039415 396. Biomed Res Int. 2014;2014:624024. doi: 10.1155/2014/624024. Epub 2014 Jun 17. Performance studies on distributed virtual screening. Krüger J(1), Grunzke R(2), Herres-Pawlis S(3), Hoffmann A(3), de la Garza L(1), Kohlbacher O(1), Nagel WE(2), Gesing S(4). Author information: (1)Center for Bioinformatics, Quantitative Biology Center, and Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany. (2)Technische Universität Dresden, Zellescher Weg 12-14, 01069 Dresden, Germany. (3)Ludwig-Maximilians-Universität München, Butenandtstr aße 5-13, 81377 München, Germany. (4)Center for Research Computing, University of Notre Dame, P.O. Box 539, Notre Dame, IN 46556, USA. Virtual high-throughput screening (vHTS) is an invaluable method in modern drug discovery. It permits screening large datasets or databases of chemical structures for those structures binding possibly to a drug target. Virtual screening is typically performed by docking code, which often runs sequentially. Processing of huge vHTS datasets can be parallelized by chunking the data because individual docking runs are independent of each other. The goal of this work is to find an optimal splitting maximizing the speedup while considering overhead and available cores on Distributed Computing Infrastructures (DCIs). We have conducted thorough performance studies accounting not only for the runtime of the docking itself, but also for structure preparation. Performance studies were conducted via the workflow-enabled science gateway MoSGrid (Molecular Simulation Grid). As input we used benchmark datasets for protein kinases. Our performance studies show that docking workflows can be made to scale almost linearly up to 500 concurrent processes distributed even over large DCIs, thus accelerating vHTS campaigns significantly. DOI: 10.1155/2014/624024 PMCID: PMC4083208 PMID: 25032219 [Indexed for MEDLINE] 397. Curr Drug Metab. 2010 May;11(4):379-406. Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers. González-Díaz H(1), Duardo-Sanchez A, Ubeira FM, Prado-Prado F, Pérez-Montoto LG, Concu R, Podda G, Shen B. Author information: (1)Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Santiago de Compostela (USC), Santiago de Compostela, 15782, Spain. In this communication we carry out an in-depth review of a very versatile QSPR-like method. The method name is MARCH-INSIDE (MARkov CHains Ivariants for Network Selection and DEsign) and is a simple but efficient computational approach to the study of QSPR-like problems in biomedical sciences. The method uses the theory of Markov Chains to generate parameters that numerically describe the structure of a system. This approach generates two principal types of parameters Stochastic Topological Indices (sto-TIs). The use of these parameters allows the rapid collection, annotation, retrieval, comparison and mining structures of molecular, macromolecular, supramolecular, and non-molecular systems within large databases. Here, we review and comment by the first time on the several applications of MARCH-INSIDE to predict drugs ADMET, Activity, Metabolizing Enzymes, and Toxico-Proteomics biomarkers discovery. The MARCH-INSIDE models reviewed are: a) drug-tissue distribution profiles, b) assembling drug-tissue complex networks, c) multi-target models for anti-parasite/anti-microbial activity, c) assembling drug-target networks, d) drug toxicity and side effects, e) web-server for drug metabolizing enzymes, f) models in drugs toxico-proteomics. We close the review with some legal remarks related to the use of this class of QSPR-like models. PMID: 20446904 [Indexed for MEDLINE] 398. J Chem Inf Model. 2006 Mar-Apr;46(2):708-16. Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors. Rella M(1), Rushworth CA, Guy JL, Turner AJ, Langer T, Jackson RM. Author information: (1)Institute of Molecular and Cellular Biology, University of Leeds, Leeds LS2 9JT, UK. The metallopeptidase Angiotensin Converting Enzyme (ACE) is an important drug target for the treatment of hypertension, heart, kidney, and lung disease. Recently, a close and unique human ACE homologue termed ACE2 has been identified and found to be an interesting new cardiorenal disease target. With the recently resolved inhibitor-bound ACE2 crystal structure available, we have attempted a structure-based approach to identify novel potent and selective inhibitors. Computational approaches focus on pharmacophore-based virtual screening of large compound databases. Selectivity was ensured by initial screening for ACE inhibitors within an internal database and the Derwent World Drug Index, which could be reduced to zero false positives and 0.1% hit rate, respectively. An average hit reduction of 0.44% was achieved with a five feature hypothesis, searching approximately 3.8 million compounds from various commercial databases. Seventeen compounds were selected based on high fit values as well as diverse structure and subjected to experimental validation in a bioassay. We show that all compounds displayed an inhibitory effect on ACE2 activity, the six most promising candidates exhibiting IC50 values in the range of 62-179 microM. DOI: 10.1021/ci0503614 PMID: 16563001 [Indexed for MEDLINE] 399. Comb Chem High Throughput Screen. 2010 May;13(4):366-74. MoStBioDat--molecular and structural bioinformatics database. Bak A(1), Polanski J, Stockner T, Kurczyk A. Author information: (1)Department of Organic Chemistry, Institute of Chemistry, University of Silesia, PL-40-006 Katowice, Poland. abak@us.edu.pl Computer simulations play a crucial role in contemporary chemical investigations, generating enormous amounts of data. The constraint of sharing data and results is regarded as a major impediment in drug discovery. Among the steepest barriers to overcome in the high throughput screening studies is the limited number of suitable, freely accessible repositories for storing drug and drug target data. By offering a uniform data storage and retrieval mechanism, various data might be compared and exchanged easily. This paper presents the stages of the MoStBioDat software platform development, originally designed for the efficient storage, management and access of SDF and PDB data. The detailed architecture and software implementation of this project are described, indicating also the disadvantages of the solutions chosen. The current implementation of the first prototype is written in Python, an open-source, high-level, object-oriented scripting language. The modular architecture of the package enables future extension with the necessary functionalities. The main objective of the MoStBioDat is to serve as an alternative, extensible open-source database derived partly from SDF and PDB files. PMID: 20438449 [Indexed for MEDLINE] 400. J Hum Genet. 2016 May;61(5):427-33. doi: 10.1038/jhg.2015.170. Epub 2016 Jan 14. Genome-wide association study of serum lipids confirms previously reported associations as well as new associations of common SNPs within PCSK7 gene with triglyceride. Kurano M(1), Tsukamoto K(2), Kamitsuji S(3), Kamatani N(3), Hara M(4), Ishikawa T(5), Kim BJ(6), Moon S(6), Jin Kim Y(6), Teramoto T(5). Author information: (1)Department of Clinical Laboratory Medicine, Graduate School of Medicine, University of Tokyo, Tokyo, Japan. (2)Department of Metabolism, Diabetes and Nephrology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan. (3)StaGen, Statistical Genetics Analysis Division, Tokyo, Japan. (4)Department of Medicine IV, Mizonokuchi Hospital, Teikyo University School of Medicine, Kawasaki, Japan. (5)Department of Internal Medicine, Teikyo University School of Medicine, Tokyo, Japan. (6)Division of Structural and Functional Genomics, Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Korea. Previous reports including genome-wide association studies (GWASs) have described associations of serum lipids with genomic variations. In the present study, we examined the association of ∼2.5 million single-nucleotide polymorphisms (SNPs) from 3041 Japanese healthy volunteers obtained from the Japan Pharmacogenomics Data Science Consortium (JPDSC) database with serum lipids. We confirmed the previously reported associations of 14 SNPs in 5 regions for low-density lipoprotein (LDL) cholesterol, 23 SNPs in 12 regions for high-density lipoprotein (HDL) cholesterol, 16 SNPs in 6 regions for triglyceride and 5 SNPs in 1 region for phospholipid. Furthermore, we identified 16 possible novel candidate genes associated with LDL cholesterol, HDL cholesterol or triglycerides, where SNPs had P-values of <1 × 10(-5). Further replication analyses of these genes with Korean data revealed significant associations of SNPs located within the PCSK7 gene and triglyceride (Pmeta=7.98 × 10(-9) and 1.91 × 10(-8) for rs508487 and rs236911, respectively). These associations remained significant even by the conditional analysis adjusting for three neighboring variations associated with triglyceride. Our present data suggest that PCSK7 as well as PCSK9 may be associated with lipids, especially triglyceride, and may serve as a candidate for a new drug target to treat lipid abnormality syndromes. DOI: 10.1038/jhg.2015.170 PMID: 26763881 [Indexed for MEDLINE] 401. Curr Comput Aided Drug Des. 2013 Dec;9(4):482-90. A neural network-based QSAR approach for exploration of diverse multi-tyrosine kinase inhibitors and its comparison with a fragment- based approach. Ajmani S, Viswanadhan VN(1). Author information: (1)Department of Computational Chemistry, Jubilant Biosys Limited, #96, Industrial Suburb, 2nd Stage, Yeshwanthpur, Bangalore - 560 022, India. Vellarkad_Viswanadhan@jubilantbiosys.com. Receptor and non-receptor tyrosine kinases have emerged as clinically useful drug target for treating certain types of cancer. It is well known that tyrosine kinase inhibitors with multi-kinases inhibitory potency are useful in anticancer therapy. In recent study, we have demonstrated application of a novel Group based QSAR (GQSAR) method to assist in lead optimization of multi-tyrosine kinase (PDGFR-beta, FGFR-1 and SRC) inhibitors. Although GQSAR method provides an alternative way to design new compounds, it could not be applied for virtual screening of large databases, because of its limitation to fragment each of the compound in the diverse database. So to circumvent this limitation of GQSAR method, herein we present the development of multi-kinase QSAR model using artificial neural networks. Various simple, easy and fast to calculate 2D/3D descriptors were used in the present analysis. The resulting neural network based QSAR (NN-QSAR) model was found to be statistically significant and provided insight into common structural requirements to inhibit different tyrosine kinases. The NN-QSAR model suggests five descriptors viz. number of rotatable bonds, number of hydrogen bond donors, number of building blocks, polar surface area and sum of nitrogen and oxygen atoms to be of major importance in explaining the activity variation in all the three kinases. In addition, this multi-target QSAR model could be useful to predict the activities of new compounds designed as tyrosine kinase inhibitors. PMID: 24138419 [Indexed for MEDLINE] 402. Bioinformation. 2012;8(12):568-73. doi: 10.6026/97320630008568. Epub 2012 Jun 28. Designing of Protein Kinase C β-II Inhibitors against Diabetic complications: Structure Based Drug Design, Induced Fit docking and analysis of active site conformational changes. Vijayakumar B, Velmurugan D. Protein Kinase C β-II (PKC β-II) is an important enzyme in the development of diabetic complications like cardiomyopathy, retinopathy, neuropathy, nephropathy and angiopathy. PKC β-II is activated in vascular tissues during diabetic vascular abnormalities. Thus, PKC β-II is considered as a potent drug target and the crystal structure of the kinase domain of PKC β-II (PDB id: 2I0E) was used to design inhibitors using Structure-Based Drug Design (SBDD) approach. Sixty inhibitors structurally similar to Staurosporine were retrieved from PubChem Compound database and High Throughput Virtual screening (HTVs) was carried out with PKC β-II. Based on the HTVs results and the nature of active site residues of PKC β-II, Staurosporine inhibitors were designed using SBDD. Induced Fit Docking (IFD) studies were carried out between kinase domain of PKC β-II and the designed inhibitors. These IFD complexes showed favorable docking score, glide energy, glide emodel and hydrogen bond and hydrophobic interactions with the active site of PKC β-II. Binding free energy was calculated for IFD complexes using Prime MM-GBSA method. The conformational changes induced by the inhibitor at the active site of PKC β-II were observed for the back bone Cα atoms and side-chain chi angles. PASS prediction tool was used to analyze the biological activities for the designed inhibitors. The various physicochemical properties were calculated for the compounds. One of the designed inhibitors successively satisfied all the in silico parameters among the others and seems to be a potent inhibitor against PKC β-II. DOI: 10.6026/97320630008568 PMCID: PMC3398787 PMID: 22829732 403. BMC Bioinformatics. 2012 Jun 28;13:153. doi: 10.1186/1471-2105-13-153. Molecular evolution of dihydrouridine synthases. Kasprzak JM(1), Czerwoniec A, Bujnicki JM. Author information: (1)Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Umultowska 89, PL-61-614 Poznan, Poland. BACKGROUND: Dihydrouridine (D) is a modified base found in conserved positions in the D-loop of tRNA in Bacteria, Eukaryota, and some Archaea. Despite the abundant occurrence of D, little is known about its biochemical roles in mediating tRNA function. It is assumed that D may destabilize the structure of tRNA and thus enhance its conformational flexibility. D is generated post-transcriptionally by the reduction of the 5,6-double bond of a uridine residue in RNA transcripts. The reaction is carried out by dihydrouridine synthases (DUS). DUS constitute a conserved family of enzymes encoded by the orthologous gene family COG0042. In protein sequence databases, members of COG0042 are typically annotated as "predicted TIM-barrel enzymes, possibly dehydrogenases, nifR3 family". RESULTS: To elucidate sequence-structure-function relationships in the DUS family, a comprehensive bioinformatic analysis was carried out. We performed extensive database searches to identify all members of the currently known DUS family, followed by clustering analysis to subdivide it into subfamilies of closely related sequences. We analyzed phylogenetic distributions of all members of the DUS family and inferred the evolutionary tree, which suggested a scenario for the evolutionary origin of dihydrouridine-forming enzymes. For a human representative of the DUS family, the hDus2 protein suggested as a potential drug target in cancer, we generated a homology model. While this article was under review, a crystal structure of a DUS representative has been published, giving us an opportunity to validate the model. CONCLUSIONS: We compared sequences and phylogenetic distributions of all members of the DUS family and inferred the phylogenetic tree, which provides a framework to study the functional differences among these proteins and suggests a scenario for the evolutionary origin of dihydrouridine formation. Our evolutionary and structural classification of the DUS family provides a background to study functional differences among these proteins that will guide experimental analyses. DOI: 10.1186/1471-2105-13-153 PMCID: PMC3674756 PMID: 22741570 [Indexed for MEDLINE] 404. Proteins. 2014 Jul;82(7):1283-300. doi: 10.1002/prot.24494. Epub 2014 Jan 15. Discovery of multiple interacting partners of gankyrin, a proteasomal chaperone and an oncoprotein--evidence for a common hot spot site at the interface and its functional relevance. Nanaware PP(1), Ramteke MP, Somavarapu AK, Venkatraman P. Author information: (1)Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, India. Gankyrin, a non-ATPase component of the proteasome and a chaperone of proteasome assembly, is also an oncoprotein. Gankyrin regulates a variety of oncogenic signaling pathways in cancer cells and accelerates degradation of tumor suppressor proteins p53 and Rb. Therefore gankyrin may be a unique hub integrating signaling networks with the degradation pathway. To identify new interactions that may be crucial in consolidating its role as an oncogenic hub, crystal structure of gankyrin-proteasome ATPase complex was used to predict novel interacting partners. EEVD, a four amino acid linear sequence seems a hot spot site at this interface. By searching for EEVD in exposed regions of human proteins in PDB database, we predicted 34 novel interactions. Eight proteins were tested and seven of them were found to interact with gankyrin. Affinity of four interactions is high enough for endogenous detection. Others require gankyrin overexpression in HEK 293 cells or occur endogenously in breast cancer cell line- MDA-MB-435, reflecting lower affinity or presence of a deregulated network. Mutagenesis and peptide inhibition confirm that EEVD is the common hot spot site at these interfaces and therefore a potential polypharmacological drug target. In MDA-MB-231 cells in which the endogenous CLIC1 is silenced, trans-expression of Wt protein (CLIC1_EEVD) and not the hot spot site mutant (CLIC1_AAVA) resulted in significant rescue of the migratory potential. Our approach can be extended to identify novel functionally relevant protein-protein interactions, in expansion of oncogenic networks and in identifying potential therapeutic targets. © 2013 Wiley Periodicals, Inc. DOI: 10.1002/prot.24494 PMID: 24338975 [Indexed for MEDLINE] 405. Nucleic Acids Res. 2009 Jan;37(Database issue):D195-200. doi: 10.1093/nar/gkn618. Epub 2008 Oct 8. SuperSite: dictionary of metabolite and drug binding sites in proteins. Bauer RA(1), Günther S, Jansen D, Heeger C, Thaben PF, Preissner R. Author information: (1)Institute of Molecular Biology and Bioinformatics, Structural Bioinformatics Group, Charité- Medical University Berlin, Arnimallee 22, 14195 Berlin, Germany. The increasing structural information about target-bound compounds provide a rich basis to study the binding mechanisms of metabolites and drugs. SuperSite is a database, which combines the structural information with various tools for the analysis of molecular recognition. The main data is made up of 8000 metabolites including 1300 drugs, bound to about 290,000 different receptor binding sites. The analysis tools include features, like the highlighting of evolutionary conserved receptor residues, the marking of putative binding pockets and the superpositioning of different binding sites of the same ligand. User-defined compounds can be edited or uploaded and will be superimposed with the most similar co-crystallized ligand. The user can examine all results online with the molecule viewer Jmol. An implemented search algorithm allows the screening of uploaded proteins, in order to detect potential drug binding sites, which are similar to known binding pockets. The huge data set of target-bound compounds in combination with the provided analysis tools allow to inspect the characteristics of molecular recognition, especially for drug target interactions. SuperSite is publicly available at: http://bioinformatics.charite.de/supersite. DOI: 10.1093/nar/gkn618 PMCID: PMC2686477 PMID: 18842629 [Indexed for MEDLINE] 406. Sci Rep. 2018 Mar 20;8(1):4894. doi: 10.1038/s41598-018-23246-0. Identification of new inhibitors against human Great wall kinase using in silico approaches. Ammarah U(1), Kumar A(2)(3), Pal R(1), Bal NC(4), Misra G(5). Author information: (1)Amity Institute of Biotechnology, Amity University, Noida, 201313, U.P., India. (2)Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, via Marengo 2, 09123, Cagliari, Italy. (3)Modeling and Simulations group, Center for advanced study research and development in Sardinia (CRS4), Loc. Piscina Manna, 09010, Pula, Italy. (4)KIIT University, Bhubaneshwar, Orissa, India. (5)Amity Institute of Biotechnology, Amity University, Noida, 201313, U.P., India. kamgauri@gmail.com. Microtubule associated serine/threonine kinase (MASTL) is an important Ser/Thr kinase belonging to the family of AGC kinases. It is the human orthologue of Greatwall kinase (Gwl) that plays a significant role in mitotic progression and cell cycle regulation. Upregulation of MASTL in various cancers and its association with poor patient survival establishes it as an important drug target in cancer therapy. Nevertheless, the target remains unexplored with the paucity of studies focused on identification of inhibitors against MASTL, which emphasizes the relevance of our present study. We explored various drug databases and performed virtual screening of compounds from both natural and synthetic sources. A list of promising compounds displaying high binding characteristics towards MASTL protein is reported. Among the natural compounds, we found a 6-hydroxynaphthalene derivative ZINC85597499 to display best binding energy value of -9.32 kcal/mol. While among synthetic compounds, a thieno-pyrimidinone based tricyclic derivative ZINC53845290 compound exhibited best binding affinity of value -7.85 kcal/mol. MASTL interactions with these two compounds were further explored using molecular dynamics simulations. Altogether, this study identifies potential inhibitors of human Gwl kinase from both natural and synthetic origin and calls for studying these compounds as potential drugs for cancer therapy. DOI: 10.1038/s41598-018-23246-0 PMCID: PMC5861128 PMID: 29559668 407. PLoS One. 2015 Oct 16;10(10):e0139889. doi: 10.1371/journal.pone.0139889. eCollection 2015. Gene-Set Local Hierarchical Clustering (GSLHC)--A Gene Set-Based Approach for Characterizing Bioactive Compounds in Terms of Biological Functional Groups. Chung FH(1), Jin ZH(2), Hsu TT(2), Hsu CL(2), Liu HC(2), Lee HC(3). Author information: (1)Institute of Systems Biology and Bioinformatics, National Central University, Zhongli, 32001, Taiwan; Center for Dynamical Biomarkers and Translational Medicine, National Central University, Zhongli, 32001, Taiwan. (2)Institute of Systems Biology and Bioinformatics, National Central University, Zhongli, 32001, Taiwan. (3)Institute of Systems Biology and Bioinformatics, National Central University, Zhongli, 32001, Taiwan; Center for Dynamical Biomarkers and Translational Medicine, National Central University, Zhongli, 32001, Taiwan; Department of Physics, Chung Yuan Christian University, Zhongli, 32023, Taiwan; Physics Division, National Center for Theoretical Sciences, Hsinchu, 30043, Taiwan. Gene-set-based analysis (GSA), which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA), which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap), an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode. Here, we constructed a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap), in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. We showed that GSCMap essentially eliminated cell-type dependence, a weakness of CMap in IGA mode, and yielded significantly better performance on sample clustering and drug-target association. As a first application of GSCMap we constructed the platform Gene-Set Local Hierarchical Clustering (GSLHC) for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. The GSLHC website http://cloudr.ncu.edu.tw/gslhc/ contains 1,857 local hierarchical clusters accessible by querying 555 of the 1,309 drugs and small molecules listed in CMap. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases. DOI: 10.1371/journal.pone.0139889 PMCID: PMC4652590 PMID: 26473729 [Indexed for MEDLINE] 408. Curr Opin Biotechnol. 2000 Feb;11(1):42-6. Biotechnology match making: screening orphan ligands and receptors. Williams C(1). Author information: (1)Millennium Pharmaceuticals, Cambridge, MA 02139-4853, USA. To date there has been a considerable amount of interest and success in the pharmaceutical industry in the discovery of drug targets and diagnostics via genomic technologies, namely DNA sequencing, mutation/polymorphism detection and expression monitoring of mRNA. As the ultimate targets for the majority of these methods are actually proteins, more and more emphasis has been placed upon protein-based methods in an effort to define the function of proteins discovered by genomic technologies. One of the most challenging areas of drug target discovery facing researchers today is the search for novel receptor-ligand pairs. Database mining techniques in conjunction with other computational methods are able to identify many novel sequences of putative receptors, but the ability to similarly identify the receptor's natural ligand is not possible by these methods. The past few years have seen an increase in methodology and instrumentation focused on the ability to discover and characterize protein-protein interactions, as well as receptor-ligand pairs. Significant advances have been made in the areas of instrumentation (biosensors and fluorescent plate readers) as well as methodologies relating to phage/ribosome display and library construction. PMID: 10679346 [Indexed for MEDLINE] 409. J Chem Biol. 2010 May 13;3(4):175-87. doi: 10.1007/s12154-010-0040-8. Virtual screening for potential inhibitors of homology modeled Leptospira interrogans MurD ligase. Umamaheswari A, Pradhan D, Hemanthkumar M. The life-threatening infections caused by Leptospira serovars remain a global challenge since long time. Prevention of infection by controlling environmental factors being difficult to practice in developing countries, there is a need for designing potent anti-leptospirosis drugs. ATP-dependent MurD involved in biosynthesis of peptidoglycan was identified as common drug target among pathogenic Leptospira serovars through subtractive genomic approach. Peptidoglycan biosynthesis pathway being unique to bacteria and absent in host represents promising target for antimicrobial drug discovery. Thus, MurD 3D models were generated using crystal structures of 1EEH and 2JFF as templates in Modeller9v7. Structural refinement and energy minimization of the model was carried out in Maestro 9.0 applying OPLS-AA 2001 force field and was evaluated through Procheck, ProSA, PROQ, and Profile 3D. The active site residues were confirmed from the models in complex with substrate and inhibitor. Four published MurD inhibitors (two phosphinics, one sulfonamide, and one benzene 1,3-dicarbixylic acid derivative) were queried against more than one million entries of Ligand.Info Meta-Database to generate in-house library of 1,496 MurD inhibitor analogs. Our approach of virtual screening of the best-ranked compounds with pharmacokinetics property prediction has provided 17 novel MurD inhibitors for developing anti-leptospirosis drug targeting peptidoglycan biosynthesis pathway. DOI: 10.1007/s12154-010-0040-8 PMCID: PMC2957891 PMID: 21566738 410. Mol Divers. 2016 May;20(2):439-51. doi: 10.1007/s11030-015-9641-z. Epub 2015 Dec 21. Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models. Lian W(1), Fang J(1), Li C(1), Pang X(1), Liu AL(2)(3)(4), Du GH(1)(5)(6). Author information: (1)Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, People's Republic of China. (2)Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, People's Republic of China. liuailin@imm.ac.cn. (3)Beijing Key Laboratory of Drug Target Research and Drug Screening, Beijing, 100050, People's Republic of China. liuailin@imm.ac.cn. (4)State Key Laboratory of Bioactive Substance and Function of Natural Medicines, 1 Xian Nong Tan Street, Beijing, 100050, People's Republic of China. liuailin@imm.ac.cn. (5)Beijing Key Laboratory of Drug Target Research and Drug Screening, Beijing, 100050, People's Republic of China. (6)State Key Laboratory of Bioactive Substance and Function of Natural Medicines, 1 Xian Nong Tan Street, Beijing, 100050, People's Republic of China. Neuraminidase (NA) is a critical enzyme in the life cycle of influenza virus, which is known as a successful paradigm in the design of anti-influenza agents. However, to date there are no classification models for the virtual screening of NA inhibitors. In this work, we built support vector machine and Naïve Bayesian models of NA inhibitors and non-inhibitors, with different ratios of active-to-inactive compounds in the training set and different molecular descriptors. Four models with sensitivity or Matthews correlation coefficients greater than 0.9 were chosen to predict the NA inhibitory activities of 15,600 compounds in our in-house database. We combined the results of four optimal models and selected 60 representative compounds to assess their NA inhibitory profiles in vitro. Nine NA inhibitors were identified, five of which were oseltamivir derivatives with large C-5 substituents exhibiting potent inhibition against H1N1 NA with IC50 values in the range of 12.9-185.0 nM, and against H3N2 NA with IC50 values between 18.9 and 366.1 nM. The other four active compounds belonged to novel scaffolds, with IC50 values ranging 39.5-63.8 μM against H1N1 NA and 44.5-114.1 μM against H3N2 NA. This is the first time that classification models of NA inhibitors and non-inhibitors are built and their prediction results validated experimentally using in vitro assays. DOI: 10.1007/s11030-015-9641-z PMID: 26689205 [Indexed for MEDLINE] 411. Mol Inform. 2014 Feb;33(2):124-34. doi: 10.1002/minf.201300023. Epub 2014 Feb 2. Pharmacophore Mapping, In Silico Screening and Molecular Docking to Identify Selective Trypanosoma brucei Pteridine Reductase Inhibitors. Dube D(1), Sharma S(1), Singh TP(1), Kaur P(2). Author information: (1)Department of Biophysics, All India Institute of Medical Sciences, New Delhi-110029, India. (2)Department of Biophysics, All India Institute of Medical Sciences, New Delhi-110029, India. punitkaur1@hotmail.com. Trypanosoma brucei Pteridine reductase (TbPTR1) is of vital importance and is an established drug target for dreaded Human African trypanosomiasis (HAT). Pharmacophore perception strategy has been employed to identify key chemical features responsible for the biological activity for TbPTR1. The findings suggest that three different pharmacophore features can be associated with T. brucei anti-PTR1 activity namely: H-bond donors (D), Hydrophobic aromatic (H) and Ring aromatic (R). The resulting hypothesis is able to predict the activity of other existing TbPTR1 inhibitors with a correlation coefficient (r) of 0.89. An in silico database screening, based on the best hypothesis, has been used to identify some potential nanomolar range TbPTR1 inhibitors. These compounds were then checked by molecular docking and subjected to ADMET analysis. Further, a detailed comparison of the pharmacophore behavior and differential analysis of binding pockets of T. brucei and L. major was made which revealed subtle differences in terms of their shape and charge properties. This investigation can form the basis for tweaking the specificity of compounds for generating new improved species specific inhibitor molecules for Pteridine reductase in these different parasitic protozoans. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. DOI: 10.1002/minf.201300023 PMID: 27485569 412. Mol Cell Endocrinol. 2009 Mar 25;301(1-2):225-8. doi: 10.1016/j.mce.2008.08.030. Epub 2008 Sep 6. Pharmacophore modelling of 17beta-HSD1 enzyme based on active inhibitors and enzyme structure. Karkola S(1), Alho-Richmond S, Wahala K. Author information: (1)Laboratory of Organic Chemistry, Department of Chemistry, PO Box 55, University of Helsinki, FIN-00014 Helsinki, Finland. The 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) enzyme regulates the conversion of estrone (E1) to the biologically active estradiol (E2). Due to its role as a key enzyme in female hormone production, it has emerged as an attractive drug target for inhibitor development in relation to hormone-dependent breast cancer. Herein, we report four pharmacophore models of 17beta-HSD1 based on a crystal structure, a relaxed crystal structure, a library of 17beta-HSD1 inhibitors and on a docked complex of 17betaHSD1 enzyme and a potent inhibitor. The models were used in screening two databases, which produced novel compounds to be used as leads in our drug design project. The results were validated by docking the compounds to the active site of the 17beta-HSD1 enzyme. With the help of our 3D-QSAR model, these results will be used to develop new inhibitors of 17beta-HSD1 as drug candidates. DOI: 10.1016/j.mce.2008.08.030 PMID: 18822344 [Indexed for MEDLINE] 413. Gene. 2014 Feb 10;535(2):233-8. doi: 10.1016/j.gene.2013.11.028. Epub 2013 Nov 27. Understanding the transcriptional regulation of cervix cancer using microarray gene expression data and promoter sequence analysis of a curated gene set. Srivastava P(1), Mangal M(2), Agarwal SM(3). Author information: (1)Integrative Genomics and Medicine, MRC Clinical Sciences, Imperial College, London, UK. (2)Bioinformatics Division, Institute of Cytology and Preventive Oncology, Noida-201301, India. (3)Bioinformatics Division, Institute of Cytology and Preventive Oncology, Noida-201301, India. Electronic address: smagarwal@yahoo.com. Cervical cancer, the malignant neoplasm of the cervix uteri is the second most common cancer among women worldwide and the top-most cancer in India. Several factors are responsible for causing cervical cancer, which alter the expression of oncogenic genes resulting in up or down-regulation of gene expression and inactivation of tumor-suppressor genes/gene products. Gene expression is regulated by interactions between transcription factors (TFs) and specific regulatory elements in the promoter regions of target genes. Thus, it is important to decipher and analyze TFs that bind to regulatory regions of diseased genes and regulate their expression. In the present study, computational methods involving the combination of gene expression data from microarray experiments and promoter sequence analysis of a curated gene set involved in the cervical cancer causation have been utilized for identifying potential regulatory elements. Consensus predictions of two approaches led to the identification of twelve TFs that might be crucial to the regulation of cervical cancer progression. Subsequently, TF enrichment and oncomine expression analysis suggested that the transcription factor family E2F played an important role for the regulation of genes involve in cervical carcinogenesis. Our results suggest that E2F possesses diagnostic/prognostic value and can act as a potential drug target in cervical cancer. Copyright © 2013 Elsevier B.V. All rights reserved. DOI: 10.1016/j.gene.2013.11.028 PMID: 24291025 [Indexed for MEDLINE] 414. Bioinformation. 2013;9(3):158-64. doi: 10.6026/97320630009158. Epub 2013 Feb 6. Molecular modeling, dynamics studies and virtual screening of Fructose 1, 6 biphosphate aldolase-II in community acquired- methicillin resistant Staphylococcus aureus (CA-MRSA). Yadav PK(1), Singh G, Gautam B, Singh S, Yadav M, Srivastav U, Singh B. Author information: (1)Department of Computational Biology & Bioinformatics, Sam Higginbottom Institute of Agriculture, Technology & Sciences (Deemed University), Allahabad-211007, India. Community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) has recently emerged as a nosocomial pathogen to the community which commonly causes skin and soft-tissue infections (SSTIs). This strain (MW2) has now become resistant to the most of the beta-lactam antibiotics; therefore it is the urgent need to identify the novel drug targets. Recently fructose 1,6 biphosphate aldolase-II (FBA) has been identified as potential drug target in CA-MRSA. The FBA catalyses the retro-ketolic cleavage of fructose-1,6-bisphosphate (FBP) to yield dihydroxyacetone phosphate (DHAP) and glyceraldehyde-3-phosphate (G3P) in glycolytic pathway. In the present research work the 3D structure of FBA was predicted using the homology modeling method followed by validation. The molecular dynamics simulation (MDS) of the predicted model was carried out using the 2000 ps time scale and 1000000 steps. The MDS results suggest that the modeled structure is stable. The predicted model of FBA was used for virtual screening against the NCI diversity subset-II ligand databases which contain 1364 compounds. Based on the docking energy scores, it was found that top four ligands i.e. ZINC01690699, ZINC13154304, ZINC29590257 and ZINC29590259 were having lower energy scores which reveal higher binding affinity towards the active site of FBA. These ligands might act as potent inhibitors for the FBA so that the menace of antimicrobial resistance in CA-MRSA can be conquered. However, pharmacological studies are required to confirm the inhibitory activity of these ligands against the FBA in CA-MRSA. DOI: 10.6026/97320630009158 PMCID: PMC3569604 PMID: 23423142 415. PLoS One. 2013 Dec 30;8(12):e83496. doi: 10.1371/journal.pone.0083496. eCollection 2013. Exploration of virtual candidates for human HMG-CoA reductase inhibitors using pharmacophore modeling and molecular dynamics simulations. Son M(1), Baek A(1), Sakkiah S(2), Park C(1), John S(1), Lee KW(1). Author information: (1)Division of Applied Life Science (BK21 Plus Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), Gazwa-dong, Jinju, Republic of Korea. (2)Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California, United States of America. 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) is a rate-controlling enzyme in the mevalonate pathway which involved in biosynthesis of cholesterol and other isoprenoids. This enzyme catalyzes the conversion of HMG-CoA to mevalonate and is regarded as a drug target to treat hypercholesterolemia. In this study, ten qualitative pharmacophore models were generated based on chemical features in active inhibitors of HMGR. The generated models were validated using a test set. In a validation process, the best hypothesis was selected based on the statistical parameters and used for virtual screening of chemical databases to find novel lead candidates. The screened compounds were sorted by applying drug-like properties. The compounds that satisfied all drug-like properties were used for molecular docking study to identify their binding conformations at active site of HMGR. The final hit compounds were selected based on docking score and binding orientation. The HMGR structures in complex with the hit compounds were subjected to 10 ns molecular dynamics simulations to refine the binding orientation as well as to check the stability of the hits. After simulation, binding modes including hydrogen bonding patterns and molecular interactions with the active site residues were analyzed. In conclusion, four hit compounds with new structural scaffold were suggested as novel and potent HMGR inhibitors. DOI: 10.1371/journal.pone.0083496 PMCID: PMC3875450 PMID: 24386216 [Indexed for MEDLINE] 416. Chem Biol Drug Des. 2017 May;89(5):714-722. doi: 10.1111/cbdd.12894. Epub 2016 Dec 2. Graph theoretical analysis, insilico modeling, design, and synthesis of compounds containing benzimidazole skeleton as antidepressant agents. Theivendren P(1), Subramanian A(2), Murugan I(2), Joshi SD(3), More UA(3). Author information: (1)International Research Centre, Kalasalingam University, Krishnankoil, Tamilnadu, India. (2)National Centre for Advanced Research in Discrete Mathematics, Kalasalingam University, Krishnankoil, Tamilnadu, India. (3)Department of Pharmaceutical Chemistry, "SET's" College of Pharmacy, Dharwad, Karnataka, India. In this study, drug target was identified using KEGG database and network analysis through Cytoscape software. Designed series of novel benzimidazoles were taken along with reference standard Flibanserin for insilico modeling. The novel 4-(1H-benzo[d]imidazol-2-yl)-N-(substituted phenyl)-4-oxobutanamide (3a-j) analogs were synthesized and evaluated for their antidepressant activity. Reaction of 4-(1H-benzo[d]imidazol-2-yl)-4-oxobutanoic acid (1) with 4-(1H-benzo [d] imidazol-2-yl)-4-oxobutanoyl chloride (2) furnished novel 4-(1H-benzo [d] imidazol-2-yl)-N-(substituted phenyl)-4-oxobutanamide (3a-j). All the newly synthesized compounds were characterized by IR, 1 H-NMR, and mass spectral analysis. The antidepressant activities of synthesized derivatives were compared with standard drug clomipramine at a dose level of 20 mg/kg. Among the derivatives tested, most of the compounds were found to have potent activity against depression. The high level of activity was shown by the compounds 3d, 3e, 3i, and it significantly reduced the duration of immobility time at the dose level of 50 mg/kg. © 2016 John Wiley & Sons A/S. DOI: 10.1111/cbdd.12894 PMID: 27797457 [Indexed for MEDLINE] 417. Commun Integr Biol. 2012 Jul 1;5(4):350-61. doi: 10.4161/cib.20005. Plasmodium falciparum RuvB proteins: Emerging importance and expectations beyond cell cycle progression. Ahmad M(1), Tuteja R. Author information: (1)Malaria Group; International Centre for Genetic Engineering and Biotechnology; New Delhi, India. The urgent requirement of next generation antimalarials has been of recent interest due to the emergence of drug-resistant parasite. The genome-wide analysis of Plasmodium falciparum helicases revealed three RuvB proteins. Due to the presence of helicase motif I and II in PfRuvBs, there is a high probability that they contain ATPase and possibly helicase activity. The Plasmodium database has homologs of several key proteins that interact with RuvBs and are most likely involved in the cell cycle progression, chromatin remodeling, and other cellular activities. Phylogenetically PfRuvBs are closely related to Saccharomyces cerevisiae RuvB, which is essential for cell cycle progression and survival of yeast. Thus PfRuvBs can serve as potential drug target if they show an essential role in the survival of parasite. DOI: 10.4161/cib.20005 PMCID: PMC3460840 PMID: 23060959 418. J Biomol Struct Dyn. 2018 May 24:1-17. doi: 10.1080/07391102.2018.1468282. [Epub ahead of print] Identification and evaluation of bioactive natural products as potential inhibitors of human microtubule affinity-regulating kinase 4 (MARK4). Mohammad T(1), Khan FI(2), Lobb KA(2), Islam A(1), Ahmad F(1), Hassan MI(1). Author information: (1)a Centre for Interdisciplinary Research in Basic Sciences , Jamia Millia Islamia , Jamia Nagar, New Delhi , 110025 , India. (2)b Computational Mechanistic Chemistry and Drug Discovery , Rhodes University , Grahamstown , South Africa. Microtubule affinity-regulating kinase 4 (MARK4) has recently been identified as a potential drug target for several complex diseases including cancer, diabetes and neurodegenerative disorders. Inhibition of MARK4 activity is an appealing therapeutic option to treat such diseases. Here, we have performed structure-based virtual high-throughput screening of 100,000 naturally occurring compounds from ZINC database against MARK4 to find its potential inhibitors. The resulted hits were selected, based on the binding affinities, docking scores and selectivity. Further, binding energy calculation, Lipinski filtration and ADMET prediction were carried out to find safe and better hits against MARK4. Best 10 compounds bearing high specificity and binding efficiency were selected, and their binding pattern to MARK4 was analyzed in detail. Finally, 100 ns molecular dynamics simulation was performed to evaluate; the dynamics stability of MARK4-compound complex. In conclusion, these selected natural compounds from ZINC database might be potential leads against MARK4, and can further be exploited in drug design and development for associated diseases. DOI: 10.1080/07391102.2018.1468282 PMID: 29683402 419. J Hypertens. 2018 May;36(5):1094-1103. doi: 10.1097/HJH.0000000000001680. A system view and analysis of essential hypertension. Botzer A(1), Grossman E(2)(3), Moult J(4), Unger R(1). Author information: (1)The Mina & Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan. (2)Department of Internal Medicine D and Hypertension Unit, The Chaim Sheba Medical Center, Tel-Hashomer, Ramat Gan. (3)Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel. (4)Department of Cell Biology and Molecular Genetics, University of Maryland - Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA. OBJECTIVES: The goal of this study was to investigate genes associated with essential hypertension from a system perspective, making use of bioinformatic tools to gain insights that are not evident when focusing at a detail-based resolution. METHODS: Using various databases (pathways, Genome Wide Association Studies, knockouts etc.), we compiled a set of about 200 genes that play a major role in hypertension and identified the interactions between them. This enabled us to create a protein-protein interaction network graph, from which we identified key elements, based on graph centrality analysis. Enriched gene regulatory elements (transcription factors and microRNAs) were extracted by motif finding techniques and knowledge-based tools. RESULTS: We found that the network is composed of modules associated with functions such as water retention, endothelial vasoconstriction, sympathetic activity and others. We identified the transcription factor SP1 and the two microRNAs miR27 (a and b) and miR548c-3p that seem to play a major role in regulating the network as they exert their control over several modules and are not restricted to specific functions. We also noticed that genes involved in metabolic diseases (e.g. insulin) are central to the network. CONCLUSION: We view the blood-pressure regulation mechanism as a system-of-systems, composed of several contributing subsystems and pathways rather than a single module. The system is regulated by distributed elements. Understanding this mode of action can lead to a more precise treatment and drug target discovery. Our analysis suggests that insulin plays a primary role in hypertension, highlighting the tight link between essential hypertension and diseases associated with the metabolic syndrome. DOI: 10.1097/HJH.0000000000001680 PMID: 29369145 420. Hum Mutat. 2010 Apr;31(4):407-13. doi: 10.1002/humu.21207. The Roche Cancer Genome Database (RCGDB). Küntzer J(1), Eggle D, Lenhof HP, Burtscher H, Klostermann S. Author information: (1)Roche Diagnostics GmbH, Pharma Research Scientific Informatics, Nonnenwald 2, Penzberg, Germany. jan.kuentzer@roche.com Sequence variations are being studied for a better understanding of the mechanism and development of cancer as a mutation-driven disease. The systematic sequencing of genes in tumors and technological advances in high-throughput techniques combined with efficient data acquisition methods have resulted in an explosion of available cancer genome-related data. Despite the technological progress and increase of data, improvements in the application area, for example, drug target discovery, have failed to keep pace with increased research and development spending. One reason for this discrepancy is the ever increasing number of databases and the absence of a unified access to the mutation data. Currently, researchers typically have to browse several, often highly specialized databases to obtain the required information. A more complete understanding of relations and dependencies between mutations and cancer, however, requires the availability of an efficient integrative cancer genome information system. To facilitate this, we developed the Roche Cancer Genome Database (RCGDB), a freely available biological information system integrating different kinds of mutation data. The database is the first comprehensive integration of disparate cancer genome data like single nucleotide variants, single nucleotide polymorphisms, and chromosomal aberrations (CGH and FISH). RCGDB is freely accessible via a Google-like Web interface at http://rcgdb.bioinf.uni-sb.de/MutomeWeb/. (c) 2010 Wiley-Liss, Inc. DOI: 10.1002/humu.21207 PMID: 20127971 [Indexed for MEDLINE] 421. J Biochem. 2006 Sep;140(3):305-11. Epub 2006 Aug 4. Structural basis for induced-fit binding of Rho-kinase to the inhibitor Y-27632. Yamaguchi H(1), Miwa Y, Kasa M, Kitano K, Amano M, Kaibuchi K, Hakoshima T. Author information: (1)Structural Biology Laboratory, Nara Institute of Science and Technology, and CREST, Japan . Rho-kinase is a main player in the regulation of cytoskeletal events and a promising drug target in the treatment of both vascular and neurological disorders. Here we report the crystal structure of the Rho-kinase catalytic domain in complex with the specific inhibitor Y-27632. Comparison with the structure of PKA bound to this inhibitor revealed a potential induced-fit binding mode that can be accommodated by the phosphate binding loop. This binding mode resembles to that observed in the Rho-kinase-fasudil complex. A structural database search indicated that a pocket underneath the phosphate-binding loop is present that favors binding to a small aromatic ring. Introduction of such a ring group might spawn a new modification scheme of pre-existing protein kinase inhibitors for improved binding capability. DOI: 10.1093/jb/mvj172 PMID: 16891330 [Indexed for MEDLINE] 422. J Cell Biochem. 2018 Sep 11. doi: 10.1002/jcb.27538. [Epub ahead of print] Structural and energetic understanding of novel natural inhibitors of Mycobacterium tuberculosis malate synthase. Shukla R(1), Shukla H(1), Tripathi T(1). Author information: (1)Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Umshing, Shillong, India. Persistent infection by Mycobacterium tuberculosis requires the glyoxylate shunt. This is a bypass to the tricarboxylic acid cycle in which isocitrate lyase (ICL) and malate synthase (MS) catalyze the net incorporation of carbon during mycobacterial growth on acetate or fatty acids as the primary carbon source. To identify a potential antitubercular compound, we performed a structure-based screening of natural compounds from the ZINC database (n = 1 67 740) against the M tuberculosis MS (MtbMS) structure. The ligands were screened against MtbMS, and 354 ligands were found to have better docking score. These compounds were assessed for Lipinski and absorption, distribution, metabolism, excretion, and toxicity prediction where 15 compounds were found to fit well for redocking studies. After refinement by molecular docking and drug-likeness analysis, four potential inhibitors (ZINC1483899, ZINC1754310, ZINC2269664, and ZINC15729522) were identified. These four ligands with phenyl-diketo acid were further subjected to molecular dynamics simulation to compare the dynamics and stability of the protein structure after ligand binding. The binding energy analysis was calculated to determine the intermolecular interactions. Our results suggested that the four compounds had a binding free energy of -201.96, -242.02, -187.03, and -169.02 kJ·mol-1 , for compounds with IDs ZINC1483899, ZINC1754310, ZINC2269664, and ZINC15729522, respectively. We concluded that two compounds (ZINC1483899 and ZINC1754310) displayed considerable structural and pharmacological properties and could be probable drug candidates to fight against M tuberculosis parasites. © 2018 Wiley Periodicals, Inc. DOI: 10.1002/jcb.27538 PMID: 30206985 423. J Cheminform. 2013 Jan 14;5(1):2. doi: 10.1186/1758-2946-5-2. Enhanced ranking of PknB Inhibitors using data fusion methods. Seal A(1), Yogeeswari P, Sriram D; OSDD Consortium, Wild DJ. Author information: (1)Computer-Aided Drug Design Laboratory, Department of Pharmacy Birla Institute of Technology, Hyderabad Campus, Shameerpet, Hyderbad, 500078, India. pyogee@bits-hyderabad.ac.in. BACKGROUND: Mycobacterium tuberculosis encodes 11 putative serine-threonine proteins Kinases (STPK) which regulates transcription, cell development and interaction with the host cells. From the 11 STPKs three kinases namely PknA, PknB and PknG have been related to the mycobacterial growth. From previous studies it has been observed that PknB is essential for mycobacterial growth and expressed during log phase of the growth and phosphorylates substrates involved in peptidoglycan biosynthesis. In recent years many high affinity inhibitors are reported for PknB. Previously implementation of data fusion has shown effective enrichment of active compounds in both structure and ligand based approaches .In this study we have used three types of data fusion ranking algorithms on the PknB dataset namely, sum rank, sum score and reciprocal rank. We have identified reciprocal rank algorithm is capable enough to select compounds earlier in a virtual screening process. We have also screened the Asinex database with reciprocal rank algorithm to identify possible inhibitors for PknB. RESULTS: In our work we have used both structure-based and ligand-based approaches for virtual screening, and have combined their results using a variety of data fusion methods. We found that data fusion increases the chance of actives being ranked highly. Specifically, we found that the ranking of Pharmacophore search, ROCS and Glide XP fused with a reciprocal ranking algorithm not only outperforms structure and ligand based approaches but also capable of ranking actives better than the other two data fusion methods using the BEDROC, robust initial enhancement (RIE) and AUC metrics. These fused results were used to identify 45 candidate compounds for further experimental validation. CONCLUSION: We show that very different structure and ligand based methods for predicting drug-target interactions can be combined effectively using data fusion, outperforming any single method in ranking of actives. Such fused results show promise for a coherent selection of candidates for biological screening. DOI: 10.1186/1758-2946-5-2 PMCID: PMC3600029 PMID: 23317154 424. In Silico Pharmacol. 2013 Jul 29;1:11. doi: 10.1186/2193-9616-1-11. eCollection 2013. Inhibition of VEGF: a novel mechanism to control angiogenesis by Withania somnifera's key metabolite Withaferin A. Saha S(1), Islam MK(1), Shilpi JA(2), Hasan S(3). Author information: (1)Pharmacy Discipline, Life Science School, Khulna University, Khulna, 9208 Bangladesh. (2)Pharmacy Discipline, Life Science School, Khulna University, Khulna, 9208 Bangladesh ; Centre for Natural Products and Drug (CENAR), University of Malaya, 50603 Kuala Lumpur, Malaysia. (3)School of Medicine, The University of Queensland (UQ), Brisbane, Queensland Australia ; Bioinformatics Lab, Queensland Institute of Medical Research (QIMR), Brisbane, Queensland Australia. PURPOSE: Angiogenesis, or new blood vessel formation from existing one, plays both beneficial and detrimental roles in living organisms in different aspects. Vascular endothelial growth factor (VEGF), a signal protein, well established as key regulator of vasculogenesis and angiogenesis. VEGF ensures oxygen supply to the tissues when blood supply is not adequate, or tissue environment is in hypoxic condition. Limited expression of VEGF is necessary, but if it is over expressed, then it can lead to serious disease like cancer. Cancers that have ability to express VEGF are more efficient to grow and metastasize because solid cancers cannot grow larger than a limited size without adequate blood and oxygen supply. Anti-VEGF drugs are already available in the market to control angiogenesis, but they are often associated with severe side-effects like fetal bleeding and proteinuria in the large number of patients. To avoid such side-effects, new insight is required to find potential compounds as anti-VEGF from natural sources. In the present investigation, molecular docking studies were carried out to find the potentiality of Withaferin A, a key metabolite of Withania somnifera, as an inhibitor of VEGF. METHODS: Molecular Docking studies were performed in DockingServer and SwissDock. Bevacizumab, a commercial anti-VEGF drug, was used as reference to compare the activity of Withaferin A. X-ray crystallographic structure of VEGF, was retrieved from Protein Data Bank (PDB), and used as drug target protein. Structure of Withaferin A and Bevacizumab was obtained from PubChem and ZINC databases. Molecular visualization was performed using UCSF Chimera. RESULTS: Withaferin A showed favorable binding with VEGF with low binding energy in comparison to Bevacizumab. Molecular Docking studies also revealed potential protein-ligand interactions for both Withaferin A and Bevacizumab. CONCLUSIONS: Conclusively our results strongly suggest that Withaferin A is a potent anti-VEGF agent as ascertained by its potential interaction with VEGF. This scientific hypothesis might provide a better insight to control angiogenesis as well as to control solid cancer growth and metastasis. DOI: 10.1186/2193-9616-1-11 PMCID: PMC4230651 PMID: 25505656 425. Cell Cycle. 2010 Jan 1;9(1):104-20. Epub 2010 Jan 5. Genome wide identification of Plasmodium falciparum helicases: a comparison with human host. Tuteja R(1). Author information: (1)Malaria Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi, India. renu@icgeb.res.in Comment in Cell Cycle. 2010 Feb 15;9(4):642. Helicases are enzymes which catalyze the unwinding of nucleic acid substrate in an energy-dependent manner. These are characterized by the presence of nine well defined conserved motifs and are essential for almost all the processes involving nucleic acids. Plasmodium falciparum causes the most virulent form of malaria. The control of malaria is becoming complicated due to the spread of resistance of both the mosquito vector and the parasite to insecticides and anti-malarial drugs. Helicases could be used as feasible drug target for control of malaria. The P. falciparum genome is completely sequenced but the annotation is still in progress. To identify members of various well defined helicase families, I used the bioinformatics approach and helicase domain sequences to search the P. falciparum genome sequence database. In addition to the homologues for a number of human helicases, some novel parasite specific helicases were also identified. I describe the members of DEAD-box, DEAH box, RuvB, Superkiller family, RecQ and repair helicases from P. falciparum. The detailed studies of these helicases will help in identifying a specific enzyme, which could be used as potential target to control the replication and transmission of the malaria parasite. DOI: 10.4161/cc.9.1.10241 PMID: 20016272 [Indexed for MEDLINE] 426. J Recept Signal Transduct Res. 2015 Feb;35(1):15-25. doi: 10.3109/10799893.2014.926924. Epub 2014 Jul 23. Effective interaction studies for inhibition of DNA ligase protein from Staphylococcus aureus. Vijayalakshmi P(1), Daisy P. Author information: (1)PG & Research Department of Biotechnology & Bioinformatics, Bioinformatics Centre (BIF), Holy Cross College (Autonomous) , Tiruchirapalli, Tamil Nadu , India. Staphylococcus aureus has been recognized as an important human pathogen for more than 100 years. It is among the most important causative agent of human infections in the twenty-first century. DNA ligase is the main protein responsible for the replication of S. aureus. In order to control the replication mechanism, DNA ligase is a successive drug target, hence we have chosen this protein for this study. We performed virtual screening using ZINC database for identification of potent inhibitor against DNA ligase. Based on the scoring methods, we have selected best five compounds from the ZINC database. In order to improve the accuracy, selected compounds were subjected into Quantum Polarized Ligand Docking (QPLD) docking, for which the results showed high docking score, compared to glide docking score. QPLD is more accurate as it includes charges in the scoring function, which was not available in the glide docking. Binding energy calculation results also indicated that selected compounds have good binding capacity with the target protein. In addition, these compounds on screening have good absorption, distribution, metabolism, excretion and toxicity property. In this study, we identified few compounds that particularly work against DNA ligase protein, having better interaction phenomenon and it would help further the experimental analysis. DOI: 10.3109/10799893.2014.926924 PMID: 25055026 [Indexed for MEDLINE] 427. Proteomics. 2004 Jun;4(6):1712-26. Has the yo-yo stopped? An assessment of human protein-coding gene number. Southan C(1). Author information: (1)Oxford GlycoSciences, Abingdon, UK. christopher.southan@astrazeneca.com Since the identification of approximately 25,000 proteins from the draft human genome assembly in 2001, estimates of the total have oscillated between 30,000 and 70,000. The recently announced genome closure has not generated a consensus gene count despite this being a key parameter for many areas of biology including drug target discovery and characterization of the human proteome. Contrary to earlier predictions of constitutive under-detection for eukaryotic genes, the latest model organism updates have produced minor increases in the worm but fly and yeast gene numbers have decreased. The postdraft, precompletion interval has produced large increases in human transcript coverage, continuous improvements in genome assembly and refinements in automated genomic annotation. Notably these enhancements have resulted in an Ensembl human protein-coding gene number of 22,184, a decrease of 1862 since the first release. Longitudinal database surveys indicate that redundancy-reduced human mRNA and protein collections are flattening out at approximately 28,000, although Ensembl maps approximately 20,000 known sequences. Observations suggest high-throughput cloning projects are predominantly extending known genes or sampling new splice forms and novel protein discovery has slowed to a trickle. The hypothesis that substantial numbers of short proteins remain experimentally and computationally undetected in mammalian genomes is neither supported by sequence data nor by the extensive homology between mouse and human proteins. Aggregating the independent annotations for complete transcripts from seven completed human chromosomes extrapolates to approximately 25,000 genes. The inclusion of partial putative genes would increase this to above 30,000 but recent data suggest these represent predominantly nonprotein-coding transcripts. Mass spectrometry-based proteomics has already verified more than 10% of human genes but has not identified significant numbers of unpredicted proteins. The available data are thus converging to a basal protein-coding gene number well below 30,000, which could even be as low as 25,000. DOI: 10.1002/pmic.200300700 PMID: 15174140 [Indexed for MEDLINE] 428. Expert Opin Biol Ther. 2018 Jul;18(sup1):23-31. doi: 10.1080/14712598.2018.1474198. Deciphering cellular biological processes to clinical application: a new perspective for Tα1 treatment targeting multiple diseases. Matteucci C(1), Argaw-Denboba A(1), Balestrieri E(1), Giovinazzo A(1), Miele M(1), D'Agostini C(1), Pica F(1), Grelli S(1), Paci M(2), Mastino A(3)(4), Sinibaldi Vallebona P(1)(4), Garaci E(5), Tomino C(5)(6). Author information: (1)a Department of Experimental Medicine and Surgery , University of Rome "Tor Vergata" , Rome , Italy. (2)b Department of Chemical Sciences and Technologies , University of Rome "Tor Vergata" , Rome , Italy. (3)c Department of Chemical, Biological, Pharmaceutical and Environmental Sciences , University of Messina , Messina , Italy. (4)d National Research Council , Institute of Translational Pharmacology , Rome , Italy. (5)e Università San Raffaele Pisana , Roma , Italy. (6)f IRCSS San Raffaele Pisana , Scientific Institute for Research, Hospitalization and Health Care , Roma , Italy. BACKGROUND: Thymosin alpha 1 (Tα1) is a well-recognized immune response modulator in a wide range of disorders, particularly infections and cancer. The bioinformatic analysis of public databases allows drug repositioning, predicting a new potential area of clinical intervention. We aimed to decipher the cellular network induced by Tα1 treatment to confirm present use and identify new potential clinical applications. RESEARCH DESIGN AND METHODS: We used the transcriptional profile of human peripheral blood mononuclear cells treated in vitro with Tα1 to perform the enrichment network analysis by the Metascape online tools and the disease enrichment analysis by the DAVID online tool. RESULTS: Networked cellular responses reflected Tα1 regulated biological processes including immune and metabolic responses, response to compounds and oxidative stress, ion homeostasis, peroxisome biogenesis and drug metabolic process. Beyond cancer and infections, the analysis evidenced the association with disorders such as kidney chronic failure, diabetes, cardiovascular, chronic respiratory, neuropsychiatric, neurodegenerative and autoimmune diseases. CONCLUSIONS: In addition to the known ability to promote immune response pathways, the network enrichment analysis demonstrated that Tα1 regulates cellular metabolic processes and oxidative stress response. Notable, the analysis highlighted the association with several diseases, suggesting new translational implication of Tα1 treatment in pathological conditions unexpected until now. DOI: 10.1080/14712598.2018.1474198 PMID: 30063863 [Indexed for MEDLINE] 429. Endocr Metab Immune Disord Drug Targets. 2018 Nov 27. doi: 10.2174/1871530319666181128100903. [Epub ahead of print] Identification of novel pancreatic lipase inhibitors using structure based virtual screening, docking and simulations studies. Panwar U(1), Singh SK(1). Author information: (1)Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 004, Tamil Nadu. India. BACKGROUND: Obesity is well known multifactorial disorder towards the public health concern in front of the world. Increasing rates of obesity has characterized with liver diseases, chronic diseases, diabetes mellitus, hypertension, and stroke, hearts improper function, reproductive and gastrointestinal, gallstones. An essential enzyme pancreatic lipase recognized for the digestion and absorption of lipids, can be a pleasing drug target towards the future development of anti-obesity therapeutics in cure of obesity disorders. OBJECTIVE: The purpose of present study is to identify an effective potential therapeutic agent for the inhibition of pancreatic lipase. METHOD: Using trio of in-silico procedure of HTVS, SP and XP in Glide module, Schrodinger with default parameters, were applied on Specs databases to identify a best potential compound based on receptor grid. Finally, based on binding interaction, docking score and glide energy, selected compounds were taken forward into the platform of IFD, ADME, MMGBSA, DFT, and MDS for analyzing the ligands behavior into the protein binding site. RESULTS: Using in silico protocol of structure based virtual screening on pancreatic lipase were reported top two compounds AN-465/43369242 & AN-465/43384139 from Specs database. Result suggested both the compounds are competitive inhibitors with higher docking score and greatest binding affinity than reported inhibitor. CONCLUSION: Thus, we anticipate that results could be future therapeutic agents and may present an idea toward the experimental studies against the inhibition of pancreatic lipase. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1871530319666181128100903 PMID: 30484411 430. Cancer Manag Res. 2018 Dec 12;10:6897-6904. doi: 10.2147/CMAR.S174815. eCollection 2018. Clinicopathological significance of DAPK promoter methylation in non-small-cell lung cancer: a systematic review and meta-analysis. Zhang Y(1), Wu J(1), Huang G(1), Xu S(2). Author information: (1)Department of Pathology, Huaihe Hospital, Henan University. (2)School of Life Sciences, Henan University, Kaifeng 475004, People's Republic of China, shouming.xu99@gmail.com. Background: Lung carcinogenesis is related to silencing of tumor suppressor genes and activation of oncogenes. The aim was to investigate the significance of death-associated protein kinase (DAPK) methylation in non-small-cell lung cancer (NSCLC) through a meta-analysis. Methods: A detailed literature search was made in PubMed, Embase, and Web of Science databases. All analysis was performed with Review Manager 5.2. Results: In total, 28 studies with a total of 2,148 patients were involved. The frequency of DAPK promoter hypermethylation was 40.50% in NSCLC, significantly higher than in nonmalignant lung tissue; the pooled OR was 5.69, P<0.00001. Additionally, DAPK promoter hypermethylation was significantly correlated with poor overall survival in patients with NSCLC. However, there was no significant difference found while comparing the rate of DAPK promoter hypermethylation in adenocarcinoma and squamous cell cancer. The rate of DAPK promoter hypermethylation was similar between stage III/IV and stage I/II. In addition, the data showed that DAPK promoter hypermethylation was not associated with smoking behavior in patients with NSCLC. Conclusion: DAPK promoter hypermethylation is correlated with risk of NSCLC and is a potential biomarker for prediction of poor prognosis in patients with NSCLC. DOI: 10.2147/CMAR.S174815 PMCID: PMC6296685 PMID: 30588095 Conflict of interest statement: Disclosure The authors report no conflicts of interest in this work. 431. Brief Funct Genomic Proteomic. 2007 Dec;6(4):265-81. doi: 10.1093/bfgp/elm034. Epub 2008 Jan 22. Microarray data analysis and mining approaches. Cordero F(1), Botta M, Calogero RA. Author information: (1)Department of Informatics, University of Torino, Italy. Microarray based transcription profiling is now a consolidated methodology and has widespread use in areas such as pharmacogenomics, diagnostics and drug target identification. Large-scale microarray studies are also becoming crucial to a new way of conceiving experimental biology. A main issue in microarray transcription profiling is data analysis and mining. When microarrays became a methodology of general use, considerable effort was made to produce algorithms and methods for the identification of differentially expressed genes. More recently, the focus has switched to algorithms and database development for microarray data mining. Furthermore, the evolution of microarray technology is allowing researchers to grasp the regulative nature of transcription, integrating basic expression analysis with mRNA characteristics, i.e. exon-based arrays, and with DNA characteristics, i.e. comparative genomic hybridization, single nucleotide polymorphism, tiling and promoter structure. In this article, we will review approaches used to detect differentially expressed genes and to link differential expression to specific biological functions. DOI: 10.1093/bfgp/elm034 PMID: 18216026 [Indexed for MEDLINE] 432. PLoS One. 2011;6(10):e26277. doi: 10.1371/journal.pone.0026277. Epub 2011 Oct 25. A new methodology to associate SNPs with human diseases according to their pathway related context. Bakir-Gungor B(1), Sezerman OU. Author information: (1)Biological Sciences and Bioengineering, Faculty of Engineering, Sabancı University, İstanbul, Turkey. burcu.gungor@bahcesehir.edu.tr Genome-wide association studies (GWAS) with hundreds of żthousands of single nucleotide polymorphisms (SNPs) are popular strategies to reveal the genetic basis of human complex diseases. Despite many successes of GWAS, it is well recognized that new analytical approaches have to be integrated to achieve their full potential. Starting with a list of SNPs, found to be associated with disease in GWAS, here we propose a novel methodology to devise functionally important KEGG pathways through the identification of genes within these pathways, where these genes are obtained from SNP analysis. Our methodology is based on functionalization of important SNPs to identify effected genes and disease related pathways. We have tested our methodology on WTCCC Rheumatoid Arthritis (RA) dataset and identified: i) previously known RA related KEGG pathways (e.g., Toll-like receptor signaling, Jak-STAT signaling, Antigen processing, Leukocyte transendothelial migration and MAPK signaling pathways); ii) additional KEGG pathways (e.g., Pathways in cancer, Neurotrophin signaling, Chemokine signaling pathways) as associated with RA. Furthermore, these newly found pathways included genes which are targets of RA-specific drugs. Even though GWAS analysis identifies 14 out of 83 of those drug target genes; newly found functionally important KEGG pathways led to the discovery of 25 out of 83 genes, known to be used as drug targets for the treatment of RA. Among the previously known pathways, we identified additional genes associated with RA (e.g. Antigen processing and presentation, Tight junction). Importantly, within these pathways, the associations between some of these additionally found genes, such as HLA-C, HLA-G, PRKCQ, PRKCZ, TAP1, TAP2 and RA were verified by either OMIM database or by literature retrieved from the NCBI PubMed module. With the whole-genome sequencing on the horizon, we show that the full potential of GWAS can be achieved by integrating pathway and network-oriented analysis and prior knowledge from functional properties of a SNP. DOI: 10.1371/journal.pone.0026277 PMCID: PMC3201947 PMID: 22046267 [Indexed for MEDLINE] 433. J Med Chem. 2004 Sep 23;47(20):4818-28. Efficient method for high-throughput virtual screening based on flexible docking: discovery of novel acetylcholinesterase inhibitors. Mizutani MY(1), Itai A. Author information: (1)Institute of Medicinal Molecular Design, Inc., 5-24-5 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan. A method of easily finding ligands, with a variety of core structures, for a given target macromolecule would greatly contribute to the rapid identification of novel lead compounds for drug development. We have developed an efficient method for discovering ligand candidates from a number of flexible compounds included in databases, when the three-dimensional (3D) structure of the drug target is available. The method, named ADAM&EVE, makes use of our automated docking method ADAM, which has already been reported. Like ADAM, ADAM&EVE takes account of the flexibility of each molecule in databases, by exploring the conformational space fully and continuously. Database screening has been made much faster than with ADAM through the tuning of parameters, so that computational screening of several hundred thousand compounds is possible in a practical time. Promising ligand candidates can be selected according to various criteria based on the docking results and characteristics of compounds. Furthermore, we have developed a new tool, EVE-MAKE, for automatically preparing the additional compound data necessary for flexible docking calculation, prior to 3D database screening. Among several successful cases of lead discovery by ADAM&EVE, the finding of novel acetylcholinesterase (AChE) inhibitors is presented here. We performed a virtual screening of about 160 000 commercially available compounds against the X-ray crystallographic structure of AChE. Among 114 compounds that could be purchased and assayed, 35 molecules with various core structures showed inhibitory activities with IC(50) values less than 100 microM. Thirteen compounds had IC(50) values between 0.5 and 10 microM, and almost all their core structures are very different from those of known inhibitors. The results demonstrate the effectiveness and validity of the ADAM&EVE approach and provide a starting point for development of novel drugs to treat Alzheimer's disease. DOI: 10.1021/jm030605g PMID: 15369385 [Indexed for MEDLINE] 434. Bioinformation. 2010 Mar 31;4(9):405-8. A ClpP protein model as tuberculosis target for screening marine compounds. Tiwari A(1), Gupta S, Srivastava S, Srivastava R, Rawat AK. Author information: (1)Biotechnology and Bioinformatics Division, BIOBRAINZ, 566/29 J, Jai Prakash Nagar, Alambagh, Lucknow 226005, U.P., India. ATP-dependent Clp protease (ClpP) is a core unit of a major bacterial protease complex employing as a new attractive drug target for that isolates, which are resistant to antibiotics. Mycobacterium tuberculosis, a gram-positive bacterium, is one of the major causes of hospital acquired infections. ClpP in Mycobacterium tuberculosis is usually tightly regulated and strictly requires a member of the family of Clp-ATPase and often further accessory proteins for proteolytic activation. Inhibition of ClpP eliminates these safeguards and start proteolytic degradation. Such uncontrolled proteolysis leads to inhibition of bacterial cell division and eventually cell death. In order to inhibit Clp protease, at first three dimensional structure model of ClpP in Mycobacterium tuberculosis was determined by comparative homology modeling program MODELLER based on crystal structure of the proteolytic component of the caseinolytic Clp protease (ClpP) from E. coli as a template protein and has 55%sequence identity with ClpP protein. The computed model's energy was minimized and validated using PROCHECK to obtain a stable model structure and is submitted in Protein Model Database (PMDB-ID: PM0075741). Stable model was further used for virtual screening against marine derived bioactive compound database through molecular docking studies using AutoDock 3.05. The docked complexes were validated and enumerated based on the AutoDock Scoring function to pick out the best marine inhibitors based on docked Energy. Thus from the entire 186 Marine compounds which were Docked, we got best 5 of them with optimal docked Energy (Ara-A: -14.31 kcal/mol, Dysinosin C: - 14.90kcal/mol, Nagelamide A: -20.49 kcal/mol, Strobilin: -8.02 kcal/mol, Manoalide: -8.81 kcal/mol). Further the five best-docked complexes were analyzed through Python Molecular Viewer software for their interaction studies. Thus from the Complex scoring and binding ability its deciphered that these Marine compounds could be promising inhibitors for ClpP as Drug target yet pharmacological studies have to confirm it. PMCID: PMC2951636 PMID: 20975890 435. Heliyon. 2018 May 8;4(5):e00612. doi: 10.1016/j.heliyon.2018.e00612. eCollection 2018 May. Discovery of small molecules through pharmacophore modeling, docking and molecular dynamics simulation against Plasmodium vivax Vivapain-3 (VP-3). Saddala MS(1)(2), Adi PJ(3). Author information: (1)Centre for Agricultural Bioinformatics, ICAR-IASRI, New Delhi, India. (2)Johns Hopkins University School of Medicine, Baltimore, MD, USA. (3)Sri Venkateswara University, Tirupati, 517502, Andhra Pradesh, India. Vivapain-3(VP-3) protein is a family of cysteine rich proteases of malaria parasite is extensively reported to participate in a range of wide cellular processes including survival. VP-3 of plasmodium recognized as an attractive drug target in vector-borne diseases like malaria. In the present study we robust a homology model of VP-3 protein and generated the pharmacophore based models adapted to screen the best drug like compounds from PubChem database. Our results finds the fourteen best lead molecules were mapped with core pharmacophore features of VP-3 and top hits were further evaluated by molecular dynamics simulation and docking studies. Based on the molecular dynamics simulation and docking results and binding vicinity of ligand molecules, top five i.e., CID 74427945, CID 74427946, CID 360883, CID193721 and CID 51416859 showed the best docking scores with good molecular interactions against VP-3. Furthermore in silico ADMET and in vitro assays clearly exhibited that out of five three CID74427946, CID74427945 and CID360883 ligand molecules showed the best promising inhibition against VP-3. The present study believed to provide significant information of potential ligand inhibitors against VP-3 to design and develop the next generation malaria therapeutics through computational approach. DOI: 10.1016/j.heliyon.2018.e00612 PMCID: PMC5944417 PMID: 29756074 436. Genomics Proteomics Bioinformatics. 2010 Dec;8(4):246-55. doi: 10.1016/S1672-0229(10)60026-5. Identification of potential Leptospira phosphoheptose isomerase inhibitors through virtual high-throughput screening. Umamaheswari A(1), Pradhan D, Hemanthkumar M. Author information: (1)SVIMS Bioinformatics Centre, SVIMS University, Tirupati 517507, India. svims.btisnet@nic.in The life-threatening infections caused by Leptospira serovars demand the need for designing anti-leptospirosis drugs. The present study encompasses exploring inhibitors against phosphoheptose isomerase (GmhA) of Leptospira, which is vital for lipopolysaccharide (LPS) biosynthesis and is identified as a common drug target through the subtractive genomic approach. GmhA model was built in Modeller 9v7. Structural refinement and energy minimization of the predicted model was carried out using Maestro 9.0. The refined model reliability was assessed through Procheck, ProSA, ProQ and Profile 3D. The substrate-based virtual high-throughput screening (VHTS) in Ligand. Info Meta-Database tool generated an in-house library of 354 substrate structural analogs. Furthermore, structure-based VHTS from the in-house library with different conformations of each ligand provided 14 novel competitive inhibitors. The model together with insight gained from the VHTS would be a promising starting point for developing anti-leptospirosis competitive inhibitors targeting LPS biosynthesis pathway. Copyright © 2010 Beijing Genomics Institute. Published by Elsevier Ltd. All rights reserved. DOI: 10.1016/S1672-0229(10)60026-5 PMCID: PMC5054147 PMID: 21382593 [Indexed for MEDLINE] 437. J Mol Graph Model. 2012 Sep;38:235-42. doi: 10.1016/j.jmgm.2012.06.016. Epub 2012 Aug 4. Targeting essential cell wall lipase Rv3802c for potential therapeutics against tuberculosis. Saravanan P(1), Avinash H, Dubey VK, Patra S. Author information: (1)Department of Biotechnology, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India. Cell wall and lipid metabolism plays a vital role in the survival and infection of Mycobacterium tuberculosis. Increase in the incidences of life-threatening multidrug-resistant (MDR) and extreme drug-resistant (XDR) tuberculosis worsens the existing scenario and urge the need of new druggable targets and new drugs. Targeting Rv3802c, an essential cell wall lipase, can open up a new arsenal to fight the dreadful opportunistic pathogen. Our current study highlights the essentiality of Rv3802c. Its 3D structure is predicted for the first time which provides insight in identifying the ligand binding sites. Our analysis showed Rv3802c is highly conserved throughout mycobacterial species with no significant sequence homolog found in human proteome. Virtual screening followed by comparative docking studies of Rv3802c with its closest human structural homolog has been carried out to identify potential inhibitors effective towards mycobacterial proteins. Two diverse molecules from ZINC database, ZINC26726377 and ZINC43866786 have been identified as potential inhibitors effective towards Rv3802c based on the difference in predicted binding free energy of -3.99 and -3.28kcal/mol respectively. Rv3802c is a promising drug target and also a step towards understanding and targeting the pathogen's cell wall and lipid metabolism simultaneously to combat tuberculosis. Copyright © 2012 Elsevier Inc. All rights reserved. DOI: 10.1016/j.jmgm.2012.06.016 PMID: 23085165 [Indexed for MEDLINE] 438. Mol Divers. 2012 Feb;16(1):193-202. doi: 10.1007/s11030-011-9338-x. Epub 2011 Nov 1. Identification of novel potential HIF-prolyl hydroxylase inhibitors by in silico screening. Teli MK(1), Rajanikant GK. Author information: (1)School of Biotechnology, Bioinformatics Infrastructure Facility, National Institute of Technology Calicut, Calicut, 673601, Kerala, India. Suppression of HIF-prolyl hydroxylase (PHD) activity by small-molecule inhibitors leads to the stabilization of hypoxia inducible factor and has been recognized as promising drug target for the treatment of ischemic diseases. In this study, pharmacophore-based virtual screening and molecular docking approaches were concurrently used with suitable modifications to identify target-specific PHD inhibitors with better absorption, distribution, metabolism, and excretion properties and to readily minimize false positives and false negatives. A customized method based on the active site information of the enzyme was used to generate a pharmacophore hypothesis (AAANR). The hypothesis was validated and utilized for chemical database screening and the retrieved hit compounds were subjected to molecular docking for further refinement. AAANR hypothesis comprised three H-bond acceptor, one negative ionizable group and one aromatic ring feature. The hypothesis was validated using decoy set with a goodness of fit score of 2 and was used as a 3D query for database screening. After manual selection, molecular docking and further refinement based on the molecular interactions of inhibitors with the essential amino acid residues, 18 hits with good absorption, distribution, metabolism, and excretion (ADME) properties were selected as excellent PHD inhibitors. The best pharmacophore hypothesis, AAANR, contains chemical features required for the effective inhibition of PHD. Using AAANR, we have identified 18 potential and diverse virtual leads, which can be readily evaluated for their potency as novel inhibitors of PHD. It can be concluded that the combination of pharmacophore, molecular docking, and manual interpretation approaches can be more successful than the traditional approach alone for discovering more effective inhibitors. DOI: 10.1007/s11030-011-9338-x PMID: 22042609 [Indexed for MEDLINE] 439. BMC Cancer. 2018 Jan 4;18(1):22. doi: 10.1186/s12885-017-3939-4. The exploration of contrasting pathways in Triple Negative Breast Cancer (TNBC). Narrandes S(1), Huang S(1)(2), Murphy L(3)(2), Xu W(4)(5)(6). Author information: (1)Research Institute of Oncology and Hematology, CancerCare Manitoba & University of Manitoba, Winnipeg, Canada. (2)College of Pharmacy, University of Manitoba, Winnipeg, Canada. (3)Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Canada. (4)Research Institute of Oncology and Hematology, CancerCare Manitoba & University of Manitoba, Winnipeg, Canada. Wayne.xu@umanitoba.ca. (5)Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Canada. Wayne.xu@umanitoba.ca. (6)College of Pharmacy, University of Manitoba, Winnipeg, Canada. Wayne.xu@umanitoba.ca. BACKGROUND: Triple Negative Breast Cancers (TNBCs) lack the appropriate targets for currently used breast cancer therapies, conferring an aggressive phenotype, more frequent relapse and poorer survival rates. The biological heterogeneity of TNBC complicates the clinical treatment further. We have explored and compared the biological pathways in TNBC and other subtypes of breast cancers, using an in silico approach and the hypothesis that two opposing effects (Yin and Yang) pathways in cancer cells determine the fate of cancer cells. Identifying breast subgroup specific components of these opposing pathways may aid in selecting potential therapeutic targets as well as further classifying the heterogeneous TNBC subtype. METHODS: Gene expression and patient clinical data from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) were used for this study. Gene Set Enrichment Analysis (GSEA) was used to identify the more active pathways in cancer (Yin) than in normal and the more active pathways in normal (Yang) than in cancer. The clustering analysis was performed to compare pathways of TNBC with other types of breast cancers. The association of pathway classified TNBC sub-groups to clinical outcomes was tested using Cox regression model. RESULTS: Among 4729 curated canonical pathways in GSEA database, 133 Yin pathways (FDR < 0.05) and 71 Yang pathways (p-value <0.05) were discovered in TNBC. The FOXM1 is the top Yin pathway while PPARα is the top Yang pathway in TNBC. The TNBC and other types of breast cancers showed different pathways enrichment significance profiles. Using top Yin and Yang pathways as classifier, the TNBC can be further subtyped into six sub-groups each having different clinical outcomes. CONCLUSION: We first reported that the FOMX1 pathway is the most upregulated and the PPARα pathway is the most downregulated pathway in TNBC. These two pathways could be simultaneously targeted in further studies. Also the pathway classifier we performed in this study provided insight into the TNBC heterogeneity. DOI: 10.1186/s12885-017-3939-4 PMCID: PMC5753474 PMID: 29301506 [Indexed for MEDLINE] 440. J Proteome Res. 2013 Jan 4;12(1):33-44. doi: 10.1021/pr300829r. Epub 2012 Dec 20. Decoding the disease-associated proteins encoded in the human chromosome 4. Chen LC(1), Liu MY, Hsiao YC, Choong WK, Wu HY, Hsu WL, Liao PC, Sung TY, Tsai SF, Yu JS, Chen YJ. Author information: (1)Institute of Information Science, Academia Sinica, Taipei, Taiwan. Chromosome 4 is the fourth largest chromosome, containing approximately 191 megabases (~6.4% of the human genome) with 757 protein-coding genes. A number of marker genes for many diseases have been found in this chromosome, including genetic diseases (e.g., hepatocellular carcinoma) and biomedical research (cardiac system, aging, metabolic disorders, immune system, cancer and stem cell) related genes (e.g., oncogenes, growth factors). As a pilot study for the chromosome 4-centric human proteome project (Chr 4-HPP), we present here a systematic analysis of the disease association, protein isoforms, coding single nucleotide polymorphisms of these 757 protein-coding genes and their experimental evidence at the protein level. We also describe how the findings from the chromosome 4 project might be used to drive the biomarker discovery and validation study in disease-oriented projects, using the examples of secretomic and membrane proteomic approaches in cancer research. By integrating with cancer cell secretomes and several other existing databases in the public domain, we identified 141 chromosome 4-encoded proteins as cancer cell-secretable/shedable proteins. Additionally, we also identified 54 chromosome 4-encoded proteins that have been classified as cancer-associated proteins with successful selected or multiple reaction monitoring (SRM/MRM) assays developed. From literature annotation and topology analysis, 271 proteins were recognized as membrane proteins while 27.9% of the 757 proteins do not have any experimental evidence at the protein-level. In summary, the analysis revealed that the chromosome 4 is a rich resource for cancer-associated proteins for biomarker verification projects and for drug target discovery projects. DOI: 10.1021/pr300829r PMID: 23256888 [Indexed for MEDLINE] 441. Genome Biol. 2011 Jul 6;12(7):R63. doi: 10.1186/gb-2011-12-7-r63. Genome sequence and global sequence variation map with 5.5 million SNPs in Chinese rhesus macaque. Fang X(1), Zhang Y, Zhang R, Yang L, Li M, Ye K, Guo X, Wang J, Su B. Author information: (1)Beijing Genomics Institute-Shenzhen, Chinese Academy of Sciences, Shenzhen 518083, China. BACKGROUND: Rhesus macaque (Macaca mulatta) is the most widely used nonhuman primate animal in biomedical research. A global map of genetic variations in rhesus macaque is valuable for both evolutionary and functional studies. RESULTS: Using next-generation sequencing technology, we sequenced a Chinese rhesus macaque genome with 11.56-fold coverage. In total, 96% of the reference Indian macaque genome was covered by at least one read, and we identified 2.56 million homozygous and 2.94 million heterozygous SNPs. We also detected a total of 125,150 structural variations, of which 123,610 were deletions with a median length of 184 bp (ranging from 25 bp to 10 kb); 63% of these deletions were located in intergenic regions and 35% in intronic regions. We further annotated 5,187 and 962 nonsynonymous SNPs to the macaque orthologs of human disease and drug-target genes, respectively. Finally, we set up a genome-wide genetic variation database with the use of Gbrowse. CONCLUSIONS: Genome sequencing and construction of a global sequence variation map in Chinese rhesus macaque with the concomitant database provide applicable resources for evolutionary and biomedical research. DOI: 10.1186/gb-2011-12-7-r63 PMCID: PMC3218825 PMID: 21733155 [Indexed for MEDLINE] 442. Mol Divers. 2009 Nov;13(4):501-17. doi: 10.1007/s11030-009-9141-0. Epub 2009 Apr 4. Molecular modeling studies, synthesis, and biological evaluation of Plasmodium falciparum enoyl-acyl carrier protein reductase (PfENR) inhibitors. Morde VA(1), Shaikh MS, Pissurlenkar RR, Coutinho EC. Author information: (1)Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Kalina, Santacruz (E), Mumbai, India. The search for new antimalarial agents is necessary as current drugs in the market become vulnerable due to the emergence of resistance strains of Plasmodium falciparum (P. falciparum). The biosynthetic pathway for fatty acids has been recognized and validated as an important drug target in P.falciparum. One of the important enzymes in this pathway that has a determinant role in completing the cycles of chain elongation is Enoyl-ACP reductase (ENR) also popularly known as FabI. In this paper we report the design, synthesis, and microbial evaluation of inhibitors of Plasmodium enoyl reductase (PfENR). The search for inhibitors involved a virtual screening of the iResearch database with docking simulations. One of the hits was selected and modified to optimize its binding to PfENR; this resulted in the development of analogues of N-benzylidene-4-phenyl-1,3-thiazol-2-amine. The activity of these analogues was predicted from comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models constructed from a dataset of 43 known inhibitors of PfENR. The most promising molecules were synthesized and their structures characterized by spectroscopic techniques. The molecules were screened for in vitro antimalarial activity by whole-cell assay method. Two molecules, viz. VRC-007 and VRC-009, were found to be active at 4.67 and 7.01 microM concentrations, respectively. DOI: 10.1007/s11030-009-9141-0 PMID: 19347595 [Indexed for MEDLINE] 443. BMC Res Notes. 2011 Nov 28;4:520. doi: 10.1186/1756-0500-4-520. Integration of gene expression data with prior knowledge for network analysis and validation. Ante M(1), Wingender E, Fuchs M. Author information: (1)Department of Bioinformatics, Medical School, Georg-August-University Goettingen, Goldschmidtstr, 1, 37077 Goettingen, Germany. m.ante@bioinf.med.uni-goettingen.de. BACKGROUND: Reconstruction of protein-protein interaction or metabolic networks based on expression data often involves in silico predictions, while on the other hand, there are unspecific networks of in vivo interactions derived from knowledge bases.We analyze networks designed to come as close as possible to data measured in vivo, both with respect to the set of nodes which were taken to be expressed in experiment as well as with respect to the interactions between them which were taken from manually curated databases RESULTS: A signaling network derived from the TRANSPATH database and a metabolic network derived from KEGG LIGAND are each filtered onto expression data from breast cancer (SAGE) considering different levels of restrictiveness in edge and vertex selection.We perform several validation steps, in particular we define pathway over-representation tests based on refined null models to recover functional modules. The prominent role of the spindle checkpoint-related pathways in breast cancer is exhibited. High-ranking key nodes cluster in functional groups retrieved from literature. Results are consistent between several functional and topological analyses and between signaling and metabolic aspects. CONCLUSIONS: This construction involved as a crucial step the passage to a mammalian protein identifier format as well as to a reaction-based semantics of metabolism. This yielded good connectivity but also led to the need to perform benchmark tests to exclude loss of essential information. Such validation, albeit tedious due to limitations of existing methods, turned out to be informative, and in particular provided biological insights as well as information on the degrees of coherence of the networks despite fragmentation of experimental data.Key node analysis exploited the networks for potentially interesting proteins in view of drug target prediction. DOI: 10.1186/1756-0500-4-520 PMCID: PMC3298547 PMID: 22123172 444. Bioinformation. 2012;8(14):678-83. doi: 10.6026/97320630008678. Epub 2012 Jul 21. Molecular docking of (5E)-3-(2-aminoethyl)-5-(2- thienylmethylene)-1, 3-thiazolidine-2, 4-dione on HIV-1 reverse transcriptase: novel drug acting on enzyme. Seniya C(1), Yadav A, Uchadia K, Kumar S, Sagar N, Shrivastava P, Shrivastava S, Wadhwa G. Author information: (1)Department of Biotechnology, Madhav Institute of Technology & Science Gwalior - 474005, M. P., India. The study of Human immunodeficiency virus (HIV) in humans and animal models in last 31 years suggested that it is a causative agent of AIDS. This causes serious pandemic public health concern globally. It was reported that the HIV-1 reverse transcriptase (RT) played a critical role in the life cycle of HIV. Therefore, inhibition of HIV-1RT enzyme is one of the major and potential targets in the treatment of AIDS. The enzyme (HIV-1RT) was successfully targeted by non nucleotide reverse transcriptase inhibitors (NNRTIs). But frequent application of NNRTIs led drug resistance mutation on HIV infections. Therefore, there is a need to search new NNRTIs with appropriate pharmacophores. For the purpose, a virtually screened 3D model of unliganded HIV-1RT (1DLO) was explored. The unliganded HIV-1RT (1DLO) was docked with 4-thiazolidinone and its derivatives (ChemBank Database) by using AutoDock4. The best seven docking solutions complex were selected and analyzed by Ligplot. The analysis showed that derivative (5E)-3-(2- aminoethyl)-5-(2- thienylmethylene)-1, 3-thiazolidine-2, 4-dione (CID 3087795) has maximum potential against unliganded HIV-1RT (1DLO). The analysis was done on the basis of scoring and binding ability. The derivative (5E)-3-(2- aminoethyl)-5-(2- thienylmethylene)-1, 3-thiazolidine-2, 4-dione (CID 3087795) indicated minimum energy score and highest number of interactions with active site residue and could be a promising inhibitor for HIV-1 RT as Drug target. DOI: 10.6026/97320630008678 PMCID: PMC3449371 PMID: 23055609 445. Cancer Genomics Proteomics. 2018 Jul-Aug;15(4):273-278. doi: 10.21873/cgp.20085. Glioblastoma Multiforme: Fewer Tumor Copy Number Segments of the SGK1 Gene Are Associated with Poorer Survival. Lehrer S(1), Rheinstein PH(2), Rosenzweig KE(2). Author information: (1)Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, U.S.A. steven.lehrer@mssm.edu. (2)Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, U.S.A. BACKGROUND/AIM: Glioblastoma multiforme (GBM) is the most common primary tumor of the central nervous system. The serum and glucocorticoid-regulated kinase SGK1 gene is required for the growth and survival of GBM stem-like cells under both normoxic and hypoxic conditions. It has been reported that oxygenation significantly affects cellular genetic expression; 30% of the genes required in hypoxia were not required under normoxic conditions. Therefore, we examined SGK1 expression to determine if it may be a novel potential drug target for GBM. MATERIALS AND METHODS: We assessed the association between SGK1 and glioblastoma patient overall survival using the GBM cohort in TCGA (The Cancer Genome Atlas) database (TCGA-GBM). To access and analyze the data we used the UCSC Xena browser (https://xenabrowser.net). Survival data of the GBM subgroup were extracted for analysis and generation of Kaplan-Meier curves for overall survival. The best cut-off was identified by methods described in the R2 web-based application (http://r2.amc.nl). RESULTS: We analyzed patient survival by tumor SGK1 copy number segments after removal of common germ-line copy-number variants (CNVs). Copy number segments (log2 tumor/normal) ≤0.009700 were associated with significantly poorer survival (p=0.016). CONCLUSION: Increased median overall survival associated with increased SGK1 copy number segments may be a reflection of better tumor oxygenation. Therefore, besides being a drug target, SGK1 may also be a prognostic marker. Among molecular tumor markers, only the methylation status of the O-6-methylguanine-DNA methyltransferase (MGMT) gene has shown a significant association with survival in patients with GBM. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved. DOI: 10.21873/cgp.20085 PMCID: PMC6070715 PMID: 29976632 [Indexed for MEDLINE] 446. Biopolymers. 1999-2000;52(4):157-67. The energetics of small internal loops in RNA. Schroeder SJ(1), Burkard ME, Turner DH. Author information: (1)Department of Chemistry, University of Rochester, RC Box 270216, Rochester, NY 14627-0216, USA. The energetics of small internal loops are important for prediction of RNA secondary and tertiary structure, selection of drug target sites, and understanding RNA structure-function relationships. Hydrogen bonding, base stacking, electrostatic interactions, backbone distortion, and base-pair size compatibility all contribute to the energetics of small internal loops. Thus, the sequence dependence of these energetics are idiosyncratic. Current approximations for predicting the free energies of internal loops consider size, asymmetry, closing base pairs, and the potential to form GA, GG, or UU pairs. The database of known three-dimensional structures allows for comparison with the models used for predicting stability from sequence. Copyright 2001 John Wiley & Sons, Inc. DOI: 10.1002/1097-0282(1999)52:4<157::AID-BIP1001>3.0.CO;2-E PMID: 11295748 [Indexed for MEDLINE] 447. In Silico Biol. 2003;3(4):429-40. Accelerating comparative genomics using parallel computing. Janaki C(1), Joshi RR. Author information: (1)Bioinformatics Team, Scientific and Engineering Computing Group, Centre for Development of Advanced Computing, Pune University Campus, Ganeshkhind, Pune-411007, India. In the past decade there has been an increase in the number of completely sequenced genomes due to the race of multibillion-dollar genome-sequencing projects. The enormous biological sequence data thus flooding into the sequence databases necessitates the development of efficient tools for comparative genome sequence analysis. The information deduced by such analysis has various applications viz. structural and functional annotation of novel genes and proteins, finding gene order in the genome, gene fusion studies, constructing metabolic pathways etc. Such study also proves invaluable for pharmaceutical industries, such as in silico drug target identification and new drug discovery. There are various sequence analysis tools available for mining such useful information of which FASTA and Smith-Waterman algorithms are widely used. However, analyzing large datasets of genome sequences using the above codes seems to be impractical on uniprocessor machines. Hence there is a need for improving the performance of the above popular sequence analysis tools on parallel cluster computers. Performance of the Smith-Waterman (SSEARCH) and FASTA programs were studied on PARAM 10000, a parallel cluster of workstations designed and developed in-house. FASTA and SSEARCH programs, which are available from the University of Virginia, were ported on PARAM and were optimized. In this era of high performance computing, where the paradigm is shifting from conventional supercomputers to the cost-effective general-purpose cluster of workstations and PCs, this study finds extreme relevance. Good performance of sequence analysis tools on a cluster of workstations was demonstrated, which is important for accelerating identification of novel genes and drug targets by screening large databases. PMID: 12954086 [Indexed for MEDLINE] 448. Curr Comput Aided Drug Des. 2011 Sep 1;7(3):159-72. Simplified receptor based pharmacophore approach to retrieve potent PTP-LAR inhibitors using apoenzyme. Ajay D(1), Sobhia ME. Author information: (1)Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Punjab, India. The design of biological active compounds from the apoenzyme is still a challenging task. Herein a simple yet efficient technique is reported to generate a receptor based pharmacophore solely using a ligand-free protein crystal structure. Human leukocyte antigen-related phosphatase (PTP-LAR) is an apoenzyme and a receptor like transmembrane phosphatase that has emerged as a drug target for diabetes, obesity and cancer. The prior knowledge of the active residues responsible for the mechanism of action of the protein was used to generate the LUDI interaction map. Then, the complement negative image of the binding site was used to generate the pharmacophore features. A unique strategy was followed to design a pharmacophore query maintaining crucial interactions with all the active residues, essential for the enzyme inhibition. The same query was used to screen several databases consisting of the Specs, IBS, MiniMaybridge, NCI and an in-house PTP inhibitor databases. In order to overcome the common bioavailability problem associated with phosphatases, the hits obtained were filtered by Lipinski's Rule of Five, SADMET properties and validated by docking studies in Glide and GOLD. These docking studies not only suggest the essential ligand binding interactions but also the binding patterns necessary for the LAR inhibition. The ligand pharmacophore mapping studies further validated the screened protocol and supported that the final screened molecules, presumably, showed potent inhibitory activity. Subsequently, these molecules were subjected to Derek toxicity predictions and nine new molecules with different scaffold were obtained as non-toxic PTP-LAR inhibitors. The present prospective strategy is a powerful technique to identify potent inhibitors using the protein 3D structure alone and is a valid alternative to other structure-based and random docking approaches. PMID: 21726194 [Indexed for MEDLINE] 449. J Biomol Struct Dyn. 2018 Nov 1:1-14. doi: 10.1080/07391102.2018.1479310. [Epub ahead of print] High throughput screening, docking, and molecular dynamics studies to identify potential inhibitors of human calcium/calmodulin-dependent protein kinase IV. Beg A(1), Khan FI(2), Lobb KA(2), Islam A(1), Ahmad F(1), Hassan MI(1). Author information: (1)a Centre for Interdisciplinary Research in Basic Sciences , Jamia Millia Islamia , New Delhi , India. (2)b Computational Mechanistic Chemistry and Drug Discovery , Rhodes University , Grahamstown , South Africa. Calcium/calmodulin-dependent protein kinase IV (CAMKIV) is associated with many diseases including cancer and neurodegenerative disorders and thus being considered as a potential drug target. Here, we have employed the knowledge of three-dimensional structure of CAMKIV to identify new inhibitors for possible therapeutic intervention. We have employed virtual high throughput screening of 12,500 natural compounds of Zinc database to screen the best possible inhibitors of CAMKIV. Subsequently, 40 compounds which showed significant docking scores (-11.6 to -10.0 kcal/mol) were selected and further filtered through Lipinski rule and drug likeness parameter to get best inhibitors of CAMKIV. Docking results are indicating that ligands are binding to the hydrophobic cavity of the kinase domain of CAMKIV and forming a significant number of non-covalent interactions. Four compounds, ZINC02098378, ZINC12866674, ZINC04293413, and ZINC13403020, showing excellent binding affinity and drug likeness were subjected to molecular dynamics simulation to evaluate their mechanism of interaction and stability of protein-ligand complex. Our observations clearly suggesting that these selected ligands may be further employed for therapeutic intervention to address CAMKIV associated diseases. Communicated by Ramaswamy H. Sarma. DOI: 10.1080/07391102.2018.1479310 PMID: 30044185 450. J Mol Recognit. 2018 Jul;31(7):e2706. doi: 10.1002/jmr.2706. Epub 2018 Apr 6. Structural insights into suppressor of cytokine signaling 1 protein- identification of new leads for type 2 diabetes mellitus. Dumpati R(1), Ramatenki V(1), Vadija R(1), Vellanki S(1), Vuruputuri U(1). Author information: (1)Department of Chemistry, University College of Science, Osmania University, Hyderabad, Telangana State, India. The study considers the Suppressor of cytokine signaling 1 (SOCS1) protein as a novel Type 2 diabetes mellitus (T2DM) drug target. T2DM in human beings is also triggered by the over expression of SOCS proteins. The SOCS1 acts as a ubiquitin ligase (E3), degrades Insulin Receptor Substrate 1 and 2 (IRS1 and IRS2) proteins, and causes insulin resistance. Therefore, the structure of the SOCS1 protein was evaluated using homology-modeling and molecular dynamics methods and validated using standard computational protocols. The Protein-Protein docking study of SOCS1 with its natural substrates, IRS1 and IRS2, and subsequent solvent accessible surface area analysis gave insight into the binding region of the SOCS1 protein. The in silico active site prediction tools highlight the residues Val155 to Ile211 in SOCS1 being implicated in the ubiquitin mediated protein degradation of the proteins IRS1 and IRS2. Virtual screening in the active site region, using large structural databases, results in selective lead structures with 3-Pyridinol, Xanthine, and Alanine moieties as Pharmacophore. The virtual screening study shows that the residues Glu149, Gly187, Arg188, Leu191, and Ser205 of the SOCS1 are important for binding. The docking study with current anti-diabetic therapeutics shows that the drugs Glibenclamide and Glyclopyramide have a partial affinity towards SOCS1. The predicted ADMET and IC50 properties for the identified ligands are within the acceptable range with drug-like properties. The structural data of SOCS1, its active site, and the identified lead structures are expedient in the development of new T2DM therapeutics. Copyright © 2018 John Wiley & Sons, Ltd. DOI: 10.1002/jmr.2706 PMID: 29630758 451. Eur J Med Chem. 2008 Aug;43(8):1603-11. doi: 10.1016/j.ejmech.2007.11.014. Epub 2007 Nov 29. A three-dimensional pharmacophore model for dipeptidyl peptidase IV inhibitors. Lu IL(1), Tsai KC, Chiang YK, Jiaang WT, Wu SH, Mahindroo N, Chien CH, Lee SJ, Chen X, Chao YS, Wu SY. Author information: (1)Division of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan Town, Miaoli County 350, Taiwan. Erratum in Eur J Med Chem. 2009 Jun;44(6):2763. Dipeptidyl peptidase IV (DPP-IV) is a valid drug target for type-2 diabetes and DPP-IV inhibitors have been proven to efficiently improve glucose tolerance. In our study, 3D pharmacophore models were generated using a training set of 22 DPP-IV inhibitors. The best model consisted of important chemical features and mapped well into the active site of DPP-IV. The model gave high correlation coefficients of 0.97 and 0.84 for the training set and the test set, respectively, showing its good predictive ability for biological activity. Furthermore, the pharmacophore model demonstrated the capability to retrieve inhibitors from database with a high enrichment factor of 42.58. All results suggest that the model provides a useful tool for designing novel DPP-IV inhibitors. DOI: 10.1016/j.ejmech.2007.11.014 PMID: 18207285 [Indexed for MEDLINE] 452. Sci Rep. 2018 Aug 24;8(1):12715. doi: 10.1038/s41598-018-30579-3. ESCC ATLAS: A population wide compendium of biomarkers for Esophageal Squamous Cell Carcinoma. Tungekar A(1)(2), Mandarthi S(1)(3), Mandaviya PR(1)(2), Gadekar VP(1)(2)(4), Tantry A(1)(5), Kotian S(1)(2), Reddy J(1)(2), Prabha D(1), Bhat S(1)(2), Sahay S(1), Mascarenhas R(1)(2)(6), Badkillaya RR(1)(7), Nagasampige MK(1)(8), Yelnadu M(1)(5)(9)(10), Pawar H(10), Hebbar P(11)(12), Kashyap MK(13)(14)(15)(16). Author information: (1)Mbiomics, Manipal, Karnataka, India. (2)Manipal Life Sciences Center, Manipal University, Manipal, Karnataka, India. (3)Department of Biochemistry, Kasturba Medical College, Manipal University, Manipal, Karnataka, India. (4)Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, 1090, Vienna, Austria. (5)Manipal Center for Information Sciences, Manipal University, Manipal, Karnataka, India. (6)Newcastle University Medicine Malaysia, Johor Bahru, 79200, Malaysia. (7)Department of Biotechnology, Alva's college, Moodubidre, Karnataka, India. (8)Department of Biotechnology, Sikkim Manipal University, Gangtok, Sikkim, 737102, India. (9)Infosys Technologies Ltd, Bangalore, Karnataka, India. (10)Faculty of Biology, Technion-Israel Institute of Technology, Haifa, 3200003, Israel. (11)Mbiomics, Manipal, Karnataka, India. phebbar@mbiomics.org. (12)Manipal Life Sciences Center, Manipal University, Manipal, Karnataka, India. phebbar@mbiomics.org. (13)Mbiomics, Manipal, Karnataka, India. manojkkashyap@gmail.com. (14)Faculty of Applied Sciences and Biotechnology, Shoolini University of Biotechnology and Management Sciences, Bajhol, Solan, Himachal Pradesh 173229, India. manojkkashyap@gmail.com. (15)School of Life and Allied Health Sciences, Glocal University, Saharanpur, Uttar Pradesh, 247001, India. manojkkashyap@gmail.com. (16)Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, 1090, Vienna, Austria. manojkkashyap@gmail.com. Esophageal cancer (EC) is the eighth most aggressive malignancy and its treatment remains a challenge due to the lack of biomarkers that can facilitate early detection. EC is identified in two major histological forms namely - Adenocarcinoma (EAC) and Squamous cell carcinoma (ESCC), each showing differences in the incidence among populations that are geographically separated. Hence the detection of potential drug target and biomarkers demands a population-centric understanding of the molecular and cellular mechanisms of EC. To provide an adequate impetus to the biomarker discovery for ESCC, which is the most prevalent esophageal cancer worldwide, here we have developed ESCC ATLAS, a manually curated database that integrates genetic, epigenetic, transcriptomic, and proteomic ESCC-related genes from the published literature. It consists of 3475 genes associated to molecular signatures such as, altered transcription (2600), altered translation (560), contain copy number variation/structural variations (233), SNPs (102), altered DNA methylation (82), Histone modifications (16) and miRNA based regulation (261). We provide a user-friendly web interface ( http://www.esccatlas.org , freely accessible for academic, non-profit users) that facilitates the exploration and the analysis of genes among different populations. We anticipate it to be a valuable resource for the population specific investigation and biomarker discovery for ESCC. DOI: 10.1038/s41598-018-30579-3 PMCID: PMC6109081 PMID: 30143675 453. Mol Biosyst. 2011 Sep;7(9):2702-10. doi: 10.1039/c1mb05228d. Epub 2011 Jul 21. In silico pharmacology suggests ginger extracts may reduce stroke risks. Chang TT(1), Chen KC, Chang KW, Chen HY, Tsai FJ, Sun MF, Chen CY. Author information: (1)Laboratory of Computational and Systems Biology, School of Chinese Medicine, China Medical University, Taichung, 40402, Taiwan. Aberrations in cyclic adenosine monophosphate (cAMP) signaling cascade has been linked to the allergic responses that associate with the risks of stroke or cardiovascular diseases. Phosphodiesterase 4D (PDE4D) has been shown to be highly involved in cAMP regulation and is hence implied to be a potential drug target in stroke prevention. To identify potential PDE4D inhibitors from traditional Chinese medicine (TCM), we employed machine learning modeling techniques to screen a comprehensive TCM database. The multiple linear regression (MLR) and support vector machine (SVM) models constructed have correlation coefficients of 0.8234 and 0.7854 respectively. Three candidates from the ginger family were identified based on the prediction models. Molecular dynamics simulation further validated the binding stabilities of each candidate in comparison to the control inhibitor L-454560. The intermolecular distances suggested that the candidates could hinder PDE4D from binding to cAMP. Furthermore, the HypoGen validation suggested that top2, top3, and the control L-454560 mapped with the predicted pharmacophores. The results suggested that the 3 compounds identified from the ginger family were capable in inhibiting cAMP binding and hydrolysis by PDE4D. We further identified and characterized the ligand binding properties that are associated with the inhibition of PDE4D. DOI: 10.1039/c1mb05228d PMID: 21776525 [Indexed for MEDLINE] 454. Methods Mol Biol. 2009;528:159-76. doi: 10.1007/978-1-60327-310-7_12. Membrane protease degradomics: proteomic identification and quantification of cell surface protease substrates. Butler GS(1), Dean RA, Smith D, Overall CM. Author information: (1)Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada. The modification of cell surface proteins by plasma membrane and soluble proteases is important for physiological and pathological processes. Methods to identify shed and soluble substrates are crucial to further define the substrate repertoire, termed the substrate degradome, of individual proteases. Identifying protease substrates is essential to elucidate protease function and involvement in different homeostatic and disease pathways. This characterisation is also crucial for drug target identification and validation, which would then allow the rational design of specific targeted inhibitors for therapeutic intervention. We describe two methods for identifying and quantifying shed cell surface protease targets in cultured cells utilising Isotope-Coded Affinity Tags (ICAT) and Isobaric Tags for Relative and Absolute Quantification (iTRAQ). As a model system to develop these techniques, we chose a cell-membrane expressed matrix metalloproteinase, MMP-14, but the concepts can be applied to proteases of other classes. By over-expression, or conversely inhibition, of a particular protease with careful selection of control conditions (e.g. vector or inactive protease) and differential labelling, shed proteins can be identified and quantified by mass spectrometry (MS), MS/MS fragmentation and database searching. DOI: 10.1007/978-1-60327-310-7_12 PMID: 19153692 [Indexed for MEDLINE] 455. Proteins. 2008 Jul;72(1):367-81. doi: 10.1002/prot.21933. Method for comparing the structures of protein ligand-binding sites and application for predicting protein-drug interactions. Minai R(1), Matsuo Y, Onuki H, Hirota H. Author information: (1)RIKEN Genomic Sciences Center, Tsurumi-ku, Yokohama 230-0045, Japan. Many drugs, even ones that are designed to act selectively on a target protein, bind unintended proteins. These unintended bindings can explain side effects or indicate additional mechanisms for a drug's medicinal properties. Structural similarity between binding sites is one of the reasons for binding to multiple targets. We developed a method for the structural alignment of atoms in the solvent-accessible surface of proteins that uses similarities in the local atomic environment, and carried out all-against-all structural comparisons for 48,347 potential ligand-binding regions from a nonredundant protein structure subset (nrPDB, provided by NCBI). The relationships between the similarity of ligand-binding regions and the similarity of the global structures of the proteins containing the binding regions were examined. We found 10,403 known ligand-binding region pairs whose structures were similar despite having different global folds. Of these, we detected 281 region pairs that had similar ligands with similar binding modes. These proteins are good examples of convergent evolution. In addition, we found a significant correlation between Z-score of structural similarity and true positive rate of "active" entries in the PubChem BioAssay database. Moreover, we confirmed the interaction between ibuprofen and a new target, porcine pancreatic elastase, by NMR experiment. Finally, we used this method to predict new drug-target protein interactions. We obtained 540 predictions for 105 drugs (e.g., captopril, lovastatin, flurbiprofen, metyrapone, and salicylic acid), and calculated the binding affinities using AutoDock simulation. The results of these structural comparisons are available at http://www.tsurumi.yokohama-cu.ac.jp/fold/database.html. 2008 Wiley-Liss, Inc. DOI: 10.1002/prot.21933 PMID: 18214952 [Indexed for MEDLINE] 456. Front Pharmacol. 2018 Mar 6;9:146. doi: 10.3389/fphar.2018.00146. eCollection 2018. QSAR-Driven Design and Discovery of Novel Compounds With Antiplasmodial and Transmission Blocking Activities. Lima MNN(1), Melo-Filho CC(1), Cassiano GC(2), Neves BJ(1)(3), Alves VM(1), Braga RC(1), Cravo PVL(4), Muratov EN(5)(6), Calit J(7), Bargieri DY(7), Costa FTM(2), Andrade CH(1)(2). Author information: (1)LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil. (2)Laboratory of Tropical Diseases - Prof. Dr. Luiz Jacintho da Silva, Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, UNICAMP, Campinas, Brazil. (3)Laboratory of Cheminformatics, PPG-SOMA, University Center of Anápolis/UniEVANGELICA, Anápolis, Brazil. (4)Global Health and Tropical Medicine Centre, Unidade de Parasitologia Médica, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal. (5)Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States. (6)Department of Chemical Technology, Odessa National Polytechnic University, Odessa, Ukraine. (7)Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil. Malaria is a life-threatening infectious disease caused by parasites of the genus Plasmodium, affecting more than 200 million people worldwide every year and leading to about a half million deaths. Malaria parasites of humans have evolved resistance to all current antimalarial drugs, urging for the discovery of new effective compounds. Given that the inhibition of deoxyuridine triphosphatase of Plasmodium falciparum (PfdUTPase) induces wrong insertions in plasmodial DNA and consequently leading the parasite to death, this enzyme is considered an attractive antimalarial drug target. Using a combi-QSAR (quantitative structure-activity relationship) approach followed by virtual screening and in vitro experimental evaluation, we report herein the discovery of novel chemical scaffolds with in vitro potency against asexual blood stages of both P. falciparum multidrug-resistant and sensitive strains and against sporogonic development of P. berghei. We developed 2D- and 3D-QSAR models using a series of nucleosides reported in the literature as PfdUTPase inhibitors. The best models were combined in a consensus approach and used for virtual screening of the ChemBridge database, leading to the identification of five new virtual PfdUTPase inhibitors. Further in vitro testing on P. falciparum multidrug-resistant (W2) and sensitive (3D7) parasites showed that compounds LabMol-144 and LabMol-146 demonstrated fair activity against both strains and presented good selectivity versus mammalian cells. In addition, LabMol-144 showed good in vitro inhibition of P. berghei ookinete formation, demonstrating that hit-to-lead optimization based on this compound may also lead to new antimalarials with transmission blocking activity. DOI: 10.3389/fphar.2018.00146 PMCID: PMC5845645 PMID: 29559909 457. Mol Inform. 2019 Jan;38(1-2):e1800046. doi: 10.1002/minf.201800046. Epub 2018 Sep 14. HToPred: A Tool for Human Topoisomerase II Inhibitor Prediction. Tripathi N(1), Shaikh N(1), Bharatam PV(1)(2), Garg P(1). Author information: (1)Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Sector 67, S.A.S. Nagar, Mohali, Punjab, 160062, India. (2)Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research (NIPER), Sector 67, S.A.S. Nagar, Mohali, Punjab, 160062, India. The enzyme human topoisomerase IIα (hTopoIIα) is an important anticancer drug target. Due to the availability of multiple inhibitor-binding sites in this enzyme, the anti-hTopoII agents possess high chemical diversity. Chemoinformatics methods can be used to identify lead compounds from large databases for hTopoII inhibitory activity and classify them. In this work, we report the use of machine learning methods to develop classification models for the identification of possible anti-hTopoIIα agents and to classify them as catalytic inhibitors vs. poisons. Initially, an extensive dataset of small molecules which are reported to be evaluated towards hTopoIIα inhibition was collected from ChEMBL database and literature. Using this dataset, predictive models for classifying small molecules into hTopoIIα inhibitors and non-inhibitors were developed. Additionally, the model development was taken up for the prediction of the type of hTopoIIα inactivation. Several molecular fingerprints and physicochemical descriptors of the molecules in the dataset were calculated using the chemoinformatics tool RDKit. Various classifiers were evaluated to establish suitable protocol. Further, ensemble models were developed by bagging of homogenous classifier and selective fusion of heterogeneous classifiers. The models were thoroughly validated with 5-fold cross validation and external validation. The best performing models were incorporated into a tool christened as Human Topoisomerase IIα Inhibitor Prediction (HToPred, http://14.139.57.41/HToPred). A molecular docking based validation for the successful application of HToPred in predicting the mode of enzyme inhibition was performed, which further established the acceptability of this tool. This tool can serve as an important platform to prescreen compounds for anti-hTopoIIα potential. © 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. DOI: 10.1002/minf.201800046 PMID: 30216700 458. BMC Bioinformatics. 2008 Aug 12;9 Suppl 9:S5. doi: 10.1186/1471-2105-9-S9-S5. ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining. Huan T(1), Sivachenko AY, Harrison SH, Chen JY. Author information: (1)School of Informatics, Indiana University - Purdue University, Indianapolis, IN 46202, USA. huant@iupui.edu BACKGROUND: New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. RESULTS: We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network. CONCLUSION: The architectural design of ProteoLens makes it suitable for bioinformatics expert data analysts who are experienced with relational database management to perform large-scale integrated network visual explorations. ProteoLens is a promising visual analytic platform that will facilitate knowledge discoveries in future network and systems biology studies. DOI: 10.1186/1471-2105-9-S9-S5 PMCID: PMC2537576 PMID: 18793469 [Indexed for MEDLINE] 459. J Comput Aided Mol Des. 2008 Dec;22(12):925-33. doi: 10.1007/s10822-008-9229-0. Epub 2008 Aug 7. Molecular dynamics simulation study of PTP1B with allosteric inhibitor and its application in receptor based pharmacophore modeling. Bharatham K(1), Bharatham N, Kwon YJ, Lee KW. Author information: (1)Division of Applied Life Science (BK21 Program), Environmental Biotechnology National Core Research Center, Gyeongsang National University, Jinju, 660-701, Korea. Allosteric inhibition of protein tyrosine phosphatase 1B (PTP1B), has paved a new path to design specific inhibitors for PTP1B, which is an important drug target for the treatment of type II diabetes and obesity. The PTP1B1-282-allosteric inhibitor complex crystal structure lacks alpha7 (287-298) and moreover there is no available 3D structure of PTP1B1-298 in open form. As the interaction between alpha7 and alpha6-alpha3 helices plays a crucial role in allosteric inhibition, alpha7 was modeled to the PTP1B1-282 in open form complexed with an allosteric inhibitor (compound-2) and a 5 ns MD simulation was performed to investigate the relative orientation of the alpha7-alpha6-alpha3 helices. The simulation conformational space was statistically sampled by clustering analyses. This approach was helpful to reveal certain clues on PTP1B allosteric inhibition. The simulation was also utilized in the generation of receptor based pharmacophore models to include the conformational flexibility of the protein-inhibitor complex. Three cluster representative structures of the highly populated clusters were selected for pharmacophore model generation. The three pharmacophore models were subsequently utilized for screening databases to retrieve molecules containing the features that complement the allosteric site. The retrieved hits were filtered based on certain drug-like properties and molecular docking simulations were performed in two different conformations of protein. Thus, performing MD simulation with alpha7 to investigate the changes at the allosteric site, then developing receptor based pharmacophore models and finally docking the retrieved hits into two distinct conformations will be a reliable methodology in identifying PTP1B allosteric inhibitors. DOI: 10.1007/s10822-008-9229-0 PMID: 18685809 [Indexed for MEDLINE] 460. Iran J Public Health. 2012;41(7):24-33. Epub 2012 Jul 31. Computational Prediction of Phylogenetically Conserved Sequence Motifs for Five Different Candidate Genes in Type II Diabetic Nephropathy. Sindhu T(1), Rajamanikandan S, Srinivasan P. Author information: (1)Dept. of Bioinformatics, Alagappa University, Karaikudi, India. BACKGROUND: Computational identification of phylogenetic motifs helps to understand the knowledge about known functional features that includes catalytic site, substrate binding epitopes, and protein-protein interfaces. Furthermore, they are strongly conserved among orthologs, indicating their evolutionary importance. The study aimed to analyze five candidate genes involved in type II diabetic nephropathy and to predict phylogenetic motifs from their corresponding orthologous protein sequences. METHODS: AKR1B1, APOE, ENPP1, ELMO1 and IGFBP1 are the genes that have been identified as an important target for type II diabetic nephropathy through experimental studies. Their corresponding protein sequences, structures, orthologous sequences were retrieved from UniprotKB, PDB, and PHOG database respectively. Multiple sequence alignments were constructed using ClustalW and phylogenetic motifs were identified using MINER. The occurrence of amino acids in the obtained phylogenetic motifs was generated using WebLogo and false positive expectations were calculated against phylogenetic similarity. RESULTS: In total, 17 phylogenetic motifs were identified from the five proteins and the residues such as glycine, leucine, tryptophan, aspartic acid were found in appreciable frequency whereas arginine identified in all the predicted PMs. The result implies that these residues can be important to the functional and structural role of the proteins and calculated false positive expectations implies that they were generally conserved in traditional sense. CONCLUSION: The prediction of phylogenetic motifs is an accurate method for detecting functionally important conserved residues. The conserved motifs can be used as a potential drug target for type II diabetic nephropathy. PMCID: PMC3469011 PMID: 23113206 461. Curr Opin Drug Discov Devel. 2009 Sep;12(5):628-43. A new generation of anti-histamines: Histamine H4 receptor antagonists on their way to the clinic. Engelhardt H(1), Smits RA, Leurs R, Haaksma E, de Esch IJ. Author information: (1)Boehringer Ingelheim RCV GmbH & Co KG, Department of Medicinal Chemistry, Vienna, Austria. At the turn of the millennium, the DNA sequence encoding the histamine H4 receptor (H4R) was identified in data from human genome databases. Considering the clinical importance of H1R and H2R ligands, and the clinical trials that are ongoing for H3R ligands, the latest addition to the histamine receptor family was noted with interest by the pharmaceutical industry. Initial studies describing the expression of the H4R, and the activity of this receptor in (patho)physiology, suggested that the H4R played a role in the immune system. The introduction of the reference H4R antagonist JNJ-7777120 (Johnson & Johnson Pharmaceutical Research & Development LLC/Abbott Laboratories), and proof of the efficacy of this agent in models of asthma, allergic rhinitis and pruritus, highlighted the H4R as a novel drug target. The first clinical candidates targeting the H4R have been identified, and new H4R antagonists are expected to enter the clinic in the near future. PMID: 19736622 [Indexed for MEDLINE] 462. Trans R Soc Trop Med Hyg. 2002 Jan-Feb;96(1):7-17. The Brugia malayi genome project: expressed sequence tags and gene discovery. Blaxter M(1), Daub J, Guiliano D, Parkinson J, Whitton C; Filarial Genome Project. Author information: (1)Institute of Cell, Animal and Population Biology, Ashworth Laboratories, Kings Buildings, University of Edinburgh, Edinburgh EH9 3JT, UK. mark.blaxter@ed.ac.uk To advance and facilitate molecular studies of Brugia malayi, one of the causative agents of human lymphatic filariasis, an expressed sequence tag (EST)-based gene discovery programme has been carried out. Over 22,000 ESTs have been produced and deposited in the public databases by a consortium of laboratories from endemic and non-endemic countries. The ESTs have been analysed using custom informatic tools to reveal patterns of individual gene expression that may point to potential targets for future research on anti-filarial drugs and vaccines. Many genes first discovered as ESTs are now being analysed by researchers for immunodiagnostic, vaccine and drug target potential. Building on the success of the B. malayi EST programme, significant EST datasets are being generated for a number of other major parasites of humans and domesticated animals, and model parasitic species. PMID: 11925998 [Indexed for MEDLINE] 463. EBioMedicine. 2018 Nov;37:56-67. doi: 10.1016/j.ebiom.2018.10.008. Epub 2018 Oct 9. Mitochondrial enzyme GLUD2 plays a critical role in glioblastoma progression. Franceschi S(1), Corsinovi D(2), Lessi F(3), Tantillo E(3), Aretini P(3), Menicagli M(3), Scopelliti C(3), Civita P(3), Pasqualetti F(4), Naccarato AG(5), Ori M(6), Mazzanti CM(3). Author information: (1)Fondazione Pisana per la Scienza ONLUS, Pisa, Italy. Electronic address: s.franceschi@fpscience.it. (2)Department of Translational Research and of New Surgical and Medical Technologies, University Hospital of Pisa, Pisa, Italy; Department of Biology, University of Pisa, Pisa, Italy. (3)Fondazione Pisana per la Scienza ONLUS, Pisa, Italy. (4)Radiotherapy Department, University Hospital of Pisa, Pisa, Italy. (5)Department of Translational Research and of New Surgical and Medical Technologies, University Hospital of Pisa, Pisa, Italy. (6)Department of Biology, University of Pisa, Pisa, Italy. Electronic address: michela.ori@unipi.it. BACKGROUND: Glioblastoma (GBM) is the most frequent and malignant primary brain tumor in adults and despite the progress in surgical procedures and therapy options, the overall survival remains very poor. Glutamate and α-KG are fundamental elements necessary to support the growth and proliferation of GBM cells. Glutamate oxidative deamination, catalyzed by GLUD2, is the predominant pathway for the production of α-KG. METHODS: GLUD2 emerged from the RNA-seq analysis of 13 GBM patients, performed in our laboratory and a microarray analysis of 77 high-grade gliomas available on the Geo database. Thereafter, we investigated GLUD2 relevance in cancer cell behavior by GLUD2 overexpression and silencing in two different human GBM cell lines. Finally, we overexpressed GLUD2 in-vivo by using zebrafish embryos and monitored the developing central nervous system. FINDINGS: GLUD2 expression was found associated to the histopathological classification, prognosis and survival of GBM patients. Moreover, through in-vitro functional studies, we showed that differences in GLUD2 expression level affected cell proliferation, migration, invasion, colony formation abilities, cell cycle phases, mitochondrial function and ROS production. In support of these findings, we also demonstrated, with in-vivo studies, that GLUD2 overexpression affects glial cell proliferation without affecting neuronal development in zebrafish embryos. INTERPRETATION: We concluded that GLUD2 overexpression inhibited GBM cell growth suggesting a novel potential drug target for control of GBM progression. The possibility to enhance GLUD2 activity in GBM could result in a blocked/reduced proliferation of GBM cells without affecting the survival of the surrounding neurons. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved. DOI: 10.1016/j.ebiom.2018.10.008 PMCID: PMC6284416 PMID: 30314897 464. Stand Genomic Sci. 2018 Oct 10;13:21. doi: 10.1186/s40793-018-0325-z. eCollection 2018. First genome sequencing and comparative analyses of Corynebacterium pseudotuberculosis strains from Mexico. Parise D(#)(1), Parise MTD(#)(1), Viana MVC(1), Muñoz-Bucio AV(2), Cortés-Pérez YA(2), Arellano-Reynoso B(2), Díaz-Aparicio E(2), Dorella FA(3), Pereira FL(3), Carvalho AF(3), Figueiredo HCP(3), Ghosh P(4), Barh D(1)(5)(6), Gomide ACP(1), Azevedo VAC(1). Author information: (1)1Laboratory of Cellular and Molecular Genetics, Institute of Biologic Sciences, Federal University of Minas Gerais, Belo Horizonte, MG Brazil. (2)2Department of Microbiology and Immunology, Faculty of Veterinary Medicine and Zootechnics, National Autonomous University of Mexico, Mexico City, Mexico. (3)3Aquacen - National Reference Laboratory for Aquatic Animal Diseases, Federal University of Minas Gerais, Belo Horizonte, MG Brazil. (4)4Department of Computer Science, Virginia Commonwealth University, Richmond, VA-23284 USA. (5)Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal 721172 India. (6)6Division of Bioinformatics and Computational Genomics, NITTE University Center for Science Education and Research (NUCSER), NITTE (Deemed to be University), Deralakatte, Mangaluru, Karnataka India. (#)Contributed equally Corynebacterium pseudotuberculosis is a pathogenic bacterium which has been rapidly spreading all over the world, causing economic losses in the agricultural sector and sporadically infecting humans. Six C. pseudotuberculosis strains were isolated from goats, sheep, and horses with distinct abscess locations. For the first time, Mexican genomes of this bacterium were sequenced and studied in silico. All strains were sequenced using Ion Personal Genome Machine sequencer, assembled using Newbler and SPAdes software. The automatic genome annotation was done using the software RAST and in-house scripts for transference, followed by manual curation using Artemis software and BLAST against NCBI and UniProt databases. The six genomes are publicly available in NCBI database. The analysis of nucleotide sequence similarity and the generated phylogenetic tree led to the observation that the Mexican strains are more similar between strains from the same host, but the genetic structure is probably more influenced by transportation of animals between farms than host preference. Also, a putative drug target was predicted and in silico analysis of 46 strains showed two gene clusters capable of differentiating the biovars equi and ovis: Restriction Modification system and CRISPR-Cas cluster. DOI: 10.1186/s40793-018-0325-z PMCID: PMC6180578 PMID: 30338024 Conflict of interest statement: The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 465. BMC Bioinformatics. 2006 Mar 14;7:135. Gene functional similarity search tool (GFSST). Zhang P(1), Zhang J, Sheng H, Russo JJ, Osborne B, Buetow K. Author information: (1)Laboratory of Population Genetics, National Cancer Institute, NIH, Bethesda, USA. zhangpeis@mail.nih.gov BACKGROUND: With the completion of the genome sequences of human, mouse, and other species and the advent of high throughput functional genomic research technologies such as biomicroarray chips, more and more genes and their products have been discovered and their functions have begun to be understood. Increasing amounts of data about genes, gene products and their functions have been stored in databases. To facilitate selection of candidate genes for gene-disease research, genetic association studies, biomarker and drug target selection, and animal models of human diseases, it is essential to have search engines that can retrieve genes by their functions from proteome databases. In recent years, the development of Gene Ontology (GO) has established structured, controlled vocabularies describing gene functions, which makes it possible to develop novel tools to search genes by functional similarity. RESULTS: By using a statistical model to measure the functional similarity of genes based on the Gene Ontology directed acyclic graph, we developed a novel Gene Functional Similarity Search Tool (GFSST) to identify genes with related functions from annotated proteome databases. This search engine lets users design their search targets by gene functions. CONCLUSION: An implementation of GFSST which works on the UniProt (Universal Protein Resource) for the human and mouse proteomes is available at GFSST Web Server. GFSST provides functions not only for similar gene retrieval but also for gene search by one or more GO terms. This represents a powerful new approach for selecting similar genes and gene products from proteome databases according to their functions. DOI: 10.1186/1471-2105-7-135 PMCID: PMC1421445 PMID: 16536867 [Indexed for MEDLINE] 466. J Proteome Res. 2010 Feb 5;9(2):1182-90. doi: 10.1021/pr900827b. Trypano-PPI: a web server for prediction of unique targets in trypanosome proteome by using electrostatic parameters of protein-protein interactions. Rodriguez-Soca Y(1), Munteanu CR, Dorado J, Pazos A, Prado-Prado FJ, González-Díaz H. Author information: (1)Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain. Trypanosoma brucei causes African trypanosomiasis in humans (HAT or African sleeping sickness) and Nagana in cattle. The disease threatens over 60 million people and uncounted numbers of cattle in 36 countries of sub-Saharan Africa and has a devastating impact on human health and the economy. On the other hand, Trypanosoma cruzi is responsible in South America for Chagas disease, which can cause acute illness and death, especially in young children. In this context, the discovery of novel drug targets in Trypanosome proteome is a major focus for the scientific community. Recently, many researchers have spent important efforts on the study of protein-protein interactions (PPIs) in pathogen Trypanosome species concluding that the low sequence identities between some parasite proteins and their human host render these PPIs as highly promising drug targets. To the best of our knowledge, there are no general models to predict Unique PPIs in Trypanosome (TPPIs). On the other hand, the 3D structure of an increasing number of Trypanosome proteins is reported in databases. In this regard, the introduction of a new model to predict TPPIs from the 3D structure of proteins involved in PPI is very important. For this purpose, we introduced new protein-protein complex invariants based on the Markov average electrostatic potential xi(k)(R(i)) for amino acids located in different regions (R(i)) of i-th protein and placed at a distance k one from each other. We calculated more than 30 different types of parameters for 7866 pairs of proteins (1023 TPPIs and 6823 non-TPPIs) from more than 20 organisms, including parasites and human or cattle hosts. We found a very simple linear model that predicts above 90% of TPPIs and non-TPPIs both in training and independent test subsets using only two parameters. The parameters were (d)xi(k)(s) = |xi(k)(s(1)) - xi(k)(s(2))|, the absolute difference between the xi(k)(s(i)) values on the surface of the two proteins of the pairs. We also tested nonlinear ANN models for comparison purposes but the linear model gives the best results. We implemented this predictor in the web server named TrypanoPPI freely available to public at http://miaja.tic.udc.es/Bio-AIMS/TrypanoPPI.php. This is the first model that predicts how unique a protein-protein complex in Trypanosome proteome is with respect to other parasites and hosts, opening new opportunities for antitrypanosome drug target discovery. DOI: 10.1021/pr900827b PMID: 19947655 [Indexed for MEDLINE] 467. Bioorg Med Chem Lett. 2009 Jul 15;19(14):3832-5. doi: 10.1016/j.bmcl.2009.04.021. Epub 2009 Apr 10. 3-(aminomethyl)piperazine-2,5-dione as a novel NMDA glycine site inhibitor from the chemical universe database GDB. Nguyen KT(1), Luethi E, Syed S, Urwyler S, Bertrand S, Bertrand D, Reymond JL. Author information: (1)Department of Chemistry and Biochemistry, University of Berne, Berne, Switzerland. Docking of randomly selected compounds from the chemical universe database GDB-11, which contains all organic molecules up to 11 atoms of C, N, O, F possible under consideration of simple chemical stability and synthetic feasibility rules, into the NMDA receptor glycine site (1pb7.pdb) lead to the identification of 3-(aminomethyl)piperazine-2,5-dione 3 and its close analog 5-(aminomethyl)piperazine-2,3-dione 4 as possible new ligands for this drug target, which is implicated in synaptic plasticity, neuronal development, learning and memory. Synthesis of these compounds in 4 and 6 steps, respectively, and testing by radioligand displacement assays and electrophysiological measurements in Xenopus oocytes show that while 4 is inactive, 3 is indeed an inhibitor of glycine, with an estimated K(D) of 50 microM. DOI: 10.1016/j.bmcl.2009.04.021 PMID: 19394821 [Indexed for MEDLINE] 468. Biomed Res Int. 2015;2015:254838. doi: 10.1155/2015/254838. Epub 2015 Oct 1. METSP: a maximum-entropy classifier based text mining tool for transporter-substrate identification with semistructured text. Zhao M(1), Chen Y(2), Qu D(2), Qu H(3). Author information: (1)School of Engineering, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Maroochydore DC, QLD 4558, Australia. (2)School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China. (3)Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing 100871, China. The substrates of a transporter are not only useful for inferring function of the transporter, but also important to discover compound-compound interaction and to reconstruct metabolic pathway. Though plenty of data has been accumulated with the developing of new technologies such as in vitro transporter assays, the search for substrates of transporters is far from complete. In this article, we introduce METSP, a maximum-entropy classifier devoted to retrieve transporter-substrate pairs (TSPs) from semistructured text. Based on the high quality annotation from UniProt, METSP achieves high precision and recall in cross-validation experiments. When METSP is applied to 182,829 human transporter annotation sentences in UniProt, it identifies 3942 sentences with transporter and compound information. Finally, 1547 confidential human TSPs are identified for further manual curation, among which 58.37% pairs with novel substrates not annotated in public transporter databases. METSP is the first efficient tool to extract TSPs from semistructured annotation text in UniProt. This tool can help to determine the precise substrates and drugs of transporters, thus facilitating drug-target prediction, metabolic network reconstruction, and literature classification. DOI: 10.1155/2015/254838 PMCID: PMC4606149 PMID: 26495291 [Indexed for MEDLINE] 469. Protein J. 2012 Apr;31(4):345-52. doi: 10.1007/s10930-012-9410-0. Cloning, expression, purification and characterization of UMP kinase from Staphylococcus aureus. Hari Prasad O(1), Nanda Kumar Y, Reddy OV, Chaudhary A, Sarma PV. Author information: (1)Department of Biotechnology, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India. Uridine monophosphate kinase (UMPK) an enzyme of de novo biosynthesis catalyses the formation of UDP and it is involved in cell wall and RNA biosynthesis. In the present study UMPK of Staphylococcus aureus ATCC12600 was characterized. Analysis of purified UMPK by gel filtration chromatography on Sephadex G-200 indicated a molecular weight of 150 kDa and exhibited monomeric form with molecular weight of 25 kDa in SDS-PAGE confirming homohexamer nature of UMPK in solution. The enzyme kinetics of UMPK showed K(m) of 2.80 ± 0.1 μM and Vmax 51.38 ± 1.39 μM of NADH/min/mg. The enzyme exhibited cooperative kinetics with ATP as substrate, as GTP decreased this cooperativity and increased affinity for ATP. The UMPK gene was amplified, sequenced (Accession number: FJ415072), cloned in pQE30 vector and overexpressed in Escherichia coli DH5α. The purified recombinant UMPK showed similar properties of native UMPK. The UMPK gene sequence showed complete homology with pyrH gene sequence of all S. aureus strains reported in the database, the 3D structure of S. aureus UMPK built from the deduced amino acid sequence was super imposed with human UMPK (PDB ID: 1TEV) to find out the structural identity using the MATRAS programme gave an RMSD value 4.24 Å indicating very low homology and extensive structural variations with human UMPK structure. Thus, UMPK may be a potential drug target in the development of antimicrobials. DOI: 10.1007/s10930-012-9410-0 PMID: 22528139 [Indexed for MEDLINE] 470. Bioinformation. 2012;8(19):931-7. doi: 10.6026/97320630008931. Epub 2012 Oct 1. Computational finding of potential inhibitor for Cytochrome P450 Mono-oxygenases Enzyme of Mycobacterium tuberculosis. Sharma A(1), Subbias KK, Robine O, Chaturvedi I, Nigam A, Sharma N, Chaudhary PP. Author information: (1)Center of drug discovery research, New Era Proteomics, C-1/31, Yamuna Vihar, New Delhi-110053, India. Cytochrome P450 mono-oxygenases (2UUQ) enzyme from Mycobacterium tuberculosis catalyzes oxidation of organic compounds such as lipids and steroidal hormones therefore remain as potential drug target. Currently available first line anti-tuberculosis drugs have been caused several side effects in the body as well as resistance development by mycobacterium against these drugs, necessitates the considerable need for finding new drugs. Therefore, we propose a structure based computational method to find a new potential inhibitor for cytochrome P450 mono-oxygenases enzyme. Compounds from several ligand databases were docked against the functional sites of 2UUQ (A) through the standard GEMDOCK v2.0 and AUTODOCK4.0 molecular docking tools. Commercially available chemical compound ZINC00004165 (5-[3-(2-nitroimidazol-1-yl) propyl] phenanthridine) has produced top rank with lowest interaction energy of -113.2 (via GEMDOCK) and lowest docking energy of -9.80 kcal/mol (via AUTODOCK) as compared to first line anti TB compounds. Z score and normal distribution analysis verified that the ZINC00004165 compound has more affinity towards 2UUQ in comparison to large number of random population of compounds. ZINC00004165 is also in agreement with the drug likeness properties of Lipinski rule of five without any violation. Therefore, our finding concludes that the commercial compound ZINC00004165 can act as a potential inhibitor against cytochrome P450 mono-oxygenases enzyme of Mycobacterium tuberculosis. DOI: 10.6026/97320630008931 PMCID: PMC3488835 PMID: 23144553 471. Nucleic Acids Res. 2019 Jan 8;47(D1):D344-D350. doi: 10.1093/nar/gky1063. iEKPD 2.0: an update with rich annotations for eukaryotic protein kinases, protein phosphatases and proteins containing phosphoprotein-binding domains. Guo Y(1), Peng D(1), Zhou J(1), Lin S(1), Wang C(1), Ning W(1), Xu H(1), Deng W(1), Xue Y(1). Author information: (1)Department of Bioinformatics & Systems Biology, Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China. Here, we described the updated database iEKPD 2.0 (http://iekpd.biocuckoo.org) for eukaryotic protein kinases (PKs), protein phosphatases (PPs) and proteins containing phosphoprotein-binding domains (PPBDs), which are key molecules responsible for phosphorylation-dependent signalling networks and participate in the regulation of almost all biological processes and pathways. In total, iEKPD 2.0 contained 197 348 phosphorylation regulators, including 109 912 PKs, 23 294 PPs and 68 748 PPBD-containing proteins in 164 eukaryotic species. In particular, we provided rich annotations for the regulators of eight model organisms, especially humans, by compiling and integrating the knowledge from 100 widely used public databases that cover 13 aspects, including cancer mutations, genetic variations, disease-associated information, mRNA expression, DNA & RNA elements, DNA methylation, molecular interactions, drug-target relations, protein 3D structures, post-translational modifications, protein expressions/proteomics, subcellular localizations and protein functional annotations. Compared with our previously developed EKPD 1.0 (∼0.5 GB), iEKPD 2.0 contains ∼99.8 GB of data with an ∼200-fold increase in data volume. We anticipate that iEKPD 2.0 represents a more useful resource for further study of phosphorylation regulators. DOI: 10.1093/nar/gky1063 PMCID: PMC6324023 PMID: 30380109 472. Int Immunopharmacol. 2019 Jan;66:236-241. doi: 10.1016/j.intimp.2018.11.031. Epub 2018 Nov 24. ERK inhibitor JSI287 alleviates imiquimod-induced mice skin lesions by ERK/IL-17 signaling pathway. Huang X(1), Yu P(1), Liu M(1), Deng Y(1), Dong Y(2), Liu Q(1), Zhang J(3), Wu T(4). Author information: (1)State Key Laboratory of Drug Innovation and Pharmaceutical Technology, Shanghai Institute of Pharmaceutical Industry, China state Institute of Pharmaceutical Industry, Shanghai 200437, China. (2)Shanghai Bioenergy Medicine Science &Technology Co., Ltd., Shanghai, China. (3)JS InnoPharm (Shanghai) Ltd., Shanghai, China. (4)State Key Laboratory of Drug Innovation and Pharmaceutical Technology, Shanghai Institute of Pharmaceutical Industry, China state Institute of Pharmaceutical Industry, Shanghai 200437, China. Electronic address: tongwu88@163.com. Many studies confirmed that the over-activation of RAF-MEK-ERK signaling pathway plays a central role in human cancers. To avoid drug resistance during cancer treatment, many researchers focused on the study of the downstream therapeutic target of RAF-MEK-ERK signaling pathway. Therefore, ERK1/2 became a hot anticancer target. It has been shown that ERK phosphorylation could activate Th17 cells and therefore induce inflammatory diseases. Due to these results, inhibition of ERK, as a potential drug target, could provide a solution for autoimmune diseases, especially T cell mediated diseases. In this study, a small synthetic molecule JSI287 was found with the function of alleviating IMQ-induced mice skin lesions through ERK/IL-17 signaling pathway during the screening of small molecule databases targeting ERK. The results showed that JS1287 small molecule alleviated epidermal thickness, epidermis congestion, edema and inflammatory cell infiltration, decreased release of inflammatory cytokines of IL-6, IL-12 and IL-17A, and further regulated the mRNA expression of ATF1 and protein expression of ERK1/2 in IMQ-induced skin lesions. Our study suggested that ERK inhibitor JSI287 could be a promising candidate for psoriasis treatment. Copyright © 2018 Elsevier B.V. All rights reserved. DOI: 10.1016/j.intimp.2018.11.031 PMID: 30481683 473. Int J Mol Sci. 2012 Dec 14;13(12):17185-209. doi: 10.3390/ijms131217185. Virtual screening of specific insulin-like growth factor 1 receptor (IGF1R) inhibitors from the National Cancer Institute (NCI) molecular database. Fan C(1), Huang YX, Bao YL, Sun LG, Wu Y, Yu CL, Zhang Y, Song ZB, Zheng LH, Sun Y, Wang GN, Li YX. Author information: (1)National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China. huangyx356@nenu.edu.cn. Insulin-like growth factor 1 receptor (IGF1R) is an attractive drug target for cancer therapy and research on IGF1R inhibitors has had success in clinical trials. A particular challenge in the development of specific IGF1R inhibitors is interference from insulin receptor (IR), which has a nearly identical sequence. A few potent inhibitors that are selective for IGF1R have been discovered experimentally with the aid of computational methods. However, studies on the rapid identification of IGF1R-selective inhibitors using virtual screening and confidence-level inspections of ligands that show different interactions with IGF1R and IR in docking analysis are rare. In this study, we established virtual screening and binding-mode prediction workflows based on benchmark results of IGF1R and several kinase receptors with IGF1R-like structures. We used comprehensive analysis of the known complexes of IGF1R and IR with their binding ligands to screen specific IGF1R inhibitors. Using these workflows, 17 of 139,735 compounds in the NCI (National Cancer Institute) database were identified as potential specific inhibitors of IGF1R. Calculations of the potential of mean force (PMF) with GROMACS were further conducted for three of the identified compounds to assess their binding affinity differences towards IGF1R and IR. DOI: 10.3390/ijms131217185 PMCID: PMC3546745 PMID: 23242155 [Indexed for MEDLINE] 474. Pharmacogenet Genomics. 2011 May;21(5):251-62. doi: 10.1097/FPC.0b013e3283438865. Pharmacogenomics genes show varying perceptibility to microRNA regulation. Rukov JL(1), Vinther J, Shomron N. Author information: (1)Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. OBJECTIVE: The aim of pharmacogenomics is to identify individual differences in genome and transcriptome composition and their effect on drug efficacy. MicroRNAs (miRNAs) are short noncoding RNAs that negatively regulate expression of the majority of animal genes, including many genes involved in drug efficacy. Consequently, differences in the miRNA expression among individuals could be an important factor contributing to differential drug response. Pharmacogenomics genes can be divided into drug target genes termed as pharmacodynamics genes (PD) and genes involved in drug transport and metabolism termed as pharmacokinetics genes (PK). To clarify the regulatory potential of miRNAs in pharmacogenomics, we have examined the potential regulation by miRNAs of PK and PD genes. METHODS: We identified PK and PD genes as annotated by the Pharmacogenomics Knowledge Base and examined miRNA targeting of genes in the two groups according to several miRNA target prediction databases. We furthermore studied how differences between the two groups are reflected in the gene structure and across gene families. Lastly, we studied changes in expression levels of PK versus PD genes in cells depleted for miRNAs by shRNA-mediated knockdown of the miRNA-processing enzyme Dicer. RESULTS: Our analysis identify a striking difference in the level of miRNA regulation between PK and PD genes, with the former having less than half predicted conserved miRNA binding sites compared with the latter. Importantly, this finding is reflected in a highly significant difference in the shift in expression levels of PD versus PK genes after depletion of miRNAs. CONCLUSION: Our study emphasizes an intrinsic difference between PK and PD genes and helps clarify the role of miRNAs in pharmacogenomics. DOI: 10.1097/FPC.0b013e3283438865 PMID: 21499217 [Indexed for MEDLINE] 475. Chem Biol Drug Des. 2019 Mar;93(3):325-336. doi: 10.1111/cbdd.13418. Epub 2018 Nov 23. Structure-based pharmacophore models to probe anticancer activity of inhibitors of protein kinase B-beta (PKB β). Akhtar N(1), Jabeen I(1), Jalal N(2), Antilla J(2). Author information: (1)Research Centre for Modeling and Simulation (RCMS), National University of Sciences and Technology (NUST), Islamabad, Pakistan. (2)School of Pharmaceutical Science and Technology, Tianjin University, Tianjin City, China. Protein kinase B-beta (PKBβ/Akt2) is a non-receptor kinase that has attracted a great deal of attention as a promising cancer therapy drug target. In mammalian cells, hyperactivation of Akt2 exclusively facilitates the survival of solid tumors by interfering with cell cycle progression. This definite function of Akt2 in tumor survival/maintenance provides the basis for the development of its antagonists with the aim of desensitizing cell proliferation. In order to find novel and potent Akt2 inhibitors, structure-based pharmacophore models have been developed and validated by the test set prediction. The final pharmacophore model was used for hits identification using public chemical databases. The hits were further prioritized using drug-like filters which revealed 14 potential hit compounds having novel chemical scaffolds. Our results elucidate the importance of three hydrogen bond acceptors (A), one hydrogen bond donor (D), one hydrophobic group (H), and one positive ionic charge (P) toward inhibition of the Ak2. One of our selected hits showed 68% cell apoptosis at 8 μg/ml concentration. We proposed various chemical scaffolds including benzamide, carboxamide, and methyl benzimidazole targeting Akt2 and thus may act as potential leads for the further development of new anticancer agents. © 2018 John Wiley & Sons A/S. DOI: 10.1111/cbdd.13418 PMID: 30354009 476. Cancer Biol Ther. 2008 Feb;7(2):285-92. Epub 2007 Nov 14. Protein phosphatase 1H, overexpressed in colon adenocarcinoma, is associated with CSE1L. Sugiura T(1), Noguchi Y, Sakurai K, Hattori C. Author information: (1)Discovery Research Laboratory, Tokyo R&D Center, Daiichi Pharmaceutical Co. Ltd., Daiichi-Sankyo Group, Tokyo, Japan. takeyuki.uu@daiichisankyo.co.jp Comment in Cancer Biol Ther. 2008 Feb;7(2):293-4. In search for a new anticancer drug target, we explored genes involved in colon adenocarcinoma development through dysregulation of a signal transduction pathway. By using the gene expression profile database, we found protein phosphatase 1H (PPM1H), belonging to the protein phosphatase 2C (PP2C) family, upregulated in colon adenocarcinomas compared with normal colon tissues. RT-PCR analysis verified the elevated level of PPM1H expression in colon cancer cell lines relative to a normal colon cell line. PPM1H encodes a protein with a molecular mass of approximately 50 kDa that resides in the cytoplasm. PPM1H fused with maltose-binding protein expressed in E. coli exhibited phosphatase activity characteristic of the PP2C family. Co-immunoprecipitation coupled with mass spectrometry analysis identified CSE1L, a proliferation and apoptosis-related protein, as a PPM1H-interacting protein. Native, but not inactive, PPM1H expressed in HeLa cells increased the mobility of CSE1L on SDS gels and a similar mobility shift was observed for purified CSE1L after treatment with PPM1H in vitro, supporting the notion that CSE1L is a substrate of PPM1H. Dominant negative PPM1H protected HeLa cells from cell death triggered by staurosporine or taxol. Additionally, knockdown of PPM1H expression with small interfering RNAs suppressed the growth of MCF-7 cells weakly but consistently. PPM1H controls cell cycle and proliferation of cancer cells potentially through dephosphorylation of CSE1L and might be a new target of anticancer drugs. PMID: 18059182 [Indexed for MEDLINE] 477. Genomics. 2012 Jul;100(1):1-7. doi: 10.1016/j.ygeno.2012.05.006. Epub 2012 May 17. Relax with CouchDB--into the non-relational DBMS era of bioinformatics. Manyam G(1), Payton MA, Roth JA, Abruzzo LV, Coombes KR. Author information: (1)Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. With the proliferation of high-throughput technologies, genome-level data analysis has become common in molecular biology. Bioinformaticians are developing extensive resources to annotate and mine biological features from high-throughput data. The underlying database management systems for most bioinformatics software are based on a relational model. Modern non-relational databases offer an alternative that has flexibility, scalability, and a non-rigid design schema. Moreover, with an accelerated development pace, non-relational databases like CouchDB can be ideal tools to construct bioinformatics utilities. We describe CouchDB by presenting three new bioinformatics resources: (a) geneSmash, which collates data from bioinformatics resources and provides automated gene-centric annotations, (b) drugBase, a database of drug-target interactions with a web interface powered by geneSmash, and (c) HapMap-CN, which provides a web interface to query copy number variations from three SNP-chip HapMap datasets. In addition to the web sites, all three systems can be accessed programmatically via web services. Copyright © 2012 Elsevier Inc. All rights reserved. DOI: 10.1016/j.ygeno.2012.05.006 PMCID: PMC3383915 PMID: 22609849 [Indexed for MEDLINE] 478. J Biomed Nanotechnol. 2011 Feb;7(1):91-2. In silico approaches: prediction of biological targets for fullerene derivatives. Gupta SK(1), Dhawan A, Shanker R. Author information: (1)Nanomaterial and Environmental Toxicology Groups, Indian Institute of Toxicology Research (Council of Scientific and Industrial Research, India), P.O. Box 80, M.G. Marg, Lucknow 226001, India. Fullerene and their derivatives have many potential new applications. However, there is increasing concern regarding toxicity as very little information is available about fullerene derivatives--protein interactions. In the present work, to identify proteins interacting with chiral fullerene derivatives, Potential Drug Target Database was searched using reverse docking approach. Hypoxanthine phosphoribosyltransferase and Beta-secretase-1 were found to be the most favorable protein targets for fullerene derivatives. PMID: 21485819 [Indexed for MEDLINE] 479. PLoS One. 2011 May 3;6(5):e19240. doi: 10.1371/journal.pone.0019240. Signalogs: orthology-based identification of novel signaling pathway components in three metazoans. Korcsmáros T(1), Szalay MS, Rovó P, Palotai R, Fazekas D, Lenti K, Farkas IJ, Csermely P, Vellai T. Author information: (1)Department of Genetics, Eötvös Loránd University, Budapest, Hungary. BACKGROUND: Uncovering novel components of signal transduction pathways and their interactions within species is a central task in current biological research. Orthology alignment and functional genomics approaches allow the effective identification of signaling proteins by cross-species data integration. Recently, functional annotation of orthologs was transferred across organisms to predict novel roles for proteins. Despite the wide use of these methods, annotation of complete signaling pathways has not yet been transferred systematically between species. PRINCIPAL FINDINGS: Here we introduce the concept of 'signalog' to describe potential novel signaling function of a protein on the basis of the known signaling role(s) of its ortholog(s). To identify signalogs on genomic scale, we systematically transferred signaling pathway annotations among three animal species, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and humans. Using orthology data from InParanoid and signaling pathway information from the SignaLink database, we predict 88 worm, 92 fly, and 73 human novel signaling components. Furthermore, we developed an on-line tool and an interactive orthology network viewer to allow users to predict and visualize components of orthologous pathways. We verified the novelty of the predicted signalogs by literature search and comparison to known pathway annotations. In C. elegans, 6 out of the predicted novel Notch pathway members were validated experimentally. Our approach predicts signaling roles for 19 human orthodisease proteins and 5 known drug targets, and suggests 14 novel drug target candidates. CONCLUSIONS: Orthology-based pathway membership prediction between species enables the identification of novel signaling pathway components that we referred to as signalogs. Signalogs can be used to build a comprehensive signaling network in a given species. Such networks may increase the biomedical utilization of C. elegans and D. melanogaster. In humans, signalogs may identify novel drug targets and new signaling mechanisms for approved drugs. DOI: 10.1371/journal.pone.0019240 PMCID: PMC3086880 PMID: 21559328 [Indexed for MEDLINE] 480. J Biomol Struct Dyn. 2011 Oct;29(2):369-77. The structural stability of wild-type horse prion protein. Zhang J(1). Author information: (1)School of Sciences, Information Technology and Engineering, University of Ballarat, Mount Helen, Ballarat, Victoria 3353, Australia. jiapu_zhang@hotmail.com Prion diseases (e.g. Creutzfeldt-Jakob disease (CJD), variant CJD (vCJD), Gerstmann-Straussler-Scheinker syndrome (GSS), Fatal Familial Insomnia (FFI) and Kuru in humans, scrapie in sheep, bovine spongiform encephalopathy (BSE or 'mad-cow' disease) and chronic wasting disease (CWD) in cattles) are invariably fatal and highly infectious neurodegenerative diseases affecting humans and animals. However, by now there have not been some effective therapeutic approaches or medications to treat all these prion diseases. Rabbits, dogs, and horses are the only mammalian species reported to be resistant to infection from prion diseases isolated from other species. Recently, the β2-α2 loop has been reported to contribute to their protein structural stabilities. The author has found that rabbit prion protein has a strong salt bridge ASP177-ARG163 (like a taut bow string) keeping this loop linked. This paper confirms that this salt bridge also contributes to the structural stability of horse prion protein. Thus, the region of β2-α2 loop might be a potential drug target region. Besides this very important salt bridge, other four important salt bridges GLU196-ARG156-HIS187, ARG156-ASP202 and GLU211-HIS177 are also found to greatly contribute to the structural stability of horse prion protein. Rich databases of salt bridges, hydrogen bonds and hydrophobic contacts for horse prion protein can be found in this paper. DOI: 10.1080/07391102.2011.10507391 PMID: 21875155 [Indexed for MEDLINE] 481. Bioorg Med Chem. 2011 Nov 1;19(21):6302-8. doi: 10.1016/j.bmc.2011.09.004. Epub 2011 Sep 10. Identification of new antimalarial leads by use of virtual screening against cytochrome bc₁. Rodrigues T(1), Moreira R, Gut J, Rosenthal PJ, O Neill PM, Biagini GA, Lopes F, dos Santos DJ, Guedes RC. Author information: (1)Research Institute for Medicines and Pharmaceutical Sciences (iMed.UL), Faculty of Pharmacy, University of Lisbon, Av. Prof. Gama Pinto, 1649-019 Lisbon, Portugal. Cytochrome bc(1) is a validated drug target in malaria parasites. The spread of Plasmodium falciparum strains resistant to multiple antimalarials emphasizes the urgent need for new drugs. We screened in silico the ZINC and MOE databases, using ligand- and structure-based approaches, to identify new leads for development. The most active compound presented an IC(50) value against cultured P. falciparum of 2 μM and a docking pose consistent with its activity. Copyright © 2011 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.bmc.2011.09.004 PMID: 21958736 [Indexed for MEDLINE] 482. Biochim Biophys Acta. 2006 Jul;1760(7):1027-38. Epub 2006 Mar 29. Molecular characterization and localization of Plasmodium falciparum choline kinase. Choubey V(1), Guha M, Maity P, Kumar S, Raghunandan R, Maulik PR, Mitra K, Halder UC, Bandyopadhyay U. Author information: (1)Division of Drug Target Discovery and Development, Central Drug Research Institute, Chatter Manzil Palace, Mahatma Gandhi Marg, Lucknow-226001, Uttar Pradesh, India. Generation of phosphocholine by choline kinase is important for phosphatidylcholine biosynthesis via Kennedy pathway and phosphatidylcholine biosynthesis is essential for intraerythrocytic growth of malaria parasite. A putative gene (Gene ID PF14_0020) in chromosome 14, having highest sequence homology with choline kinase, has been identified by BLAST searches from P. falciparum genome sequence database. This gene has been PCR amplified, cloned, over-expressed and characterized. Choline kinase activity of the recombinant protein (PfCK) was validated as it catalyzed the formation of phosphocholine from choline in presence of ATP. The K(m) values for choline and ATP are found to be 145+/-20 microM and 2.5+/-0.3 mM, respectively. PfCK can phosphorylate choline efficiently but not ethanolamine. Southern blotting indicates that PfCK is a single copy gene and it is a cytosolic protein as evidenced by Western immunoblotting and confocal microscopy. A model structure of PfCK was constructed based on the crystal structure of choline kinase of C. elegans to search the structural homology. Consistent with the homology modeling predictions, CD analysis indicates that the alpha and beta content of PfCK are 33% and 14%, respectively. Since choline kinase plays a vital role for growth and multiplication of P. falciparum during intraerythrocytic stages, we can suggest that this well characterized PfCK may be exploited in the screening of new choline kinase inhibitors to evaluate their antimalarial activity. DOI: 10.1016/j.bbagen.2006.03.003 PMID: 16626864 [Indexed for MEDLINE] 483. Mol Inform. 2011 Oct;30(10):873-83. doi: 10.1002/minf.201100085. Epub 2011 Sep 7. Identification of Novel Inhibitors of UDP-Galactopyranose Mutase by Structure-Based Virtual Screening. Karunan Partha S(1), Sadeghi-Khomami A(2), Cren S(2), Robinson RI(2), Woodward S(2), Slowski K(1), Berast L(1), Zheng B(3), Thomas NR(4), Sanders DA(5). Author information: (1)Department of Chemistry, University of Saskatchewa, 110 Science Place, Saskatoon, SK, Canada S7N 5C9. (2)Centre for Biomolecular Sciences, School of Chemistry, The University of Nottingham, University Park, Nottingham, UK. (3)Department of Chemistry, University of Alberta, Edmonton, AB, Canada. (4)Centre for Biomolecular Sciences, School of Chemistry, The University of Nottingham, University Park, Nottingham, UK. neil.thomas@nottingham.ac.uk. (5)Department of Chemistry, University of Saskatchewa, 110 Science Place, Saskatoon, SK, Canada S7N 5C9. david.sanders@usask.ca. UDP-galactopyranose mutase (UGM) is a flavo-enzyme involved in the bacterial cell wall biosynthesis. UGM catalyzes the reversible isomerization of UDP-galactopyranose (UDP-Galp) to UDP-galactofuranose (UDP-Galf). UDP-Galf is the activated precursor of galactofuranose (Galf) residues that are essential components of the cell wall of certain pathogenic bacteria such as Klebsiella pneumoniae and Mycobacterium tuberculosis. Neither UGM nor Galf residues are found in humans, making Galf biosynthesis a potential drug target for developing antibacterial agents. We report the identification of novel inhibitors of UGM by in silico docking of the LeadQuest compound database against UGM from Escherichia coli. The 13 most promising inhibitors were then evaluated against K. pneumonia and M. tuberculosis UGMs by enzyme inhibition studies. Two inhibitors were identified with IC50 values of ∼1 µM and subsequently these compounds were docked into the recently solved X-ray structure of Deinococcus radiodurans UGM. The structure-activity relationships of the initial 13 compounds evaluated as inhibitors are discussed. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. DOI: 10.1002/minf.201100085 PMID: 27468107 484. Pancreatology. 2009;9(4):340-3. doi: 10.1159/000212082. Epub 2009 May 18. A web-based platform for mining pancreatic expression datasets. Chelala C(1), Lemoine NR, Hahn SA, Crnogorac-Jurcevic T. Author information: (1)Centre for Molecular Oncology, Institute of Cancer and CR-UK Clinical Centre, Barts and The London School of Medicine (QMUL), London, UK. Claude.Chelala@cancer.org.uk BACKGROUND AND AIMS: We have recently constructed the pancreatic expression database (http://www.pancreasexpression.org/). It is a freely available web-based tool that currently holds 7,636 gene expression measurements from various pancreatic cancer types, precursor lesions (PanINs) and chronic pancreatitis. It was constructed with the aim of enabling mining of the large body of publicly available genomic/proteomic data in the pancreatic field and thus furthering our understanding of the complex basis of pancreatic cancer within the realm of systems biology. METHODS AND RESULTS: Annotated, integrated pancreatic datasets were stored in a data management system based on the BioMart technology alongside public annotations. Within our first database report we incorporated 32 datasets and have also included several easy examples of its usage. We now provide further, more sophisticated query types that we hope will serve as a prototype for possible questions of interest that might be addressed in the pancreatic biology community. CONCLUSION: The systematic understanding of the pathobiology underlying pancreatic cancer initiation and progression is a prerequisite for devising timely diagnostic tools and successful drug target discovery. As the database is only as comprehensive as its contents and as user-friendly as its query engine, we invite the pancreatic biology community for feedback and submission of further datasets. Copyright 2009 S. Karger AG, Basel. DOI: 10.1159/000212082 PMID: 19451743 [Indexed for MEDLINE] 485. J Mol Graph Model. 2010 Jun;28(8):870-82. doi: 10.1016/j.jmgm.2010.03.007. Epub 2010 Apr 4. Benchmarking docking and scoring protocol for the identification of potential acetylcholinesterase inhibitors. Zaheer-ul-Haq(1), Halim SA, Uddin R, Madura JD. Author information: (1)Dr Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan. zaheer.qasmi@iccs.edu Acetylcholinesterase (AChE) plays a crucial role in nerve impulse transmission at cholinergic synapses by rapid hydrolysis of the neurotransmitter acetylcholine (ACh). AChE has become an important drug target because partial inhibition of AChE results in modest increase in ACh levels that can have therapeutic benefits, thus AChE inhibitors have proved useful in the symptomatic treatment of Alzheimer's disease. To establish an effective docking protocol for virtual screening of AChE, a comparative molecular docking study was performed. For this purpose six docking/scoring approaches (AutoDock, FlexX, MOE, Surflex-Dock, GOLD and FRED) were compared to determine their ability to reproduce the binding poses in twenty six complexes of AChE. Docking accuracy was evaluated by calculating the RMSD of the docked complexes. FRED was found to be the best in reproducing the experimental pose by placing it near the top of its ranking. The performance of scoring functions was evaluated by identifying known actives out of large database of inactive compounds. A dataset of 5000 "drug like" decoys were retrieved from NCI database and docked into the binding site of AChE with six known inhibitors using FRED in combination with five scoring functions, i.e., Chemgauss2, Chemgauss3, ChemScore, Shapegauss and PLP. The poses obtained by FRED were re-scored using GOLD score, ChemScore and ASP as implemented in GOLD while G_Score, D_Score, ChemScore and PMF as implemented in the CScore module of SYBYL7.3. D_Score presented significantly better enrichment than others and 50% of the active inhibitors were identified in top 20% of the ranked database. Crown Copyright (c) 2010. Published by Elsevier Inc. All rights reserved. DOI: 10.1016/j.jmgm.2010.03.007 PMID: 20447848 [Indexed for MEDLINE] 486. Mol Inform. 2014 Sep;33(9):597-609. doi: 10.1002/minf.201400058. Epub 2014 Aug 26. Discovery of Novel Mycobacterial DNA Gyrase B Inhibitors: In Silico and In Vitro Biological Evaluation. Saxena S(1), Renuka J(1), Yogeeswari P(1), Sriram D(2). Author information: (1)D. Sriram, S. Saxena, J. Renuka, P. Yogeeswari, Department of Pharmacy, Birla Institute of Technology & Science-Pilani, Hyderabad Campus, Jawahar Nagar, Hyderabad 500078, Andhra, Pradesh, India tel: +91-40663030506; fax: +91-4066303998. (2)D. Sriram, S. Saxena, J. Renuka, P. Yogeeswari, Department of Pharmacy, Birla Institute of Technology & Science-Pilani, Hyderabad Campus, Jawahar Nagar, Hyderabad 500078, Andhra, Pradesh, India tel: +91-40663030506; fax: +91-4066303998. dsriram@hyderabad.bits-pilani.ac.in, drdsriram@yahoo.com. DNA gyrase of Mycobacterium tuberculosis (MTB) is a type II topoisomerase that ensures the regulation of DNA topology and has been genetically demonstrated to be a bactericidal drug target. Availability of crystal structure of M. smegmatics GyrB in complex with one of the aminopyrazinamides facilitated us to employ structure-based virtual screening approach to obtain new hits from a commercial library of Asinex database using energy-optimized pharmacophore modeling. Further the model was validated using enrichment calculations, and finally three models were employed for high-throughput virtual screening and docking to identify novel DNA gyrase B inhibitors. This study led to the identification of fifteen potential compounds with IC50 values in the range of 1.5-45.5 µM against M. smegmatics GyrB and 1.16-25 µM in MTB supercoiling assay. Lead 11 emerged as the most potential compound, exhibiting inhibition of MTB DNA gyrase supercoiling with an IC50 of 1.16±0.25 µM, and M. smegmatics GyrB IC50 of 1.5±0.12 µM and hence could be further developed as novel inhibitor for mycobacterial GyrB. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. DOI: 10.1002/minf.201400058 PMID: 27486079 487. J Med Chem. 2004 Feb 26;47(5):1079-80. Sabadinine: a potential non-peptide anti-severe acute-respiratory-syndrome agent identified using structure-aided design. Toney JH(1), Navas-Martín S, Weiss SR, Koeller A. Author information: (1)Department of Chemistry and Biochemistry, Montclair State University, 1 Normal Avenue, Upper Montclair, New Jersey 07043, USA. toneyje@mail.montclair.edu A novel human coronavirus has been reported to be the causative agent of severe acute respiratory syndrome (SARS). Since replication of HcoVs depends on extensive proteolytic processing, the main proteinase, 3CLpro, is an attractive drug target for anti-SARS agents. We have employed molecular docking of a chemical database into the active site of 3CLpro to search for non-peptidyl inhibitors. One compound was identified to be the natural product sabadinine, isolated from a historical herbal remedy. DOI: 10.1021/jm034137m PMID: 14971887 [Indexed for MEDLINE] 488. Shi Yan Sheng Wu Xue Bao. 2003 Oct;36(5):342-6. [Development of a K562 multidrug-resistant cell line and study on proteins with altered expression]. [Article in Chinese] Wang Y(1), Cao J, Zeng S. Author information: (1)Cancer Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China. In an attempt to study the whole protein expression alterations of tumer cells after becoming multidrug-resistant, which may provide useful information on new drug target identification, an adriamycin-resistant variant of the human leukemia cell line K562 (K562/ADR) was developed in vitro by continuous exposure to adrimycin. MTT assay was used to determine IC50 of K562/ADR cells to adriamycin (ADR), cisplatin (DDP), 5-fluorouracil (5-FU) and vincristin (VCR). The total proteins of K562 and K562/ADR were separated by two-dimensional gel electrophoresis and visualized by silver staining. Proteins with significant expression alterations were selected and their peptide mass fingerprints (PMFs) were obtained by matrix-assisted laser desorption/ionization time of flying mass spectrometry (MALDI-TOF-MS). The PMFs were used to search NCBInr database by AutoMS-Fit software. The results showed that K562/ADR cell demonstrated cross-resistance to other antineoplastic drugs. The IC50 of K562/ADR cells to ADR, DDP, 5-FU, VDR were much higher than those of K562. The proteins differentially expressed in the two cell lines were identified as cell cycle-related proteins, zinc finger protein 165, etc. These proteins are involved in cell cycling and transcription regulation, whose expression alterations may contribute to the multidrug resistant phenotype of K562/ADR cells. PMID: 14724945 [Indexed for MEDLINE] 489. J Med Chem. 2005 Dec 15;48(25):8009-15. Diketo acid pharmacophore. 2. Discovery of structurally diverse inhibitors of HIV-1 integrase. Dayam R(1), Sanchez T, Neamati N. Author information: (1)Department of Pharmaceutical Sciences, University of Southern California, School of Pharmacy, 1985 Zonal Avenue, PSC304, Los Angeles, California 90089, USA. Because of its unique role in the viral replication process, HIV-1 integrase (IN) is an important antiretroviral drug target. The beta-diketo acid class of IN inhibitors has played a major role in validating IN as a legitimate target for antiretroviral drug design. S-1360 (1) and L-870,810 (2) are examples of beta-diketo acid related compounds to enter clinical trials. With an aim to discover novel lead compounds with diverse structural scaffolds, we employed common feature pharmacophore models using four known beta-diketo acid analogues including S-1360 (J. Med. Chem. 2005, 1, 111-120). The best-ranked pharmacophore model (Hypo1) contained a hydrophobic (HYA), an H-bond acceptor (HBA), and two H-bond donor (HBD) features. A search of a 3D database containing approximately 150,000 small molecules using Hypo1 found 1700 compounds that satisfied all the features of the pharmacophore query. Of the 1700 compounds, 110 were selected for in vitro screening studies on the basis of their docking scores, predicted binding location inside the active site of IN, and their druglike properties. Forty-eight compounds inhibited IN catalytic activities with an IC50 value less than 100 microM. Twenty-seven structurally diverse inhibitors are reported here. Out of the 27 compounds, 13 compounds inhibited strand transfer activity of IN with an IC50 value less than 30 microM. These compounds are novel, druglike, and readily amenable for synthetic optimization. DOI: 10.1021/jm050837a PMID: 16335925 [Indexed for MEDLINE] 490. Mol Divers. 2012 Aug;16(3):563-77. doi: 10.1007/s11030-012-9388-8. Epub 2012 Aug 14. Rationalizing lead optimization by consensus 2D- CoMFA CoMSIA GRIND (3D) QSAR guided fragment hopping in search of γ-secretase inhibitors. Manoharan P(1), Ghoshal N. Author information: (1)Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, Jadavpur, Kolkata 700032, India. γ-Secretase (Gamma Secretase) is a potential drug target in Alzheimer's disease therapeutics. A sequel lead design study was undertaken on a series of bicyclononanes with an aim of identifying potent isofunctional chemotypes. Fragment-based bioisosteric replacement, which considers shape, chemistry, and electrostatics was carried out to mine over four million medicinally relevant fragments of Brood database. The resulting subset, thus, obtained was further mined using consensus QSAR developed from 2D and CoMFA, CoMSIA, GRIND (3D) QSAR predicted endpoints with superior statistical results. The employed consensus prediction and the predicted endpoint values were found to be in good agreement with the experimental values. The predictive ability of the generated model was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. Few analogs designed, using the concept of bioisosterism, were found to be promising and could be considered for synthesis and subsequent screening. DOI: 10.1007/s11030-012-9388-8 PMID: 22890960 [Indexed for MEDLINE] 491. PLoS One. 2011 Jan 11;6(1):e15228. doi: 10.1371/journal.pone.0015228. Identification of critical residues of the mycobacterial dephosphocoenzyme a kinase by site-directed mutagenesis. Walia G(1), Gajendar K, Surolia A. Author information: (1)Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India. Dephosphocoenzyme A kinase performs the transfer of the γ-phosphate of ATP to dephosphocoenzyme A, catalyzing the last step of coenzyme A biosynthesis. This enzyme belongs to the P-loop-containing NTP hydrolase superfamily, all members of which posses a three domain topology consisting of a CoA domain that binds the acceptor substrate, the nucleotide binding domain and the lid domain. Differences in the enzymatic organization and regulation between the human and mycobacterial counterparts, have pointed out the tubercular CoaE as a high confidence drug target (HAMAP database). Unfortunately the absence of a three-dimensional crystal structure of the enzyme, either alone or complexed with either of its substrates/regulators, leaves both the reaction mechanism unidentified and the chief players involved in substrate binding, stabilization and catalysis unknown. Based on homology modeling and sequence analysis, we chose residues in the three functional domains of the enzyme to assess their contributions to ligand binding and catalysis using site-directed mutagenesis. Systematically mutating the residues from the P-loop and the nucleotide-binding site identified Lys14 and Arg140 in ATP binding and the stabilization of the phosphoryl intermediate during the phosphotransfer reaction. Mutagenesis of Asp32 and Arg140 showed catalytic efficiencies less than 5-10% of the wild type, indicating the pivotal roles played by these residues in catalysis. Non-conservative substitution of the Leu114 residue identifies this leucine as the critical residue from the hydrophobic cleft involved in leading substrate, DCoA binding. We show that the mycobacterial enzyme requires the Mg(2+) for its catalytic activity. The binding energetics of the interactions of the mutant enzymes with the substrates were characterized in terms of their enthalpic and entropic contributions by ITC, providing a complete picture of the effects of the mutations on activity. The properties of mutants defective in substrate recognition were consistent with the ordered sequential mechanism of substrate addition for CoaE. DOI: 10.1371/journal.pone.0015228 PMCID: PMC3019153 PMID: 21264299 [Indexed for MEDLINE] 492. Bioorg Med Chem Lett. 2009 Feb 1;19(3):589-96. doi: 10.1016/j.bmcl.2008.12.065. Epub 2008 Dec 24. Discovery of novel inhibitors of Trypanosoma cruzi trans-sialidase from in silico screening. Neres J(1), Brewer ML, Ratier L, Botti H, Buschiazzo A, Edwards PN, Mortenson PN, Charlton MH, Alzari PM, Frasch AC, Bryce RA, Douglas KT. Author information: (1)School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester M13 9PT, UK. jneres@hotmail.com trans-Sialidase from Trypanosoma cruzi (TcTS) has emerged as a potential drug target for treatment of Chagas disease. Here, we report the results of virtual screening for the discovery of novel TcTS inhibitors, which targeted both the sialic acid and sialic acid acceptor sites of this enzyme. A library prepared from the Evotec database of commercially available compounds was screened using the molecular docking program GOLD, following the application of drug-likeness filters. Twenty-three compounds selected from the top-scoring ligands were purchased and assayed using a fluorimetric assay. Novel inhibitor scaffolds, with IC(50) values in the submillimolar range were discovered. The 3-benzothiazol-2-yl-4-phenyl-but-3-enoic acid scaffold was studied in more detail, and TcTS inhibition was confirmed by an alternative sialic acid transfer assay. Attempts to obtain crystal structures of these compounds with TcTS proved unsuccessful but provided evidence of ligand binding at the active site. DOI: 10.1016/j.bmcl.2008.12.065 PMID: 19144516 [Indexed for MEDLINE] 493. ACS Chem Neurosci. 2011 May 18;2(5):232-5. doi: 10.1021/cn200025w. IUPHAR-DB: an open-access, expert-curated resource for receptor and ion channel research. Sharman JL(1), Mpamhanga CP. Author information: (1)University/BHF Centre for Cardiovascular Science, The Queen's Medical Research Institute, University of Edinburgh , Edinburgh, EH16 4TJ, United Kingdom. This contribution highlights efforts by the International Union of Basic and Clinical Pharmacology (IUPHAR) Nomenclature Committee (NC-IUPHAR) to classify human receptors and ion channels, to document their properties, and to recommend ligands that are useful for characterization. This effort has inspired the creation of an online database (IUPHAR-DB), which is intended to provide free information to all scientists, summarized from primary literature by experts. DOI: 10.1021/cn200025w PMCID: PMC3369752 PMID: 22778867 [Indexed for MEDLINE] 494. Chem Biol Drug Des. 2011 May;77(5):373-87. doi: 10.1111/j.1747-0285.2011.01088.x. Epub 2011 Mar 1. First pharmacophore model of CCR3 receptor antagonists and its homology model-assisted, stepwise virtual screening. Jain V(1), Saravanan P, Arvind A, Mohan CG. Author information: (1)Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar- 160 062, Punjab, India. CCR3, a G protein-coupled receptor, plays a central role in allergic inflammation and is an important drug target for inflammatory diseases. To understand the structure-function relationship of CCR3 receptor, different computational techniques were employed, which mainly include: (i) homology modeling of CCR3 receptor, (ii) 3D-quantitative pharmacophore model of CCR3 antagonists, (iii) virtual screening of small compound databases, and (iv) finally, molecular docking at the binding site of the CCR3 receptor homology model. Pharmacophore model was developed for the first time, on a training data set of 22 CCR3 antagonists, using CATALYST HypoRefine program. Best hypothesis (Hypo1) has three different chemical features: two hydrogen-bond acceptors, one hydrophobic, and one ring aromatic. Hypo1 model was further validated using (i) 87 test set CCR3 antagonists, (ii) Cat Scramble randomization technique, and (iii) Decoy data set. Molecular docking studies were performed on modeled CCR3 receptor using 303 virtually screened hits, obtained from small compound database virtual screening. Finally, five hits were identified as potential leads against CCR3 receptor, which exhibited good estimated activities, favorable binding interactions, and high docking scores. These studies provided useful information on the structurally vital residues of CCR3 receptor involved in the antagonist binding, and their unexplored potential for the future development of potent CCR3 receptor antagonists. © 2011 John Wiley & Sons A/S. DOI: 10.1111/j.1747-0285.2011.01088.x PMID: 21284830 [Indexed for MEDLINE] 495. Structure. 1999 Jan 15;7(1):81-9. Crystal structure of Trypanosoma cruzi trypanothione reductase in complex with trypanothione, and the structure-based discovery of new natural product inhibitors. Bond CS(1), Zhang Y, Berriman M, Cunningham ML, Fairlamb AH, Hunter WN. Author information: (1)Department of Chemistry, University of Manchester, Oxford Road, Manchester M13 9PL, UK Department of Biochemistry University of Sydney Sydney NSW 2006 Australia. BACKGROUND: Trypanothione reductase (TR) helps to maintain an intracellular reducing environment in trypanosomatids, a group of protozoan parasites that afflict humans and livestock in tropical areas. This protective function is achieved via reduction of polyamine-glutathione conjugates, in particular trypanothione. TR has been validated as a chemotherapeutic target by molecular genetics methods. To assist the development of new therapeutics, we have characterised the structure of TR from the pathogen Trypanosoma cruzi complexed with the substrate trypanothione and have used the structure to guide database searches and molecular modelling studies. RESULTS: The TR-trypanothione-disulfide structure has been determined to 2.4 A resolution. The chemical interactions involved in enzyme recognition and binding of substrate can be inferred from this structure. Comparisons with the related mammalian enzyme, glutathione reductase, explain why each enzyme is so specific for its own substrate. A CH***O hydrogen bond can occur between the active-site histidine and a carbonyl of the substrate. This interaction contributes to enzyme specificity and mechanism by producing an electronic induced fit when substrate binds. Database searches and molecular modelling using the substrate as a template and the active site as receptor have identified a class of cyclic-polyamine natural products that are novel TR inhibitors. CONCLUSIONS: The structure of the TR-trypanothione enzyme-substrate complex provides details of a potentially valuable drug target. This information has helped to identify a new class of enzyme inhibitors as novel lead compounds worthy of further development in the search for improved medicines to treat a range of parasitic infections. PMID: 10368274 [Indexed for MEDLINE] 496. J Med Chem. 2004 Oct 21;47(22):5418-26. A three-dimensional in silico pharmacophore model for inhibition of Plasmodium falciparum cyclin-dependent kinases and discovery of different classes of novel Pfmrk specific inhibitors. Bhattacharjee AK(1), Geyer JA, Woodard CL, Kathcart AK, Nichols DA, Prigge ST, Li Z, Mott BT, Waters NC. Author information: (1)Division of Experimental Therapeutics, Walter Reed Army Institute of Research, Silver Spring, Maryland 20910-7500, USA. apurba.bhattacharjee@na.amedd.army.mil The cell division cycle is regulated by a family of cyclin-dependent protein kinases (CDKs) that are functionally conserved among many eukaryotic species. The characterization of plasmodial CDKs has identified them as a leading antimalarial drug target in our laboratory. We have developed a three-dimensional QSAR pharmacophore model for inhibition of a Plasmodium falciparum CDK, known as Pfmrk, from a set of fifteen structurally diverse kinase inhibitors with a wide range of activity. The model was found to contain two hydrogen bond acceptor functions and two hydrophobic sites including one aromatic-ring hydrophobic site. Although the model was not developed from X-ray structural analysis of the known CDK2 structure, it is consistent with the structure-functional requirements for binding of the CDK inhibitors in the ATP binding pocket. Using the model as a template, a search of the in-house three-dimensional multiconformer database resulted in the discovery of sixteen potent Pfmrk inhibitors. The predicted inhibitory activities of some of these Pfmrk inhibitors from the molecular model agree exceptionally well with the experimental inhibitory values from the in vitro CDK assay. DOI: 10.1021/jm040108f PMID: 15481979 [Indexed for MEDLINE] 497. Exp Parasitol. 2009 Jan;121(1):96-104. doi: 10.1016/j.exppara.2008.10.007. Epub 2008 Oct 21. Schistosoma spp.: Isolation of microtubule associated proteins in the tegument and the definition of dynein light chains components. Githui EK(1), Damian RT, Aman RA, Ali MA, Kamau JM. Author information: (1)Department of Molecular Genetics, Institute of Primate Research/National Museums of Kenya, P.O. Box 40658, Nairobi, Kenya. kegithui@yahoo.com Schistosomes are parasitic blood flukes that reside in human mesenteric veins or urinary bladder veins, depending on species of the parasite. The syncytial tegument of these parasites represents a dynamic interface that regulates nutritional and immunological interactions between the parasite and the host. It is known that the components for biogenesis and maintenance of the tegument are supplied via vesicles from the nucleated cell bodies beneath the syncytium and muscle layer. To investigate the common motor components of vesicular transport in the tegument of schistomes, we extracted Schistosoma mansoni tegumental microtubule associated proteins utilizing detergent/high-salt procedure and raised antiserum against these proteins. The antiserum was applied to screen Schistosoma haematobium lambda gt11 expression library and some of the isolated clones were sequenced. Blast search for the sequences against NCBI database identified clones that are dynein light chains and myosin genes. Further analysis of schistosome dynein genes in the databases identified three families of dynein light chains (Dlcs). The Tctex family protein sequences are significantly different from the mammalian homologs and, therefore, offer a potential vaccine/drug target against schistosomes. DOI: 10.1016/j.exppara.2008.10.007 PMID: 18996374 [Indexed for MEDLINE] 498. Korean J Parasitol. 2011 Sep;49(3):221-8. doi: 10.3347/kjp.2011.49.3.221. Epub 2011 Sep 30. Expressed sequence tag analysis of the erythrocytic stage of Plasmodium berghei. Seok JW(1), Lee YS, Moon EK, Lee JY, Jha BK, Kong HH, Chung DI, Hong Y. Author information: (1)Department of Parasitology, Kyungpook National University School of Medicine, Daegu 700-422, Korea. Rodent malaria parasites, such as Plasmodium berghei, are practical and useful model organisms for human malaria research because of their analogies to the human malaria in terms of structure, physiology, and life cycle. Exploiting the available genetic sequence information, we constructed a cDNA library from the erythrocytic stages of P. berghei and analyzed the expressed sequence tag (EST). A total of 10,040 ESTs were generated and assembled into 2,462 clusters. These EST clusters were compared against public protein databases and 48 putative new transcripts, most of which were hypothetical proteins with unknown function, were identified. Genes encoding ribosomal or membrane proteins and purine nucleotide phosphorylases were highly abundant clusters in P. berghei. Protein domain analyses and the Gene Ontology functional categorization revealed translation/protein folding, metabolism, protein degradation, and multiple family of variant antigens to be mainly prevalent. The presently-collected ESTs and its bioinformatic analysis will be useful resources to identify for drug target and vaccine candidates and validate gene predictions of P. berghei. DOI: 10.3347/kjp.2011.49.3.221 PMCID: PMC3210838 PMID: 22072821 [Indexed for MEDLINE] 499. Mol Inform. 2010 Jul 12;29(6-7):499-508. doi: 10.1002/minf.201000052. Epub 2010 Jun 28. Towards Proteome-Wide Interaction Models Using the Proteochemometrics Approach. Strömbergsson H(1), Lapins M(2), Kleywegt GJ(3), Wikberg JE(2). Author information: (1)The Linnaeus Centre for Bioinformatics, Department of Cell and Molecular Biology, Biomedical Centre, Box 598, SE-751 24, Uppsala, Sweden. helena.strombergsson@medsci.uu.se. (2)Department of Pharmaceutical Pharmacology, Biomedical Centre, Box 591, SE-751 24 Uppsala, Sweden. (3)Department of Cell and Molecular Biology, Biomedical Centre, Box 596, SE-751 24, Uppsala, Sweden. A proteochemometrics model was induced from all interaction data in the BindingDB database, comprizing in all 7078 protein-ligand complexes with representatives from all major drug target categories. Proteins were represented by alignment-independent sequence descriptors holding information on properties such as hydrophobicity, charge, and secondary structure. Ligands were represented by commonly used QSAR descriptors. The inhibition constant (pKi ) values of protein-ligand complexes were discretized into "high" and "low" interaction activity. Different machine-learning techniques were used to induce models relating protein and ligand properties to the interaction activity. The best was decision trees, which gave an accuracy of 80 % and an area under the ROC curve of 0.81. The tree pointed to the protein and ligand properties, which are relevant for the interaction. As the approach does neither require alignments nor knowledge of protein 3D structures virtually all available protein-ligand interaction data could be utilized, thus opening a way to completely general interaction models that may span entire proteomes. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. DOI: 10.1002/minf.201000052 PMID: 27463328 500. J Chem Inf Model. 2009 Jan;49(1):35-42. doi: 10.1021/ci8003607. Knowledge based identification of potent antitubercular compounds using structure based virtual screening and structure interaction fingerprints. Kumar A(1), Chaturvedi V, Bhatnagar S, Sinha S, Siddiqi MI. Author information: (1)Molecular and Structural Biology Division and Drug Target Discovery and Development Division, Central Drug Research Institute, Lucknow 226001, India. In view of the worldwide spread of multidrug resistance of Mycobacterium tuberculosis, there is an urgent need to discover antitubercular agents with novel structures. Thymidine monophosphate kinase from M. tuberculosis (TMPKmt) is an attractive target for antitubercular chemotherapy. We report here the identification of potent antitubercular compounds targeting TMPKmt using virtual screening methods. For this purpose we have developed a pharmacophore hypothesis based on the substrate and known TMPKmt inhibitors and employed it to screen the Maybridge small molecule database. The molecular docking was then performed in order to select the compounds on the basis of their ability to form favorable interactions with the TMPKmt active site. In addition, we applied straightforward weighting using structure interaction fingerprints to include additional knowledge into structure based virtual screening. Eight compounds were acquired and evaluated for antitubercular activity against M. tuberculosis H37Rv in vitro, and out of these 3 compounds showed MIC of 3.12 microg/mL whereas 2 compounds showed MIC of 12.5 microg/mL. All the active compounds were found to be nontoxic in Vero cell lines and mice bone marrow macrophages. All the identified hits highlighted a key hydrogen bonding interaction with Arg74. The observed pi-stacking interaction with Phe70 was also produced by the identified hits. These hits represent promising starting points for structural optimization in hit-to-lead development. DOI: 10.1021/ci8003607 PMID: 19063713 [Indexed for MEDLINE] 501. Chem Biol Drug Des. 2012 Jun;79(6):1056-62. doi: 10.1111/j.1747-0285.2012.01373.x. Epub 2012 Apr 17. Potential selective inhibitors against Rv0183 of Mycobacterium tuberculosis targeting host lipid metabolism. Saravanan P, Dubey VK, Patra S. Tuberculosis is the second leading infectious killer with 9 million new cases in 2009. Extensive use of pathogen's lipid metabolism especially in utilizing the host lipids and virulence highlights the importance of exported lipid-catabolizing enzymes. Current study aims to emphasize the importance of Rv0183, an exported monoacylglycerol lipase, involved in metabolizing the host cell membrane lipids. Sequence analysis and homology modeling shows Rv0183 is highly conserved throughout mycobacterial species even in Mycobacterium leprae and also significantly divergent from mammalian lipases. Additionally, employing virtual screening using NCI diversity set and ZINC database with criteria of molecules with higher predicted free energy of binding toward Rv0183 than human lipase, potential inhibitors have been identified for Rv0183. A tautomer of ZINC13451138, known inhibitor for HIV-1 integrase is the best hit with difference in free energy of binding of 8.72 kcal/mol. The sequence and structure analysis were helpful in identifying the ligand binding sites and molecular function of the mycobacterial specific monoacylglycerol lipase. Rv0183 represents a suitable and promising drug target and is also a step towards understanding dormancy development and reactivation, thereby addressing pathogen's drug resistance. Experimental studies on the discovered potential inhibitors in this virtual screen should further validate the therapeutic utility of Rv0183. © 2012 John Wiley & Sons A/S. DOI: 10.1111/j.1747-0285.2012.01373.x PMID: 22405030 [Indexed for MEDLINE] 502. AMIA Annu Symp Proc. 2011;2011:1127-33. Epub 2011 Oct 22. Exploring schizophrenia drug-gene interactions through molecular network and pathway modeling. Putnam DK(1), Sun J, Zhao Z. Author information: (1)Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA. In this study, we retrieved 39 schizophrenia-related antipsychotic drugs from the DrugBank database. These drugs had interactions with 142 targets, whose corresponding genes were defined as drug targeted genes. To explore the complexity between these drugs and their related genes in schizophrenia, we constructed a drug-target gene network. These genes were overrepresented in several pathways including: neuroactive ligand-receptor pathways, glutamate metabolism, and glycine metabolism. Through integrating the pathway information into a drug-gene network, we revealed a few bridge genes connected the sub-networks of the drug-gene network: GRIN2A, GRIN3B, GRIN2C, GRIN2B, DRD1, and DRD2. These genes encode ionotropic glutamate receptors belonging to the NMDA receptor family and dopamine receptors. Haloperidol was the only drug to directly interact with these pathways and receptors and consequently may have a unique action at the drug-gene interaction level during the treatment of schizophrenia. This study represents the first systematic investigation of drug-gene interactions in psychosis. PMCID: PMC3243134 PMID: 22195173 [Indexed for MEDLINE] 503. BMC Bioinformatics. 2011 Feb 15;12 Suppl 1:S3. doi: 10.1186/1471-2105-12-S1-S3. The G protein-coupled receptors in the pufferfish Takifugu rubripes. Sarkar A(1), Kumar S, Sundar D. Author information: (1)Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India. BACKGROUND: Guanine protein-coupled receptors (GPCRs) constitute a eukaryotic transmembrane protein family and function as "molecular switches" in the second messenger cascades and are found in all organisms between yeast and humans. They form the single, biggest drug-target family due to their versatility of action and their role in several physiological functions, being active players in detecting the presence of light, a variety of smells and tastes, amino acids, nucleotides, lipids, chemicals etc. in the environment of the cell. Comparative genomic studies on model organisms provide information on target receptors in humans and their function. The Japanese teleost Fugu has been identified as one of the smallest vertebrate genomes and a compact model to study the human genome, owing to the great similarity in its gene repertoire with that of human and other vertebrates. Thus the characterization of the GPCRs of Fugu would provide insights to the evolution of the vertebrate genome. RESULTS: We classified the GPCRs in the Fugu genome and our analysis of its 316 membrane-bound receptors, available on the public databases as well as from literature, detected 298 GPCRs that were grouped into five main families according to the GRAFS classification system (namely, Glutamate, Rhodopsin, Adhesion, Frizzled and Secretin). We also identified 18 other GPCRs that could not be grouped under the GRAFS family and hence were classified as 'Other 7TM' receptors. On comparison of the GPCR information from the Fugu genome with those in the human and chicken genomes, we detected 96.83% (306/316) and 96.51% (305/316) orthology in GPCRs among the Fugu-human genomes and Fugu-chicken genomes, respectively. CONCLUSIONS: This study reveals the position of pisces in vertebrate evolution from the GPCR perspective. Fugu can act as a reference model for the human genome for other protein families as well, going by the high orthology observed for GPCRs between Fugu and human. The evolutionary comparison of GPCR sequences between key vertebrate classes of mammals, birds and fish will help in identifying key functional residues and motifs so as to fill in the blanks in the evolution of GPCRs in vertebrates. DOI: 10.1186/1471-2105-12-S1-S3 PMCID: PMC3044285 PMID: 21342560 [Indexed for MEDLINE] 504. J Comput Aided Mol Des. 2010 Jan;24(1):77-87. doi: 10.1007/s10822-009-9315-y. Epub 2009 Dec 29. Characterization of dipeptidylcarboxypeptidase of Leishmania donovani: a molecular model for structure based design of antileishmanials. Baig MS(1), Kumar A, Siddiqi MI, Goyal N. Author information: (1)Division of Biochemistry, Central Drug Research Institute, Lucknow 226001, India. Leishmania donovani dipeptidylcarboxypeptidsae (LdDCP), an angiotensin converting enzyme (ACE) related metallopeptidase has been identified and characterized as a putative drug target for antileishmanial chemotherapy. The kinetic parameters for LdDCP with substrate, Hip-His-Leu were determined as, Km, 4 mM and Vmax, 1.173 micromole/ml/min. Inhibition studies revealed that known ACE inhibitors (captopril and bradykinin potentiating peptide; BPP1) were weak inhibitors for LdDCP as compared to human testicular ACE (htACE) with Ki values of 35.8 nM and 3.9 microM, respectively. Three dimensional model of LdDCP was generated based on crystal structure of Escherichia coli DCP (EcDCP) by means of comparative modeling and assessed using PROSAII, PROCHECK and WHATIF. Captopril docking with htACE, LdDCP and EcDCP and analysis of molecular electrostatic potentials (MEP) suggested that the active site domain of three enzymes has several minor but potentially important structural differences. These differences could be exploited for designing selective inhibitor of LdDCP thereby antileishmanial compounds either by denovo drug design or virtual screening of small molecule databases. DOI: 10.1007/s10822-009-9315-y PMID: 20039100 [Indexed for MEDLINE] 505. Molecules. 2018 May 30;23(6). pii: E1312. doi: 10.3390/molecules23061312. Knowledge-Based Neuroendocrine Immunomodulation (NIM) Molecular Network Construction and Its Application. Wang T(1)(2), Han L(3)(4), Zhang X(5)(6), Wu R(7)(8), Cheng X(9)(10), Zhou W(11)(12), Zhang Y(13)(14). Author information: (1)Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China. wangtongxing89@126.com. (2)State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China. wangtongxing89@126.com. (3)Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China. bmigroup2@163.com. (4)State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China. bmigroup2@163.com. (5)Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China. zhangx_r@126.com. (6)State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China. zhangx_r@126.com. (7)Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China. wrr302@163.com. (8)State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China. wrr302@163.com. (9)Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China. cxr916@163.com. (10)State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China. cxr916@163.com. (11)Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China. zhouwx@bmi.ac.cn. (12)State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China. zhouwx@bmi.ac.cn. (13)Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China. zhangyx@bmi.ac.cn. (14)State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China. zhangyx@bmi.ac.cn. Growing evidence shows that the neuroendocrine immunomodulation (NIM) network plays an important role in maintaining and modulating body function and the homeostasis of the internal environment. The disequilibrium of NIM in the body is closely associated with many diseases. In the present study, we first collected a core dataset of NIM signaling molecules based on our knowledge and obtained 611 NIM signaling molecules. Then, we built a NIM molecular network based on the MetaCore database and analyzed the signaling transduction characteristics of the core network. We found that the endocrine system played a pivotal role in the bridge between the nervous and immune systems and the signaling transduction between the three systems was not homogeneous. Finally, employing the forest algorithm, we identified the molecular hub playing an important role in the pathogenesis of rheumatoid arthritis (RA) and Alzheimer's disease (AD), based on the NIM molecular network constructed by us. The results showed that GSK3B, SMARCA4, PSMD7, HNF4A, PGR, RXRA, and ESRRA might be the key molecules for RA, while RARA, STAT3, STAT1, and PSMD14 might be the key molecules for AD. The molecular hub may be a potentially druggable target for these two complex diseases based on the literature. This study suggests that the NIM molecular network in this paper combined with the forest algorithm might provide a useful tool for predicting drug targets and understanding the pathogenesis of diseases. Therefore, the NIM molecular network and the corresponding online tool will not only enhance research on complex diseases and system biology, but also promote the communication of valuable clinical experience between modern medicine and Traditional Chinese Medicine (TCM). DOI: 10.3390/molecules23061312 PMCID: PMC6099962 PMID: 29848990 [Indexed for MEDLINE] 506. J Am Soc Mass Spectrom. 2017 Oct;28(10):2078-2089. doi: 10.1007/s13361-017-1706-z. Epub 2017 Jul 27. Quantitative Proteomic Analysis of Optimal Cutting Temperature (OCT) Embedded Core-Needle Biopsy of Lung Cancer. Zhao X(1)(2)(3), Huffman KE(4), Fujimoto J(5), Canales JR(5), Girard L(4), Nie G(6)(7), Heymach JV(8), Wistuba II(5), Minna JD(4), Yu Y(9). Author information: (1)CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, People's Republic of China. (2)University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China. (3)Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, USA. (4)Hamon Center for Therapeutic Oncology Research, Simmons Comprehensive Cancer Center, Pharmacology and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA. (5)Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA. (6)CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, People's Republic of China. niegj@nanoctr.cn. (7)University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China. niegj@nanoctr.cn. (8)Department of Head and Neck and Thoracic Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA. (9)Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, USA. Yonghao.yu@utsouthwestern.edu. With recent advances in understanding the genomic underpinnings and oncogenic drivers of pathogenesis in different subtypes, it is increasingly clear that proper pretreatment diagnostics are essential for the choice of appropriate treatment options for non-small cell lung cancer (NSCLC). Tumor tissue preservation in optimal cutting temperature (OCT) compound is commonly used in the surgical suite. However, proteins recovered from OCT-embedded specimens pose a challenge for LC-MS/MS experiments, due to the large amounts of polymers present in OCT. Here we present a simple workflow for whole proteome analysis of OCT-embedded NSCLC tissue samples, which involves a simple trichloroacetic acid precipitation step. Comparisons of protein recovery between frozen versus OCT-embedded tissue showed excellent consistency with more than 9200 proteins identified. Using an isobaric labeling strategy, we quantified more than 5400 proteins in tumor versus normal OCT-embedded core needle biopsy samples. Gene ontology analysis indicated that a number of proliferative as well as squamous cell carcinoma (SqCC) marker proteins were overexpressed in the tumor, consistent with the patient's pathology based diagnosis of "poorly differentiated SqCC". Among the most downregulated proteins in the tumor sample, we noted a number of proteins with potential immunomodulatory functions. Finally, interrogation of the aberrantly expressed proteins using a candidate approach and cross-referencing with publicly available databases led to the identification of potential druggable targets in DNA replication and DNA damage repair pathways. We conclude that our approach allows LC-MS/MS proteomic analyses on OCT-embedded lung cancer specimens, opening the way to bring powerful proteomics into the clinic. Graphical Abstract ᅟ. DOI: 10.1007/s13361-017-1706-z PMCID: PMC5693617 PMID: 28752479 507. Drug Des Devel Ther. 2015 Sep 18;9:5277-85. doi: 10.2147/DDDT.S86929. eCollection 2015. Clinicopathological significance and potential drug target of CDH1 in breast cancer: a meta-analysis and literature review. Huang R(1), Ding P(1), Yang F(1). Author information: (1)Department of Occupational and Environmental Health, School of Public Health, Central South University, Changsha, Hunan, People's Republic of China. CDH1, as a tumor suppressor gene, contributes sporadic breast cancer (BC) progression. However, the association between CDH1 hypermethylation and BC, and its clinicopathological significance remains unclear. We conducted a meta-analysis to investigate the relationship between the CDH1 methylation profile and the major clinicopathological features. A detailed literature was searched through the electronic databases PubMed, Web of Science™, and EMBASE™ for related research publications. The data were extracted and assessed by two reviewers independently. Odds ratios (ORs) with corresponding confidence intervals (CIs) were calculated and summarized respectively. The frequency of CDH1 methylation was significantly higher in invasive ductal carcinoma than in normal breast tissues (OR =5.83, 95% CI 3.76-9.03, P<0.00001). CDH1 hypermethylation was significantly higher in estrogen receptor (ER)-negative BC than in ER-positive BC (OR =0.62, 95% CI 0.43-0.87, P=0.007). In addition, we found that the CDH1 was significantly methylated in HER2-negative BC than in HER2-positive BC (OR =0.26, 95% CI 0.15-0.44, P<0.00001). However, CDH1 methylation frequency was not associated with progesterone receptor (PR) status, or with grades, stages, or lymph node metastasis of BC patients. Our results indicate that CDH1 hypermethylation is a potential novel drug target for developing personalized therapy. CDH1 hypermethylation is strongly associated with ER-negative and HER2-negative BC, respectively, suggesting CDH1 methylation status could contribute to the development of novel therapeutic approaches for the treatment of ER-negative or HER2-negative BC with aggressive tumor biology. DOI: 10.2147/DDDT.S86929 PMCID: PMC4583122 PMID: 26425077 [Indexed for MEDLINE] 508. J Med Chem. 2004 Oct 7;47(21):5076-84. Soft docking and multiple receptor conformations in virtual screening. Ferrari AM(1), Wei BQ, Costantino L, Shoichet BK. Author information: (1)Department of Pharmaceutical Chemistry, University of California-San Francisco, Genentech Hall, 600 16th Street, San Francisico, CA 94143-2240, USA. Protein conformational change is an important consideration in ligand-docking screens, but it is difficult to predict. A simple way to account for protein flexibility is to soften the criterion for steric fit between ligand and receptor. A more comprehensive but more expensive method would be to sample multiple receptor conformations explicitly. Here, these two approaches are compared. A "soft" scoring function was created by attenuating the repulsive term in the Lennard-Jones potential, allowing for a closer approach between ligand and protein. The standard, "hard" Lennard-Jones potential was used for docking to multiple receptor conformations. The Available Chemicals Directory (ACD) was screened against two cavity sites in the T4 lysozyme. These sites undergo small but significant conformational changes on ligand binding, making them good systems for soft docking. The ACD was also screened against the drug target aldose reductase, which can undergo large conformational changes on ligand binding. We evaluated the ability of the scoring functions to identify known ligands from among the over 200 000 decoy molecules in the database. The soft potential was always better at identifying known ligands than the hard scoring function when only a single receptor conformation was used. Conversely, the soft function was worse at identifying known leads than the hard function when multiple receptor conformations were used. This was true even for the cavity sites and was especially true for aldose reductase. To test the multiple-conformation method predictively, we screened the ACD for molecules that preferentially docked to the expanded conformation of aldose reductase, known to bind larger ligands. Six novel molecules that ranked among the top 0.66% of hits from the multiple-conformation calculation, but ranked relatively poorly in the soft docking calculation, were tested experimentally for enzyme inhibition. Four of these six inhibited the enzyme, the best with an IC(50) of 8 microM. Although ligands can get better scores in soft docking, the same is also true for decoys. The improved ranking of such decoys can come at the expense of true ligands. DOI: 10.1021/jm049756p PMCID: PMC1413506 PMID: 15456251 [Indexed for MEDLINE] 509. Expert Opin Ther Targets. 2003 Jun;7(3):427-40. Secreted phospholipase A2 enzymes as therapeutic targets. Scott KF(1), Graham GG, Bryant KJ. Author information: (1)St Vincent's Hospital Clinical School, School of Medical Sciences, The University of New South Wales, Sydney, Australia. k.scott@garvan.org.au Homology cloning through in silico database search analysis has led to the definition of ten structurally-related mammalian secreted phospholipase A(2) (sPLA(2)) enzyme forms at present, each expressed in a species-, genotype- and cell-type-specific manner and with different enzymatic properties. These studies have shown that models based on the premise that there is only one PLA(2) drug target are now inadequate. Type IIA sPLA(2) remains the most advanced clinical target, with rationally designed inhibitors in Phase II clinical trials. However, progress in our understanding of the functional role of the ten secreted enzymes in phospholipid (PL) metabolism and in eicosanoid-mediated disorders, together with their emerging activity-independent and receptor-mediated functions, is likely to significantly impact on current and future drug development efforts. DOI: 10.1517/14728222.7.3.427 PMID: 12783578 [Indexed for MEDLINE] 510. Am J Cancer Res. 2018 May 1;8(5):792-809. eCollection 2018. ERBB3, IGF1R, and TGFBR2 expression correlate with PDGFR expression in glioblastoma and participate in PDGFR inhibitor resistance of glioblastoma cells. Song K(1)(2), Yuan Y(1)(2), Lin Y(1)(2), Wang YX(1)(2), Zhou J(1)(2), Gai QJ(1)(2), Zhang L(1)(2), Mao M(1)(2), Yao XX(1)(2), Qin Y(1)(2), Lu HM(1)(2), Zhang X(1)(2), Cui YH(1)(2), Bian XW(1)(2), Zhang X(1)(2), Wang Y(1)(2). Author information: (1)Department of Pathology, Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University Chongqing 400038, China. (2)Key Laboratory of Tumor Immunology and Pathology of Ministry of Education Chongqing 400038, China. Glioma, the most prevalent malignancy in brain, is classified into four grades (I, II, III, and IV), and grade IV glioma is also known as glioblastoma multiforme (GBM). Aberrant activation of receptor tyrosine kinases (RTKs), including platelet-derived growth factor receptor (PDGFR), are frequently observed in glioma. Accumulating evidence suggests that PDGFR plays critical roles during glioma development and progression and is a promising drug target for GBM therapy. However, PDGFR inhibitor (PDGFRi) has failed in clinical trials, at least partially, due to the activation of other RTKs, which compensates for PDGFR inhibition and renders tumor cells resistance to PDGFRi. Therefore, identifying the RTKs responsible for PDGFRi resistance might provide new therapeutic targets to synergetically enhance the efficacy of PDGFRi. In this study, we analyzed the TCGA glioma database and found that the mRNA expressions of three RTKs, i.e. ERBB3, IGF1R, and TGFBR2, were positively correlated with that of PDGFR. Co-immunoprecipitation assay indicated novel interactions between the three RTKs and PDGFR in GBM cells. Moreover, concurrent expression of PDGFR with ERBB3, IGF1R, or TGFBR2 in GBM cells attenuated the toxicity of PDGFRi and maintained the activation of PDGFR downstream targets under the existence of PDGFRi. Thus, ERBB3, IGF1R, and TGFBR2 might participate in PDGFRi resistance of GBM cells. Consistent with this notion, combination of PDGFRi with inhibitor targeting either ERBB3 or IGF1R more potently suppressed the growth of GBM cells than each inhibitor alone. The positive correlations of PDGFR with ERBB3, IGF1R, and TGFBR2 were further confirmed in 66 GBM patient samples. Intriguingly, survival analysis showed that ERBB3 predicted poor prognosis in GBM patients with high PDGFRA expression. Altogether, our work herein suggested that ERBB3, IGF1R, and TGFBR2 were responsible for PDGFRi resistance and revealed that ERBB3 acted as potential prognostic marker and therapeutic target for GBM with high PDGFRA expression. PMCID: PMC5992513 PMID: 29888103 Conflict of interest statement: None. 511. Biophys Rep. 2018;4(1):1-16. doi: 10.1007/s41048-017-0045-8. Epub 2018 Feb 1. Docking-based inverse virtual screening: methods, applications, and challenges. Xu X(1)(2)(3)(4), Huang M(1)(3), Zou X(1)(2)(3)(4). Author information: (1)1Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211 USA. (2)2Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211 USA. (3)3Informatics Institute, University of Missouri, Columbia, MO 65211 USA. (4)4Department of Biochemistry, University of Missouri, Columbia, MO 65211 USA. Identifying potential protein targets for a small-compound ligand query is crucial to the process of drug development. However, there are tens of thousands of proteins in human alone, and it is almost impossible to scan all the existing proteins for a query ligand using current experimental methods. Recently, a computational technology called docking-based inverse virtual screening (IVS) has attracted much attention. In docking-based IVS, a panel of proteins is screened by a molecular docking program to identify potential targets for a query ligand. Ever since the first paper describing a docking-based IVS program was published about a decade ago, the approach has been gradually improved and utilized for a variety of purposes in the field of drug discovery. In this article, the methods employed in docking-based IVS are reviewed in detail, including target databases, docking engines, and scoring function methodologies. Several web servers developed for non-expert users are also reviewed. Then, a number of applications are presented according to different research purposes, such as target identification, side effects/toxicity, drug repositioning, drug-target network development, and receptor design. The review concludes by discussing the challenges that docking-based IVS needs to overcome to become a robust tool for pharmaceutical engineering. DOI: 10.1007/s41048-017-0045-8 PMCID: PMC5860130 PMID: 29577065 Conflict of interest statement: Compliance with Ethical StandardsXianjin Xu, Marshal Huang, and Xiaoqin Zou declare that they have no conflict of interest.This article does not contain any studies with human or animal subjects performed by any of the authors. 512. Mol Inform. 2011 Oct;30(10):851-62. doi: 10.1002/minf.201100049. Epub 2011 Aug 3. Optimization of Structure Based Virtual Screening Protocols Against Thymidine Monophosphate Kinase Inhibitors as Antitubercular Agents. Ul-Haq Z(1)(2), Uddin R(3)(4), Gul S(1). Author information: (1)Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi-75270, Pakistan. (2)Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS) gGmbH, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany. (3)Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi-75270, Pakistan. zaheer.qasmi@iccs.edu, mriazuddin@iccs.edu. (4)Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 52a, A-6020 Innsbruck, Austria. zaheer.qasmi@iccs.edu, mriazuddin@iccs.edu. Thymidine monophosphate kinase from Mycobacterium tuberculosis (TMPKMtub ) is an established drug target against tuberculosis. The enzyme TMPKMtub is responsible for the survival of bacterium MTB and required to synthesize an essential building block of the bacterial DNA which is thymidine triphosphate (TTP). There are several potent inhibitors available against the target enzyme but the majority are substrate analogues. Recently, three dimensional structures of the enzyme TMPKMtub inhibitor complexes were resolved using X-ray crystallography. These available crystal structures were the basis of initiating a structure based lead identification campaign against TMPKMtub . The available information was utilized to perform structure-based virtual screening against TMPKMtub with the hope to diversify the structures of the current inhibitors. In order to setup the protocol, 10 000 out of 45 000 drug-like molecules were randomly selected from National Cancer Institute's (NCI) database. Additionally 105 known inhibitors along with 11 natural substrates were mixed with the 10 000 selected compounds. For the current study, a rigid based docking algorithm, i.e., FRED has been utilized to set up an efficient docking and scoring protocol. The methods including enrichment curves, consensus scoring and ROC curves are providing useful insights into the setting up of a suitable structure-based docking protocol against TMPKMtub . As a result, an optimum docking and scoring function has been identified for future large scale virtual screening. In the present work, we have demonstrated a rational choice of protocol for structure based virtual screening of chemical libraries and help to understand the influence of receptor flexibility by using multiple geometries. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. DOI: 10.1002/minf.201100049 PMID: 27468105 513. Eur J Clin Pharmacol. 2015 Apr;71(4):461-71. doi: 10.1007/s00228-015-1814-2. Epub 2015 Feb 11. Drug-target based cross-sectional analysis of olfactory drug effects. Lötsch J(1), Daiker H, Hähner A, Ultsch A, Hummel T. Author information: (1)Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany, j.loetsch@em.uni-frankfurt.de. BACKGROUND: Drug effects on the human sense of smell attract increasing interest, yet systematic evidence from controlled studies is sparse. The present cross-sectional approach to olfactory drug effects made use of the recent developments in informatics, knowledge discovery, and data mining allowing connecting drug-related information from humans with underlying molecular drug targets. METHODS: In this prospective cross-sectional study, n = 1008 outpatients at a general practitioner were enrolled. All currently taken medications were obtained, and olfactory function was assessed by means of a clinically established 12-item odor identification test. The association between the patients' sense of smell and the administered medications was based (i) on the active pharmacological substances and (ii) on the molecular targets queried from the publicly accessible DrugBank database. RESULTS: Of the 168 different substances, six were taken sufficiently often to be analyzed. The administration of levothyroxine was associated with a higher olfactory score (p = 0.033). For the 168 drugs, 323 different targets could be queried. Thirty-one gene products were addressed sufficiently often to be analyzed. Besides agonistic targeting of thyroid hormone receptors (genes THRA1, THRB1) agreeing with the above result, antagonistically targeting the adrenoceptor alpha 1A (gene ADRA1A) by several unrelated medications was associated with a significantly higher olfactory score (p = 0.012). CONCLUSIONS: The identified drug effects on olfaction are both biologically plausible based on supportive information from basic science studies. The novel molecular target-based approach suggested clear advantages over the classical drug or drug class-based approach. It increased the analyzable data volume fivefold and provided plausible hypotheses about mechanistic drug effects opening possibilities for drug discovery and repurposing. DOI: 10.1007/s00228-015-1814-2 PMID: 25666029 [Indexed for MEDLINE] 514. Prog Mol Biol Transl Sci. 2014;121:95-131. doi: 10.1016/B978-0-12-800101-1.00004-1. Targeting GPR119 for the potential treatment of type 2 diabetes mellitus. Mo XL(1), Yang Z(1), Tao YX(1). Author information: (1)Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, Alabama, USA. G protein-coupled receptor 119 (GPR119) was initially identified as an orphan receptor through mining the human genome database. In 2005, GPR119 was deorphanized and shown to be a receptor for fatty acid metabolites, including some phospholipids and fatty acid amide derivatives. GPR119 regulates various physiological processes that improve glucose homeostasis, including glucose-dependent insulin secretion from pancreatic β-cells, gastrointestinal incretin hormone secretion, appetite control, epithelial electrolyte homeostasis, gastric emptying, and β-cell proliferation and cytoprotection. Therefore, GPR119, the sensing receptor for fatty acid metabolites, represents a novel drug target for the treatment of type 2 diabetes mellitus. © 2014 Elsevier Inc. All rights reserved. DOI: 10.1016/B978-0-12-800101-1.00004-1 PMID: 24373236 [Indexed for MEDLINE] 515. Interdiscip Sci. 2012 Mar;4(1):74-82. doi: 10.1007/s12539-011-0109-2. Epub 2012 Mar 6. Preliminary analysis to target pyruvate phosphate dikinase from wolbachia endosymbiont of Brugia malayi for designing anti-filarial agents. Palayam M(1), Lakshminarayanan K, Radhakrishnan M, Krishnaswamy G. Author information: (1)Centre of Advanced study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai, 600025, India. Filariasis causing nematode Brugia malayi is shown to harbor wolbachia bacteria as symbionts. The sequenced genome of the wolbachia endosymbiont from B.malayi (wBm) offers an unprecedented opportunity to identify new wolbachia drug targets. Genome analysis of the glycolytic/gluconeogenic pathway has revealed that wBm lacks pyruvate kinase (PK) and may instead utilize the enzyme pyruvate phosphate dikinase (PPDK; ATP: pyruvate, orthophosphate phosphotransferase, EC 2.7.9.1). PPDK catalyses the reversible conversion of AMP, PPi and phosphoenolpyruvate into ATP, Pi and pyruvate. Most organisms including mammals exclusively possess PK. Therefore the absence of PPDK in mammals makes this enzyme as attractive wolbachia drug target. In the present study we have modeled the three dimensional structure of wBm PPDK. The template with 50% identity and 67% similarity in amino acid sequence was employed for homology-modeling approach. The putative active site consists of His476, Arg360, Glu358, Asp344, Arg112, Lys43 and Glu346 was selected as site of interest for designing suitable inhibitor molecules. Docking studies were carried out using induced fit algorithms with OPLS force field of Schrödinger's Glide. The lead molecules which inhibit the PPDK activity are taken from the small molecule library (Pubchem database) and the interaction analysis showed that these compounds may inhibit the function of PPDK in wBm. DOI: 10.1007/s12539-011-0109-2 PMID: 22392278 [Indexed for MEDLINE] 516. Annu Rev Pharmacol Toxicol. 2012;52:505-21. doi: 10.1146/annurev-pharmtox-010611-134520. Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Zhao S(1), Iyengar R. Author information: (1)Department of Pharmacology and Systems Therapeutics, and Systems Biology Center New York, Mount Sinai School of Medicine, New York, New York 10029, USA. Systems approaches have long been used in pharmacology to understand drug action at the organ and organismal levels. The application of computational and experimental systems biology approaches to pharmacology allows us to expand the definition of systems pharmacology to include network analyses at multiple scales of biological organization and to explain both therapeutic and adverse effects of drugs. Systems pharmacology analyses rely on experimental "omics" technologies that are capable of measuring changes in large numbers of variables, often at a genome-wide level, to build networks for analyzing drug action. A major use of omics technologies is to relate the genomic status of an individual to the therapeutic efficacy of a drug of interest. Combining pathway and network analyses, pharmacokinetic and pharmacodynamic models, and a knowledge of polymorphisms in the genome will enable the development of predictive models of therapeutic efficacy. Network analyses based on publicly available databases such as the U.S. Food and Drug Administration's Adverse Event Reporting System allow us to develop an initial understanding of the context within which molecular-level drug-target interactions can lead to distal effectors in a process that results in adverse phenotypes at the organ and organismal levels. The current state of systems pharmacology allows us to formulate a set of questions that could drive future research in the field. The long-term goal of such research is to develop polypharmacology for complex diseases and predict therapeutic efficacy and adverse event risk for individuals prior to commencement of therapy. DOI: 10.1146/annurev-pharmtox-010611-134520 PMCID: PMC3619403 PMID: 22235860 [Indexed for MEDLINE] 517. J Med Chem. 2008 Nov 27;51(22):7205-15. doi: 10.1021/jm800825n. Discovery of ligands for a novel target, the human telomerase RNA, based on flexible-target virtual screening and NMR. Pinto IG(1), Guilbert C, Ulyanov NB, Stearns J, James TL. Author information: (1)Department of Pharmaceutical Chemistry, MC 2280, University of Californias San Francisco, 600 16th Street, San Francisco, California 94158-2517, USA. The human ribonucleoprotein telomerase is a validated anticancer drug target, and hTR-P2b is a part of the human telomerase RNA (hTR) essential for its activity. Interesting ligands that bind hTR-P2b were identified by iteratively using a tandem structure-based approach: docking of potential ligands from small databases to hTR-P2b via the program MORDOR, which permits flexibility in both ligand and target, with subsequent NMR screening of high-ranking compounds. A high percentage of the compounds tested experimentally were found via NMR to bind to the U-rich region of hTR-P2b; most have MW < 500 Da and are from different compound classes, and several possess a charge of 0 or +1. Of the 48 ligands identified, 24 exhibit a decided preference to bind hTR-P2b RNA rather than A-site rRNA and 10 do not bind A-site rRNA at all. Binding affinity was measured by monitoring RNA imino proton resonances for some of the compounds that showed hTR binding preference. DOI: 10.1021/jm800825n PMCID: PMC2651004 PMID: 18950148 [Indexed for MEDLINE] 518. Nucleic Acids Res. 2007 Jan;35(Database issue):D347-53. Epub 2006 Dec 1. The National Microbial Pathogen Database Resource (NMPDR): a genomics platform based on subsystem annotation. McNeil LK(1), Reich C, Aziz RK, Bartels D, Cohoon M, Disz T, Edwards RA, Gerdes S, Hwang K, Kubal M, Margaryan GR, Meyer F, Mihalo W, Olsen GJ, Olson R, Osterman A, Paarmann D, Paczian T, Parrello B, Pusch GD, Rodionov DA, Shi X, Vassieva O, Vonstein V, Zagnitko O, Xia F, Zinner J, Overbeek R, Stevens R. Author information: (1)National Center for Supercomputing Applications, Urbana, IL 61801, USA. lkmcneil@ncsa.uiuc.edu The National Microbial Pathogen Data Resource (NMPDR) (http://www.nmpdr.org) is a National Institute of Allergy and Infections Disease (NIAID)-funded Bioinformatics Resource Center that supports research in selected Category B pathogens. NMPDR contains the complete genomes of approximately 50 strains of pathogenic bacteria that are the focus of our curators, as well as >400 other genomes that provide a broad context for comparative analysis across the three phylogenetic Domains. NMPDR integrates complete, public genomes with expertly curated biological subsystems to provide the most consistent genome annotations. Subsystems are sets of functional roles related by a biologically meaningful organizing principle, which are built over large collections of genomes; they provide researchers with consistent functional assignments in a biologically structured context. Investigators can browse subsystems and reactions to develop accurate reconstructions of the metabolic networks of any sequenced organism. NMPDR provides a comprehensive bioinformatics platform, with tools and viewers for genome analysis. Results of precomputed gene clustering analyses can be retrieved in tabular or graphic format with one-click tools. NMPDR tools include Signature Genes, which finds the set of genes in common or that differentiates two groups of organisms. Essentiality data collated from genome-wide studies have been curated. Drug target identification and high-throughput, in silico, compound screening are in development. DOI: 10.1093/nar/gkl947 PMCID: PMC1751540 PMID: 17145713 [Indexed for MEDLINE] 519. Methods Mol Biol. 2018;1711:277-296. doi: 10.1007/978-1-4939-7493-1_14. Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing. Madhukar NS(1), Elemento O(2). Author information: (1)Department of Physiology and Biophysics, Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medical College, 1305 York Avenue, New York, NY, 10021, USA. (2)Department of Physiology and Biophysics, Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medical College, 1305 York Avenue, New York, NY, 10021, USA. ole2001@med.cornell.edu. Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. First, we explain the importance of customized drug regimens to the future of medical care. Second, we discuss the different public databases and community efforts that can be leveraged to develop new methods for identifying new predictive biomarkers. Third, we cover the basic methods that are currently used to identify markers or signatures of drug response, without any prior knowledge of the drug's mechanism of action. We further discuss how one can integrate knowledge about drug targets, mechanisms, and predictive markers to better estimate drug response in a diverse set of samples. We begin this section with a primer on popular methods to identify targets and mechanism of action for new small molecules. This discussion also includes a set of computational methods that incorporate other drug features, which do not relate to drug-induced genetic changes or sequencing data such as drug structures, side-effects, and efficacy profiles. Those additional drug properties can aid in gaining higher accuracy for the identification of drug target and mechanism of action. We then progress to discuss using these targets in combination with disease-specific expression patterns, known pathways, and genetic interaction networks to aid drug choice. Finally, we conclude this chapter with a general overview of machine learning methods that can integrate multiple pieces of sequencing data along with prior drug or biological knowledge to drastically improve response prediction. DOI: 10.1007/978-1-4939-7493-1_14 PMID: 29344895 [Indexed for MEDLINE] 520. Medicine (Baltimore). 2017 Mar;96(12):e6424. doi: 10.1097/MD.0000000000006424. Clinical effects of p53 overexpression in squamous cell carcinoma of the sinonasal tract: A systematic meta-analysis with PRISMA guidelines. Wang X(1), Lv W, Qi F, Gao Z, Yang H, Wang W, Gao Y. Author information: (1)Department of ENT, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China. BACKGROUND: The level of p53 protein expression in sinonasal squamous cell carcinoma (SNSCC) has been estimated, but the results remain inconsistent and the point of consensus has not been reached. This study was first determined to evaluate the clinical effects of p53 expression in SCC of the sinonasal tract. METHODS: According to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement criteria, the potential literature was searched from diverse databases. The pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated to assess the strength of association between p53 expression and SNSCC. RESULTS: Final 17 eligible studies were included in a total of 258 cases and 748 controls. The result of p53 expression was shown to be notably higher in SNSCC than in benign sinonasal papillomas and normal sinonasal mucosa (OR = 26.93, P < 0.001; OR = 39.79, P < 0.001; respectively). Subgroup analyses of ethnicity revealed that p53 expression had significant association with SNSCC in Asian and Caucasian populations in cancer versus benign sinonasal papillomas or normal sinonasal mucosa. The expression of p53 was notably higher in moderately or poorly differentiated SNSCC than in well-differentiated SNSCC (OR = 3.51, P = 0.021), while p53 expression was not associated with histological type. CONCLUSION: The results suggested that p53 overexpression may be correlated with the carcinogenesis and progression of SNSCC. The p53 gene may become a novel drug target of SNSCC. Additional studies on the correlation of p53 expression with clinicopathological features are needed. DOI: 10.1097/MD.0000000000006424 PMCID: PMC5371485 PMID: 28328848 [Indexed for MEDLINE] 521. Curr Comput Aided Drug Des. 2017;13(2):101-111. doi: 10.2174/1573409912666161124144725. In-Silico Characterization of a Hypothetical Protein, Rv1288 of Mycobacterium tuberculosis Containing an Esterase Signature and an Uncommon LytE Domain. Kumar A(1), Maan P(1), Singh G(1), Kaur J(1). Author information: (1)Department of Biotechnology, BMS Block-1, South Campus, Punjab University, Chandigarh 160014, India. BACKGROUND: Death toll due to tuberculosis is still rising day by day. Whole genome sequence of Mycobacterium tuberculosis has provided a platform to conduct research in order to identify the probable drug target. OBJECTIVES: Out of 4000 gene products of M. tuberculosis, approximately 40% of proteins are annotated as hypothetical. Identifying and characterizing these proteins could provide a new prescriptive for developing new TB drugs. Rv1288, a protein of M. tuberculosis H37Rv has been annotated as a hypothetical protein in database. Attempt has been made to assign a meaningful role to rv1288 gene product in M. tuberculosis life cycle. METHODS: A homology 3D structure of both domains was separately generated and assigned as Rv1288LytE and Rv1288est. Molecular simulation of Rv1288est was carried out for proper structure analysis. To further confirm the predictive role of Rv1288 in mycobacterium life cycle, molecular docking was performed. N-acetyl glucosamine, a major constituent of cell wall was docked with LytE domain, whereas, esterase domain was docked with lipolytic substrate, pNP-ester derivatives and inhibitors THL/PMSF. RESULTS: In-silico analysis revealed that Rv1288 is a two domain protein, an N-terminal LytE domain containing three consecutive LysM motifs and a C-terminal esterase domain of esterase D family. LytE domain has the property to bind N-acetyl glucosamine moieties of peptidoglycan, a major component of cell wall. Detailed in-silico sequence analysis revealed that this LytE domain may help in positioning the esterase domain to the cell wall of mycobacterium. Esterase domain comprised a tetrapeptide motif HGGG, a pentapeptide sequence motif GxSxG and conserved amino acid residues Ser-141, Asp-238 and His-272 which constitute a catalytic triad characteristic of other hormone sensitive lipases/ esterases. Docking studies suggested that THL and PMSF could be the potent inhibitors for Rv1288 protein. CONCLUSION: In the present investigation, we bioinformatically confirmed that Rv1288 is most likely a LytE domain containing lipolytic enzyme showing similarity to hormone sensitive lipases/esterases. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1573409912666161124144725 PMID: 27890013 [Indexed for MEDLINE] 522. Parasit Vectors. 2010 Nov 23;3:113. doi: 10.1186/1756-3305-3-113. Bovipain-2, the falcipain-2 ortholog, is expressed in intraerythrocytic stages of the tick-transmitted hemoparasite Babesia bovis. Mesplet M(1), Echaide I, Dominguez M, Mosqueda JJ, Suarez CE, Schnittger L, Florin-Christensen M. Author information: (1)Instituto de Patobiología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, INTA-Castelar, Argentina. mflorin@cnia.inta.gov.ar. BACKGROUND: Cysteine proteases have been shown to be highly relevant for Apicomplexan parasites. In the case of Babesia bovis, a tick-transmitted hemoparasite of cattle, inhibitors of these enzymes were shown to hamper intraerythrocytic replication of the parasite, underscoring their importance for survival. RESULTS: Four papain-like cysteine proteases were found to be encoded by the B. bovis genome using the MEROPS database. One of them, the ortholog of Plasmodium falciparum falcipain-2, here named bovipain-2, was further characterized. Bovipain-2 is encoded in B. bovis chromosome 4 by an ORF of 1.3 kb, has a predicted molecular weight of 42 kDa, and is hydrophilic with the exception of a transmembrane region. It has orthologs in several other apicomplexans, and its predicted amino acid sequence shows a high degree of conservation among several B. bovis isolates from North and South America. Synteny studies demonstrated that the bovipain-2 gene has expanded in the genomes of two related piroplasmids, Theileria parva and T. annulata, into families of 6 and 7 clustered genes respectively. The bovipain-2 gene is transcribed in in vitro cultured intra-erythrocyte forms of a virulent and an attenuated B. bovis strain from Argentina, and has no introns, as shown by RT-PCR followed by sequencing. Antibodies against a recombinant form of bovipain-2 recognized two parasite protein bands of 34 and 26 kDa, which coincide with the predicted sizes of the pro-peptidase and mature peptidase, respectively. Immunofluorescence studies showed an intracellular localization of bovipain-2 in the middle-rear region of in vitro cultured merozoites, as well as diffused in the cytoplasm of infected erythrocytes. Anti-bovipain-2 antibodies also reacted with B. bigemina-infected erythrocytes giving a similar pattern, which suggests cross-reactivity among these species. Antibodies in sera of two out of six B. bovis-experimentally infected bovines tested, reacted specifically with recombinant bovipain-2 in immunoblots, thus demonstrating expression and immunogenicity during bovine-infecting stages. CONCLUSIONS: Overall, we present the characterization of bovipain-2 and demonstrate its in vitro and in vivo expression in virulent and attenuated strains. Given the involvement of apicomplexan cysteine proteases in essential parasite functions, bovipain-2 constitutes a new vaccine candidate and potential drug target for bovine babesiosis. DOI: 10.1186/1756-3305-3-113 PMCID: PMC3003645 PMID: 21092313 523. J Recept Signal Transduct Res. 2017 Oct;37(5):470-480. doi: 10.1080/10799893.2017.1342129. Epub 2017 Jul 3. Identification and characterization of ErbB4 kinase inhibitors for effective breast cancer therapy. Sahu A(1)(2), Patra PK(1), Yadav MK(1), Varma M(2). Author information: (1)a Department of Biochemistry , Pt. J.N.M. Medical College , Raipur , India. (2)b Department of Biochemistry , Sri Aurobindo Institute of Medical Sciences , Indore , India. The overexpression of ErbB4 is associated with aggressive disease biology and reduced the survival of breast cancer patients. We have used ErbB4 receptor as a novel drug target to spearhead the rational drug design. The present study is divided into two parts. In the first part, we have exploited the hidden information inside ErbB4 kinase receptor both at sequence and structural level. PSI-BLAST algorithm is used to search similar sequences against ErbB4 kinase sequence. Top 15 sequences with high identity were selected for finding conserved and variable regions among sequences using multiple sequence alignment. In the second part, available 3 D structure of ErbB4 kinase is curated using loop modeling, and anomalies in the modeled structure is improved by energy minimization. The resultant structure is validated by analyzing dihedral angles by Ramachandran plot analysis. Furthermore, the potential binding sites were detected by using DoGSite and CASTp server. The similarity-search criterion is used for the preparation of our in-house database of drugs from DrugBank database. In total, 409 drugs yet to be tested against ErbB4 kinase is used for screening purpose. Virtual screening results in identification of 11 compounds with better binding affinity than lapatinib and canertinib. Study of protein-ligand interactions reveals information about amino acid residues; Lys726, Thr771, Met774, Cys778, Arg822, Thr835, Asp836 and Phe837 at the binding pocket. The physicochemical properties and bioactivity score calculation of selected compounds suggest them as biological active. This study presents a rich array that assist in expediting new drug discovery for breast cancer. DOI: 10.1080/10799893.2017.1342129 PMID: 28670936 [Indexed for MEDLINE] 524. Geroscience. 2018 Dec;40(5-6):523-538. doi: 10.1007/s11357-018-0046-7. Epub 2018 Oct 29. Report: NIA workshop on translating genetic variants associated with longevity into drug targets. Schork NJ(1), Raghavachari N(2); Workshop Speakers and Participants. Author information: (1)Department of Quantitative Medicine, The Translational Genomics Research Institute, Phoenix, AZ, USA. (2)National Institute on Aging, Bethesda, MD, USA. nalini.raghavachari@nih.gov. To date, candidate gene and genome-wide association studies (GWAS) have led to the discovery of longevity-associated variants (LAVs) in genes such as FOXO3A and APOE. Unfortunately, translating variants into drug targets is challenging for any trait, and longevity is no exception. Interdisciplinary and integrative multi-omics approaches are needed to understand how LAVs affect longevity-related phenotypes at the molecular physiologic level in order to leverage their discovery to identify new drug targets. The NIA convened a workshop in August 2017 on emerging and novel in silico (i.e., bioinformatics and computational) approaches to the translation of LAVs into drug targets. The goal of the workshop was to identify ways of enabling, enhancing, and facilitating interactions among researchers from different disciplines whose research considers either the identification of LAVs or the mechanistic or causal pathway(s) and protective factors they influence for discovering drug targets. Discussions among the workshop participants resulted in the identification of critical needs for enabling the translation of LAVs into drug targets in several areas. These included (1) the initiation and better use of cohorts with multi-omics profiling on the participants; (2) the generation of longitudinal information on multiple individuals; (3) the collection of data from non-human species (both long and short-lived) for comparative biology studies; (4) the refinement of computational tools for integrative analyses; (5) the development of novel computational and statistical inference techniques for assessing the potential of a drug target; (6) the identification of available drugs that could modulate a target in a way that could potentially provide protection against age-related diseases and/or enhance longevity; and (7) the development or enhancement of databases and repositories of relevant information, such as the Longevity Genomics website ( https://www.longevitygenomics.org ), to enhance and help motivate future interdisciplinary studies. Integrative approaches that examine the influence of LAVs on molecular physiologic phenotypes that might be amenable to pharmacological modulation are necessary for translating LAVs into drugs to enhance health and life span. DOI: 10.1007/s11357-018-0046-7 PMCID: PMC6294726 PMID: 30374935 525. BMJ. 2018 Sep 10;362:k3529. doi: 10.1136/bmj.k3529. Efficacy of PD-1 or PD-L1 inhibitors and PD-L1 expression status in cancer: meta-analysis. Shen X(1), Zhao B(1). Author information: (1)Center for Precision Medicine, 109 Xueyuan West Road, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China. OBJECTIVE: To evaluate the relative efficacy of programmed cell death 1 (PD-1) or programmed cell death ligand 1 (PD-L1) inhibitors versus conventional drugs in patients with cancer that were PD-L1 positive and PD-L1 negative. DESIGN: Meta-analysis of randomised controlled trials. DATA SOURCES: PubMed, Embase, Cochrane database, and conference abstracts presented at the American Society of Clinical Oncology and European Society of Medical Oncology up to March 2018. REVIEW METHODS: Studies of PD-1 or PD-L1 inhibitors (avelumab, atezolizumab, durvalumab, nivolumab, and pembrolizumab) that had available hazard ratios for death based on PD-L1 positivity or negativity were included. The threshold for PD-L1 positivity or negativity was that PD-L1 stained cell accounted for 1% of tumour cells, or tumour and immune cells, assayed by immunohistochemistry staining methods. RESULTS: 4174 patients with advanced or metastatic cancers from eight randomised controlled trials were included in this study. Compared with conventional agents, PD-1 or PD-L1 inhibitors were associated with significantly prolonged overall survival in both patients that were PD-L1 positive (n=2254, hazard ratio 0.66, 95% confidence interval 0.59 to 0.74) and PD-L1 negative (1920, 0.80, 0.71 to 0.90). However, the efficacies of PD-1 or PD-L1 blockade treatment in patients that were PD-L1 positive and PD-L1 negative were significantly different (P=0.02 for interaction). Additionally, in both patients that were PD-L1 positive and PD-L1 negative, the long term clinical benefits from PD-1 or PD-L1 blockade were observed consistently across interventional agent, cancer histotype, method of randomisation stratification, type of immunohistochemical scoring system, drug target, type of control group, and median follow-up time. CONCLUSIONS: PD-1 or PD-L1 blockade therapy is a preferable treatment option over conventional therapy for both patients that are PD-L1 positive and PD-L1 negative. This finding suggests that PD-L1 expression status alone is insufficient in determining which patients should be offered PD-1 or PD-L1 blockade therapy. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions. DOI: 10.1136/bmj.k3529 PMCID: PMC6129950 PMID: 30201790 Conflict of interest statement: Competing interests: All authors have completed the ICMJE form disclosure form at www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. 526. Eur J Pharm Sci. 2017 Aug 30;106:198-211. doi: 10.1016/j.ejps.2017.06.003. Epub 2017 Jun 4. Identification of Histone Deacetylase (HDAC) as a drug target against MRSA via interolog method of protein-protein interaction prediction. Uddin R(1), Tariq SS(2), Azam SS(3), Wadood A(4), Moin ST(5). Author information: (1)Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Pakistan. Electronic address: mriazuddin@iccs.edu. (2)Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Pakistan. (3)National Centre for Bioinformatics, Quaid-i-Azam University, Islamabad, Pakistan. (4)Department of Biochemistry, Abdul Wali Khan University, Mardan, Pakistan. (5)H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Pakistan. Patently, Protein-Protein Interactions (PPIs) lie at the core of significant biological functions and make the foundation of host-pathogen relationships. Hence, the current study is aimed to use computational biology techniques to predict host-pathogen Protein-Protein Interactions (HP-PPIs) between MRSA and Humans as potential drug targets ultimately proposing new possible inhibitors against them. As a matter of fact this study is based on the Interolog method which implies that homologous proteins retain their ability to interact. A distant homolog approach based on Interolog method was employed to speculate MRSA protein homologs in Humans using PSI-BLAST. In addition the protein interaction partners of these homologs as listed in Database of Interacting Proteins (DIP) were predicted to interact with MRSA as well. Moreover, a direct approach using BLAST was also applied so as to attain further confidence in the strategy. Consequently, the common HP-PPIs predicted by both approaches are suggested as potential drug targets (22%) whereas, the unique HP-PPIs estimated only through distant homolog approach are presented as novel drug targets (12%). Furthermore, the most repeated entry in our results was found to be MRSA Histone Deacetylase (HDAC) which was then modeled using SWISS-MODEL. Eventually, small molecules from ZINC, selected randomly, were docked against HDAC using Auto Dock and are suggested as potential binders (inhibitors) based on their energetic profiles. Thus the current study provides basis for further in-depth analysis of such data which not only include MRSA but other deadly pathogens as well. Copyright © 2017 Elsevier B.V. All rights reserved. DOI: 10.1016/j.ejps.2017.06.003 PMID: 28591562 [Indexed for MEDLINE] 527. Front Microbiol. 2017 Jan 20;8:25. doi: 10.3389/fmicb.2017.00025. eCollection 2017. A Systematic Review of In vitro and In vivo Activities of Anti-Toxoplasma Drugs and Compounds (2006-2016). Montazeri M(1), Sharif M(2), Sarvi S(2), Mehrzadi S(3), Ahmadpour E(4), Daryani A(2). Author information: (1)Toxoplasmosis Research Center, Mazandaran University of Medical SciencesSari, Iran; Student Research Committee, Mazandaran University of Medical SciencesSari, Iran. (2)Toxoplasmosis Research Center, Mazandaran University of Medical SciencesSari, Iran; Department of Parasitology and Mycology, Sari Medical School, Mazandaran University of Medical SciencesSari, Iran. (3)Department of Pharmacology, School of Medicine, Iran University of Medical Sciences Tehran Iran. (4)Drug Applied Research Center, Tabriz University of Medical Sciences Tabriz, Iran. The currently available anti-Toxoplasma agents have serious limitations. This systematic review was performed to evaluate drugs and new compounds used for the treatment of toxoplasmosis. Data was systematically collected from published papers on the efficacy of drugs/compounds used against Toxoplasma gondii (T. gondii) globally during 2006-2016. The searched databases were PubMed, Google Scholar, Science Direct, ISI Web of Science, EBSCO, and Scopus. One hundred and eighteen papers were eligible for inclusion in this systematic review, which were both in vitro and in vivo studies. Within this review, 80 clinically available drugs and a large number of new compounds with more than 39 mechanisms of action were evaluated. Interestingly, many of the drugs/compounds evaluated against T. gondii act on the apicoplast. Therefore, the apicoplast represents as a potential drug target for new chemotherapy. Based on the current findings, 49 drugs/compounds demonstrated in vitro half-maximal inhibitory concentration (IC50) values of below 1 μM, but most of them were not evaluated further for in vivo effectiveness. However, the derivatives of the ciprofloxacin, endochin-like quinolones and 1-[4-(4-nitrophenoxy) phenyl] propane-1-one (NPPP) were significantly active against T. gondii tachyzoites both in vitro and in vivo. Thus, these compounds are promising candidates for future studies. Also, compound 32 (T. gondii calcium-dependent protein kinase 1 inhibitor), endochin-like quinolones, miltefosine, rolipram abolish, and guanabenz can be repurposed into an effective anti-parasitic with a unique ability to reduce brain tissue cysts (88.7, 88, 78, 74, and 69%, respectively). Additionally, no promising drugs are available for congenital toxoplasmosis. In conclusion, as current chemotherapy against toxoplasmosis is still not satisfactory, development of well-tolerated and safe specific immunoprophylaxis in relaxing the need of dependence on chemotherapeutics is a highly valuable goal for global disease control. However, with the increasing number of high-risk individuals, and absence of a proper vaccine, continued efforts are necessary for the development of novel treatment options against T. gondii. Some of the novel compounds reviewed here may represent good starting points for the discovery of effective new drugs. In further, bioinformatic and in silico studies are needed in order to identify new potential toxoplasmicidal drugs. DOI: 10.3389/fmicb.2017.00025 PMCID: PMC5247447 PMID: 28163699 528. Microb Pathog. 2017 Feb;103:40-56. doi: 10.1016/j.micpath.2016.12.003. Epub 2016 Dec 7. Discovery of potent inhibitors targeting Vibrio harveyi LuxR through shape and e-pharmacophore based virtual screening and its biological evaluation. Rajamanikandan S(1), Jeyakanthan J(1), Srinivasan P(2). Author information: (1)Department of Bioinformatics, Science campus Alagappa University, Karaikudi, Tamilnadu, India. (2)Department of Animal Health and Management, Science campus Alagappa University, Karaikudi, Tamilnadu, India. Electronic address: sri.bioinformatics@gmail.com. Quorum sensing is widely recognized as an efficient mechanism in the regulation and production of several virulence factors, biofilm formation and stress responses. For this reason, quorum sensing circuit is emerging as a novel drug target for the development of anti-infective. Recently, cinnamaldehyde derivatives have been found to interfere with master quorum sensing transcriptional regulator and thereby decreasing the DNA binding ability of LuxR. However, the exact mode of cinnamaldehyde binding with LuxR and receptor interaction still remains inconclusive. In the current study, combined method of molecular docking and molecular dynamics simulations were performed to investigate the binding mode, dynamic and energy aspects of cinnamaldehyde derivatives into the binding site of LuxR. Based on the experimental and computational evidences, LuxR-3,4-dichloro-cinnamaldehyde complex was chosen for the development of e-pharmacophore model. Further, shape and e-pharmacophore based virtual screening were performed against ChemBridge database to find potent and suitable ligands for LuxR. By comparing the results of shape and e-pharmacophore based virtual screening; best 9 hit molecules were selected for further studies including ADMET prediction, molecular dynamics simulations and Prime MM-GBSA calculations. From the 9 hit molecules, the top most compound 3-(2,4-dichlorophenyl)-1-(1H-pyrrol-2-yl)-2-propen-1-one (ChemBridge-7364106) was selected for in vitro assays using Vibrio harveyi. The result revealed that ChemBridge-7364106 significantly reduced the bioluminescence production in a dose dependent manner. In addition, ChemBridge-7364106 showed a significant inhibition in biofilm formation and motility in V. harveyi. The results from the study suggest that ChemBridge-7364106 could serve as an anti-quorum sensing molecule for V. harveyi. Copyright © 2016 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.micpath.2016.12.003 PMID: 27939874 [Indexed for MEDLINE] 529. Int J Parasitol Drugs Drug Resist. 2014 Jul 30;4(3):347-54. doi: 10.1016/j.ijpddr.2014.06.001. eCollection 2014 Dec. Tyrosine aminotransferase from Leishmania infantum: A new drug target candidate. Moreno MA(1), Alonso A(1), Alcolea PJ(1), Abramov A(2), de Lacoba MG(1), Abendroth J(3), Zhang S(2), Edwards T(3), Lorimer D(3), Myler PJ(4), Larraga V(1). Author information: (1)Departamento de Microbiología Molecular y Servicio de Bioinformática y Bioestadística, Centro de Investigaciones Biológicas, Consejo Superior de Investigaciones Científicas (CSIC), calle Ramiro de Maeztu, 9, 28040 Madrid, Spain. (2)Seattle Structural Genomics Center for Infectious Disease (SSGCID), USA ; Seattle Biomedical Research Institute, 307 Westlake Avenue North, Seattle, WA 98109, USA. (3)Seattle Structural Genomics Center for Infectious Disease (SSGCID), USA ; Emerald Bio Inc., 7869 NE Day Road West, Bainbridge Island, WA 98110, USA. (4)Seattle Structural Genomics Center for Infectious Disease (SSGCID), USA ; Seattle Biomedical Research Institute, 307 Westlake Avenue North, Seattle, WA 98109, USA ; Department of Global Health, University of Washington, Seattle, WA 98125, USA ; Department of Biomedical Informatics & Medical Education, University of Washington, Seattle, WA 98125, USA. Leishmania infantum is the etiological agent of zoonotic visceral leishmaniasis in the Mediterranean basin. The disease is fatal without treatment, which has been based on antimonial pentavalents for more than 60 years. Due to resistances, relapses and toxicity to current treatment, the development of new drugs is required. The structure of the L. infantum tyrosine aminotransferase (LiTAT) has been recently solved showing important differences with the mammalian orthologue. The characterization of LiTAT is reported herein. This enzyme is cytoplasmic and is over-expressed in the more infective stages and nitric oxide resistant parasites. Unlike the mammalian TAT, LiTAT is able to use ketomethiobutyrate as co-substrate. The pharmacophore model of LiTAT with this specific co-substrate is described herein. This may allow the identification of new inhibitors present in the databases. All the data obtained support that LiTAT is a good target candidate for the development of new anti-leishmanial drugs. DOI: 10.1016/j.ijpddr.2014.06.001 PMCID: PMC4266777 PMID: 25516846 530. J Biomol Struct Dyn. 2013;31(1):44-58. doi: 10.1080/07391102.2012.691361. Epub 2012 Jul 18. Rationalization and prediction of drug resistant mutations in targets for clinical anti-tubercular drugs. Padiadpu J(1), Mukherjee S, Chandra N. Author information: (1)Department of Biochemistry, Indian Institute of Science, Bangalore, 560012, India. Resistance to therapy limits the effectiveness of drug treatment in many diseases. Drug resistance can be considered as a successful outcome of the bacterial struggle to survive in the hostile environment of a drug-exposed cell. An important mechanism by which bacteria acquire drug resistance is through mutations in the drug target. Drug resistant strains (multi-drug resistant and extensively drug resistant) of Mycobacterium tuberculosis are being identified at alarming rates, increasing the global burden of tuberculosis. An understanding of the nature of mutations in different drug targets and how they achieve resistance is therefore important. An objective of this study is to first decipher sequence as well as structural bases for the observed resistance in known drug resistant mutants and then to predict positions in each target that are more prone to acquiring drug resistant mutations. A curated database containing hundreds of mutations in the 38 drug targets of nine major clinical drugs, associated with resistance is studied here. Mutations have been classified into those that occur in the binding site itself, those that occur in residues interacting with the binding site and those that occur in outer zones. Structural models of the wild type and mutant forms of the target proteins have been analysed to seek explanations for reduction in drug binding. Stability analysis of an entire array of 19 mutations at each of the residues for each target has been computed using structural models. Conservation indices of individual residues, binding sites and whole proteins are computed based on sequence conservation analysis of the target proteins. The analyses lead to insights about which positions in the polypeptide chain have a higher propensity to acquire drug resistant mutations. Thus critical insights can be obtained about the effect of mutations on drug binding, in terms of which amino acid positions and therefore which interactions should not be heavily relied upon, which in turn can be translated into guidelines for modifying the existing drugs as well as for designing new drugs. The methodology can serve as a general framework to study drug resistant mutants in other micro-organisms as well. DOI: 10.1080/07391102.2012.691361 PMID: 22803837 [Indexed for MEDLINE] 531. Biochem Biophys Res Commun. 2010 Feb 26;393(1):55-60. doi: 10.1016/j.bbrc.2010.01.076. Epub 2010 Jan 25. Identification of genes related to heart failure using global gene expression profiling of human failing myocardium. Min KD(1), Asakura M, Liao Y, Nakamaru K, Okazaki H, Takahashi T, Fujimoto K, Ito S, Takahashi A, Asanuma H, Yamazaki S, Minamino T, Sanada S, Seguchi O, Nakano A, Ando Y, Otsuka T, Furukawa H, Isomura T, Takashima S, Mochizuki N, Kitakaze M. Author information: (1)Department of Cardiovascular Medicine, Osaka, Japan. Although various management methods have been developed for heart failure, it is necessary to investigate the diagnostic or therapeutic targets of heart failure. Accordingly, we have developed different approaches for managing heart failure by using conventional microarray analyses. We analyzed gene expression profiles of myocardial samples from 12 patients with heart failure and constructed datasets of heart failure-associated genes using clinical parameters such as pulmonary artery pressure (PAP) and ejection fraction (EF). From these 12 genes, we selected four genes with high expression levels in the heart, and examined their novelty by performing a literature-based search. In addition, we included four G-protein-coupled receptor (GPCR)-encoding genes, three enzyme-encoding genes, and one ion-channel protein-encoding gene to identify a drug target for heart failure using in silico microarray database. After the in vitro functional screening using adenovirus transfections of 12 genes into rat cardiomyocytes, we generated gene-targeting mice of five candidate genes, namely, MYLK3, GPR37L1, GPR35, MMP23, and NBC1. The results revealed that systolic blood pressure differed significantly between GPR35-KO and GPR35-WT mice as well as between GPR37L1-Tg and GPR37L1-KO mice. Further, the heart weight/body weight ratio between MYLK3-Tg and MYLK3-WT mice and between GPR37L1-Tg and GPR37L1-KO mice differed significantly. Hence, microarray analysis combined with clinical parameters can be an effective method to identify novel therapeutic targets for the prevention or management of heart failure. Copyright 2010 Elsevier Inc. All rights reserved. DOI: 10.1016/j.bbrc.2010.01.076 PMID: 20100464 [Indexed for MEDLINE] 532. J Biomed Semantics. 2018 Sep 6;9(1):23. doi: 10.1186/s13326-018-0189-6. Using predicate and provenance information from a knowledge graph for drug efficacy screening. Vlietstra WJ(1), Vos R(2)(3), Sijbers AM(4), van Mulligen EM(2), Kors JA(2). Author information: (1)Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, 3015, GE, the Netherlands. w.vlietstra@erasmusmc.nl. (2)Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, 3015, GE, the Netherlands. (3)Department of Methodology and Statistics, Maastricht University, Maastricht, 6200, MD, the Netherlands. (4)Centre for Molecular and Biomolecular Informatics, Radboudumc, Nijmegen, 6525, GA, the Netherlands. BACKGROUND: Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relationship between subject and object. A triple can also contain provenance information, which consists of references to the sources of the triple (e.g. scientific publications or database entries). Knowledge graphs have been used to classify drug-disease pairs for drug efficacy screening, but existing computational methods have often ignored predicate and provenance information. Using this information, we aimed to develop a supervised machine learning classifier and determine the added value of predicate and provenance information for drug efficacy screening. To ensure the biological plausibility of our method we performed our research on the protein level, where drugs are represented by their drug target proteins, and diseases by their disease proteins. RESULTS: Using random forests with repeated 10-fold cross-validation, our method achieved an area under the ROC curve (AUC) of 78.1% and 74.3% for two reference sets. We benchmarked against a state-of-the-art knowledge-graph technique that does not use predicate and provenance information, obtaining AUCs of 65.6% and 64.6%, respectively. Classifiers that only used predicate information performed superior to classifiers that only used provenance information, but using both performed best. CONCLUSION: We conclude that both predicate and provenance information provide added value for drug efficacy screening. DOI: 10.1186/s13326-018-0189-6 PMCID: PMC6127943 PMID: 30189889 533. Expert Opin Drug Discov. 2018 May;13(5):411-423. doi: 10.1080/17460441.2018.1443076. Epub 2018 Feb 28. Opportunities and challenges in drug discovery targeting metabotropic glutamate receptor 4. Volpi C(1), Fallarino F(1), Mondanelli G(1), Macchiarulo A(2), Grohmann U(1). Author information: (1)a Department of Experimental Medicine , University of Perugia , Perugia , Italy. (2)b Department of Pharmaceutical Sciences , University of Perugia , Perugia , Italy. INTRODUCTION: Until recently, metabotropic glutamate receptor 4 (mGlu4) has not received adequate attention in terms of drug targeting when compared to other members of the same mGlu receptor family, possibly because of the difficulties encountered in developing highly selective, either orthosteric or allosteric, ligands for this receptor. Areas covered: This review gives to discussion to the past and recent advances (between 2012-2017) in targeting the mGlu4 receptor for the treatment of disorders of the central nervous system (CNS) as well as immunological (neuroinflammation) and metabolic diseases (diabetes). Chemical structures, properties, and pharmacological properties discussed herein were retrieved from the scientific literature databases, PubMed and Google Scholar. Expert opinion: The fertile field of mGlu receptor positive allosteric modulators (PAMs) has recently led to the discovery of foliglurax, a highly selective mGlu4 receptor PAM with optimal bioavailability after oral administration and excellent brain distribution. However, further elucidation of the biological properties of the mGlu4 receptor, including expression and its signalling profile in distinct tissues and cells are still awaited in order to establish the mGlu4 receptor as a definite drug target in several CNS and non-CNS diseases. DOI: 10.1080/17460441.2018.1443076 PMID: 29486616 [Indexed for MEDLINE] 534. Antimicrob Agents Chemother. 2012 Mar;56(3):1190-201. doi: 10.1128/AAC.05528-11. Epub 2011 Dec 5. Identification of lead compounds targeting the cathepsin B-like enzyme of Eimeria tenella. Schaeffer M(1), Schroeder J, Heckeroth AR, Noack S, Gassel M, Mottram JC, Selzer PM, Coombs GH. Author information: (1)Wellcome Trust Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, 120 University Place, Glasgow, Scotland, United Kingdom. Cysteine peptidases have been implicated in the development and pathogenesis of Eimeria. We have identified a single-copy cathepsin B-like cysteine peptidase gene in the genome database of Eimeria tenella (EtCatB). Molecular modeling of the predicted protein suggested that it differs significantly from host enzymes and could be a good drug target. EtCatB was expressed and secreted as a soluble, active, glycosylated mature enzyme from Pichia pastoris. Biochemical characterization of the recombinant enzyme confirmed that it is cathepsin B-like. Screening of a focused library against the enzyme identified three inhibitors (a nitrile, a thiosemicarbazone, and an oxazolone) that can be used as leads for novel drug discovery against Eimeria. The oxazolone scaffold is a novel cysteine peptidase inhibitor; it may thus find widespread use. DOI: 10.1128/AAC.05528-11 PMCID: PMC3294901 PMID: 22143531 [Indexed for MEDLINE] 535. Comput Biol Chem. 2018 Jun;74:115-122. doi: 10.1016/j.compbiolchem.2018.02.017. Epub 2018 Mar 8. Prioritization of potential drug targets against P. aeruginosa by core proteomic analysis using computational subtractive genomics and Protein-Protein interaction network. Uddin R(1), Jamil F(2). Author information: (1)Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Pakistan. Electronic address: mriazuddin@iccs.edu. (2)Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Pakistan. Pseudomonas aeruginosa is an opportunistic gram-negative bacterium that has the capability to acquire resistance under hostile conditions and become a threat worldwide. It is involved in nosocomial infections. In the current study, potential novel drug targets against P. aeruginosa have been identified using core proteomic analysis and Protein-Protein Interactions (PPIs) studies. The non-redundant reference proteome of 68 strains having complete genome and latest assembly version of P. aeruginosa were downloaded from ftp NCBI RefSeq server in October 2016. The standalone CD-HIT tool was used to cluster ortholog proteins (having >=80% amino acid identity) present in all strains. The pan-proteome was clustered in 12,380 Clusters of Orthologous Proteins (COPs). By using in-house shell scripts, 3252 common COPs were extracted out and designated as clusters of core proteome. The core proteome of PAO1 strain was selected by fetching PAO1's proteome from common COPs. As a result, 1212 proteins were shortlisted that are non-homologous to the human but essential for the survival of the pathogen. Among these 1212 proteins, 321 proteins are conserved hypothetical proteins. Considering their potential as drug target, those 321 hypothetical proteins were selected and their probable functions were characterized. Based on the druggability criteria, 18 proteins were shortlisted. The interacting partners were identified by investigating the PPIs network using STRING v10 database. Subsequently, 8 proteins were shortlisted as 'hub proteins' and proposed as potential novel drug targets against P. aeruginosa. The study is interesting for the scientific community working to identify novel drug targets against MDR pathogens particularly P. aeruginosa. Copyright © 2018 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.compbiolchem.2018.02.017 PMID: 29587180 [Indexed for MEDLINE] 536. Trends Pharmacol Sci. 2014 Nov;35(11):604-20. doi: 10.1016/j.tips.2014.09.007. Epub 2014 Oct 10. Advances in kinase targeting: current clinical use and clinical trials. Rask-Andersen M(1), Zhang J(2), Fabbro D(3), Schiöth HB(4). Author information: (1)Department of Neuroscience, Division of Functional Pharmacology, Uppsala University, Biomedicinska Centrum (BMC), Uppsala 751 24, Sweden. Electronic address: Mathias.Rask-Andersen@neuro.uu.se. (2)Department of Neuroscience, Division of Functional Pharmacology, Uppsala University, Biomedicinska Centrum (BMC), Uppsala 751 24, Sweden; Department of Chemistry, Umeå Universitet, 901 87 Umeå, Sweden. (3)PIQUR Therapeutics AG, Hohe Winde-Strasse 120, 4059 Basel, Switzerland. (4)Department of Neuroscience, Division of Functional Pharmacology, Uppsala University, Biomedicinska Centrum (BMC), Uppsala 751 24, Sweden. Phosphotransferases, also known as kinases, are the most intensively studied protein drug target category in current pharmacological research, as evidenced by the vast number of kinase-targeting agents enrolled in active clinical trials. This development has emerged following the great success of small-molecule, orally available protein kinase inhibitors for the treatment of cancer, starting with the introduction of imatinib (Gleevec®) in 2003. The pharmacological utility of kinase-targeting has expanded to include treatment of inflammatory diseases, and rapid development is ongoing for kinase-targeted therapies in a broad array of indications in ophthalmology, analgesia, central nervous system (CNS) disorders, and the complications of diabetes, osteoporosis, and otology. In this review we highlight specifically the kinase drug targets and kinase-targeting agents being explored in current clinical trials. This analysis is based on a recent estimate of all established and clinical trial drug mechanisms of action, utilizing private and public databases to create an extensive dataset detailing aspects of more than 3000 approved and experimental drugs. Copyright © 2014 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.tips.2014.09.007 PMID: 25312588 [Indexed for MEDLINE] 537. Interdiscip Sci. 2013 Jun;5(2):136-44. doi: 10.1007/s12539-013-0164-y. Epub 2013 Jun 6. Homology modeling of LmxMPK4 of Leishmania mexicana and virtual screening of potent inhibitors against it. Gupta CL(1), Khan MK, Khan MF, Tiwari AK. Author information: (1)Bioinformatics Centre, Indian Veterinary Research Institute, Izatnagar 243122, India. chhedilalgupta009@gmail.com Leishmaniasis is one of the most important diseases of mankind. In the life cycle of Leishmania mexicana, two most important developmental stages are observed. In insect vector it is in promastigote form and in mammalian macrophages is the amastigote form. The family of protein kinases are extremely important regulators of many different cellular processes such as transcriptional control, cell cycle development and differentiation, and also draw much attention as possible drug targets for protozaon parasites. Leishmania mexicana mitogen activated protein kinase 4 (LmxMPK4) is essential for proliferation and survival of the parasite promastigote and amastigote forms and is a potential drug target for leishmaniasis. The existing therapy for leishmaniasis is not enough due to host toxicity and drug resistance. The experimental 3D structure of this protein has not yet been determined. In this study, we have used homology modelling techniques to generate the 3D structure of LmxMPK4 and selected effective inhibitors by ZINC database on the basis of structure of berberine alkaloid for molecular docking studies with LmxMPK4. The inhibitors ZINC05999210, ZINC40402312 and ZINC40977377 were found to be more potent for inhibition of leishmaniasis due to the robust binding affinity and strong inhibition constant (Ki) of the protein-ligand interactions. This finding may help to understand the nature of MAP kinase and development of specific anti-leishmanial therapies. DOI: 10.1007/s12539-013-0164-y PMID: 23740395 [Indexed for MEDLINE] 538. Mol Biosyst. 2010 Feb;6(2):339-48. doi: 10.1039/b916446d. Epub 2009 Dec 8. Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE. Kim HU(1), Kim TY, Lee SY. Author information: (1)Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of Korea. Acinetobacter baumannii has emerged as a new clinical threat to human health, particularly to ill patients in the hospital environment. Current lack of effective clinical solutions to treat this pathogen urges us to carry out systems-level studies that could contribute to the development of an effective therapy. Here we report the development of a strategy for identifying drug targets by combined genome-scale metabolic network and essentiality analyses. First, a genome-scale metabolic network of A. baumannii AYE, a drug-resistant strain, was reconstructed based on its genome annotation data, and biochemical knowledge from literatures and databases. In order to evaluate the performance of the in silico model, constraints-based flux analysis was carried out with appropriate constraints. Simulations were performed from both reaction (gene)- and metabolite-centric perspectives, each of which identifies essential genes/reactions and metabolites critical to the cell growth. The gene/reaction essentiality enables validation of the model and its comparative study with other known organisms' models. The metabolite essentiality approach was undertaken to predict essential metabolites that are critical to the cell growth. The EMFilter, a framework that filters initially predicted essential metabolites to find the most effective ones as drug targets, was also developed. EMFilter considers metabolite types, number of total and consuming reaction linkage with essential metabolites, and presence of essential metabolites and their relevant enzymes in human metabolism. Final drug target candidates obtained by this system framework are presented along with implications of this approach. DOI: 10.1039/b916446d PMID: 20094653 [Indexed for MEDLINE] 539. Protein Sci. 2006 Sep;15(9):2071-81. Epub 2006 Aug 1. Peptide deformylase is a potential target for anti-Helicobacter pylori drugs: reverse docking, enzymatic assay, and X-ray crystallography validation. Cai J(1), Han C, Hu T, Zhang J, Wu D, Wang F, Liu Y, Ding J, Chen K, Yue J, Shen X, Jiang H. Author information: (1)Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Graduate School of Chinese Academy of Sciences, China. Colonization of human stomach by the bacterium Helicobacter pylori is a major causative factor for gastrointestinal illnesses and gastric cancer. However, the discovery of anti-H. pylori agents is a difficult task due to lack of mature protein targets. Therefore, identifying new molecular targets for developing new drugs against H. pylori is obviously necessary. In this study, the in-house potential drug target database (PDTD, http://www.dddc.ac.cn/tarfisdock/) was searched by the reverse docking approach using an active natural product (compound 1) discovered by anti-H. pylori screening as a probe. Homology search revealed that, among the 15 candidates discovered by reverse docking, only diaminopimelate decarboxylase (DC) and peptide deformylase (PDF) have homologous proteins in the genome of H. pylori. Enzymatic assay demonstrated compound 1 and its derivative compound 2 are the potent inhibitors against H. pylori PDF (HpPDF) with IC50 values of 10.8 and 1.25 microM, respectively. X-ray crystal structures of HpPDF and the complexes of HpPDF with 1 and 2 were determined for the first time, indicating that these two inhibitors bind well with HpPDF binding pocket. All these results indicate that HpPDF is a potential target for screening new anti-H. pylori agents. In addition, compounds 1 and 2 were predicted to bind to HpPDF with relatively high selectivity, suggesting they can be used as leads for developing new anti-H. pylori agents. The results demonstrated that our strategy, reverse docking in conjunction with bioassay and structural biology, is effective and can be used as a complementary approach of functional genomics and chemical biology in target identification. DOI: 10.1110/ps.062238406 PMCID: PMC2242601 PMID: 16882991 [Indexed for MEDLINE] 540. Biochem J. 2006 Jun 1;396(2):287-95. Phosphatidylinositol synthesis is essential in bloodstream form Trypanosoma brucei. Martin KL(1), Smith TK. Author information: (1)Division of Biological Chemistry and Molecular Microbiology, School of Life Sciences, University of Dundee, Dundee DD1 5EH, Scotland, UK. PI (phosphatidylinositol) is a ubiquitous eukaryotic phospholipid which serves as a precursor for messenger molecules and GPI (glycosylphosphatidylinositol) anchors. PI is synthesized either de novo or by head group exchange by a PIS (PI synthase). The synthesis of GPI anchors has previously been validated both genetically and chemically as a drug target in Trypanosoma brucei, the causative parasite of African sleeping sickness. However, nothing is known about the synthesis of PI in this organism. Database mining revealed a putative TbPIS gene in the T. brucei genome and by recombinant expression and characterization it was shown to encode a catalytically active PIS, with a high specificity for myo-inositol. Immunofluorescence revealed that in T. brucei, PIS is found in both the endoplasmic reticulum and Golgi. We created a conditional double knockout of TbPIS in the bloodstream form of T. brucei, which when grown under non-permissive conditions, clearly showed that TbPIS is an essential gene. In vivo labelling of these conditional double knockout cells confirmed this result, showing a decrease in the amount of PI formed by the cells when grown under non-permissive conditions. Furthermore, quantitative and qualitative analysis by GLC-MS and ESI-MS/MS (electrospray ionization MS/MS) respectively showed a significant decrease (70%) in cellular PI, which appears to affect all major PI species equally. A consequence of this fall in PI level is a knock-on reduction in GPI biosynthesis which is essential for the parasite's survival. The results presented here show that PI synthesis is essential for bloodstream form T. brucei, and to our knowledge this is the first report of the dependence on PI synthesis of a protozoan parasite by genetic validation. DOI: 10.1042/BJ20051825 PMCID: PMC1462709 PMID: 16475982 [Indexed for MEDLINE] 541. J Biomol Struct Dyn. 2018 Jan 4:1-17. doi: 10.1080/07391102.2017.1415820. [Epub ahead of print] Unravelling novel congeners from acetyllysine mimicking ligand targeting a lysine acetyltransferase PCAF bromodomain. Suryanarayanan V(1), Singh SK(1). Author information: (1)a Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics , Alagappa University , Karaikudi , Tamil Nadu 630004 , India. p300/CBP Associated Factor (PCAF) bromodomain (BRD), a lysine acetyltransferases, has emerged as a promising drug target as its dysfunction is linked to onset and progression of several diseases like cancer, diabetes, AIDS, etc. In this study, a three featured E-Pharmacophore (ARR) was generated based on acetyllysine mimicking inhibitor of PCAF BRD which is available as co-crystal structure (PDB ID: 5FDZ). It was used for filtering small molecule databases followed by molecular docking and consequently validated using enrichment calculation. The resulted hits were found to be congeners which show the predictive power of E-Pharmacophore hypothesis. Further, Induced Fit Docking method, Binding energy calculation, ADME prediction, Single Point Energy calculation and Molecular Dynamics simulation were performed to find better hits against PCAF BRD. Based on the results, it was concluded that Asn803, Tyr809 and Tyr802 along with a water molecule (HOH1001) plays crucial role in binding with inhibitor. It is also proposed that four hits from Life Chemicals database namely, F2276-0099, F2276-0008, F2276-0104 and F2276-0106 could act as potent drug molecules for PCAF BRD. Thus, the present study is strongly believed to have bright impact on rational drug design of potent and novel congeners of PCAF BRD inhibitors. DOI: 10.1080/07391102.2017.1415820 PMID: 29228881 542. FEBS J. 2017 Aug;284(15):2425-2441. doi: 10.1111/febs.14136. Epub 2017 Jul 7. Streptococcus pyogenes quinolinate-salvage pathway-structural and functional studies of quinolinate phosphoribosyl transferase and NH3 -dependent NAD+ synthetase. Booth WT(1), Morris TL(1), Mysona DP(1), Shah MJ(1), Taylor LK(1), Karlin TW(1), Clary K(1), Majorek KA(2), Offermann LR(1)(3), Chruszcz M(1). Author information: (1)Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC, USA. (2)Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA. (3)Department of Chemistry, Davidson College, NC, USA. Streptococcus pyogenes, also known as Group A Strep (GAS), is an obligate human pathogen that is responsible for millions of infections and numerous deaths per year. Infection manifestations can range from simple, acute pharyngitis to more complex, necrotizing fasciitis. To date, most treatments for GAS infections involve the use of common antibiotics including tetracycline and clindamycin. Unfortunately, new strains have been identified that are resistant to these drugs, therefore, new targets must be identified to treat drug-resistant strains. This work is focused on the structural and functional characterization of three proteins: spNadC, spNadD, and spNadE. These enzymes are involved in the biosynthesis of nicotinamide adenine dinucleotide (NAD+ ). The structures of spNadC and spNadE were determined. SpNadC is suggested to play a role in GAS virulence, while spNadE, functions as an NAD synthetase and is considered to be a new drug target. Determination of the spNadE structure uncovered a putative, NH3 channel, which may provide insight into the mechanistic details of NH3 -dependent NAD+ synthetases in prokaryotes.ENZYMES: Quinolinate phosphoribosyltransferase: EC2.4.2.19 and NAD synthetase: EC6.3.1.5. DATABASE: Protein structures for spNadC, spNadCΔ69A , and spNadE are deposited into Protein Data Bank under the accession codes 5HUL, 5HUO & 5HUP, and 5HUH & 5HUJ, respectively. © 2017 Federation of European Biochemical Societies. DOI: 10.1111/febs.14136 PMCID: PMC5551413 PMID: 28618168 [Indexed for MEDLINE] 543. Drug Des Devel Ther. 2016 Mar 11;10:1147-57. doi: 10.2147/DDDT.S97043. eCollection 2016. Toward antituberculosis drugs: in silico screening of synthetic compounds against Mycobacterium tuberculosisl,d-transpeptidase 2. Billones JB(1), Carrillo MC(2), Organo VG(2), Macalino SJ(2), Sy JB(2), Emnacen IA(2), Clavio NA(2), Concepcion GP(3). Author information: (1)Office of the Vice President for Academic Affairs - Emerging Interdisciplinary Research Program: "Computer-aided Discovery of Compounds for the treatment of Tuberculosis in the Philippines," Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Diliman, Diliman, Quezon City, Philippines; Institute of Pharmaceutical Sciences, National Institutes of Health, University of the Philippines Manila, Manila, Philippines. (2)Office of the Vice President for Academic Affairs - Emerging Interdisciplinary Research Program: "Computer-aided Discovery of Compounds for the treatment of Tuberculosis in the Philippines," Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Diliman, Diliman, Quezon City, Philippines. (3)Marine Science Institute, University of the Philippines Diliman, Diliman, Quezon City, Philippines. Mycobacterium tuberculosis (Mtb) the main causative agent of tuberculosis, is the main reason why this disease continues to be a global public health threat. It is therefore imperative to find a novel antitubercular drug target that is unique to the structural machinery or is essential to the growth and survival of the bacterium. One such target is the enzyme l,d-transpeptidase 2, also known as LdtMt2, a protein primarily responsible for the catalysis of 3→3 cross-linkages that make up the mycolyl-arabinogalactan-peptidoglycan complex of Mtb. In this study, structure-based pharmacophore screening, molecular docking, and in silico toxicity evaluations were employed in screening compounds from a database of synthetic compounds. Out of the 4.5 million database compounds, 18 structures were identified as high-scoring, high-binding hits with very satisfactory absorption, distribution, metabolism, excretion, and toxicity properties. Two out of the 18 compounds were further subjected to in vitro bioactivity assays, with one exhibiting a good inhibitory activity against the Mtb H37Ra strain. DOI: 10.2147/DDDT.S97043 PMCID: PMC4795573 PMID: 27042006 [Indexed for MEDLINE] 544. Toxicol Appl Pharmacol. 2013 Sep 15;271(3):395-404. doi: 10.1016/j.taap.2011.01.015. Epub 2011 Feb 1. Integrating mechanistic and polymorphism data to characterize human genetic susceptibility for environmental chemical risk assessment in the 21st century. Mortensen HM(1), Euling SY. Author information: (1)Office of Research and Development, US Environmental Protection Agency, National Center for Computational Toxicology, US EPA, 109 TW Alexander Dr., Mailcode B205-01, Research Triangle Park, NC 27711, USA. Electronic address: mortensen.holly@epa.gov. Response to environmental chemicals can vary widely among individuals and between population groups. In human health risk assessment, data on susceptibility can be utilized by deriving risk levels based on a study of a susceptible population and/or an uncertainty factor may be applied to account for the lack of information about susceptibility. Defining genetic susceptibility in response to environmental chemicals across human populations is an area of interest in the NAS' new paradigm of toxicity pathway-based risk assessment. Data from high-throughput/high content (HT/HC), including -omics (e.g., genomics, transcriptomics, proteomics, metabolomics) technologies, have been integral to the identification and characterization of drug target and disease loci, and have been successfully utilized to inform the mechanism of action for numerous environmental chemicals. Large-scale population genotyping studies may help to characterize levels of variability across human populations at identified target loci implicated in response to environmental chemicals. By combining mechanistic data for a given environmental chemical with next generation sequencing data that provides human population variation information, one can begin to characterize differential susceptibility due to genetic variability to environmental chemicals within and across genetically heterogeneous human populations. The integration of such data sources will be informative to human health risk assessment. Copyright © 2011. Published by Elsevier Inc. DOI: 10.1016/j.taap.2011.01.015 PMID: 21291902 [Indexed for MEDLINE] 545. EBioMedicine. 2018 Nov;37:168-176. doi: 10.1016/j.ebiom.2018.10.005. Epub 2018 Oct 10. S1PR1 predicts patient survival and promotes chemotherapy drug resistance in gastric cancer cells through STAT3 constitutive activation. Song S(1), Min H(1), Niu M(1), Wang L(1), Wu Y(1), Zhang B(2), Chen X(3), Liang Q(1), Wen Y(1), Wang Y(1), Yi L(1), Wang H(4), Gao Q(5). Author information: (1)Center for Translational Medicine and Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210093, Jiangsu Province, China. (2)Central Laboratory, Nanjing Chest Hospital, Medical School of Southeast University, Nanjing 210029, Jiangsu Province, China. (3)Department of Biochemistry, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China. (4)Center for Translational Medicine and Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210093, Jiangsu Province, China. Electronic address: hwang@nju.edu.cn. (5)Center for Translational Medicine and Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210093, Jiangsu Province, China. Electronic address: qian_gao@nju.edu.cn. BACKGROUND: S1PR1-STAT3 inter-regulatory loop was initially suggested to be oncogenic in several cancer cells. However, the clinical relevance of this mechanism in tumor progression, disease prognosis and drug response was not established. METHODS: The correlations between S1PR1 transcription, overall survival and chemotherapy response of GC patients were tested using a large clinical database. The relevance of S1PR1 expression and STAT3 activation in both tumor tissues and cancer cell lines was also tested. The effect of S1PR1 high expression achieved by persistent STAT3 activation on tumor cell drug resistance was investigated in vitro and in vivo. FINDINGS: An enhanced S1PR1 expression was highly related with a reduced overall survival time and a worse response to chemotherapy drug and closer correlation to STAT3 in gastric cancer patients. The issue chip analysis showed that the expressions of S1PR1 and STAT3 activation were increased in higher graded gastric cancer (GC) tissues. Cellular studies supported the notion that the high S1PR1 expression was responsible for drug resistance in GC cells through a molecular pattern derived by constitutive activation of STAT3. The disruption of S1PR1-STAT3 signaling significantly re-sensitized drug resistance in GC cells in vitro and in vivo. INTERPRETATION: S1PR1-STAT3 signaling may participate drug resistance in GC, thus could serve as a drug target to increase the efficacy of GC treatment. FUND: This work was supported by the National Natural Science Foundation of China (No. 81570775, 81471095), the grant from the research projects in traditional Chinese medicine industry of China (No. 201507004-2). Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved. DOI: 10.1016/j.ebiom.2018.10.005 PMCID: PMC6284371 PMID: 30316864 546. Ann Rheum Dis. 2018 Jul;77(7):1078-1084. doi: 10.1136/annrheumdis-2018-213093. Epub 2018 Apr 6. Identification of ST3AGL4, MFHAS1, CSNK2A2 and CD226 as loci associated with systemic lupus erythematosus (SLE) and evaluation of SLE genetics in drug repositioning. Wang YF(#)(1), Zhang Y(#)(2), Zhu Z(#)(3), Wang TY(1), Morris DL(4), Shen JJ(1), Zhang H(1), Pan HF(5), Yang J(1), Yang S(3), Ye DQ(5), Vyse TJ(4), Cui Y(6), Zhang X(3), Sheng Y(3), Lau YL(1), Yang W(1). Author information: (1)Department of Paediatrics and Adolescent Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong. (2)Guangzhou Institute of Paediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China. (3)Institute/Department of Dermatology, No.1 Hospital, Anhui Medical University, Hefei, China. (4)Division of Genetics and Molecular Medicine, King's College London, London, UK. (5)Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China. (6)Departmentof Dermatology, China-Japan Friendship Hospital, Beijing, China. (#)Contributed equally OBJECTIVES: Systemic lupus erythematosus (SLE) is a prototype autoimmune disease with a strong genetic component in its pathogenesis. Through genome-wide association studies (GWAS), we recently identified 10 novel loci associated with SLE and uncovered a number of suggestive loci requiring further validation. This study aimed to validate those loci in independent cohorts and evaluate the role of SLE genetics in drug repositioning. METHODS: We conducted GWAS and replication studies involving 12 280 SLE cases and 18 828 controls, and performed fine-mapping analyses to identify likely causal variants within the newly identified loci. We further scanned drug target databases to evaluate the role of SLE genetics in drug repositioning. RESULTS: We identified three novel loci that surpassed genome-wide significance, including ST3AGL4 (rs13238909, pmeta=4.40E-08), MFHAS1 (rs2428, pmeta=1.17E-08) and CSNK2A2 (rs2731783, pmeta=1.08E-09). We also confirmed the association of CD226 locus with SLE (rs763361, pmeta=2.45E-08). Fine-mapping and functional analyses indicated that the putative causal variants in CSNK2A2 locus reside in an enhancer and are associated with expression of CSNK2A2 in B-lymphocytes, suggesting a potential mechanism of association. In addition, we demonstrated that SLE risk genes were more likely to be interacting proteins with targets of approved SLE drugs (OR=2.41, p=1.50E-03) which supports the role of genetic studies to repurpose drugs approved for other diseases for the treatment of SLE. CONCLUSION: This study identified three novel loci associated with SLE and demonstrated the role of SLE GWAS findings in drug repositioning. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted. DOI: 10.1136/annrheumdis-2018-213093 PMID: 29625966 Conflict of interest statement: Competing interests: None declared. 547. AAPS J. 2017 Dec 4;20(1):11. doi: 10.1208/s12248-017-0172-7. Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling. Vlot AHC(1), de Witte WEA(1), Danhof M(1), van der Graaf PH(1)(2), van Westen GJP(3), de Lange ECM(4). Author information: (1)Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333, CC, Leiden, The Netherlands. (2)Certara Quantitative Systems Pharmacology, Canterbury Innovation Centre, Canterbury, CT2 7FG, UK. (3)Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333, CC, Leiden, The Netherlands. (4)Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333, CC, Leiden, The Netherlands. ecmdelange@lacdr.leidenuniv.nl. Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ8-tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity. DOI: 10.1208/s12248-017-0172-7 PMID: 29204742 [Indexed for MEDLINE] 548. mSphere. 2017 Aug 30;2(4). pii: e00340-17. doi: 10.1128/mSphereDirect.00340-17. eCollection 2017 Jul-Aug. Genetic Validation of Leishmania donovani Lysyl-tRNA Synthetase Shows that It Is Indispensable for Parasite Growth and Infectivity. Chadha S(1), Mallampudi NA(2), Mohapatra DK(2), Madhubala R(1). Author information: (1)School of Life Sciences, Jawaharlal Nehru University, New Delhi, India. (2)Natural Products Chemistry Division, CSIR-Indian Institute of Chemical Technology, Hyderabad, India. Leishmania donovani is a protozoan parasite that causes visceral leishmaniasis. Increasing resistance and severe side effects of existing drugs have led to the need to identify new chemotherapeutic targets. Aminoacyl-tRNA synthetases (aaRSs) are ubiquitous and are required for protein synthesis. aaRSs are known drug targets for bacterial and fungal pathogens. Here, we have characterized and evaluated the essentiality of L. donovani lysyl-tRNA synthetase (LdLysRS). Two different coding sequences for lysyl-tRNA synthetases are annotated in the Leishmania genome database. LdLysRS-1 (LdBPK_150270.1), located on chromosome 15, is closer to apicomplexans and eukaryotes, whereas LdLysRS-2 (LdBPK_300130.1), present on chromosome 30, is closer to bacteria. In the present study, we have characterized LdLysRS-1. Recombinant LdLysRS-1 displayed aminoacylation activity, and the protein localized to the cytosol. The LdLysRS-1 heterozygous mutants had a restrictive growth phenotype and attenuated infectivity. LdLysRS-1 appears to be an essential gene, as a chromosomal knockout of LdLysRS-1 could be generated when the gene was provided on a rescuing plasmid. Cladosporin, a fungal secondary metabolite and a known inhibitor of LysRS, was more potent against promastigotes (50% inhibitory concentration [IC50], 4.19 µM) and intracellular amastigotes (IC50, 1.09 µM) than were isomers of cladosporin (3-epi-isocladosporin and isocladosporin). These compounds exhibited low toxicity to mammalian cells. The specificity of inhibition of parasite growth caused by these inhibitors was further assessed using LdLysRS-1 heterozygous mutant strains and rescue mutant promastigotes. These inhibitors inhibited the aminoacylation activity of recombinant LdLysRS. Our data provide a framework for the development of a new class of drugs against this parasite. IMPORTANCE Aminoacyl-tRNA synthetases are housekeeping enzymes essential for protein translation, providing charged tRNAs for the proper construction of peptide chains. These enzymes provide raw materials for protein translation and also ensure fidelity of translation. L. donovani is a protozoan parasite that causes visceral leishmaniasis. It is a continuously proliferating parasite that depends heavily on efficient protein translation. Lysyl-tRNA synthetase is one of the aaRSs which charges lysine to its cognate tRNA. Two different coding sequences for lysyl-tRNA synthetases (LdLysRS) are present in this parasite. LdLysRS-1 is closer to apicomplexans and eukaryotes, whereas LdLysRS-2 is closer to bacteria. Here, we have characterized LdLysRS-1 of L. donovani. LdLysRS-1 appears to be an essential gene, as the chromosomal null mutants did not survive. The heterozygous mutants showed slower growth kinetics and exhibited attenuated virulence. This study also provides a platform to explore LdLysRS-1 as a potential drug target. DOI: 10.1128/mSphereDirect.00340-17 PMCID: PMC5577655 PMID: 28875178 549. Front Cell Infect Microbiol. 2016 Oct 18;6:124. eCollection 2016. Genetic-and-Epigenetic Interspecies Networks for Cross-Talk Mechanisms in Human Macrophages and Dendritic Cells during MTB Infection. Li CW(1), Lee YL(1), Chen BS(1). Author information: (1)Laboratory of Control and Systems Biology, National Tsing Hua University Hsinchu, Taiwan. Tuberculosis is caused by Mycobacterium tuberculosis (Mtb) infection. Mtb is one of the oldest human pathogens, and evolves mechanisms implied in human evolution. The lungs are the first organ exposed to aerosol-transmitted Mtb during gaseous exchange. Therefore, the guards of the immune system in the lungs, such as macrophages (Mϕs) and dendritic cells (DCs), are the most important defense against Mtb infection. There have been several studies discussing the functions of Mϕs and DCs during Mtb infection, but the genome-wide pathways and networks are still incomplete. Furthermore, the immune response induced by Mϕs and DCs varies. Therefore, we analyzed the cross-talk genome-wide genetic-and-epigenetic interspecies networks (GWGEINs) between Mϕs vs. Mtb and DCs vs. Mtb to determine the varying mechanisms of both the host and pathogen as it relates to Mϕs and DCs during early Mtb infection. First, we performed database mining to construct candidate cross-talk GWGEIN between human cells and Mtb. Then we constructed dynamic models to characterize the molecular mechanisms, including intraspecies gene/microRNA (miRNA) regulation networks (GRNs), intraspecies protein-protein interaction networks (PPINs), and the interspecies PPIN of the cross-talk GWGEIN. We applied a system identification method and a system order detection scheme to dynamic models to identify the real cross-talk GWGEINs using the microarray data of Mϕs, DCs and Mtb. After identifying the real cross-talk GWGEINs, the principal network projection (PNP) method was employed to construct host-pathogen core networks (HPCNs) between Mϕs vs. Mtb and DCs vs. Mtb during infection process. Thus, we investigated the underlying cross-talk mechanisms between the host and the pathogen to determine how the pathogen counteracts host defense mechanisms in Mϕs and DCs during Mtb H37Rv early infection. Based on our findings, we propose Rv1675c as a potential drug target because of its important defensive role in Mϕs. Furthermore, the membrane essential proteins v1098c, and Rv1696 (or cytoplasm for Rv0667), in Mtb could also be potential drug targets because of their important roles in Mtb survival in both cell types. We also propose the drugs Lopinavir, TMC207, ATSM, and GTSM as potential therapeutic treatments for Mtb infection since they target the above potential drug targets. DOI: 10.3389/fcimb.2016.00124 PMCID: PMC5067469 PMID: 27803888 [Indexed for MEDLINE] 550. J Mol Model. 2015 Apr;21(4):102. doi: 10.1007/s00894-015-2647-8. Epub 2015 Apr 2. In silico identification of novel kinase inhibitors by targeting B-Raf(v660e) from natural products database. Wang ZJ(1), Wan ZN, Chen XD, Wu CF, Gao GL, Liu R, Shi Z, Bao JK. Author information: (1)School of Life Sciences and Key Laboratory of Bio-resources, Ministry of Education, & State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610064, China. The Ras/Raf/MEK/ERK (MAPK) signaling pathway has gained much attention from scientific community for therapeutic intervention in the past decades, specifically in oncology. Notably, a most prevalent B-Raf(v600e) mutant in Raf kinase family exhibits elevated kinase activity and results in constitutive activation of the MAPK pathway, thus making it a promising drug target for cancer therapy. Herein, virtual screening is applied to identify its potential inhibitors. Following the 25 ns molecular dynamic (MD) simulations, ZINC38541768, ZINC38541767, and ZINC12496469 are identified as B-Raf(v600e) potential inhibitors in a DFG-in conformation. Furthermore, according to the molecular mechanics/generalized born surface area (MM/GBSA) method, these three small molecules exhibit similar and good binding affinity toward B-Raf(v600e) (-38.76 kcal mol(-1), -42.60 kcal mol(-1), and -39.04 kcal mol(-1)). At the same time, several critical residues, such as I463, V471 in the P-loop, and DFG motif residue D594 within the A-loop, are also well clarified. All these results may not only indicate some future applications of inhibitors targeting B-Raf(v600e), but also benefit B-Raf(v600e) harboring cancer patients. DOI: 10.1007/s00894-015-2647-8 PMID: 25832798 [Indexed for MEDLINE] 551. Comput Biol Chem. 2018 Jun;74:1-11. doi: 10.1016/j.compbiolchem.2018.02.019. Epub 2018 Mar 6. Discovery of novel drug candidates for inhibition of soluble epoxide hydrolase of arachidonic acid cascade pathway implicated in atherosclerosis. Gurung AB(1), Mayengbam B(1), Bhattacharjee A(2). Author information: (1)Computational Biology Laboratory, Department of Biotechnology and Bioinformatics, North-Eastern Hill University, Shillong, Meghalaya, 793022, India. (2)Computational Biology Laboratory, Department of Biotechnology and Bioinformatics, North-Eastern Hill University, Shillong, Meghalaya, 793022, India; Bioinformatics Centre, North-Eastern Hill University, Shillong, Meghalaya, 793022, India. Electronic address: abhattacharjee@nehu.ac.in. Soluble epoxide hydrolase (sEH), a key enzyme belonging to cytochrome P450 pathway of arachidonic acid cascade is a novel therapeutic drug target against atherosclerosis. The enzyme breaks down epoxyeicosatrienoic acid (EETs) to dihydroxy-eicosatrienoic acids (DHETs) and reduces beneficial cardiovascular properties of EETs. Thus, the present work is aimed at identification of potential leads as sEH inhibitors which will sustain the beneficial properties of EETs in vivo. PubChem and ZINC databases were screened for drug-like compounds based on Lipinski's rule of five and in silico toxicity filters. The binding potential of the drug-like compounds with sEH was explored using molecular docking. The top ranked lead (ZINC23099069) showed higher GOLD score compared with that of the control, 12-(3-adamantan-1-yl-ureido)-dodecanoic acid butyl ester (AUDA-BE) and displayed two hydrogen bonds with Tyr383 and His420 and eleven residues involved in hydrophobic interactions with sEH. The apo_sEH and sEH_ZINC23099069 complex showed stable trajectories during 20 ns time scale of molecular dynamics (MD) simulation. Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) binding free energy analysis showed that electrostatic energy is the driving energy component for interaction of the lead with sEH. These results demonstrate ZINC23099069 to be a promising drug candidate as sEH inhibitor against atherosclerosis instead of the present urea-based inhibitors. Copyright © 2018 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.compbiolchem.2018.02.019 PMID: 29522918 [Indexed for MEDLINE] 552. Adv Neurobiol. 2017;17:349-384. doi: 10.1007/978-3-319-58811-7_13. Phosphodiesterase 1: A Unique Drug Target for Degenerative Diseases and Cognitive Dysfunction. Wennogle LP(1), Hoxie H(2), Peng Y(3), Hendrick JP(2). Author information: (1)Alexandria Center for Life Science, Intra-Cellular Therapies, Inc., New York, 10016, NY, USA. lwennogle@intracellulartherapies.com. (2)Alexandria Center for Life Science, Intra-Cellular Therapies, Inc., New York, 10016, NY, USA. (3)Rutgers University, 7 College Ave, New Brunswick, NJ, 08901, USA. The focus of this chapter is on the cyclic nucleotide phosphodiesterase 1 (PDE1) family. PDE1 is one member of the 11 PDE families (PDE 1-11). It is the only phosphodiesterase family that is calcium/calmodulin activated. As a result, whereas other families of PDEs 2-11 play a dominant role controlling basal levels of cyclic nucleotides, PDE1 is involved when intra-cellular calcium levels are elevated and, thus, has an "on demand" or activity-dependent involvement in the control of cyclic nucleotides in excitatory cells including neurons, cardiomyocytes and smooth muscle. As a Class 1 phosphodiesterase, PDE1 hydrolyzes the 3' bond of 3'-5'-cyclic nucleotides, cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP). Here, we review evidence for this family of enzymes as drug targets for development of therapies aimed to address disorders of the central nervous system (CNS) and of degenerative diseases. The chapter includes sections on the potential for cognitive enhancement in mental disorders, as well as a review of PDE1 enzyme structure, enzymology, tissue distribution, genomics, inhibitors, pharmacology, clinical trials, and therapeutic indications. Information is taken from public databases. A number of excellent reviews of the phosphodiesterase family have been written as well as reviews of the PDE1 family. References cited here are not comprehensive, rather pointing to major reviews and key publications. DOI: 10.1007/978-3-319-58811-7_13 PMID: 28956339 [Indexed for MEDLINE] 553. Rapid Commun Mass Spectrom. 2007;21(3):429-36. Direct identification of proteins from T47D cells and murine brain tissue by matrix-assisted laser desorption/ionization post-source decay/collision-induced dissociation. Pevsner PH(1), Naftolin F, Hillman DE, Miller DC, Fadiel A, Kogus A, Stern A, Samuels HH. Author information: (1)Department of Pharmacology, New York University School of Medicine, New York, NY 10016, USA. paul.pevsner@med.nyu.edu The purpose of this study is to determine the feasibility of the direct matrix-assisted laser desorption/ionization (MALDI) identification of proteins in fixed T47D breast cancer cells and murine brain tissues. The ability to identify proteins from cells and tissue may lead to biomarkers that effectively predict the onset of defined disease states, and their dynamic behavior could be an important hint for drug target discoveries. Direct tissue application of trypsin allows protein identification in cells and tissues, while maintaining spatial integrity and intracellular organization. Using a chemical printer, matrix was co-registered on trypsinized human T47D breast cancer cells and cryo-preserved sections of murine brain tissue, followed by MALDI post-source decay (PSD) or MALDI collision-induced dissociation (CID), respectively. Mass-to-charge (m/z) data from the cells and brain tissues were processed using Mascot software interrogation of the National Center for Biotechnology Information (NCBI) database. Histone H2B was identified from cultured T47D human breast cancer cells. Tubulin beta2 was identified from mouse brain cortex following an induced stroke. These results suggest that MALDI PSD/CID, combined with bioinformatics, can be used for the direct identification of proteins from cells and tissues. Refinements in preparation techniques may improve this approach to provide a tool for quantitative proteomics and clinical analysis. Copyright 2007 John Wiley & Sons, Ltd. DOI: 10.1002/rcm.2849 PMID: 17216666 [Indexed for MEDLINE] 554. J Biomol Struct Dyn. 2016;34(2):239-49. doi: 10.1080/07391102.2015.1022603. Epub 2015 Apr 9. Molecular docking based virtual screening of natural compounds as potential BACE1 inhibitors: 3D QSAR pharmacophore mapping and molecular dynamics analysis. Kumar A(1), Roy S(2), Tripathi S(1), Sharma A(1). Author information: (1)a CSIR-Central Institute of Medicinal and Aromatic Plants , P.O. - CIMAP, Near Kukrail Picnic Spot, Lucknow 226 015 , India. (2)b Faculty of Electronics and Communication, Department of BioMedical Engineering , Brno University of Technology , Antonínská 548/1, 601 90 Brno , Czech Republic. Beta-site APP cleaving enzyme1 (BACE1) catalyzes the rate determining step in the generation of Aβ peptide and is widely considered as a potential therapeutic drug target for Alzheimer's disease (AD). Active site of BACE1 contains catalytic aspartic (Asp) dyad and flap. Asp dyad cleaves the substrate amyloid precursor protein with the help of flap. Currently, there are no marketed drugs available against BACE1 and existing inhibitors are mostly pseudopeptide or synthetic derivatives. There is a need to search for a potent inhibitor with natural scaffold interacting with flap and Asp dyad. This study screens the natural database InterBioScreen, followed by three-dimensional (3D) QSAR pharmacophore modeling, mapping, in silico ADME/T predictions to find the potential BACE1 inhibitors. Further, molecular dynamics of selected inhibitors were performed to observe the dynamic structure of protein after ligand binding. All conformations and the residues of binding region were stable but the flap adopted a closed conformation after binding with the ligand. Bond oligosaccharide interacted with the flap as well as catalytic dyad via hydrogen bond throughout the simulation. This led to stabilize the flap in closed conformation and restricted the entry of substrate. Carbohydrates have been earlier used in the treatment of AD because of their low toxicity, high efficiency, good biocompatibility, and easy permeability through the blood-brain barrier. Our finding will be helpful in identify the potential leads to design novel BACE1 inhibitors for AD therapy. DOI: 10.1080/07391102.2015.1022603 PMID: 25707809 [Indexed for MEDLINE] 555. Biomed Res Int. 2014;2014:139492. doi: 10.1155/2014/139492. Epub 2014 Jun 23. In silico investigation of potential mTOR inhibitors from traditional Chinese medicine for treatment of Leigh syndrome. Chen KC(1), Lee WY(2), Chen HY(3), Chen CY(4). Author information: (1)School of Pharmacy, China Medical University, Taichung 40402, Taiwan. (2)School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan ; Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan ; Department of Neurosurgery, China Medical University Hospital, Taichung 40447, Taiwan. (3)Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan. (4)School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan ; Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan ; Human Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan ; Research Center for Chinese Medicine & Acupuncture, China Medical University, Taichung 40402, Taiwan. A recent research demonstrates that the inhibition of mammalian target of rapamycin (mTOR) improves survival and health for patients with Leigh syndrome. mTOR proteins can be treated as drug target proteins against Leigh syndrome and other mitochondrial disorders. In this study, we aim to identify potent TCM compounds from the TCM Database@Taiwan as lead compounds of mTOR inhibitors. PONDR-Fit protocol was employed to predict the disordered disposition in mTOR protein before virtual screening. After virtual screening, the MD simulation was employed to validate the stability of interactions between each ligand and mTOR protein in the docking poses from docking simulation. The top TCM compounds, picrasidine M and acerosin, have higher binding affinities with target protein in docking simulation than control. There have H-bonds with residues Val2240 and π interactions with common residue Trp2239. After MD simulation, the top TCM compounds maintain similar docking poses under dynamic conditions. The top two TCM compounds, picrasidine M and acerosin, were extracted from Picrasma quassioides (D. Don) Benn. and Vitex negundo L. Hence, we propose the TCM compounds, picrasidine M and acerosin, as potential candidates as lead compounds for further study in drug development process with the mTOR protein against Leigh syndrome and other mitochondrial disorders. DOI: 10.1155/2014/139492 PMCID: PMC4090453 PMID: 25045657 [Indexed for MEDLINE] 556. PLoS One. 2012;7(3):e33521. doi: 10.1371/journal.pone.0033521. Epub 2012 Mar 28. Chemoinformatic identification of novel inhibitors against Mycobacterium tuberculosis L-aspartate α-decarboxylase. Sharma R(1), Kothapalli R, Van Dongen AM, Swaminathan K. Author information: (1)Department of Biological Sciences, National University of Singapore, Singapore. L-aspartate α-decarboxylase (ADC) belongs to a class of pyruvoyl dependent enzymes and catalyzes the conversion of aspartate to β-alanine in the pantothenate pathway, which is critical for the growth of several micro-organisms, including Mycobacterium tuberculosis (Mtb). Its presence only in micro-organisms, fungi and plants and its absence in animals, particularly human, make it a promising drug target. We have followed a chemoinformatics-based approach to identify potential drug-like inhibitors against Mycobacterium tuberculosis L-aspartate α-decarboxylase (MtbADC). The structure-based high throughput virtual screening (HTVS) mode of the Glide program was used to screen 333,761 molecules of the Maybridge, National Cancer Institute (NCI) and Food and Drug Administration (FDA) approved drugs databases. Ligands were rejected if they cross-reacted with S-adenosylmethionine (SAM) decarboxylase, a human pyruvoyl dependent enzyme. The lead molecules were further analyzed for physicochemical and pharmacokinetic parameters, based on Lipinski's rule of five, and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties. This analysis resulted in eight small potential drug-like inhibitors that are in agreement with the binding poses of the crystallographic ADC:fumarate and ADC:isoasparagine complex structures and whose backbone scaffolds seem to be suitable for further experimental studies in therapeutic development against tuberculosis. DOI: 10.1371/journal.pone.0033521 PMCID: PMC3314653 PMID: 22470451 [Indexed for MEDLINE] 557. Parasitology. 2011 Feb;138(2):160-74. doi: 10.1017/S0031182010001198. Epub 2010 Sep 9. Anthelmintic resistance: markers for resistance, or susceptibility? Beech RN(1), Skuce P, Bartley DJ, Martin RJ, Prichard RK, Gilleard JS. Author information: (1)Institute of Parasitology, Macdonald College, McGill University, Ste Anne de Bellevue, QC H9X3V9, Canada. robin.beech@mcgill.ca The Consortium for Anthelmintic Resistance and Susceptibility (CARS) brings together researchers worldwide, with a focus of advancing knowledge of resistance and providing information on detection methods and treatment strategies. Advances in this field suggest mechanisms and features of resistance that are shared among different classes of anthelmintic. Benzimidazole resistance is characterized by specific amino acid substitutions in beta-tubulin. If present, these substitutions increase in frequency upon drug treatment and lead to treatment failure. In the laboratory, sequence substitutions in ion-channels can contribute to macrocyclic lactone resistance, but there is little evidence that they are significant in the field. Changes in gene expression are associated with resistance to several different classes of anthelmintic. Increased P-glycoprotein expression may prevent drug access to its site of action. Decreased expression of ion-channel subunits and the loss of specific receptors may remove the drug target. Tools for the identification and genetic analysis of parasitic nematodes and a new online database will help to coordinate research efforts in this area. Resistance may result from a loss of sensitivity as well as the appearance of resistance. A focus on the presence of anthelmintic susceptibility may be as important as the detection of resistance. DOI: 10.1017/S0031182010001198 PMCID: PMC3064440 PMID: 20825689 [Indexed for MEDLINE] 558. Dis Model Mech. 2016 Jul 1;9(7):749-57. doi: 10.1242/dmm.025239. Epub 2016 May 5. Genomic profiling of murine mammary tumors identifies potential personalized drug targets for p53-deficient mammary cancers. Pfefferle AD(1), Agrawal YN(2), Koboldt DC(3), Kanchi KL(3), Herschkowitz JI(4), Mardis ER(3), Rosen JM(5), Perou CM(6). Author information: (1)Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC 27599, USA Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA. (2)Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA. (3)The McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA. (4)Department of Biomedical Sciences, University at Albany, Rensselaer, NY 12144, USA. (5)Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA. (6)Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC 27599, USA Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA cperou@med.unc.edu. Targeted therapies against basal-like breast tumors, which are typically 'triple-negative breast cancers (TNBCs)', remain an important unmet clinical need. Somatic TP53 mutations are the most common genetic event in basal-like breast tumors and TNBC. To identify additional drivers and possible drug targets of this subtype, a comparative study between human and murine tumors was performed by utilizing a murine Trp53-null mammary transplant tumor model. We show that two subsets of murine Trp53-null mammary transplant tumors resemble aspects of the human basal-like subtype. DNA-microarray, whole-genome and exome-based sequencing approaches were used to interrogate the secondary genetic aberrations of these tumors, which were then compared to human basal-like tumors to identify conserved somatic genetic features. DNA copy-number variation produced the largest number of conserved candidate personalized drug targets. These candidates were filtered using a DNA-RNA Pearson correlation cut-off and a requirement that the gene was deemed essential in at least 5% of human breast cancer cell lines from an RNA-mediated interference screen database. Five potential personalized drug target genes, which were spontaneously amplified loci in both murine and human basal-like tumors, were identified: Cul4a, Lamp1, Met, Pnpla6 and Tubgcp3 As a proof of concept, inhibition of Met using crizotinib caused Met-amplified murine tumors to initially undergo complete regression. This study identifies Met as a promising drug target in a subset of murine Trp53-null tumors, thus identifying a potential shared driver with a subset of human basal-like breast cancers. Our results also highlight the importance of comparative genomic studies for discovering personalized drug targets and for providing a preclinical model for further investigations of key tumor signaling pathways. © 2016. Published by The Company of Biologists Ltd. DOI: 10.1242/dmm.025239 PMCID: PMC4958311 PMID: 27149990 [Indexed for MEDLINE] 559. Eur J Clin Pharmacol. 2016 Dec;72(12):1449-1461. Epub 2016 Oct 1. A machine-learned computational functional genomics-based approach to drug classification. Lötsch J(1)(2), Ultsch A(3). Author information: (1)Institute of Clinical Pharmacology, Goethe - University, Theodor Stern Kai 7, 60590, Frankfurt am Main, Germany. j.loetsch@em.uni-frankfurt.de. (2)Fraunhofer Institute of Molecular Biology and Applied Ecology - Project Group Translational Medicine and Pharmacology (IME-TMP), Theodor - Stern - Kai 7, 60590, Frankfurt am Main, Germany. j.loetsch@em.uni-frankfurt.de. (3)DataBionics Research Group, University of Marburg, Hans - Meerwein - Straße, 35032, Marburg, Germany. OBJECTIVE: The public accessibility of "big data" about the molecular targets of drugs and the biological functions of genes allows novel data science-based approaches to pharmacology that link drugs directly with their effects on pathophysiologic processes. This provides a phenotypic path to drug discovery and repurposing. This paper compares the performance of a functional genomics-based criterion to the traditional drug target-based classification. METHODS: Knowledge discovery in the DrugBank and Gene Ontology databases allowed the construction of a "drug target versus biological process" matrix as a combination of "drug versus genes" and "genes versus biological processes" matrices. As a canonical example, such matrices were constructed for classical analgesic drugs. These matrices were projected onto a toroid grid of 50 × 82 artificial neurons using a self-organizing map (SOM). The distance, respectively, cluster structure of the high-dimensional feature space of the matrices was visualized on top of this SOM using a U-matrix. RESULTS: The cluster structure emerging on the U-matrix provided a correct classification of the analgesics into two main classes of opioid and non-opioid analgesics. The classification was flawless with both the functional genomics and the traditional target-based criterion. The functional genomics approach inherently included the drugs' modulatory effects on biological processes. The main pharmacological actions known from pharmacological science were captures, e.g., actions on lipid signaling for non-opioid analgesics that comprised many NSAIDs and actions on neuronal signal transmission for opioid analgesics. CONCLUSIONS: Using machine-learned techniques for computational drug classification in a comparative assessment, a functional genomics-based criterion was found to be similarly suitable for drug classification as the traditional target-based criterion. This supports a utility of functional genomics-based approaches to computational system pharmacology for drug discovery and repurposing. DOI: 10.1007/s00228-016-2134-x PMID: 27695919 [Indexed for MEDLINE] 560. J Biomed Inform. 2014 Oct;51:191-9. doi: 10.1016/j.jbi.2014.05.013. Epub 2014 Jun 10. Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature. Xu R(1), Wang Q(2). Author information: (1)Medical Informatics Program, Center for Clinical Investigation, Case Western Reserve University, Cleveland, OH 44106, United States. Electronic address: rxx@case.edu. (2)ThinTek, LLC, Palo Alto, CA 94306, United States. Electronic address: qwang@thintek.com. Systems approaches to studying drug-side-effect (drug-SE) associations are emerging as an active research area for drug target discovery, drug repositioning, and drug toxicity prediction. However, currently available drug-SE association databases are far from being complete. Herein, in an effort to increase the data completeness of current drug-SE relationship resources, we present an automatic learning approach to accurately extract drug-SE pairs from the vast amount of published biomedical literature, a rich knowledge source of side effect information for commercial, experimental, and even failed drugs. For the text corpus, we used 119,085,682 MEDLINE sentences and their parse trees. We used known drug-SE associations derived from US Food and Drug Administration (FDA) drug labels as prior knowledge to find relevant sentences and parse trees. We extracted syntactic patterns associated with drug-SE pairs from the resulting set of parse trees. We developed pattern-ranking algorithms to prioritize drug-SE-specific patterns. We then selected a set of patterns with both high precisions and recalls in order to extract drug-SE pairs from the entire MEDLINE. In total, we extracted 38,871 drug-SE pairs from MEDLINE using the learned patterns, the majority of which have not been captured in FDA drug labels to date. On average, our knowledge-driven pattern-learning approach in extracting drug-SE pairs from MEDLINE has achieved a precision of 0.833, a recall of 0.407, and an F1 of 0.545. We compared our approach to a support vector machine (SVM)-based machine learning and a co-occurrence statistics-based approach. We show that the pattern-learning approach is largely complementary to the SVM- and co-occurrence-based approaches with significantly higher precision and F1 but lower recall. We demonstrated by correlation analysis that the extracted drug side effects correlate positively with both drug targets, metabolism, and indications. Copyright © 2014 Elsevier Inc. All rights reserved. DOI: 10.1016/j.jbi.2014.05.013 PMCID: PMC4589180 PMID: 24928448 [Indexed for MEDLINE] 561. BMC Syst Biol. 2013;7 Suppl 5:S6. doi: 10.1186/1752-0509-7-S5-S6. Epub 2013 Dec 9. Computational drug repositioning through heterogeneous network clustering. Wu C, Gudivada RC, Aronow BJ, Jegga AG. BACKGROUND: Given the costly and time consuming process and high attrition rates in drug discovery and development, drug repositioning or drug repurposing is considered as a viable strategy both to replenish the drying out drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational drug-repositioning candidate discovery platforms. RESULTS: Using known disease-gene and drug-target relationships from the KEGG database, we built a weighted disease and drug heterogeneous network. The nodes represent drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible drug-disease pairs (putative drug repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with drug indications that were either reported in published literature or investigated in clinical trials. CONCLUSIONS: Previous computational approaches for drug repositioning focused either on drug-drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering drug-disease relationships also. Further, we considered not only gene but also other features to build the disease drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with drug indications that are under investigation, we believe our approach could complement the current computational approaches for drug repositioning candidate discovery. DOI: 10.1186/1752-0509-7-S5-S6 PMCID: PMC4029299 PMID: 24564976 [Indexed for MEDLINE] 562. ScientificWorldJournal. 2012;2012:637953. doi: 10.1100/2012/637953. Epub 2012 Sep 17. A comprehensive review on pharmacotherapeutics of herbal bioenhancers. Dudhatra GB(1), Mody SK, Awale MM, Patel HB, Modi CM, Kumar A, Kamani DR, Chauhan BN. Author information: (1)Department of Pharmacology & Toxicology, College of Veterinary Science & Animal Husbandry, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar 385506, Gujarat, India. drgvets@gmail.com In India, Ayurveda has made a major contribution to the drug discovery process with new means of identifying active compounds. Recent advancement in bioavailability enhancement of drugs by compounds of herbal origin has produced a revolutionary shift in the way of therapeutics. Thus, bibliographic investigation was carried out by analyzing classical text books and peer-reviewed papers, consulting worldwide-accepted scientific databases from last 30 years. Herbal bioenhancers have been shown to enhance bioavailability and bioefficacy of different classes of drugs, such as antibiotics, antituberculosis, antiviral, antifungal, and anticancerous drugs at low doses. They have also improved oral absorption of nutraceuticals like vitamins, minerals, amino acids, and certain herbal compounds. Their mechanism of action is mainly through absorption process, drug metabolism, and action on drug target. This paper clearly indicates that scientific researchers and pharmaceutical industries have to give emphasis on experimental studies to find out novel active principles from such a vast array of unexploited plants having a role as a bioavailability and bioefficacy enhancer. Also, the mechanisms of action by which bioenhancer compounds exert bioenhancing effects remain to be explored. DOI: 10.1100/2012/637953 PMCID: PMC3458266 PMID: 23028251 [Indexed for MEDLINE] 563. BMC Bioinformatics. 2010 Jan 18;11 Suppl 1:S53. doi: 10.1186/1471-2105-11-S1-S53. Virtual Screening of potential drug-like inhibitors against Lysine/DAP pathway of Mycobacterium tuberculosis. Garg A(1), Tewari R, Raghava GP. Author information: (1)Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India. aarti@imtech.res.in BACKGROUND: An explosive global spreading of multidrug resistant Mycobacterium tuberculosis (Mtb) is a catastrophe, which demands an urgent need to design or develop novel/potent antitubercular agents. The Lysine/DAP biosynthetic pathway is a promising target due its specific role in cell wall and amino acid biosynthesis. Here, we report identification of potential antitubercular candidates targeting Mtb dihydrodipicolinate synthase (DHDPS) enzyme of the pathway using virtual screening protocols. RESULTS: In the present study, we generated three sets of drug-like molecules in order to screen potential inhibitors against Mtb drug target DHDPS. The first set of compounds was a combinatorial library, which comprised analogues of pyruvate (substrate of DHDPS). The second set of compounds consisted of pyruvate-like molecules i.e. structurally similar to pyruvate, obtained using 3D flexible similarity search against NCI and PubChem database. The third set constituted 3847 anti-infective molecules obtained from PubChem. These compounds were subjected to Lipinski's rule of drug-like five filters. Finally, three sets of drug-like compounds i.e. 4088 pyruvate analogues, 2640 pyruvate-like molecules and 1750 anti-infective molecules were docked at the active site of Mtb DHDPS (PDB code: 1XXX used in the molecular docking calculations) to select inhibitors establishing favorable interactions. CONCLUSION: The above-mentioned virtual screening procedures helped in the identification of several potent candidates that possess inhibitory activity against Mtb DHDPS. Therefore, these novel scaffolds/candidates which could have the potential to inhibit Mtb DHDPS enzyme would represent promising starting points as lead compounds and certainly aid the experimental designing of antituberculars in lesser time. DOI: 10.1186/1471-2105-11-S1-S53 PMCID: PMC3009526 PMID: 20122228 [Indexed for MEDLINE] 564. Comput Biol Chem. 2018 Apr;73:1-12. doi: 10.1016/j.compbiolchem.2018.01.005. Epub 2018 Jan 31. 3D QSAR Pharmacophore Based Virtual Screening for Identification of Potential Inhibitors for CDC25B. Ma Y(1), Li HL(1), Chen XB(2), Jin WY(1), Zhou H(1), Ma Y(3), Wang RL(4). Author information: (1)Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, 300070, China. (2)Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, 300070, China; Eye Hospital, Tianjin Medical University, School of Optometry and Ophthalmology, Tianjin Medical University, China. (3)Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, 300070, China. Electronic address: maying@tmu.edu.cn. (4)Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, 300070, China. Electronic address: wangrunling@tmu.edu.cn. Owing to its fundamental roles in cell cycle phases, the cell division cycle 25B (CDC25B) was broadly considered as potent clinical drug target for cancers. In this study, 3D QSAR pharmacophore models for CDC25B inhibitors were developed by the module of Hypogen. Three methods (cost analysis, test set prediction, and Fisher's test) were applied to validate that the models could be used to predict the biological activities of compounds. Subsequently, 26 compounds satisfied Lipinski's rule of five were obtained by the virtual screening of the Hypo-1-CDC25B against ZINC databases. It was then discovered that 9 identified molecules had better binding affinity than a known CDC25B inhibitors-compound 1 using docking studies. The molecular dynamics simulations showed that the compound had favorable conformations for binding to the CDC25B. Thus, our findings here would be helpful to discover potent lead compounds for the treatment of cancers. Copyright © 2018 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.compbiolchem.2018.01.005 PMID: 29413811 [Indexed for MEDLINE] 565. Drug Res (Stuttg). 2018 May;68(5):250-262. doi: 10.1055/s-0043-120198. Epub 2017 Oct 24. Graph Theoretical Analysis, In Silico Modeling, Synthesis, Anti-Microbial and Anti-TB Evaluation of Novel Quinoxaline Derivatives. Saravanan G(#)(1), Selvam TP(2), Alagarsamy V(#)(1), Kunjiappan S(#)(2), Joshi SD(#)(3), Indhumathy M(#)(4), Kumar PD(#)(5). Author information: (1)Department of Pharmaceutical Chemistry, MNR College of Pharmacy, Fasalwadi, Sangareddy-502294, Telangana, India. (2)Department of Pharmaceutical Chemistry, Karavali College of Pharmacy, Vamanjoor, Mangalore- 575028, Karnataka, India. (3)Department of Pharmaceutical Chemistry, Sonia Education Trust's College of Pharmacy, Sangolli Rayanna Nagar, Dharwad-580002, Karnataka, India. (4)Department of Biotechnology, P.S.R Engineering College, Sevalpatti, Sivakasi, Tamilnadu, India. (5)Hindu College of Pharmacy, Amaravathi Road, Guntur-522002, Andhra Pradesh, India. (#)Contributed equally BACKGROUND: We designed to synthesize a number of 2-(2-(substituted benzylidene) hydrazinyl)-N-(4-((3-(phenyl imino)-3,4-dihydro quinoxalin-2(1 H)-ylidene)amino) phenyl) acetamide S1-S13: with the hope to obtain more active and less toxic anti-microbial and anti-TB agents. METHODS: A series of novel quinoxaline Schiff bases S1-S13: were synthesized from o-phenylenediamine and oxalic acid by a multistep synthesis. In present work, we are introducing graph theoretical analysis to identify drug target. In the connection of graph theoretical analysis, we utilised KEGG database and Cytoscape software. All the title compounds were evaluated for their in-vitro anti-microbial activity by using agar well diffusion method at three different concentration levels (50, 100 and 150 µg/ml). The MIC of the compounds was also determined by agar streak dilution method. RESULTS: The identified study report through graph theoretical analysis were highlights that the key virulence factor for pathogenic mycobacteria is a eukaryotic-like serine/threonine protein kinase, termed PknG. All compounds were found to display significant activity against entire tested bacteria and fungi. In addition the synthesized scaffolds were screened for their in vitro antituberculosis (anti-TB) activity against Mycobacterium tuberculosis (Mtb) strain H37Ra using standard drug Rifampicin. CONCLUSION: A number of analogs found markedly potent anti-microbial and anti-TB activity. The relationship between the functional group variation and the biological activity of the evaluated compounds were well discussed. The observed study report was showing that the compound S6: (4-nitro substitution) exhibited most potent effective anti-microbial and anti-TB activity out of various tested compounds. © Georg Thieme Verlag KG Stuttgart · New York. DOI: 10.1055/s-0043-120198 PMID: 29065435 [Indexed for MEDLINE] Conflict of interest statement: The authors have declared no conflict of interest. 566. Proc Natl Acad Sci U S A. 2012 Jul 10;109(28):11178-83. doi: 10.1073/pnas.1204524109. Epub 2012 Jun 18. Identifying mechanism-of-action targets for drugs and probes. Gregori-Puigjané E(1), Setola V, Hert J, Crews BA, Irwin JJ, Lounkine E, Marnett L, Roth BL, Shoichet BK. Author information: (1)Department of Pharmaceutical Chemistry, University of California, 1700 Fourth Street, San Francisco, CA 94143-2550, USA. Notwithstanding their key roles in therapy and as biological probes, 7% of approved drugs are purported to have no known primary target, and up to 18% lack a well-defined mechanism of action. Using a chemoinformatics approach, we sought to "de-orphanize" drugs that lack primary targets. Surprisingly, targets could be easily predicted for many: Whereas these targets were not known to us nor to the common databases, most could be confirmed by literature search, leaving only 13 Food and Drug Administration-approved drugs with unknown targets; the number of drugs without molecular targets likely is far fewer than reported. The number of worldwide drugs without reasonable molecular targets similarly dropped, from 352 (25%) to 44 (4%). Nevertheless, there remained at least seven drugs for which reasonable mechanism-of-action targets were unknown but could be predicted, including the antitussives clemastine, cloperastine, and nepinalone; the antiemetic benzquinamide; the muscle relaxant cyclobenzaprine; the analgesic nefopam; and the immunomodulator lobenzarit. For each, predicted targets were confirmed experimentally, with affinities within their physiological concentration ranges. Turning this question on its head, we next asked which drugs were specific enough to act as chemical probes. Over 100 drugs met the standard criteria for probes, and 40 did so by more stringent criteria. A chemical information approach to drug-target association can guide therapeutic development and reveal applications to probe biology, a focus of much current interest. DOI: 10.1073/pnas.1204524109 PMCID: PMC3396511 PMID: 22711801 [Indexed for MEDLINE] 567. Cancer Res. 2018 Dec 15;78(24):6807-6817. doi: 10.1158/0008-5472.CAN-18-0989. Epub 2018 Oct 24. The NCI Transcriptional Pharmacodynamics Workbench: A Tool to Examine Dynamic Expression Profiling of Therapeutic Response in the NCI-60 Cell Line Panel. Monks A(1), Zhao Y(2), Hose C(1), Hamed H(2), Krushkal J(2), Fang J(2), Sonkin D(2), Palmisano A(2), Polley EC(3), Fogli LK(3), Konaté MM(3), Miller SB(3), Simpson MA(4), Voth AR(4), Li MC(2), Harris E(1), Wu X(5), Connelly JW(1), Rapisarda A(1), Teicher BA(3), Simon R(2), Doroshow JH(6)(7). Author information: (1)Molecular Pharmacology Group, Frederick National Laboratory for Cancer Research sponsored by the NCI, Frederick, Maryland. (2)Biometric Research Program, Division of Cancer Treatment and Diagnosis, NCI, NIH, Rockville, Maryland. (3)Division of Cancer Treatment and Diagnosis, NCI, NIH, Bethesda, Maryland. (4)Applied/Developmental Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the NCI, Frederick, Maryland. (5)Cancer Research Technology Program, Frederick National Laboratory for Cancer Research sponsored by the NCI, Frederick, Maryland. (6)Division of Cancer Treatment and Diagnosis, NCI, NIH, Bethesda, Maryland. doroshoj@mail.nih.gov. (7)Center for Cancer Research, NCI, NIH, Bethesda, Maryland. : The intracellular effects and overall efficacies of anticancer therapies can vary significantly by tumor type. To identify patterns of drug-induced gene modulation that occur in different cancer cell types, we measured gene-expression changes across the NCI-60 cell line panel after exposure to 15 anticancer agents. The results were integrated into a combined database and set of interactive analysis tools, designated the NCI Transcriptional Pharmacodynamics Workbench (NCI TPW), that allows exploration of gene-expression modulation by molecular pathway, drug target, and association with drug sensitivity. We identified common transcriptional responses across agents and cell types and uncovered gene-expression changes associated with drug sensitivity. We also demonstrated the value of this tool for investigating clinically relevant molecular hypotheses and identifying candidate biomarkers of drug activity. The NCI TPW, publicly available at https://tpwb.nci.nih.gov, provides a comprehensive resource to facilitate understanding of tumor cell characteristics that define sensitivity to commonly used anticancer drugs. SIGNIFICANCE: The NCI Transcriptional Pharmacodynamics Workbench represents the most extensive compilation to date of directly measured longitudinal transcriptional responses to anticancer agents across a thoroughly characterized ensemble of cancer cell lines. ©2018 American Association for Cancer Research. DOI: 10.1158/0008-5472.CAN-18-0989 PMCID: PMC6295263 [Available on 2019-12-15] PMID: 30355619 568. J Steroid Biochem Mol Biol. 2018 Apr;178:159-166. doi: 10.1016/j.jsbmb.2017.12.002. Epub 2017 Dec 9. Functional characterization of the G162R and D216H genetic variants of human CYP17A1. Capper CP(1), Liu J(2), McIntosh LR(3), Larios JM(4), Johnson MD(5), Hollenberg PF(3), Osawa Y(3), Auchus RJ(6), Rae JM(7). Author information: (1)Department of Pharmacology, University of Michigan, Ann Arbor, MI, USA; Division of Metabolism, Endocrinology, and Diabetes, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA. (2)Division of Metabolism, Endocrinology, and Diabetes, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA. (3)Department of Pharmacology, University of Michigan, Ann Arbor, MI, USA. (4)Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA. (5)Lombardi Comprehensive Cancer Center, Department of Oncology, Georgetown University, Washington, D.C., USA. (6)Department of Pharmacology, University of Michigan, Ann Arbor, MI, USA; Division of Metabolism, Endocrinology, and Diabetes, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA. Electronic address: rauchus@med.umich.edu. (7)Department of Pharmacology, University of Michigan, Ann Arbor, MI, USA; Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA. Cytochrome P450 17A1 (CYP17A1) is a dual-function enzyme catalyzing reactions necessary for cortisol and androgen biosynthesis. CYP17A1 is a validated drug target for prostate cancer as CYP17A1 inhibition significantly reduces circulating androgens and improves survival in castration-resistant prostate cancer. Germline CYP17A1 genetic variants with altered CYP17A1 activity manifesting as various endocrinopathies are extremely rare; however, characterizing these variants provides critical insights into CYP17A1 protein structure and function. By querying the dbSNP online database and publically available data from the 1000 genomes project (http://browser.1000genomes.org), we identified two CYP17A1 nonsynonymous genetic variants with unknown consequences for enzymatic activity and stability. We hypothesized that the resultant amino acid changes would alter CYP17A1 stability or activity. To test this hypothesis, we utilized a HEK-293T cell-based expression system to characterize the functional consequences of two CYP17A1 variants, D216H (rs200063521) and G162R (rs141821705). Cells transiently expressing the D216H variant demonstrate a selective impairment of 16α-hydroxyprogesterone synthesis by 2.1-fold compared to wild-type (WT) CYP17A1, while no effect on 17α-hydroxyprogesterone synthesis was observed. These data suggest that substrate orientations in the active site might be altered with this amino acid substitution. In contrast, the G162R substitution exhibits decreased CYP17A1 protein stability compared to WT with a near 70% reduction in protein levels as determined by immunoblot analysis. This variant is preferentially ubiquitinated and degraded prematurely, with an enzyme half-life calculated to be ∼2.5 h, and proteasome inhibitor treatment recovers G162R protein expression to WT levels. Together, these data provide new insights into CYP17A1 structure-function and stability mechanisms. Copyright © 2017 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.jsbmb.2017.12.002 PMCID: PMC5835412 [Available on 2019-04-01] PMID: 29229304 569. Comput Biol Chem. 2017 Dec;71:70-81. doi: 10.1016/j.compbiolchem.2017.08.013. Epub 2017 Sep 14. Network pharmacology-based approach of novel traditional Chinese medicine formula for treatment of acute skin inflammation in silico. Tang HC(1), Huang HJ(1), Lee CC(2), Chen CYC(3). Author information: (1)Department of Medical Research, China Medical University Hospital, Taichung 40447, Taiwan. (2)School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan. (3)Department of Medical Research, China Medical University Hospital, Taichung 40447, Taiwan; Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan; School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Guangzhou 510275, China. Electronic address: chenyuchian@mail.sysu.edu.cn. Matrix metalloproteinase-9 (MMP-9) appears to play an important role in acute skin inflammation. Subantimicrobial dose of tetracycline has been demonstrated to inhibit the activity of MMP-9 protein. However, long-term use tetracycline will induce side effect. The catalytic site of MMP-9 is located at zinc-binding amino acids, His401, His405 and His411. We attempted to search novel medicine formula as MMP-9 inhibitors from traditional Chinese medicine (TCM) database by using in silico studies. We utilized high-throughput virtual screening to find which natural compounds could bind to the zinc-binding site. The quantitative structure-activity relationship (QSAR) models, which constructed by scaffold of MMP-9 inhibitors and its activities, were employed to predict the bio-activity of the natural compounds for MMP-9. The results showed that Celacinnine, Lobelanidine and Celallocinnine were qualified to interact with zinc-binding site and displayed well predictive activity. We found that celallocinnine was the best TCM compound for zinc binging sites of MMP-9 because the stable interactions were observed under dynamic condition. In addition, Celacinnine and Lobelanidine could interact with MMP-9 related protein that identified by drug-target interaction network analysis. Thus, we suggested the herbs Hypericum patulum, Sedum acre, and Tripterygium wilfordii that containing Celallocinnine, Celacinnine and Lobelanidine might be a novel medicine formula to avoid the side effect of tetracycline and increase the efficacy of treatment. Copyright © 2017 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.compbiolchem.2017.08.013 PMID: 28987294 [Indexed for MEDLINE] 570. Bioorg Med Chem Lett. 2016 Jan 15;26(2):265-271. doi: 10.1016/j.bmcl.2015.12.043. Epub 2015 Dec 12. Comparative study between 3D-QSAR and Docking-Based Pharmacophore models for potent Plasomodium falciparum dihydroorotate dehydrogenase inhibitors. Tseng TS(1), Lee YC(2), Hsiao NW(3), Liu YR(4), Tsai KC(5). Author information: (1)National Research Institute of Chinese Medicine, Ministry of Health and Welfare, Taipei 112, Taiwan. (2)The Center of Translational Medicine, Taipei Medical University, Taipei, Taiwan; The Ph.D. Program for Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan. (3)Institute of Biotechnology, National Changhua University of Education, Changhua, Taiwan. (4)Joint Biobank, Office of Human Research, Taipei Medical University, Taipei 110, Taiwan. (5)National Research Institute of Chinese Medicine, Ministry of Health and Welfare, Taipei 112, Taiwan; The Ph.D. Program for Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan. Electronic address: tkc@nricm.edu.tw. Malaria, caused by infections of the human malaria parasites Plasmodium falciparum, is a global infectious parasitic disease. Each year, about three million people died from malaria and the majority of whom are pregnant women and young children. Recently, a number of research attempt to reduce malaria parasite resistance and the toxicity of anti-malarial drugs. Nowadays, Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) was validated as a potent drug target to inhibit malarial activity by blocking pyrimidine biosynthesis. In this study, we employed 3D-QSAR Pharmacophore Generation and Docking-Based Pharmacophore Development to build the pharmacophore by using the collected 67 effective inhibitors against PfDHODH. 3D-QSAR Pharmacophore model, Hypo1, shows the high correlation coefficient (0.935), the lowest RMS deviation (2.15), the predicting accuracy of successful rates to training set (89.4%) and test set compounds (72.4%), respectively, revealing favorable predictive ability and is a reliable for further study. Additionally, Docking-Based Pharmacophore model, DBP-All255, exhibits comparable predictive capability to that of Hypo1, while DBP-Top1 shows poor statistical significance. This study reveals pharmacophore features of Hypo1, built by 3D-QSAR Pharmacophore Generation, are well-complementary to the functional residues in the active site of PfDHODH and is of great reliable for database screening. Copyright © 2015 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.bmcl.2015.12.043 PMID: 26707392 [Indexed for MEDLINE] 571. Eur J Med Chem. 2015 Nov 13;105:182-93. doi: 10.1016/j.ejmech.2015.10.014. Epub 2015 Oct 22. Inhibition of 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase from Mycobacterium tuberculosis: in silico screening and in vitro validation. Nirmal CR(1), Rao R(1), Hopper W(2). Author information: (1)Department of Bioinformatics, School of Bioengineering, Faculty of Engineering & Technology, SRM University, Kattankulathur, 603203, Tamil Nadu, India. (2)Department of Bioinformatics, School of Bioengineering, Faculty of Engineering & Technology, SRM University, Kattankulathur, 603203, Tamil Nadu, India. Electronic address: hod.bioinfo@ktr.srmuniv.ac.in. Tuberculosis, caused by Mycobacterium tuberculosis, remains a serious global health threat, highlighting the urgent need for novel antituberculosis drugs. The shikimate pathway, responsible for aromatic amino acid biosynthesis, is required for the growth of Mycobacterium tuberculosis and is a potential drug target. 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase (mtDAH7Ps) catalyzes the first step in shikimate pathway. E-pharmacophore models for inhibitors of mtDAH7Ps - tyrosine, phenylalanine, phosphoenolpyruvate and (2S)-2,7-bis(phosphonooxy)heptanoic acid were screened against ZINC synthetic and natural compounds databases. The shortlisted compounds were subjected to induce fit docking and validated by Prime/Molecular Mechanics Generalized Born Surface Area calculation to predict ligand binding energy and ligand strain energy for ligand and receptor. The lead compounds were screened for their inhibitory activity against purified mtDAH7Ps enzyme. Lead compounds inhibited mtDAH7Ps in a concentration-dependent manner; with an IC50 value of 21 μM, 42 μM and 54 μM for α-Tocopherol, rutin and 3-Pyridine carboxyaldehyde respectively. Molecular Dynamics analysis for 50 ns of the active compounds-mtDAH7Ps complexes showed that the backbone of mtDAH7Ps was stable. These results suggest that α-tocopherol, 3 - Pyridine carboxyaldehyde and rutin could be novel drug leads to inhibit mtDAH7Ps in M. tuberculosis. Copyright © 2015 Elsevier Masson SAS. All rights reserved. DOI: 10.1016/j.ejmech.2015.10.014 PMID: 26491981 [Indexed for MEDLINE] 572. J Biomol Struct Dyn. 2015;33(5):1082-93. doi: 10.1080/07391102.2014.929535. Epub 2014 Jun 23. Shape-based virtual screening, docking, and molecular dynamics simulations to identify Mtb-ASADH inhibitors. Kumar R(1), Garg P, Bharatam PV. Author information: (1)a Department of Pharmacoinformatics , National Institute of Pharmaceutical Education and Research (NIPER) , Sector-67, S.A.S. Nagar 160 062 , Punjab , India. Aspartate β-semialdehyde dehydrogenase (ASADH) is a key enzyme for the biosynthesis of essential amino acids and several important metabolites in microbes. Inhibition of ASADH enzyme is a promising drug target strategy against Mycobacterium tuberculosis (Mtb). In this work, in silico approach was used to identify potent inhibitors of Mtb-ASADH. Aspartyl β-difluorophosphonate (β-AFP), a known lead compound, was used to understand the molecular recognition interactions (using molecular docking and molecular dynamics analysis). This analysis helped in validating the computational protocol and established the participation of Arg99, Glu224, Cys130, Arg249, and His256 amino acids as the key amino acids in stabilizing ligand-enzyme interactions for effective binding, an essential feature is H-bonding interactions with the two arginyl residues at the two ends of the ligand. Best binding conformation of β-AFP was selected as a template for shape-based virtual screening (ZINC and NCI databases) to identify compounds that competitively inhibit the Mtb-ASADH. The top rank hits were further subjected to ADME and toxicity filters. Final filter was based on molecular docking analysis. Each screened molecule carries the characteristics of the highly electronegative groups on both sides separated by an average distance of 6 Å. Finally, the best predicted 20 compounds exhibited minimum three H-bonding interactions with Arg99 and Arg249. These identified hits can be further used for designing the more potent inhibitors against ASADH family. MD simulations were also performed on two selected compounds (NSC4862 and ZINC02534243) for further validation. During the MD simulations, both compounds showed same H-bonding interactions and remained bound to key active residues of Mtb-ASADH. DOI: 10.1080/07391102.2014.929535 PMID: 24875451 [Indexed for MEDLINE] 573. J Am Med Inform Assoc. 2014 Oct;21(e2):e278-86. doi: 10.1136/amiajnl-2013-002512. Epub 2014 Mar 18. Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. Cheng F(1), Zhao Z(2). Author information: (1)Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. (2)Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, Tennessee, USA Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA. OBJECTIVE: Drug-drug interactions (DDIs) are an important consideration in both drug development and clinical application, especially for co-administered medications. While it is necessary to identify all possible DDIs during clinical trials, DDIs are frequently reported after the drugs are approved for clinical use, and they are a common cause of adverse drug reactions (ADR) and increasing healthcare costs. Computational prediction may assist in identifying potential DDIs during clinical trials. METHODS: Here we propose a heterogeneous network-assisted inference (HNAI) framework to assist with the prediction of DDIs. First, we constructed a comprehensive DDI network that contained 6946 unique DDI pairs connecting 721 approved drugs based on DrugBank data. Next, we calculated drug-drug pair similarities using four features: phenotypic similarity based on a comprehensive drug-ADR network, therapeutic similarity based on the drug Anatomical Therapeutic Chemical classification system, chemical structural similarity from SMILES data, and genomic similarity based on a large drug-target interaction network built using the DrugBank and Therapeutic Target Database. Finally, we applied five predictive models in the HNAI framework: naive Bayes, decision tree, k-nearest neighbor, logistic regression, and support vector machine, respectively. RESULTS: The area under the receiver operating characteristic curve of the HNAI models is 0.67 as evaluated using fivefold cross-validation. Using antipsychotic drugs as an example, several HNAI-predicted DDIs that involve weight gain and cytochrome P450 inhibition were supported by literature resources. CONCLUSIONS: Through machine learning-based integration of drug phenotypic, therapeutic, structural, and genomic similarities, we demonstrated that HNAI is promising for uncovering DDIs in drug development and postmarketing surveillance. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions. DOI: 10.1136/amiajnl-2013-002512 PMCID: PMC4173180 PMID: 24644270 [Indexed for MEDLINE] 574. BMC Bioinformatics. 2008 Apr 15;9:198. doi: 10.1186/1471-2105-9-198. Improving protein function prediction methods with integrated literature data. Gabow AP(1), Leach SM, Baumgartner WA, Hunter LE, Goldberg DS. Author information: (1)Department of Pharmacology, University of Colorado at Denver and Health Sciences Center, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6101, PO Box 6511, Aurora, CO 80045, USA. gabow@cbio.mskcc.org BACKGROUND: Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity. RESULTS: We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder, but co-occurrence data still proves beneficial. CONCLUSION: Co-occurrence data is a valuable supplemental source for graph-theoretic function prediction algorithms. A rapidly growing literature corpus ensures that co-occurrence data is a readily-available resource for nearly every studied organism, particularly those with small protein interaction databases. Though arguably biased toward known genes, co-occurrence data provides critical additional links to well-studied regions in the interaction network that graph-theoretic function prediction algorithms can exploit. DOI: 10.1186/1471-2105-9-198 PMCID: PMC2375131 PMID: 18412966 [Indexed for MEDLINE] 575. Hum Mol Genet. 2008 Jul 1;17(13):1916-21. doi: 10.1093/hmg/ddn089. Epub 2008 Mar 28. Endocannabinoid receptor 1 gene variations increase risk for obesity and modulate body mass index in European populations. Benzinou M(1), Chèvre JC, Ward KJ, Lecoeur C, Dina C, Lobbens S, Durand E, Delplanque J, Horber FF, Heude B, Balkau B, Borch-Johnsen K, Jørgensen T, Hansen T, Pedersen O, Meyre D, Froguel P. Author information: (1)CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France. The therapeutic effects of cannabinoid receptor blockade on obesity-associated phenotypes underline the importance of the endocannabinoid pathway on the energy balance. Using a staged-approach, we examined the contribution of the endocannabinoid receptor 1 gene (CNR1) on obesity and body mass index (BMI) in the European population. With the input of CNR1 exons and 3' and 5' regions sequencing and HapMap database, we selected and genotyped 26 tagging single-nucleotide polymorphisms (SNPs) in 1932 obese cases and 1173 non-obese controls of French European origin. Variants that showed significant associations (P < 0.05) with obesity after correction for multiple testing were further tested in two additional European cohorts including 2645 individuals. For the identification of the potential causal variant(s), we further genotyped SNPs in high linkage disequilibrium (LD) with the obesity-associated variants. Of the 25 successfully genotyped CNR1 SNPs, 12 showed nominal evidence of association with childhood obesity, class I and II and/or class III adult obesity (1.16 < OR < 1.40, 0.00003 < P < 0.04). Intronic SNPs rs806381 and rs2023239, which resisted correction for multiple testing were further associated with higher BMI in both Swiss obese subjects and Danish individuals. The genotyping of all know variants in partial LD (r(2) > 0.5) with these two SNPs in the initial case-control study, identified two better associated SNPs (rs6454674 and rs10485170). Our study of 5750 subjects shows that CNR1 variations increase the risk for obesity and modulate BMI in our European population. As CB1 is a drug target for obesity, a pharmacogenetic analysis of the endocannabinoid blockade obesity treatment may be of interest to identify best responders. DOI: 10.1093/hmg/ddn089 PMID: 18375449 [Indexed for MEDLINE] 576. Prog Neuropsychopharmacol Biol Psychiatry. 2019 Jan 19;92:207-216. doi: 10.1016/j.pnpbp.2019.01.006. [Epub ahead of print] Association of purinergic receptor P2RX7 gene polymorphisms with depression symptoms. Vereczkei A(1), Abdul-Rahman O(1), Halmai Z(2), Nagy G(3), Szekely A(4), Somogyi A(3), Faludi G(5), Nemoda Z(6). Author information: (1)Department of Medical Chemistry, Molecular Biology and Pathobiochemistry, Semmelweis University, Budapest, Hungary. (2)Department of Psychiatry, Kútvölgyi Clinical Centre, Semmelweis University, Budapest, Hungary; Bhaktivedanta College, Budapest, Hungary. (3)2nd Department of Internal Medicine, Semmelweis University, Budapest, Hungary. (4)Institute of Psychology, Eötvös Loránd University, Budapest, Hungary. (5)Department of Psychiatry, Kútvölgyi Clinical Centre, Semmelweis University, Budapest, Hungary. (6)Department of Medical Chemistry, Molecular Biology and Pathobiochemistry, Semmelweis University, Budapest, Hungary. Electronic address: nemoda.zsofia@med.semmelweis-univ.hu. INTRODUCTION: The activation of the ATP-gated P2RX7 (purinergic receptor P2X, ligand-gated ion channel, 7) produces microglial activation, a process which has been demonstrated in depression, bipolar disorder, and schizophrenia. Emerging data over the last years highlighted the importance of P2X7 cation channel as a potential drug target for these central nervous system disorders. The Gln460Arg (rs2230912) polymorphism of the P2RX7 gene has been widely studied in mood disorders, however the results are still controversial. Therefore, we aimed to investigate the C-terminal region of this gene in major depressive and bipolar disorders (MDD and BD) by studying possibly functional, non-synonymous polymorphisms within a 7 kb long region around the Gln460Arg, including Ala348Thr (rs1718119), Thr357Ser (rs2230911), and Glu496Ala (rs3751143) variants. Since Gln460Arg is located at the 3' end of the P2RX7 gene, we included additional, potentially functional single nucleotide polymorphisms (SNPs) from the 3' untranslated region (UTR), which can be in linkage with Gln460Arg. Based on in silico search, we chose two SNPs in putative microRNA target sites which are located in consecutive positions: rs1653625 and rs1718106. METHODS: P2RX7 SNPs from the C-terminal region were selected based on previous functional assays, 3' UTR variants were chosen using PolymiRTS and Patrocles databases. The genotyping of the non-synonymous SNPs was carried out by pre-designed TaqMan® kits, while the 3' UTR variants were analyzed by PCR-RFLP method. Case-control analyses were carried out between 315 inpatients with acute major depressive episode (195 MDD, 120 BD) and 406 healthy control subjects. The two subscales of the Hospital Anxiety and Depression Scale (HADS) self-report questionnaire were used for quantitative analyses, including an additional, "at-risk" population of 218 patients with diabetes mellitus. The in vitro reporter gene assays were carried out on HEK and SK-N-FI cells transiently transfected with pMIR vector constructs containing the P2RX7 3' UTR downstream of the luciferase gene. RESULTS: Haplotype analysis indicated a relatively high linkage between the analyzed P2RX7 SNPs. Our case-control study did not yield any association between P2RX7 gene variants and depression. However, dimensional analyses showed significant associations of the HADS depression severity scores with Gln460Arg (rs2230912) and Ala348Thr (rs1718119) in the depressed and diabetic patient groups. In the in vitro experiments, the P2RX7 3' UTR constructs with the lowest predicted binding efficiency to their miRNAs showed the highest expression of the gene. The combination of the depression-associated P2RX7 C-terminal and 3' UTR SNPs contributed to the highest depression severity score in the haplotype analysis. CONCLUSION: Based on our findings, we propose that a P2RX7 haplotype combination of the Gln460Arg and neighboring SNPs contribute to the observed genetic association with depressive symptoms. Copyright © 2019 Elsevier Inc. All rights reserved. DOI: 10.1016/j.pnpbp.2019.01.006 PMID: 30664971 577. Curr Drug Discov Technol. 2018;15(2):81-93. doi: 10.2174/1570163814666170816112135. Structural Insights for Drugs Developed for Phospholipase D Enzymes. Stieglitz KA(1). Author information: (1)STEM Biotechnology Division, Roxbury Community College, Roxbury, MA 02120, United States. BACKGROUND: In recent years human phospholipase D enzymes (PLD1 and PLD2 isozymes) have emerged as drug targets for various diseases such as cardiovascular disease, cancer, infectious diseases and neurodegenerative conditions such as Alzheimer's and Parkinson's disease. The interest in PLD as a drug target is due to the fact that PLD enzymes belong to a superfamily of phospholipases that are essential to intracellular and extracellular signaling. Many bioactive lipid signaling molecules are generated by these enzymes including phosphatidic and lysophosphatidic acid, arachidonic acid, and diacylglycerol (DAG). More specifically PLDs are part of one pathway that generates phosphatidic acid which is a precursor to many lipids in the intracellular de novo pathway. The lipids produced from PA regulate many cellular events considered hallmarks of pathogenesis in cells; including proliferation, migration, invasion, angiogenesis, and vesicle transport. Hence, human PLD is a valid target for a variety of drug therapies. METHODS: The focus of this review is phospholipase D inhibitory molecules. A survey of structure-based drug design studies for PLD enzymes was done by searching several literature databases. Studies that focused on the structural aspects of phospholipase D were compiled and analyzed for content. Particular attention was given to studies involving inhibitory molecules as the focus of this work. In addition, the protein data bank (PDB) was surveyed for three dimensional structures of PLD. Structural investigation via in silico docking utilizing the available three dimensional coordinates of PLD and recent potent PLD isozyme specific inhibitors was performed to gain insights into the mode of binding by drugs designed to inhibit PLDs. RESULTS: Beginning with halopemide and derivatives such as FIPI (5-fluoro-2- indoyly des-chlorohalopemide) leading to PLD isozyme selective inhibitors such as novel triazaspirone-based series of PLD inhibitors, structures and IC50 values presented were found to be in the nanomolar range for either human PLD1 or PLD2. Selective oestrogen receptor modulators (SERMS), compounds used in the treatment of oestrogen-receptor-positive breast cancer, inhibited mammalian PLD enzymes in the low micromolar range. The first universal PLD inhibitor developed was devoid of the 6-OH moiety necessary for oestrogen receptor binding and anti-proliferation action. The universal PLD inhibitor contains a N,N-dimethylamino moiety which is known to reduce SERM activity and was found to inhibit several PLDs in the low micromolar range. The literature analyzed revealed a systematic approach to the biochemical evaluation of modes of binding of these inhibitors to the PLD enzymes. Finally, docking studies of several of the more potent PLD inhibitors correlates with biochemical studies with two modes of inhibitor binding to PLD: active site binding and allosteric binding. CONCLUSION: PLD inhibitors from diverse backgrounds continue to be developed as research progresses to the most potent and highly selective human PLD inhibitors with low or no off target activities. Docking studies strongly suggest both competitive (active site) and allosteric binding of these inhibitors to PLD. The three dimensional structure of PLD co-crystallized with potent inhibitors will be paramount to confirm the modes of binding for these molecules to PLD. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1570163814666170816112135 PMID: 28814238 578. Drug Des Devel Ther. 2015 Mar 31;9:1897-912. doi: 10.2147/DDDT.S77020. eCollection 2015. Modeling, molecular dynamics, and docking assessment of transcription factor rho: a potential drug target in Brucella melitensis 16M. Pradeepkiran JA(1), Kumar KK(1), Kumar YN(2), Bhaskar M(1). Author information: (1)Division of Animal Biotechnology, Department of Zoology, Sri Venkateswara University, Tirupati, India. (2)Biomedical Informatics Centre, Vector Control Research Centre, Indian Council of Medical Research, Pondicherry, India. The zoonotic disease brucellosis, a chronic condition in humans affecting renal and cardiac systems and causing osteoarthritis, is caused by Brucella, a genus of Gram-negative, facultative, intracellular pathogens. The mode of transmission and the virulence of the pathogens are still enigmatic. Transcription regulatory elements, such as rho proteins, play an important role in the termination of transcription and/or the selection of genes in Brucella. Adverse effects of the transcription inhibitors play a key role in the non-successive transcription challenges faced by the pathogens. In the investigation presented here, we computationally predicted the transcription termination factor rho (TtFRho) inhibitors against Brucella melitensis 16M via a structure-based method. In view the unknown nature of its crystal structure, we constructed a robust three-dimensional homology model of TtFRho's structure by comparative modeling with the crystal structure of the Escherichia coli TtFRho (Protein Data Bank ID: 1PVO) as a template in MODELLER (v 9.10). The modeled structure was optimized by applying a molecular dynamics simulation for 2 ns with the CHARMM (Chemistry at HARvard Macromolecular Mechanics) 27 force field in NAMD (NAnoscale Molecular Dynamics program; v 2.9) and then evaluated by calculating the stereochemical quality of the protein. The flexible docking for the interaction phenomenon of the template consists of ligand-related inhibitor molecules from the ZINC (ZINC Is Not Commercial) database using a structure-based virtual screening strategy against minimized TtFRho. Docking simulations revealed two inhibitors compounds - ZINC24934545 and ZINC72319544 - that showed high binding affinity among 2,829 drug analogs that bind with key active-site residues; these residues are considered for protein-ligand binding and unbinding pathways via steered molecular dynamics simulations. Arg215 in the model plays an important role in the stability of the protein-ligand complex via a hydrogen bonding interaction by aromatic-π contacts, and the ADMET (absorption, distribution, metabolism, and excretion) analysis of best leads indicate nontoxic in nature with good potential for drug development. DOI: 10.2147/DDDT.S77020 PMCID: PMC4386771 PMID: 25848225 [Indexed for MEDLINE] 579. Biomed Res Int. 2014;2014:364625. doi: 10.1155/2014/364625. Epub 2014 Jun 5. Potential mitochondrial isocitrate dehydrogenase R140Q mutant inhibitor from traditional Chinese medicine against cancers. Lee WY(1), Chen KC(2), Chen HY(3), Chen CY(4). Author information: (1)Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan ; School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan ; Department of Neurosurgery, China Medical University Hospital, Taichung 40447, Taiwan. (2)School of Pharmacy, China Medical University, Taichung 40402, Taiwan. (3)Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan. (4)Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan ; School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan ; Research Center for Chinese Medicine & Acupuncture, China Medical University, Taichung 40402, Taiwan. A recent research of cancer has indicated that the mutant of isocitrate dehydrogenase 1 and 2 (IDH1 and 2) genes will induce various cancers, including chondrosarcoma, cholangiocarcinomas, and acute myelogenous leukemia due to the effect of point mutations in the active-site arginine residues of isocitrate dehydrogenase (IDH), such as IDH1/R132, IDH2/R140, and IDH2/R172. As the inhibition for those tumor-associated mutant IDH proteins may induce differentiation of those cancer cells, these tumor-associated mutant IDH proteins can be treated as a drug target proteins for a differentiation therapy against cancers. In this study, we aim to identify the potent TCM compounds from the TCM Database@Taiwan as lead compounds of IDH2 R140Q mutant inhibitor. Comparing to the IDH2 R140Q mutant protein inhibitor, AGI-6780, the top two TCM compounds, precatorine and abrine, have higher binding affinities with target protein in docking simulation. After MD simulation, the top two TCM compounds remain as the same docking poses under dynamic conditions. In addition, precatorine is extracted from Abrus precatorius L., which represents the cytotoxic and proapoptotic effects for breast cancer and several tumor lines. Hence, we propose the TCM compounds, precatorine and abrine, as potential candidates as lead compounds for further study in drug development process with the IDH2 R140Q mutant protein against cancer. DOI: 10.1155/2014/364625 PMCID: PMC4066711 PMID: 24995286 [Indexed for MEDLINE] 580. PLoS Negl Trop Dis. 2013 Nov 21;7(11):e2548. doi: 10.1371/journal.pntd.0002548. eCollection 2013 Nov. Molecular cloning and characterization of taurocyamine kinase from Clonorchis sinensis: a candidate chemotherapeutic target. Xiao JY(1), Lee JY, Tokuhiro S, Nagataki M, Jarilla BR, Nomura H, Kim TI, Hong SJ, Agatsuma T. Author information: (1)Department of Environmental Health Sciences, Kochi Medical School, Nankoku, Kochi, Japan ; Department of Parasitology, Basic Medical College, Jiamusi University, Jiamusi, China. BACKGROUND: Adult Clonorchis sinensis lives in the bile duct and causes endemic clonorchiasis in East Asian countries. Phosphagen kinases (PK) constitute a highly conserved family of enzymes, which play a role in ATP buffering in cells, and are potential targets for chemotherapeutic agents, since variants of PK are found only in invertebrate animals, including helminthic parasites. This work is conducted to characterize a PK from C. sinensis and to address further investigation for future drug development. METHODOLOGY/PRINCIPAL FINDINGS: [corrected] A cDNA clone encoding a putative polypeptide of 717 amino acids was retrieved from a C. sinensis transcriptome. This polypeptide was homologous to taurocyamine kinase (TK) of the invertebrate animals and consisted of two contiguous domains. C. sinensis TK (CsTK) gene was reported and found consist of 13 exons intercalated with 12 introns. This suggested an evolutionary pathway originating from an arginine kinase gene group, and distinguished annelid TK from the general CK phylogenetic group. CsTK was found not to have a homologous counterpart in sequences analysis of its mammalian hosts from public databases. Individual domains of CsTK, as well as the whole two-domain enzyme, showed enzymatic activity and specificity toward taurocyamine substrate. Of the CsTK residues, R58, I60 and Y84 of domain 1, and H60, I63 and Y87 of domain 2 were found to participate in binding taurocyamine. CsTK expression was distributed in locomotive and reproductive organs of adult C. sinensis. Developmentally, CsTK was stably expressed in both the adult and metacercariae stages. Recombinant CsTK protein was found to have low sensitivity and specificity toward C. sinensis and platyhelminth-infected human sera on ELISA. CONCLUSION: CsTK is a promising anti-C. sinensis drug target since the enzyme is found only in the C. sinensis and has a substrate specificity for taurocyamine, which is different from its mammalian counterpart, creatine. DOI: 10.1371/journal.pntd.0002548 PMCID: PMC3836730 PMID: 24278491 [Indexed for MEDLINE] 581. Protein Expr Purif. 2011 Aug;78(2):225-34. doi: 10.1016/j.pep.2011.04.012. Epub 2011 Apr 29. Overexpression, purification and assessment of cyclosporin binding of a family of cyclophilins and cyclophilin-like proteins of the human malarial parasite Plasmodium falciparum. Marín-Menéndez A(1), Bell A. Author information: (1)Department of Microbiology, School of Genetics and Microbiology, Moyne Institute of Preventive Medicine, Trinity College Dublin, Dublin 2, Ireland. Malaria represents a global health, economic and social burden of enormous magnitude. Chemotherapy is at the moment a largely effective weapon against the disease, but the appearance of drug-resistant parasites is reducing the effectiveness of most drugs. Finding new drug-target candidates is one approach to the development of new drugs. The family of cyclophilins may represent a group of potential targets. They are involved in protein folding and regulation due to their peptidyl-prolyl cis-trans isomerase and/or chaperone activities. They also mediate the action of the immunosuppressive drug cyclosporin A, which additionally has strong antimalarial activity. In the genome database of the most lethal human malarial parasite Plasmodium falciparum, 11 genes apparently encoding cyclophilin or cyclophilin-like proteins were found, but most of these have not yet been characterized. Previously a pET vector conferring a C-terminal His₆ tag was used for recombinant expression and purification of one member of the P. falciparum cyclophilin family in Escherichia coli. The approach here was to use an identical method to produce all of the other members of this family and thereby allow the most consistent functional comparisons. We were successful in generating all but three of the family, plus a single amino-acid mutant, in the same recombinant form as either full-length proteins or isolated cyclophilin-like domains. The recombinant proteins were assessed by thermal melt assay for correct folding and cyclosporin A binding. Copyright © 2011 Elsevier Inc. All rights reserved. DOI: 10.1016/j.pep.2011.04.012 PMID: 21549842 [Indexed for MEDLINE] 582. Curr Pharm Des. 2010;16(24):2737-64. Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues. González-Díaz H(1), Romaris F, Duardo-Sanchez A, Pérez-Montoto LG, Prado-Prado F, Patlewicz G, Ubeira FM. Author information: (1)Department of Microbiology & Parasitology, University of Santiago de Compostela, Spain. humberto.gonzalez@usc.es Quantitative Structure-Activity Relationship (QSAR) models have been used in Pharmaceutical design and Medicinal Chemistry for the discovery of anti-parasite drugs. QSAR models predict biological activity using as input different types of structural parameters of molecules. Topological Indices (TIs) are a very interesting class of these parameters. We can derive TIs from graph representations based on only nodes (atoms) and edges (chemical bonds). TIs are not time-consuming in terms of computational resources because they depend only on atom-atom connectivity information. This information expressed in the molecular graphs can be tabulated in the form of adjacency matrices easy to manipulate with computers. Consequently, TIs allow the rapid collection, annotation, retrieval, comparison and mining of molecular structures within large databases. The interest in TIs has exploded because we can use them to describe also macromolecular and macroscopic systems represented by complex networks of interactions (links) between the different parts of a system (nodes) such as: drug-target, protein-protein, metabolic, host-parasite, brain cortex, parasite disease spreading, Internet, or social networks. In this work, we review and comment on the following topics related to the use of TIs in anti-parasite drugs and target discovery. The first topic reviewed was: Topological Indices and QSAR for antiparasitic drugs. This topic included: Theoretical Background, QSAR for anti-malaria drugs, QSAR for anti-Toxoplasma drugs. The second topic was: TOMO-COMD approach to QSAR of antiparasitic drugs. We included in this topic: TOMO-COMD theoretical background and TOMO-COMD models for antihelmintic activity, Trichomonas, anti-malarials, anti-trypanosome compounds. The third section was inserted to discuss Topological Indices in the context of Complex Networks. The last section is devoted to the MARCH-INSIDE approach to QSAR of antiparasitic drugs and targets. This begins with a theoretical background for drugs and parameters for proteins. Next, we reviewed MARCH-INSIDE models for Pharmaceutical Design of antiparasitic drugs including: flukicidal drugs and anti-coccidial drugs. We close MARCH-NSIDE topic with a review of multi-target QSAR of antiparasitic drugs, MARCH-INSIDE assembly of complex networks of antiparasitic drugs. We closed the MARCH-INSIDE section discussing the prediction of proteins in parasites and MARCH-INSIDE web-servers for Protein-Protein interactions in parasites: Plasmod-PPI and Trypano-PPI web-servers. We closed this revision with an important section devoted to review some legal issues related to QSAR models. PMID: 20642428 [Indexed for MEDLINE] 583. Clin Pharmacokinet. 2010 May;49(5):277-94. doi: 10.2165/11319360-000000000-00000. Effects of hypothermia on pharmacokinetics and pharmacodynamics: a systematic review of preclinical and clinical studies. van den Broek MP(1), Groenendaal F, Egberts AC, Rademaker CM. Author information: (1)Department of Clinical Pharmacy, Division of Laboratory Medicine & Pharmacy, University Medical Center Utrecht, Utrecht, the Netherlands. m.p.h.vandenbroek@umcutrecht.nl Examples of clinical applications of therapeutic hypothermia in modern clinical medicine include traumatic cardiac arrest, ischaemic stroke and, more recently, acute perinatal asphyxia in neonates. The exact mechanism of (neuro)protection by hypothermia is unknown. Since most enzymatic processes exhibit temperature dependency, it can be expected that therapeutic hypothermia may cause alterations in both pharmacokinetic and pharmacodynamic parameters, which could result in an increased risk of drug toxicity or therapy failure. Generalizable knowledge about the effect of therapeutic hypothermia on pharmacokinetics and pharmacodynamics could lead to more appropriate dosing and thereby prediction of clinical effects. This article reviews the evidence on the influence of therapeutic hypothermia on individual pharmacokinetic and pharmacodynamic parameters. A literature search was conducted within the PubMed, Embase and Cochrane databases from January 1965 to September 2008, comparing pharmacokinetic and/or pharmacodynamic parameters in hypothermia and normothermia regarding preclinical (animal) and clinical (human) studies. During hypothermia, pharmacokinetic parameters alter, resulting in drug and metabolite accumulation in the plasma for the majority of drugs. Impaired clearance is the most striking effect. Based on impaired clearance, dosages should be decreased considerably, especially for drugs with a low therapeutic index. Hypothetically, high-clearance compounds are affected more than low-clearance compounds because of the additional effect of impaired hepatic blood flow. The volume of distribution also changes, which may lead to therapy failure when it increases and could lead to toxicity when it decreases. The pH-partitioning hypothesis could contribute to the changes in the volumes of distribution for weak bases and acids, depending on their acid dissociation constants and acid-base status. Pharmacodynamic parameters may also alter, depending on the hypothermic regimen, drug target location, pharmacological mechanism and metabolic pathway of inactivation. The pharmacological response changes when target sensitivity alters. Rewarming patients to normothermia can also result in toxicity or therapy failure. The integrated effect of hypothermia on pharmacokinetic and pharmacodynamic properties of individual drugs is unclear. Therefore, therapeutic drug monitoring is currently considered essential for drugs with a low therapeutic index, drugs with active metabolites, high-clearance compounds and drugs that are inactivated by enzymes at the site of effect. Because most of the studies (74%) included in this review contain preclinical data, clinical pharmacokinetic/pharmacodynamic studies are essential for the development of substantiated dose regimens to avoid toxicity and therapy failure in patients treated with hypothermia. DOI: 10.2165/11319360-000000000-00000 PMID: 20384391 [Indexed for MEDLINE] 584. J Chem Inf Model. 2016 Jul 25;56(7):1357-72. doi: 10.1021/acs.jcim.6b00055. Epub 2016 Jun 16. QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni. Melo-Filho CC(1), Dantas RF(2), Braga RC(1), Neves BJ(1), Senger MR(2), Valente WC(2), Rezende-Neto JM(2), Chaves WT(2), Muratov EN(3)(4), Paveley RA(5), Furnham N(5), Kamentsky L(6), Carpenter AE(6), Silva-Junior FP(2), Andrade CH(1). Author information: (1)LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias , Rua 240, Qd.87, Goiania, GO 74605-510, Brazil. (2)Laboratory of Experimental and Computational Biochemistry of Drugs, Oswaldo Cruz Institute , Av. Brasil, 4365, Rio de Janeiro, RJ 21040-900, Brazil. (3)Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina , Chapel Hill, North Carolina 27599, United States. (4)Department of Chemical Technology, Odessa National Polytechnic University , 1. Shevchenko Ave., Odessa, 65000, Ukraine. (5)Department of Pathogen Molecular Biology & Department of Infection and Immunity, London School of Hygiene and Tropical Medicine , London WC1E 7HT, United Kingdom. (6)Imaging Platform, Broad Institute of Massachusetts Institute of Technology and Harvard , Cambridge, Massachusetts 02142, United States. Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents. DOI: 10.1021/acs.jcim.6b00055 PMCID: PMC5283162 PMID: 27253773 [Indexed for MEDLINE] 585. Drug Des Devel Ther. 2015 Sep 3;9:5087-97. doi: 10.2147/DDDT.S87197. eCollection 2015. Distinct prognostic values and potential drug targets of ALDH1 isoenzymes in non-small-cell lung cancer. You Q(1), Guo H(2), Xu D(3). Author information: (1)Department of Pathology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, People's Republic of China. (2)Department of Respiratory Medicine, Shouguang Hospital of Traditional Chinese Medicine, Shouguang, People's Republic of China. (3)Department of Endocrinology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, People's Republic of China. Increased aldehyde dehydrogenase 1 (ALDH1) activity has been found in the stem cell populations of leukemia and some solid tumors including non-small-cell lung cancer (NSCLC). However, which ALDH1's isoenzymes are contributing to ALDH1 activity remains elusive. In addition, the prognostic value of individual ALDH1 isoenzyme is not clear. In the current study, we investigated the prognostic value of ALDH1 isoenzymes in NSCLC patients through the Kaplan-Meier plotter database, which contains updated gene expression data and survival information from a total of 1,926 NSCLC patients. High expression of ALDH1A1 mRNA was found to be correlated to a better overall survival (OS) in all NSCLC patients followed for 20 years (hazard ratio [HR] 0.88 [0.77-0.99], P=0.039). In addition, high expression of ALDH1A1 mRNA was also found to be correlated to better OS in adenocarcinoma (Ade) patients (HR 0.71 [0.57-0.9], P=0.0044) but not in squamous cell carcinoma (SCC) patients (HR 0.92 [0.72-1.16], P=0.48). High expression of ALDH1A2 and ALDH1B1 mRNA was found to be correlated to worser OS in all NSCLC patients, as well as in Ade, but not in SCC patients. High expression of both ALDH1A3 and ALDH1L1 mRNA was not found to be correlated to OS in all NSCLC patients. These results strongly support that ALDH1A1 mRNA in NSCLC is associated with better prognosis. In addition, our current study also supports that ALDH1A2 and ALDH1B1 might be major contributors to the ALDH1 activity in NSCLC, since high expression of ALDH1A2 and ALDH1B1 mRNA was found to be significantly correlated to worser OS in all NSCLC patients. Based on our study, ALDH1A2 and ALDH1B1 might be excellent potential drug targets for NSCLC patients. DOI: 10.2147/DDDT.S87197 PMCID: PMC4562757 PMID: 26366059 [Indexed for MEDLINE] 586. Clin Cancer Res. 2011 Mar 15;17(6):1452-62. doi: 10.1158/1078-0432.CCR-10-2694. Epub 2011 Feb 10. STAT3 expression, molecular features, inflammation patterns, and prognosis in a database of 724 colorectal cancers. Morikawa T(1), Baba Y, Yamauchi M, Kuchiba A, Nosho K, Shima K, Tanaka N, Huttenhower C, Frank DA, Fuchs CS, Ogino S. Author information: (1)Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA. PURPOSE: STAT3 is a transcription factor that is constitutively activated in some cancers. It seems to play crucial roles in cell proliferation and survival, angiogenesis, tumor-promoting inflammation, and suppression of antitumor host immune response in the tumor microenvironment. Although the STAT3 signaling pathway is a potential drug target, clinical, pathologic, molecular, or prognostic features of STAT3-activated colorectal cancer remain uncertain. EXPERIMENTAL DESIGN: Utilizing a database of 724 colon and rectal cancer cases, we evaluated phosphorylated STAT3 (p-STAT3) expression by immunohistochemistry. The Cox proportional hazards model was used to compute mortality HR, adjusting for clinical, pathologic, and molecular features, including microsatellite instability (MSI), the CpG island methylator phenotype (CIMP), LINE-1 methylation, 18q LOH, TP53 (p53), CTNNB1 (β-catenin), JC virus T-antigen, and KRAS, BRAF, and PIK3CA mutations. RESULTS: Among the 724 tumors, 131 (18%) showed high-level p-STAT3 expression (p-STAT3-high), 244 (34%) showed low-level expression (p-STAT3-low), and the remaining 349 (48%) were negative for p-STAT3. p-STAT3 overexpression was associated with significantly higher colorectal cancer-specific mortality [log-rank P = 0.0020; univariate HR (p-STAT3-high vs. p-STAT3-negative): 1.85, 95% CI: 1.30-2.63, P(trend) = 0.0005; multivariate HR: 1.61, 95% CI: 1.11-2.34, P(trend) = 0.015]. p-STAT3 expression was positively associated with peritumoral lymphocytic reaction (multivariate OR: 3.23; 95% CI: 1.89-5.53, P < 0.0001). p-STAT3 expression was not associated with MSI, CIMP, or LINE-1 hypomethylation. CONCLUSIONS: STAT3 activation in colorectal cancer is associated with adverse clinical outcome, supporting its potential roles as a prognostic biomarker and a chemoprevention and/or therapeutic target. ©2011 AACR. DOI: 10.1158/1078-0432.CCR-10-2694 PMCID: PMC3077111 PMID: 21310826 [Indexed for MEDLINE] 587. Mar Drugs. 2018 Mar 14;16(3). pii: E94. doi: 10.3390/md16030094. Anti-BACE1 and Antimicrobial Activities of Steroidal Compounds Isolated from Marine Urechis unicinctus. Zhu YZ(1), Liu JW(2), Wang X(3), Jeong IH(4), Ahn YJ(5), Zhang CJ(6). Author information: (1)College of Chemistry and Pharmaceutical Science, Qingdao Agricultural University, Changcheng Rd, Chengyang district, Qingdao 266109, China. zhuyzh2008@163.com. (2)College of Chemistry and Pharmaceutical Science, Qingdao Agricultural University, Changcheng Rd, Chengyang district, Qingdao 266109, China. mail.liujingwen@gmail.com. (3)School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China. xinyuw001@163.com. (4)Division of Crop Protection, National Institute of Agricultural Science, Rural Development Administration, Jeollabuk-do 55365, Korea. Inhongjeong@korea.kr. (5)Department of Agricultural Biotechnology, Seoul National University, 599 Gwanak-ro, Silim-dong, Gwanak-Gu, Seoul 151742, Korea. yjahn@snu.ac.kr. (6)Department of Plant Science, University of Connecticut, 1376 Storrs Road, U-4163, Storrs, CT 06269, USA. chuanjiezhang@snu.ac.kr. The human β-site amyloid cleaving enzyme (BACE1) has been considered as an effective drug target for treatment of Alzheimer's disease (AD). In this study, Urechis unicinctus (U. unicinctus), which is a Far East specialty food known as innkeeper worm, ethanol extract was studied by bioassay-directed fractionation and isolation to examine its potential β-site amyloid cleaving enzyme inhibitory and antimicrobial activity. The following compounds were characterized: hecogenin, cholest-4-en-3-one, cholesta-4,6-dien-3-ol, and hurgadacin. These compounds were identified by their mass spectrometry, ¹H, and 13C NMR spectral data, comparing those data with NIST/EPA/NIH Mass spectral database (NIST11) and published values. Hecogenin and cholest-4-en-3-one showed significant inhibitory activity against BACE1 with EC50 values of 116.3 and 390.6 µM, respectively. Cholesta-4,6-dien-3-ol and hurgadacin showed broad spectrum antimicrobial activity, particularly strongly against Escherichia coli (E. coli), Salmonella enterica (S. enterica), Pasteurella multocida (P. multocida), and Physalospora piricola (P. piricola), with minimal inhibitory concentration (MIC) ranging from 0.46 to 0.94 mg/mL. This is the first report regarding those four known compounds that were isolated from U. unicinctus and their anti-BACE1 and antimicrobial activity, highlighting the fact that known natural compounds may be a critical source of new medicine leads. These findings provide scientific evidence for potential application of those bioactive compounds for the development of AD drugs and antimicrobial agents. DOI: 10.3390/md16030094 PMCID: PMC5867638 PMID: 29538306 [Indexed for MEDLINE] Conflict of interest statement: The authors declare no competing financial interest. 588. Molecules. 2017 Dec 6;22(12). pii: E2166. doi: 10.3390/molecules22122166. In Silico Identification and In Vitro Evaluation of Natural Inhibitors of Leishmania major Pteridine Reductase I. Herrmann FC(1), Sivakumar N(2), Jose J(3), Costi MP(4), Pozzi C(5), Schmidt TJ(6). Author information: (1)Institute of Pharmaceutical Biology and Phytochemistry (IPBP), University of Muenster, PharmaCampus, Corrensstrasse 48, D-48149 Muenster, Germany. f_herr01@uni-muenster.de. (2)Institute of Pharmaceutical Biology and Phytochemistry (IPBP), University of Muenster, PharmaCampus, Corrensstrasse 48, D-48149 Muenster, Germany. nirinasiva@gmail.com. (3)Institute of Pharmaceutical and Medicinal Chemistry, University of Muenster, PharmaCampus, Correnstrasse 48, D-48149 Muenster, Germany. joachim.jose@uni-muenster.de. (4)Department of Life Sciences, University of Modena and Reggio Emilia, Via G. Campi 103, 41125 Modena, Italy. mariapaola.costi@unimore.it. (5)Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via A. Moro 2, 53100 Siena, Italy. pozzi4@unisi.it. (6)Institute of Pharmaceutical Biology and Phytochemistry (IPBP), University of Muenster, PharmaCampus, Corrensstrasse 48, D-48149 Muenster, Germany. thomschm@uni-muenster.de. In a continuation of our computational efforts to find new natural inhibitors of a variety of target enzymes from parasites causing neglected tropical diseases (NTDs), we now report on 15 natural products (NPs) that we have identified as inhibitors of Leishmania major pteridine reductase I (LmPTR1) through a combination of in silico and in vitro investigations. Pteridine reductase (PTR1) is an enzyme of the trypanosomatid parasites' peculiar folate metabolism, and has previously been validated as a drug target. Initially, pharmacophore queries were created based on four 3D structures of LmPTR1 using co-crystallized known inhibitors as templates. Each of the pharmacophore queries was used to virtually screen a database of 1100 commercially available natural products. The resulting hits were submitted to molecular docking analyses in the substrate binding site of the respective protein structures used for the pharmacophore design. This approach led to the in silico identification of a total of 18 NPs with predicted binding affinity to LmPTR1. These compounds were subsequently tested in vitro for inhibitory activity towards recombinant LmPTR1 in a spectrophotometric inhibition assay. Fifteen out of the 18 tested compounds (hit rate = 83%) showed significant inhibitory activity against LmPTR1 when tested at a concentration of 50 µM. The IC50 values were determined for the six NPs that inhibited the target enzyme by more than 50% at 50 µM, with sophoraflavanone G being the most active compound tested (IC50 = 19.2 µM). The NPs identified and evaluated in the present study may represent promising lead structures for the further rational drug design of more potent inhibitors against LmPTR1. DOI: 10.3390/molecules22122166 PMCID: PMC6149668 PMID: 29211037 [Indexed for MEDLINE] 589. J Mol Recognit. 2017 Nov;30(11). doi: 10.1002/jmr.2644. Epub 2017 Jun 13. Computational modeling of the bat HKU4 coronavirus 3CLpro inhibitors as a tool for the development of antivirals against the emerging Middle East respiratory syndrome (MERS) coronavirus. Abuhammad A(1), Al-Aqtash RA(1), Anson BJ(2), Mesecar AD(2)(3)(4), Taha MO(1). Author information: (1)Department of Pharmaceutical Sciences, School of Pharmacy, The University of Jordan, Amman, Jordan. (2)Department of Biological Sciences, Purdue University, West Lafayette, IN, USA. (3)Department of Chemistry, Purdue University, West Lafayette, IN, USA. (4)Centers for Cancer Research & Drug Discovery, Purdue University, West Lafayette, IN, USA. The Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging virus that poses a major challenge to clinical management. The 3C-like protease (3CLpro ) is essential for viral replication and thus represents a potential target for antiviral drug development. Presently, very few data are available on MERS-CoV 3CLpro inhibition by small molecules. We conducted extensive exploration of the pharmacophoric space of a recently identified set of peptidomimetic inhibitors of the bat HKU4-CoV 3CLpro . HKU4-CoV 3CLpro shares high sequence identity (81%) with the MERS-CoV enzyme and thus represents a potential surrogate model for anti-MERS drug discovery. We used 2 well-established methods: Quantitative structure-activity relationship (QSAR)-guided modeling and docking-based comparative intermolecular contacts analysis. The established pharmacophore models highlight structural features needed for ligand recognition and revealed important binding-pocket regions involved in 3CLpro -ligand interactions. The best models were used as 3D queries to screen the National Cancer Institute database for novel nonpeptidomimetic 3CLpro inhibitors. The identified hits were tested for HKU4-CoV and MERS-CoV 3CLpro inhibition. Two hits, which share the phenylsulfonamide fragment, showed moderate inhibitory activity against the MERS-CoV 3CLpro and represent a potential starting point for the development of novel anti-MERS agents. To the best of our knowledge, this is the first pharmacophore modeling study supported by in vitro validation on the MERS-CoV 3CLpro .HIGHLIGHTS: MERS-CoV is an emerging virus that is closely related to the bat HKU4-CoV. 3CLpro is a potential drug target for coronavirus infection. HKU4-CoV 3CLpro is a useful surrogate model for the identification of MERS-CoV 3CLpro enzyme inhibitors. dbCICA is a very robust modeling method for hit identification. The phenylsulfonamide scaffold represents a potential starting point for MERS coronavirus 3CLpro inhibitors development. Copyright © 2017 John Wiley & Sons, Ltd. DOI: 10.1002/jmr.2644 PMID: 28608547 [Indexed for MEDLINE] 590. PLoS Med. 2015 Jul 21;12(7):e1001854. doi: 10.1371/journal.pmed.1001854. eCollection 2015 Jul. Glitazone Treatment and Incidence of Parkinson's Disease among People with Diabetes: A Retrospective Cohort Study. Brauer R(1), Bhaskaran K(1), Chaturvedi N(2), Dexter DT(3), Smeeth L(1), Douglas I(1). Author information: (1)Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom. (2)Institute of Cardiovascular Sciences, University College London, London, United Kingdom. (3)Centre for Neuroinflammation & Neurodegeneration, Division of Brain Sciences, Faculty of Medicine, Imperial College, London, United Kingdom. Comment in BMJ. 2015;351:h3949. BACKGROUND: Recent in vitro and animal experiments suggest that peroxisome proliferation-activated receptor gamma (PPARɣ) agonist medications, such as antidiabetic glitazone (GTZ) drugs, are neuroprotective in models of Parkinson's disease (PD). These findings have not been tested in humans. We hypothesized that individuals prescribed GTZ drugs would have a lower incidence of PD compared to individuals prescribed other treatments for diabetes. METHODS AND FINDINGS: Using primary care data from the United Kingdom Clinical Practice Research Datalink (CPRD), we conducted a retrospective cohort study in which individuals with diabetes who were newly prescribed GTZ (GTZ-exposed group) were matched by age, sex, practice, and diabetes treatment stage with up to five individuals prescribed other diabetes treatments (other antidiabetic drug-exposed group). Patients were followed up from 1999 until the first recording of a PD diagnosis, end of observation in the database, or end of the study (1 August 2013). An incidence rate ratio (IRR) was calculated using conditional Poisson regression, adjusted for possible confounders. 44,597 GTZ exposed individuals were matched to 120,373 other antidiabetic users. 175 GTZ-exposed individuals were diagnosed with PD compared to 517 individuals in the other antidiabetic drug-exposed group. The incidence rate (IR) of PD in the GTZ-exposed group was 6.4 per 10,000 patient years compared with 8.8 per 10,000 patient years in those prescribed other antidiabetic treatments (IRR 0.72, 95% confidence interval [CI] 0.60-0.87). Adjustments for potential confounding variables, including smoking, other medications, head injury, and disease severity, had no material impact (fully adjusted IRR 0.75, 0.59-0.94). The risk was reduced in those with current GTZ prescriptions (current GTZ-exposed IRR 0.59, 0.46-0.77) but not reduced among those with past prescriptions (past GTZ-exposed IRR 0.85, 0.65-1.10). Our study only included patients with diabetes who did not have a PD diagnosis when they were first prescribed GTZ, and thus, it cannot establish whether GTZ use prevents or slows the progression of PD. CONCLUSIONS: In patients with diabetes, a current prescription for GTZ is associated with a reduction in incidence of PD. This suggests PPAR gamma pathways may be a fruitful drug target in PD. DOI: 10.1371/journal.pmed.1001854 PMCID: PMC4511413 PMID: 26196151 [Indexed for MEDLINE] 591. Br J Cancer. 2011 Feb 15;104(4):653-63. doi: 10.1038/sj.bjc.6606058. Epub 2011 Jan 25. Development and evaluation of human AP endonuclease inhibitors in melanoma and glioma cell lines. Mohammed MZ(1), Vyjayanti VN, Laughton CA, Dekker LV, Fischer PM, Wilson DM 3rd, Abbotts R, Shah S, Patel PM, Hickson ID, Madhusudan S. Author information: (1)Translational DNA Repair Group, Laboratory of Molecular Oncology, Academic Unit of Oncology, School of Molecular Medical Sciences, Nottingham University Hospitals, University of Nottingham, Nottingham, UK. AIMS: Modulation of DNA base excision repair (BER) has the potential to enhance response to chemotherapy and improve outcomes in tumours such as melanoma and glioma. APE1, a critical protein in BER that processes potentially cytotoxic abasic sites (AP sites), is a promising new target in cancer. In the current study, we aimed to develop small molecule inhibitors of APE1 for cancer therapy. METHODS: An industry-standard high throughput virtual screening strategy was adopted. The Sybyl8.0 (Tripos, St Louis, MO, USA) molecular modelling software suite was used to build inhibitor templates. Similarity searching strategies were then applied using ROCS 2.3 (Open Eye Scientific, Santa Fe, NM, USA) to extract pharmacophorically related subsets of compounds from a chemically diverse database of 2.6 million compounds. The compounds in these subsets were subjected to docking against the active site of the APE1 model, using the genetic algorithm-based programme GOLD2.7 (CCDC, Cambridge, UK). Predicted ligand poses were ranked on the basis of several scoring functions. The top virtual hits with promising pharmaceutical properties underwent detailed in vitro analyses using fluorescence-based APE1 cleavage assays and counter screened using endonuclease IV cleavage assays, fluorescence quenching assays and radiolabelled oligonucleotide assays. Biochemical APE1 inhibitors were then subjected to detailed cytotoxicity analyses. RESULTS: Several specific APE1 inhibitors were isolated by this approach. The IC(50) for APE1 inhibition ranged between 30 nM and 50 μM. We demonstrated that APE1 inhibitors lead to accumulation of AP sites in genomic DNA and potentiated the cytotoxicity of alkylating agents in melanoma and glioma cell lines. CONCLUSIONS: Our study provides evidence that APE1 is an emerging drug target and could have therapeutic application in patients with melanoma and glioma. DOI: 10.1038/sj.bjc.6606058 PMCID: PMC3049581 PMID: 21266972 [Indexed for MEDLINE] 592. Int J Mol Sci. 2018 Oct 17;19(10). pii: E3204. doi: 10.3390/ijms19103204. Prediction of Novel Anoctamin1 (ANO1) Inhibitors Using 3D-QSAR Pharmacophore Modeling and Molecular Docking. Lee YH(1), Yi GS(2). Author information: (1)Department of Bio and Brain engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea. yoonhuk30@kaist.ac.kr. (2)Department of Bio and Brain engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea. gsyi@kaist.ac.kr. Recently, anoctamin1 (ANO1), a calcium-activated chloride channel, has been considered an important drug target, due to its involvement in various physiological functions, as well as its possibility for treatment of cancer, pain, diarrhea, hypertension, and asthma. Although several ANO1 inhibitors have been discovered by high-throughput screening, a discovery of new ANO1 inhibitors is still in the early phase, in terms of their potency and specificity. Moreover, there is no computational model to be able to identify a novel lead candidate of ANO1 inhibitor. Therefore, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore modeling approach was employed for identifying the essential chemical features to be required in the inhibition of ANO1. The pharmacophore hypothesis 2 (Hypo2) was selected as the best model based on the highest correlation coefficient of prediction on the test set (0.909). Hypo2 comprised a hydrogen bond acceptor, a hydrogen bond donor, a hydrophobic, and a ring aromatic feature with good statistics of the total cost (73.604), the correlation coefficient of the training set (0.969), and the root-mean-square deviation (RMSD) value (0.946). Hypo2 was well assessed by the test set, Fischer randomization, and leave-one-out methods. Virtual screening of the ZINC database with Hypo2 retrieved the 580 drug-like candidates with good potency and ADMET properties. Finally, two compounds were selected as novel lead candidates of ANO1 inhibitor, based on the molecular docking score and the interaction analysis. In this study, the best pharmacophore model, Hypo2, with notable predictive ability was successfully generated, and two potential leads of ANO1 inhibitors were identified. We believe that these compounds and the 3D-QSAR pharmacophore model could contribute to discovering novel and potent ANO1 inhibitors in the future. DOI: 10.3390/ijms19103204 PMCID: PMC6214110 PMID: 30336555 [Indexed for MEDLINE] 593. Biomed Res Int. 2017;2017:9084507. doi: 10.1155/2017/9084507. Epub 2017 Nov 21. The Bioinformatic Analysis of the Dysregulated Genes and MicroRNAs in Entorhinal Cortex, Hippocampus, and Blood for Alzheimer's Disease. Pang X(1), Zhao Y(1), Wang J(1)(2), Zhou Q(1), Xu L(1), Kang(1), Liu AL(1)(2), Du GH(1)(2). Author information: (1)State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China. (2)Beijing Key Laboratory of Drug Target and Screening Research, Beijing 100050, China. Aim: The incidence of Alzheimer's disease (AD) has been increasing in recent years, but there exists no cure and the pathological mechanisms are not fully understood. This study aimed to find out the pathogenesis of learning and memory impairment, new biomarkers, potential therapeutic targets, and drugs for AD. Methods: We downloaded the microarray data of entorhinal cortex (EC) and hippocampus (HIP) of AD and controls from Gene Expression Omnibus (GEO) database, and then the differentially expressed genes (DEGs) in EC and HIP regions were analyzed for functional and pathway enrichment. Furthermore, we utilized the DEGs to construct coexpression networks to identify hub genes and discover the small molecules which were capable of reversing the gene expression profile of AD. Finally, we also analyzed microarray and RNA-seq dataset of blood samples to find the biomarkers related to gene expression in brain. Results: We found some functional hub genes, such as ErbB2, ErbB4, OCT3, MIF, CDK13, and GPI. According to GO and KEGG pathway enrichment, several pathways were significantly dysregulated in EC and HIP. CTSD and VCAM1 were dysregulated significantly in blood, EC, and HIP, which were potential biomarkers for AD. Target genes of four microRNAs had similar GO_terms distribution with DEGs in EC and HIP. In addtion, small molecules were screened out for AD treatment. Conclusion: These biological pathways and DEGs or hub genes will be useful to elucidate AD pathogenesis and identify novel biomarkers or drug targets for developing improved diagnostics and therapeutics against AD. DOI: 10.1155/2017/9084507 PMCID: PMC5735586 PMID: 29359159 [Indexed for MEDLINE] 594. J Mol Graph Model. 2017 Oct;77:168-180. doi: 10.1016/j.jmgm.2017.08.007. Epub 2017 Aug 12. In silico identification of inhibitors of ribose 5-phosphate isomerase from Trypanosoma cruzi using ligand and structure based approaches. de V C Sinatti V(1), R Baptista LP(2), Alves-Ferreira M(3), Dardenne L(4), Hermínio Martins da Silva J(5), Guimarães AC(2). Author information: (1)Fiocruz, Instituto Oswaldo Cruz, Laboratório de Genômica Funcional e Bioinformática, Av. Brasil 4365, Manguinhos, 21040-900, Rio de Janeiro, RJ, Brazil. Electronic address: nessasinatti@gmail.com. (2)Fiocruz, Instituto Oswaldo Cruz, Laboratório de Genômica Funcional e Bioinformática, Av. Brasil 4365, Manguinhos, 21040-900, Rio de Janeiro, RJ, Brazil. (3)Fiocruz, Laboratório de Modelagem de Sistemas Biológicos, Centro de Desenvolvimento Tecnológico em Saúde, Av. Brasil 4036, Manguinhos, 21040-361, Rio de Janeiro, Brazil; Instituto Nacional de Ciência e Tecnologia em Inovação em Doenças de Populações Negligenciadas, INCT-IDPN, CNPq, Brazil. (4)Laboratório Nacional de Computação Científica, Grupo de Modelagem Molecular de Sistemas Biológicos, Av. Getúlio Vargas, 333, Quitandinha, 25651-075, Petrópolis, RJ, Brazil. (5)Fiocruz-Ceará, Computational Modeling Group, Avenida Santos Dumont, 5753, Papicu, 60175-047, Fortaleza, CE, Brazil. Chagas disease, caused by the protozoan Trypanosoma cruzi, affects approximately seven million people, mainly in Latin America, and causes about 7000 deaths annually. The available treatments are unsatisfactory and search for more effective drugs against this pathogen is critical. In this context, the ribose 5-phosphate isomerase (Rpi) enzyme is a potential drug target mainly due to its function in the pentose phosphate pathway and its essentiality (previously shown in other trypanosomatids). In this study, we propose novel potential inhibitors for the Rpi of T. cruzi (TcRpi) based on a computer-aided approach, including structure-based and ligand-based pharmacophore modeling. Along with a substructural and similarity search, the selected pharmacophore hypotheses were used to screen the purchasable subset of the ZINC Database, yielding 20,183 candidate compounds. These compounds were submitted to molecular docking studies in the TcRpi and Human Rpi (HsRpi) active sites in order to identify potential selective inhibitors for the T. cruzi enzyme. After the molecular docking and ADME-T (absorption, distribution, metabolism, excretion and toxicity)/PAINS (pan-assay interference compounds) screenings, 211 molecules were selected as potential TcRpi inhibitors. Out of these, three compounds - ZINC36975961, ZINC63480117, and ZINC43763931 - were submitted to molecular dynamics simulations and two of them - ZINC36975961 and ZINC43763931- had good performance and made interactions with important active site residues over all the simulation time. These compounds could be considered potential TcRpi inhibitors candidates and also may be used as leads for developing new TcRpi inhibitors. Copyright © 2017 Elsevier Inc. All rights reserved. DOI: 10.1016/j.jmgm.2017.08.007 PMID: 28865321 [Indexed for MEDLINE] 595. Oncotarget. 2017 May 16;8(20):33225-33240. doi: 10.18632/oncotarget.16600. The design of novel inhibitors for treating cancer by targeting CDC25B through disruption of CDC25B-CDK2/Cyclin A interaction using computational approaches. Li HL(1), Ma Y(1), Ma Y(1), Li Y(1), Chen XB(1)(2), Dong WL(1), Wang RL(1). Author information: (1)Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China. (2)Eye Hospital, Tianjin Medical University, School of Optometry and Ophthalmology, Tianjin Medical University, Tianjin, China. Cell division cycle 25B is a key cell cycle regulator and widely considered as potent clinical drug target for cancers. This research focused on identifying potential compounds in theory which are able to disrupt transient interactions between CDC25B and its CDK2/Cyclin A substrate.By using the method of ZDOCK and RDOCK, the most optimized 3D structure of CDK2/Cyclin A in complex with CDC25B was constructed and validated using two methods: 1) the superimposition of proteins; 2) analysis of the hydrogen bond distances of Arg 488(N1)-Asp 206(OD1), Arg 492(NE)-Asp 206(OD1), Arg 492(N1)-Asp 206(OD2) and Tyr 497(NE)-Asp 210(OD1). A series of new compounds was gained through searching the fragment database derived from ZINC based on the known inhibitor-compound 7 by the means of "replace fragment" technique. The compounds acquired via meeting the requirements of the absorption, distribution, metabolism, and excretion (ADME) predictions. Finally, 12 compounds with better binding affinity were identified. The comp#1, as a representative, was selected to be synthesized and assayed for their CDC25B inhibitory activities. The comp#1 exhibited mild inhibitory activities against human CDC25B with IC50 values at about 39.02 μM. Molecular Dynamic (MD) simulation revealed that the new inhibitor-comp#1 had favorable conformations for binding to CDC25B and disturbing the interactions between CDC25B and CDK2/Cyclin A. DOI: 10.18632/oncotarget.16600 PMCID: PMC5464863 PMID: 28402259 [Indexed for MEDLINE] 596. BMC Bioinformatics. 2016 Feb 8;17:75. doi: 10.1186/s12859-016-0898-8. Resistance related metabolic pathways for drug target identification in Mycobacterium tuberculosis. Cloete R(1), Oppon E(2), Murungi E(3)(4), Schubert WD(5)(6), Christoffels A(7). Author information: (1)South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa. ruben@sanbi.ac.za. (2)South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa. ekow@sanbi.ac.za. (3)South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa. eddkimm@gmail.com. (4)Current address: Department of Biochemistry, Egerton University, Njoro, Kenya. eddkimm@gmail.com. (5)Department of Biotechnology, University of the Western Cape, Bellville, South Africa. wolf-dieter.schubert@up.ac.za. (6)Current address: Department of Biochemistry, University of Pretoria, Pretoria, South Africa. wolf-dieter.schubert@up.ac.za. (7)South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa. alan@sanbi.ac.za. BACKGROUND: Increasing resistance to anti-tuberculosis drugs has driven the need for developing new drugs. Resources such as the tropical disease research (TDR) target database and AssessDrugTarget can help to prioritize putative drug targets. Hower, these resources do not necessarily map to metabolic pathways and the targets are not involved in dormancy. In this study, we specifically identify drug resistance pathways to allow known drug resistant mutations in one target to be offset by inhibiting another enzyme of the same metabolic pathway. One of the putative targets, Rv1712, was analysed by modelling its three dimensional structure and docking potential inhibitors. RESULTS: We mapped 18 TB drug resistance gene products to 15 metabolic pathways critical for mycobacterial growth and latent TB by screening publicly available microarray data. Nine putative targets, Rv1712, Rv2984, Rv2194, Rv1311, Rv1305, Rv2195, Rv1622c, Rv1456c and Rv2421c, were found to be essential, to lack a close human homolog, and to share >67 % sequence identity and >87 % query coverage with mycobacterial orthologs. A structural model was generated for Rv1712, subjected to molecular dynamic simulation, and identified 10 compounds with affinities better than that for the ligand cytidine-5'-monophosphate (C5P). Each compound formed more interactions with the protein than C5P. CONCLUSIONS: We focused on metabolic pathways associated with bacterial drug resistance and proteins unique to pathogenic bacteria to identify novel putative drug targets. The ten compounds identified in this study should be considered for experimental studies to validate their potential as inhibitors of Rv1712. DOI: 10.1186/s12859-016-0898-8 PMCID: PMC4745158 PMID: 26856535 [Indexed for MEDLINE] 597. PLoS Comput Biol. 2015 Oct 7;11(10):e1004477. doi: 10.1371/journal.pcbi.1004477. eCollection 2015 Oct. Ligand Discovery for the Alanine-Serine-Cysteine Transporter (ASCT2, SLC1A5) from Homology Modeling and Virtual Screening. Colas C(1), Grewer C(2), Otte NJ(3), Gameiro A(2), Albers T(2), Singh K(2), Shere H(1), Bonomi M(4), Holst J(3), Schlessinger A(1). Author information: (1)Department of Pharmacology and Systems Therapeutics, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America. (2)Department of Chemistry, Binghamton University, Binghamton, New York, United States of America. (3)Origins of Cancer Laboratory Centenary Program, Camperdown, Australia; Sydney Medical School, University of Sydney, Sydney, Australia. (4)Department of Chemistry, University of Cambridge, Cambridge, United Kingdom. The Alanine-Serine-Cysteine transporter ASCT2 (SLC1A5) is a membrane protein that transports neutral amino acids into cells in exchange for outward movement of intracellular amino acids. ASCT2 is highly expressed in peripheral tissues such as the lung and intestines where it contributes to the homeostasis of intracellular concentrations of neutral amino acids. ASCT2 also plays an important role in the development of a variety of cancers such as melanoma by transporting amino acid nutrients such as glutamine into the proliferating tumors. Therefore, ASCT2 is a key drug target with potentially great pharmacological importance. Here, we identify seven ASCT2 ligands by computational modeling and experimental testing. In particular, we construct homology models based on crystallographic structures of the aspartate transporter GltPh in two different conformations. Optimization of the models' binding sites for protein-ligand complementarity reveals new putative pockets that can be targeted via structure-based drug design. Virtual screening of drugs, metabolites, fragments-like, and lead-like molecules from the ZINC database, followed by experimental testing of 14 top hits with functional measurements using electrophysiological methods reveals seven ligands, including five activators and two inhibitors. For example, aminooxetane-3-carboxylate is a more efficient activator than any other known ASCT2 natural or unnatural substrate. Furthermore, two of the hits inhibited ASCT2 mediated glutamine uptake and proliferation of a melanoma cancer cell line. Our results improve our understanding of how substrate specificity is determined in amino acid transporters, as well as provide novel scaffolds for developing chemical tools targeting ASCT2, an emerging therapeutic target for cancer and neurological disorders. DOI: 10.1371/journal.pcbi.1004477 PMCID: PMC4596572 PMID: 26444490 [Indexed for MEDLINE] 598. Biomed Res Int. 2014;2014:429486. doi: 10.1155/2014/429486. Epub 2014 Jun 25. In silico investigation of potential TRAF6 inhibitor from traditional Chinese medicine against cancers. Chen KC(1), Lee WY(2), Chen HY(3), Chen CY(4). Author information: (1)School of Pharmacy, China Medical University, Taichung 40402, Taiwan. (2)School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan ; Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan ; Department of Neurosurgery, China Medical University Hospital, Taichung 40447, Taiwan. (3)Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan. (4)School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan ; Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan ; Research Center for Chinese Medicine & Acupuncture, China Medical University, Taichung 40402, Taiwan ; Human Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung 40447, Taiwan. It has been indicated that tumor necrosis factor receptor-associated factor-6 (TRAF6) will upregulate the expression of hypoxia-inducible factor-1α (HIF-1α) and promote tumor angiogenesis. TRAF6 proteins can be treated as drug target proteins for a differentiation therapy against cancers. As structural disordered disposition in the protein may induce the side-effect and reduce the occupancy for ligand to bind with target protein, PONDR-Fit protocol was performed to predict the disordered disposition in TRAF6 protein before virtual screening. TCM compounds from the TCM Database@Taiwan were employed for virtual screening to identify potent compounds as lead compounds of TRAF6 inhibitor. After virtual screening, the MD simulation was performed to validate the stability of interactions between TRAF6 proteins and each ligand. The top TCM compounds, tryptophan, diiodotyrosine, and saussureamine C, extracted from Saussurea lappa Clarke, Bos taurus domesticus Gmelin, and Lycium chinense Mill., have higher binding affinities with target protein in docking simulation. However, the docking pose of TRAF6 protein with tryptophan is not stable under dynamic condition. For the other two TCM candidates, diiodotyrosine and saussureamine C maintain the similar docking poses under dynamic conditions. Hence, we propose the TCM compounds, diiodotyrosine and saussureamine C, as potential candidates as lead compounds for further study in drug development process with the TRAF6 protein against cancer. DOI: 10.1155/2014/429486 PMCID: PMC4096009 PMID: 25089269 [Indexed for MEDLINE] 599. Chem Biol Drug Des. 2013 Jun;81(6):675-87. doi: 10.1111/cbdd.12127. In silico target fishing for the potential targets and molecular mechanisms of baicalein as an antiparkinsonian agent: discovery of the protective effects on NMDA receptor-mediated neurotoxicity. Gao L(1), Fang JS, Bai XY, Zhou D, Wang YT, Liu AL, Du GH. Author information: (1)Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China. The flavonoid baicalein has been proven effective in animal models of parkinson's disease; however, the potential biological targets and molecular mechanisms underlying the antiparkinsonian action of baicalein have not been fully clarified. In the present study, the potential targets of baicalein were predicted by in silico target fishing approaches including database mining, molecular docking, structure-based pharmacophore searching, and chemical similarity searching. A consensus scoring formula has been developed and validated to objectively rank the targets. The top two ranked targets catechol-O-methyltransferase (COMT) and monoamine oxidase B (MAO-B) have been proposed as targets of baicalein by literatures. The third-ranked one (N-methyl-d-aspartic acid receptor, NMDAR) with relatively low consensus score was further experimentally tested. Although our results suggested that baicalein significantly attenuated NMDA-induced neurotoxicity including cell death, intracellular nitric oxide (NO) and reactive oxygen species (ROS) generation, extracellular NO reduction in human SH-SY5Y neuroblastoma cells, baicalein exhibited no inhibitory effect on [(3) H]MK-801 binding study, indicating that NMDAR might not be the target of baicalein. In conclusion, the results indicate that in silico target fishing is an effective method for drug target discovery, and the protective role of baicalein against NMDA-induced neurotoxicity supports our previous research that baicalein possesses antiparkinsonian activity. © 2013 John Wiley & Sons A/S. DOI: 10.1111/cbdd.12127 PMID: 23461900 [Indexed for MEDLINE] 600. 11C-Labeled rifampicin. Shan L(1). In: Molecular Imaging and Contrast Agent Database (MICAD) [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2004-2013. 2010 Aug 02. Author information: (1)National Center for Biotechnology Information, NLM, NIH Rifampicin (RIF) (or rifampin) is a rifamycin derivative with a clinically effective group of 4-methyl-1-piperazinaminyl. RIF is typically used to treat Mycobacterium infections, including tuberculosis (TB) (1, 2). By binding the β subunit, RIF inhibits the DNA-dependent RNA polymerase and thus prevents RNA and protein synthesis in bacterial cells (2, 3). RIF labeled with 11C ([11C]RIF) has been generated by Liu et al. for in vivo and real-time analysis of the RIF pharmacokinetics (PK) and biodistribution with positron emission tomography (PET) (1). The half-life of 11C is 20.4 min. The PK and biodistribution of a novel drug are traditionally determined with blood and tissue sampling and/or autoradiography. Despite high workload and huge investment in drug development, only 8% of the drugs entering clinical trials today reach the market as estimated by the U.S. Food and Drug Administration. One main reason for this attrition is insufficient exploration of the in vivo drug-target interaction (1). Traditional methods are inadequate to answer questions such as whether a drug reaches the target, how the drug interacts with its targets, and how the drug modifies the diseases. Because of the high resolution and sensitivity of newly developed imaging techniques, investigators have become increasingly interested in addressing these issues (4, 5). In the case of PET imaging, most small molecules can now be efficiently labeled with 11C or with 18F at >37 GBq/µmol (1 Ci/μmol), and they can be detected with PET in the nanomolar to picomolar concentration range (6-8). Consequently, a sufficient signal for imaging can be obtained even though the total amount of a radiotracer administered systemically is extremely low (known as microdosing, typically <1 μg for humans). Microdosing is particularly valuable for evaluating tissue exposure in the early phase of drug development when the full-range toxicology is not yet available (9, 10). Increasing evidence has demonstrated the efficiency of PET imaging in: obtaining quantitative information on drug PK and distribution in various tissues including brain; confirming drug binding with targets and elucidating the relationship between occupancy and target expression/function in vivo; assessing drug passage across the blood–brain barrier (BBB) and ensuring sufficient exposure to brain for central nervous system drugs; and dissecting the modifying effects of drugs on diseases (4, 6, 7). The current treatment regime for drug-sensitive TB involves the use of RIF, isoniazid (INH), pyrazinamide (PZA), and ethambutol or streptomycin for two months, followed by four months of continued dosing with INH and RIF (2, 3). This regime is primarily based on PK studies in serum and efficacy of treatment. The efficacy of each drug for different types of TB such as brain TB and the drug distribution in each compartment of an organ are not well understood. To provide direct insights into these drugs, Liu et al. labeled INH, RIF, and PZA with 11C and investigated their PK and biodistribution in baboons with PET (1). Liu et al. found that the organ distribution and BBB penetration of each drug differed greatly. For [11C]RIF, its ability to penetrate the BBB was lower than that of PZA and INH (PZA > INH). The RIF concentrations in the lungs and brain were 10 times and 3−4 times higher, respectively, than the RIF minimum inhibitory concentration (MIC) value against TB, supporting the use of RIF for treating TB infections in the lungs and the central nervous system. This chapter summarizes the data of [11C]RIF obtained by Liu et al. The data obtained with [11C]INH and [11C]PZA were described in the MICAD chapters on [11C]INH and [11C]PZA, respectively. PMID: 20827821 601. Biochemistry. 2010 Mar 9;49(9):1833-42. doi: 10.1021/bi901998m. Two-dimensional combinatorial screening of a bacterial rRNA A-site-like motif library: defining privileged asymmetric internal loops that bind aminoglycosides. Tran T(1), Disney MD. Author information: (1)Department of Chemistry and The Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, The State University of New York, 657 Natural Sciences Complex, Buffalo, New York 14260, USA. RNAs have diverse structures that are important for biological function. These structures include bulges and internal loops that can form tertiary contacts or serve as ligand binding sites. The most commonly exploited RNA drug target for small molecule intervention is the bacterial ribosome, more specifically the rRNA aminoacyl-tRNA site (rRNA A-site) which is a major target for the aminoglycoside class of antibiotics. The bacterial A-site is composed of a 1 x 1 nucleotide all-U internal loop and a 2 x 1 nucleotide all-A internal loop separated by a single GC base pair. Therefore, we probed the molecular recognition of a small library of four aminoglycosides for binding a 16384-member bacterial rRNA A-site-like internal loop library using two-dimensional combinatorial screening (2DCS). 2DCS is a microarray-based method that probes RNA and chemical spaces simultaneously. These studies sought to determine if aminoglycosides select their therapeutic target if given a choice of binding all possible internal loops derived from an A-site-like library. Results show that the bacterial rRNA A-site was not selected by any aminoglycoside. Analyses of selected sequences using the RNA Privileged Space Predictor (RNA-PSP) program show that each aminoglycoside preferentially binds different types of internal loops. For three of the aminoglycosides, 6''-azido-kanamycin A, 5-O-(2-azidoethyl)-neamine, and 6''-azido-tobramycin, the selected internal loops bind with approximately 10-fold higher affinity than the bacterial rRNA A-site. The internal loops selected to bind 5''-azido-neomycin B bind with an affinity similar to that of the therapeutic target. Selected internal loops that are unique for each aminoglycoside have dissociation constants ranging from 25 to 270 nM and are specific for the aminoglycoside they was selected to bind compared to the other arrayed aminoglycosides. These studies further establish a database of RNA motifs that are recognized by small molecules that could be used to enable the rational and modular design of small molecules targeting RNA. DOI: 10.1021/bi901998m PMCID: PMC2846769 PMID: 20108982 [Indexed for MEDLINE] 602. J Immunol. 2019 Mar 15;202(6):1826-1832. doi: 10.4049/jimmunol.1801063. Epub 2019 Jan 30. Characterization of Glucose Transporter 6 in Lipopolysaccharide-Induced Bone Marrow-Derived Macrophage Function. Caruana BT(1), Byrne FL(1), Knights AJ(1), Quinlan KGR(1), Hoehn KL(2)(3). Author information: (1)School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia; and. (2)School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia; and k.hoehn@unsw.edu.au. (3)Department of Pharmacology, University of Virginia, Charlottesville, VA 22908. The polarization processes for M1 versus M2 macrophages are quite distinct in the context of changes in cellular metabolism. M1 macrophages are highly glycolytic, whereas M2 macrophages require a more oxidative nutrient metabolism. An important part of M1 polarization involves upregulation of the glucose transporter (GLUT) GLUT1 to facilitate increased glucose uptake and glycolytic metabolism; however, the role of other glucose transporters in this process is largely unknown. In surveying the Functional Annotation of the Mammalian Genome and Gene Expression Omnibus Profiles databases, we discovered that the glucose transporter GLUT6 is highly upregulated in LPS-activated macrophages. In our previous work, we have not detected mouse GLUT6 protein expression in any noncancerous tissue; therefore, in this study, we investigated the expression and significance of GLUT6 in bone marrow-derived macrophages from wild-type and GLUT6 knockout C57BL/6 mice. We show that LPS-induced M1 polarization markedly upregulated GLUT6 protein, whereas naive macrophages and IL-4-induced M2 macrophages do not express GLUT6 protein. However, despite strong upregulation of GLUT6 in M1 macrophages, the absence of GLUT6 did not alter M1 polarization in the context of glucose uptake, glycolytic metabolism, or cytokine production. Collectively, these data show that GLUT6 is dispensable for LPS-induced M1 polarization and function. These findings are important because GLUT6 is an anticancer drug target, and this study suggests that inhibition of GLUT6 may not impart detrimental side effects on macrophage function to interfere with their antitumor properties. Copyright © 2019 by The American Association of Immunologists, Inc. DOI: 10.4049/jimmunol.1801063 PMID: 30700586 603. Genomics. 2017 Oct;109(5-6):408-418. doi: 10.1016/j.ygeno.2017.06.006. Epub 2017 Jul 4. Visualizing the regulatory role of Angiopoietin-like protein 8 (ANGPTL8) in glucose and lipid metabolic pathways. Siddiqa A(1), Cirillo E(2), Tareen SHK(3), Ali A(4), Kutmon M(5), Eijssen LMT(2), Ahmad J(6), Evelo CT(5), Coort SL(2). Author information: (1)Research Centre for Modeling and Simulation - RCMS, National University of Sciences and Technology, Pakistan; Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, The Netherlands. (2)Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, The Netherlands. (3)Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, The Netherlands. (4)Atta-ur-Rahman School of Applied Biosciences - ASAB, National University of Sciences and Technology, Pakistan. (5)Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, The Netherlands; Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, The Netherlands. (6)Research Centre for Modeling and Simulation - RCMS, National University of Sciences and Technology, Pakistan. Electronic address: jamil.ahmad@rcms.nust.edu.pk. ANGPTL8 (Angiopoietin-like protein 8) is a newly identified hormone emerging as a novel drug target for treatment of diabetes mellitus and dyslipidemia due to its unique metabolic nature. With increasing number of studies targeting the regulation of ANGPTL8, integration of their findings becomes indispensable. This study has been conducted with the aim to collect, analyze, integrate and visualize the available knowledge in the literature about ANGPTL8 and its regulation. We utilized this knowledge to construct a regulatory pathway of ANGPTL8 which is available at WikiPathways, an open source pathways database. It allows us to visualize ANGPTL8's regulation with respect to other genes/proteins in different pathways helping us to understand the complex interplay of novel hormones/genes/proteins in metabolic disorders. To the best of our knowledge, this is the first attempt to present an integrated pathway view of ANGPTL8's regulation and its associated pathways and is important resource for future omics-based studies. Copyright © 2017 Elsevier Inc. All rights reserved. DOI: 10.1016/j.ygeno.2017.06.006 PMID: 28684091 [Indexed for MEDLINE] 604. Sci Rep. 2016 Mar 1;6:22223. doi: 10.1038/srep22223. Significant impact of miRNA-target gene networks on genetics of human complex traits. Okada Y(1)(2), Muramatsu T(3), Suita N(1)(4), Kanai M(1), Kawakami E(5), Iotchkova V(6)(7), Soranzo N(6)(7), Inazawa J(3)(8), Tanaka T(1)(8)(9). Author information: (1)Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8510, Japan. (2)Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan. (3)Department of Molecular Cytogenetics, Medical Research Institute and Graduate School of Medical and Dental Science, Tokyo Medical and Dental University, Tokyo 113-8510, Japan. (4)Advanced Medicinal Research Laboratories, Tsukuba Research Institute, Ono Pharmaceutical CO., LTD., Tsukuba 300-4247, Japan. (5)Laboratory for Disease Systems Modeling, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan. (6)Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, CB10 1HH, UK. (7)Department of Haematology, University of Cambridge, Hills Rd, Cambridge CB2 0AH, UK. (8)Bioresource Research Center, Tokyo Medical and Dental University, Tokyo 113-8510, Japan. (9)Laboratory for Cardiovascular Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan. The impact of microRNA (miRNA) on the genetics of human complex traits, especially in the context of miRNA-target gene networks, has not been fully assessed. Here, we developed a novel analytical method, MIGWAS, to comprehensively evaluate enrichment of genome-wide association study (GWAS) signals in miRNA-target gene networks. We applied the method to the GWAS results of the 18 human complex traits from >1.75 million subjects, and identified significant enrichment in rheumatoid arthritis (RA), kidney function, and adult height (P < 0.05/18 = 0.0028, most significant enrichment in RA with P = 1.7 × 10(-4)). Interestingly, these results were consistent with current literature-based knowledge of the traits on miRNA obtained through the NCBI PubMed database search (adjusted P = 0.024). Our method provided a list of miRNA and target gene pairs with excess genetic association signals, part of which included drug target genes. We identified a miRNA (miR-4728-5p) that downregulates PADI2, a novel RA risk gene considered as a promising therapeutic target (rs761426, adjusted P = 2.3 × 10(-9)). Our study indicated the significant impact of miRNA-target gene networks on the genetics of human complex traits, and provided resources which should contribute to drug discovery and nucleic acid medicine. DOI: 10.1038/srep22223 PMCID: PMC4772006 PMID: 26927695 [Indexed for MEDLINE] 605. Drug Des Devel Ther. 2015 Apr 15;9:2171-8. doi: 10.2147/DDDT.S78537. eCollection 2015. Clinicopathological significance and potential drug targeting of CDH1 in lung cancer: a meta-analysis and literature review. Yu Q(1), Guo Q(2), Chen L(3), Liu S(1). Author information: (1)Shandong Provincial Key Laboratory of Mental Disorders, Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Beijing, People's Republic of China. (2)Respiratory Medicine, Shandong Cancer Hospital, Jinan, Beijing, People's Republic of China. (3)Department of Respiratory Diseases, People's Liberation Army General Hospital, Beijing, People's Republic of China. BACKGROUND: CDH1 is a protein encoded by the CDH1 gene in humans. Mutations in this gene are linked with several types of cancer. Loss of CDH1 function contributes to the progression of cancer by increasing proliferation, invasion, and/or metastasis. However, the association between and clinicopathological significance of CDH1 promoter methylation and lung cancer remains unclear. In this study, we systematically reviewed the studies of CDH1 promoter methylation and lung cancer, and evaluated the association between CDH1 promoter methylation and lung cancer using meta-analysis methods. METHODS: A comprehensive search of the PubMed and Embase databases was performed up to July 2014. The methodological quality of the studies was also evaluated. The data were extracted and assessed by two reviewers independently. Analyses of pooled data were performed. Odds ratios (ORs) were calculated and summarized. RESULTS: Finally, an analysis of 866 patients with non-small cell lung cancer from 13 eligible studies was performed. The CDH1 methylation level in the cancer group was significantly higher than in the controls (OR 3.89, 95% confidence interval [CI] 2.87-5.27, P<0.00001). However, there were no correlations between CDH1 promoter methylation and clinicopathological characteristics (sex status, OR 0.78, 95% CI 0.41-1.50, P=0.46; smoking history, OR 0.97, 95% CI 0.53-1.79, P=0.93; pathological type, OR 0.97, 95% CI 0.59-1.60, P=0.91; clinical staging, OR 1.48, 95% CI 0.81-2.68, P=0.2; lymph node metastasis, OR 0.68, 95% CI 0.13-3.63, P=0.65; or differentiation degree, OR 1.01, 95% CI 0.34-3.02, P=0.99). CONCLUSION: The results of this meta-analysis suggest that CDH1 methylation is associated with an increased risk of lung cancer. CDH1 hypermethylation, which induces inactivation of the CDH1 gene, plays an important role in carcinogenesis and may serve as a potential drug target in lung cancer. However, CDH1 methylation does not correlate with other factors, such as smoking history, clinical stage, pathological type, sex status, lymph node metastasis, or degree of differentiation. DOI: 10.2147/DDDT.S78537 PMCID: PMC4404966 PMID: 25931811 [Indexed for MEDLINE] 606. J Biomol Struct Dyn. 2013 Dec;31(12):1358-69. doi: 10.1080/07391102.2012.736773. Epub 2012 Nov 12. A possible strategy against head and neck cancer: in silico investigation of three-in-one inhibitors. Tsou YA(1), Chen KC, Chang SS, Wen YR, Chen CY. Author information: (1)a Laboratory of Computational and Systems Biology , China Medical University , Taichung , 40402 , Taiwan . Overexpression of epidermal growth factor receptor (EGFR), Her2, and uroporphyrinogen decarboxylase (UROD) occurs in a variety of malignant tumor tissues. UROD has potential to modulate tumor response of radiotherapy for head and neck cancer, and EGFR and Her2 are common drug targets for the treatment of head and neck cancer. This study attempts to find a possible lead compound backbone from TCM Database@Taiwan ( http://tcm.cmu.edu.tw/ ) for EGFR, Her2, and UROD proteins against head and neck cancer using computational techniques. Possible traditional Chinese medicine (TCM) lead compounds had potential binding affinities with EGFR, Her2, and UROD proteins. The candidates formed stable interactions with residues Arg803, Thr854 in EGFR, residues Thr862, Asp863 in Her2 protein, and residues Arg37, Arg41 in UROD protein, which are key residues in the binding or catalytic domain of EGFR, Her2, and UROD proteins. Thus, the TCM candidates indicated a possible molecule backbone for evolving potential inhibitors for three drug target proteins against head and neck cancer. DOI: 10.1080/07391102.2012.736773 PMID: 23140436 [Indexed for MEDLINE] 607. PLoS Negl Trop Dis. 2011 May;5(5):e1168. doi: 10.1371/journal.pntd.0001168. Epub 2011 May 24. Characterization of the phytochelatin synthase of Schistosoma mansoni. Ray D(1), Williams DL. Author information: (1)Department of Biological Sciences, Illinois State University, Normal, Illinois, USA. Treatment for schistosomiasis, which is responsible for more than 280,000 deaths annually, depends exclusively on the use of praziquantel. Millions of people are treated annually with praziquantel and drug resistant parasites are likely to evolve. In order to identify novel drug targets the Schistosoma mansoni sequence databases were queried for proteins involved in glutathione metabolism. One potential target identified was phytochelatin synthase (PCS). Phytochelatins are oligopeptides synthesized enzymatically from glutathione by PCS that sequester toxic heavy metals in many organisms. However, humans do not have a PCS gene and do not synthesize phytochelatins. In this study we have characterized the PCS of S. mansoni (SmPCS). The conserved catalytic triad of cysteine-histidine-aspartate found in PCS proteins and cysteine proteases is also found in SmPCS, as are several cysteine residues thought to be involved in heavy metal binding and enzyme activation. The SmPCS open reading frame is considerably extended at both the N- and C-termini compared to PCS from other organisms. Multiple PCS transcripts are produced from the single encoded gene by alternative splicing, resulting in both mitochondrial and cytoplasmic protein variants. Expression of SmPCS in yeast increased cadmium tolerance from less than 50 µM to more than 1,000 µM. We confirmed the function of SmPCS by identifying PCs in yeast cell extracts using HPLC-mass spectrometry. SmPCS was found to be expressed in all mammalian stages of worm development investigated. Increases in SmPCS expression were seen in ex vivo worms cultured in the presence of iron, copper, cadmium, or zinc. Collectively, these results indicate that SmPCS plays an important role in schistosome response to heavy metals and that PCS is a potential drug target for schistosomiasis treatment. This is the first characterization of a PCS from a parasitic organism. DOI: 10.1371/journal.pntd.0001168 PMCID: PMC3101182 PMID: 21629724 [Indexed for MEDLINE] 608. PLoS Negl Trop Dis. 2017 Sep 15;11(9):e0005891. doi: 10.1371/journal.pntd.0005891. eCollection 2017 Sep. The single cyclic nucleotide-specific phosphodiesterase of the intestinal parasite Giardia lamblia represents a potential drug target. Kunz S(1)(2), Balmer V(1), Sterk GJ(2), Pollastri MP(3), Leurs R(2), Müller N(1), Hemphill A(1), Spycher C(1). Author information: (1)Institute of Parasitology, Vetsuisse Faculty, University of Bern, Bern, Switzerland. (2)Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute of Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. (3)Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts, United States of America. BACKGROUND: Giardiasis is an intestinal infection correlated with poverty and poor drinking water quality, and treatment options are limited. According to the Center for Disease Control and Prevention, Giardia infections afflict nearly 33% of people in developing countries, and 2% of the adult population in the developed world. This study describes the single cyclic nucleotide-specific phosphodiesterase (PDE) of G. lamblia and assesses PDE inhibitors as a new generation of anti-giardial drugs. METHODS: An extensive search of the Giardia genome database identified a single gene coding for a class I PDE, GlPDE. The predicted protein sequence was analyzed in-silico to characterize its domain structure and catalytic domain. Enzymatic activity of GlPDE was established by complementation of a PDE-deficient Saccharomyces cerevisiae strain, and enzyme kinetics were characterized in soluble yeast lysates. The potency of known PDE inhibitors was tested against the activity of recombinant GlPDE expressed in yeast and against proliferating Giardia trophozoites. Finally, the localization of epitope-tagged and ectopically expressed GlPDE in Giardia cells was investigated. RESULTS: Giardia encodes a class I PDE. Catalytically important residues are fully conserved between GlPDE and human PDEs, but sequence differences between their catalytic domains suggest that designing Giardia-specific inhibitors is feasible. Recombinant GlPDE hydrolyzes cAMP with a Km of 408 μM, and cGMP is not accepted as a substrate. A number of drugs exhibit a high degree of correlation between their potency against the recombinant enzyme and their inhibition of trophozoite proliferation in culture. Epitope-tagged GlPDE localizes as dots in a pattern reminiscent of mitosomes and to the perinuclear region in Giardia. CONCLUSIONS: Our data strongly suggest that inhibition of G. lamblia PDE activity leads to a profound inhibition of parasite proliferation and that GlPDE is a promising target for developing novel anti-giardial drugs. DOI: 10.1371/journal.pntd.0005891 PMCID: PMC5617230 PMID: 28915270 [Indexed for MEDLINE] 609. Drug Des Devel Ther. 2017 Mar 2;11:563-574. doi: 10.2147/DDDT.S119930. eCollection 2017. In silico discovery and in vitro activity of inhibitors against Mycobacterium tuberculosis 7,8-diaminopelargonic acid synthase (Mtb BioA). Billones JB(1), Carrillo MC(2), Organo VG(2), Sy JB(2), Clavio NA(2), Macalino SJ(2), Emnacen IA(2), Lee AP(2), Ko PK(2), Concepcion GP(3). Author information: (1)OVPAA-EIDR Program, "Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis in the Philippines", Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Manila, Philippines; Institute of Pharmaceutical Sciences, National Institutes of Health, University of the Philippines Manila, Manila, Philippines. (2)OVPAA-EIDR Program, "Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis in the Philippines", Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Manila, Philippines. (3)Marine Science Institute, College of Science, University of the Philippines Diliman, Quezon City, Philippines. Computer-aided drug discovery and development approaches such as virtual screening, molecular docking, and in silico drug property calculations have been utilized in this effort to discover new lead compounds against tuberculosis. The enzyme 7,8-diaminopelargonic acid aminotransferase (BioA) in Mycobacterium tuberculosis (Mtb), primarily involved in the lipid biosynthesis pathway, was chosen as the drug target due to the fact that humans are not capable of synthesizing biotin endogenously. The computational screening of 4.5 million compounds from the Enamine REAL database has ultimately yielded 45 high-scoring, high-affinity compounds with desirable in silico absorption, distribution, metabolism, excretion, and toxicity properties. Seventeen of the 45 compounds were subjected to bioactivity validation using the resazurin microtiter assay. Among the 4 actives, compound 7 ((Z)-N-(2-isopropoxyphenyl)-2-oxo-2-((3-(trifluoromethyl)cyclohexyl)amino)acetimi dic acid) displayed inhibitory activity up to 83% at 10 μg/mL concentration against the growth of the Mtb H37Ra strain. DOI: 10.2147/DDDT.S119930 PMCID: PMC5338852 PMID: 28280303 [Indexed for MEDLINE] Conflict of interest statement: Disclosure The authors report no conflicts of interest in this work. 610. 11C-Labeled isoniazid. Shan L(1). In: Molecular Imaging and Contrast Agent Database (MICAD) [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2004-2013. 2010 Aug 02. Author information: (1)National Center for Biotechnology Information, NLM, NIH Isoniazid, also known as isonicotinylhydrazine (INH), is a first-line drug used in the prevention and treatment of tuberculosis (TB) (1, 2). INH is a prodrug that is activated by the catalase-peroxidase enzyme KatG of the mycobacteria. The activation process leads to the formation of the isonicotinic acyl-NADH complex. Subsequent binding of the complex with the enoyl-acyl carrier protein reductase InhA results in the inhibition of mycolic acid synthesis, which is essential for the wall of mycobacteria (3). INH is bactericidal to rapidly dividing mycobacteria but is bacteriostatic to slow-growing mycobacteria. INH labeled with 11C ([11C]INH) has been generated by Liu et al. for in vivo and real-time analysis of the INH pharmacokinetics (PK) and biodistribution with positron emission tomography (PET) (1). The half-life of 11C is 20.4 min, allowing for a ~60 min window to observe the PK. The PK and biodistribution of a novel drug are traditionally determined with blood and tissue sampling and/or autoradiography. Despite high workload and huge investment in drug development, only 8% of the drugs entering clinical trials today reach the market, as estimated by the U.S. Food and Drug Administration. One main reason for this attrition is insufficient exploration of the in vivo drug–target interaction (1). Traditional methods are inadequate to answer questions such as whether a drug reaches the target, how the drug interacts with its targets, and how the drug modifies the diseases. Because of the high resolution and sensitivity of newly developed imaging techniques, investigators have become increasingly interested in addressing these issues (4, 5). In the case of PET imaging, most small molecules can now be efficiently labeled with 11C or with 18F at >37 GBq/µmol (1 Ci/μmol), and they can be detected with PET in the nanomolar to picomolar concentration range (6-8). Consequently, a sufficient signal for imaging can be obtained even though the total amount of a radiotracer administered systemically is extremely low (known as microdosing, typically <1 μg for humans). Microdosing is particularly valuable for evaluating tissue exposure in the early phase of drug development when the full-range toxicology is not yet available (9, 10). Increasing evidence has demonstrated the efficiency of PET imaging in obtaining quantitative information on drug PK and distribution in various tissues including brain; confirming drug binding with targets and elucidating the relationship between occupancy and target expression/function in vivo; assessing drug passage across the blood–brain barrier (BBB) and ensuring sufficient exposure to brain for central nervous system drugs; and dissecting the modifying effects of drugs on diseases (4, 6, 7). The current treatment regime for drug-sensitive TB involves the use of rifampicin (RIF), INH, pyrazinamide (PZA), and ethambutol or streptomycin for two months, followed by four months of continued dosing with INH and RIF (11, 12). This regime is primarily based on PK studies in serum and on efficacy of treatment. The efficacy of each drug for different types of TB such as brain TB and the drug distribution in each compartment of an organ are not well understood. To provide direct insights into these drugs, Liu et al. labeled INH, RIF, and PZA with 11C and used PET to investigate their PK and biodistribution in baboons (1). Liu et al. found that the organ distribution and BBB penetration of each drug differed greatly. The ability of [11C]INH to penetrate the BBB was lower than that of PZA but higher than that of RIF (PZA > INH > RIF). The INH concentrations in the lungs and brain were ten times higher than the INH minimum inhibitory concentration (MIC) value against TB, supporting the use of INH for treating TB infections in the lungs and brain. This chapter summarizes the data obtained by Liu et al. regarding [11C]INH. The data obtained with regard to [11C]RIF and [11C]PZA are described in the MICAD chapters on [11C]RIF and [11C]PZA, respectively. PMID: 20945562 611. 11C-Labeled pyrazinamide. Shan L(1). In: Molecular Imaging and Contrast Agent Database (MICAD) [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2004-2013. 2010 Aug 02. Author information: (1)National Center for Biotechnology Information, NLM, NIH Pyrazinamide (PZA) is a prodrug used in the treatment of Mycobacterium tuberculosis (MTB) infection. PZA is converted to its active form, pyrazinoic acid, by the pyrazinamidase of MTB at the acidic site of infection. Pyrazinoic acid inhibits the type 1 fatty acid synthases of the bacilli. Accumulation of pyrazinoic acid is also thought to disrupt the membrane potential and interfere with the energy production necessary for survival of MTB. Mutations of the pyrazinamidase gene are responsible for the development of PZA resistance. PZA is largely bacteriostatic. PZA labeled with 11C ([11C]PZA) has been generated by Liu et al. for in vivo and real-time analysis of the PZA pharmacokinetics (PK) and biodistribution with positron emission tomography (PET) (1). The half-life of 11C is 20.4 min. The PK and biodistribution of a novel drug are traditionally determined with blood and tissue sampling and/or autoradiography. Despite high workload and huge investment in drug development, only 8% of the drugs entering clinical trials today reach the market, as estimated by the U.S. Food and Drug Administration. One main reason for this attrition is insufficient exploration of the in vivo drug–target interaction (1). Traditional methods are inadequate to answer questions such as whether a drug reaches the target, how the drug interacts with its targets, and how the drug modifies the diseases. Because of the high resolution and sensitivity of newly developed imaging techniques, investigators have become increasingly interested in addressing these issues (2, 3). In the case of PET imaging, most small molecules can now be efficiently labeled with 11C or with 18F at >37 GBq/µmol (1 Ci/μmol), and they can be detected with PET in the nanomolar to picomolar concentration range (4-6). Consequently, a sufficient signal for imaging can be obtained even though the total amount of a radiotracer administered systemically is extremely low (known as microdosing, typically <1 μg for humans). Microdosing is particularly valuable for evaluating tissue exposure in the early phase of drug development when the full-range toxicology is not yet available (7, 8). Increasing evidence has demonstrated the efficiency of PET imaging in obtaining quantitative information on drug PK and distribution in various tissues including brain; confirming drug binding with targets and elucidating the relationship between occupancy and target expression/function in vivo; assessing drug passage across the blood–brain barrier (BBB) and ensuring sufficient exposure to brain for central nervous system drugs; and dissecting the modifying effects of drugs on diseases (2, 4, 5). The current treatment regime for drug-sensitive TB involves the use of rifampicin (RIF), isoniazid (INH), PZA, and ethambutol or streptomycin for two months, followed by four months of continued dosing with INH and RIF (9, 10). This regime is primarily based on PK studies in serum and on efficacy of treatment. The efficacy of each drug for different types of TB such as brain TB and the drug distribution in each compartment of an organ are not well understood. To provide direct insights into these drugs, Liu et al. labeled INH, RIF, and PZA with 11C and used PET to investigate their PK and biodistribution in baboons (1). Liu et al. found that the organ distribution and BBB penetration of each drug differed greatly. [11C]PZA can easily penetrate the BBB (PZA > INH > RIF); however, the PZA concentrations in the cerebrospinal fluid and brain were only slightly higher than its minimum inhibitory concentration (MIC) value against TB. This chapter summarizes the data obtained by Liu et al. regarding [11C]PZA. The data obtained with regard to [11C]RIF and [11C]INH are described in the MICAD chapters on [11C]RIF and [11C]INH, respectively. PMID: 20945558 612. Mol Microbiol. 2002 May;44(4):989-99. The Pneumocystis carinii drug target S-adenosyl-L-methionine:sterol C-24 methyl transferase has a unique substrate preference. Kaneshiro ES(1), Rosenfeld JA, Basselin-Eiweida M, Stringer JR, Keely SP, Smulian AG, Giner JL. Author information: (1)Department of Biological Sciences, University of Cincinnati, Cincinnati, OH 45221-0006, USA. Edna.Kaneshiro@uc.edu Pneumocystis is an opportunistic pathogen that can cause pneumonitis in immunodeficient people such as AIDS patients. Pneumocystis remains difficult to study in the absence of culture methods for luxuriant growth. Recombinant protein technology now makes it possible to avoid some major obstacles. The P. carinii expressed sequence tag (EST) database contains 11 entries of a sequence encoding a protein homologous to S-adenosyl-L-methionine (SAM):C-24 sterol methyl transferase (SMT), suggesting high activity of this enzyme in the organism. We sequenced the erg6 cDNA, identified the putative peptide motifs for the sterol and SAM binding sites in the deduced amino acid sequence and expressed the protein in Escherichia coli. Unlike SAM:SMT from other organisms, the P. carinii enzyme had higher affinities for lanosterol and 24-methylenelanosterol than for zymosterol, the preferred substrate in other fungi. Cycloartenol was not a productive substrate. With lanosterol and 24-methylenelanosterol as substrates, the major reaction products were 24-methylenelanosterol and pneumocysterol respectively. Thus, the P. carinii SAM:SMT catalysed the transfer of both the first and the second methyl groups to the sterol C-24 position, and the substrate preference was found to be a unique property of the P. carinii SAM:SMT. These observations, together with the absence of SAM:SMT among mammals, further support the identification of sterol C-24 alkylation reactions as excellent targets for the development of drugs specifically directed against this pathogen. PMID: 12010494 [Indexed for MEDLINE] 613. Cancer Causes Control. 2018 Jun 18. doi: 10.1007/s10552-018-1049-5. [Epub ahead of print] Regular aspirin use and gene expression profiles in prostate cancer patients. Stopsack KH(1)(2), Ebot EM(3), Downer MK(3)(4), Gerke TA(5), Rider JR(6), Kantoff PW(7), Mucci LA(3)(4). Author information: (1)Department of Internal Medicine, Mayo Clinic, Rochester, MN, 55905, USA. stopsack@mskcc.org. (2)Department of Medicine, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA. stopsack@mskcc.org. (3)Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. (4)Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA. (5)Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, 33612, USA. (6)Department of Epidemiology, Boston University School of Public Health, Boston, MA, 02118, USA. (7)Department of Medicine, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA. PURPOSE: Pharmacoepidemiology studies suggest prognostic benefits of aspirin in prostate cancer. We hypothesized that aspirin induces transcriptional changes in tumors or normal prostate tissue. METHODS: We analyzed the prostatic transcriptome from men diagnosed with prostate cancer during follow-up of the Physicians' Health Study 1 (PHS, n = 149), initially a randomized controlled trial of aspirin. Aspirin target genes were identified through systematic literature review and a drug target database. We compared target gene expression according to regular aspirin use at cancer diagnosis and used whole-transcriptome gene set enrichment analysis to identify gene sets associated with aspirin use. Results were validated in the Health Professionals Follow-up Study (HPFS, n = 254) and in Connectivity Map. RESULTS: Of 12 target genes identified from prior studies and 540 genes from the drug target database, none were associated with aspirin use. Twenty-one gene sets were enriched in tumor tissue of aspirin users, 18 of which were clustered around ribosome function and translation. These gene sets were associated with exposure to cyclooxygenase inhibitors in Connectivity Map. Their association with cancer prognosis was U-shaped in both cohorts. No gene sets were enriched in normal tissue. In HPFS, neither the target genes nor the gene sets were associated with aspirin use. CONCLUSIONS: Regular aspirin use may affect ribosome function in prostate tumors. Other putative target genes had similar expression in tumors from aspirin users and non-users. If results are corroborated by experimental studies, a potential benefit of aspirin may be limited to a subset of prostate cancer patients. DOI: 10.1007/s10552-018-1049-5 PMCID: PMC6298857 [Available on 2019-12-18] PMID: 29915914 614. J Exp Clin Cancer Res. 2018 Sep 6;37(1):220. doi: 10.1186/s13046-018-0894-0. FOXC1 induces cancer stem cell-like properties through upregulation of beta-catenin in NSCLC. Cao S(1), Wang Z(1), Gao X(1), He W(1), Cai Y(1), Chen H(2)(3), Xu R(4)(5). Author information: (1)Department of Pharmacology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China. (2)Department of Pharmacology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China. chenhuitj@mails.tjmu.edu.cn. (3)The Key Laboratory for Drug Target Researches and Pharmacodynamic Evaluation of Hubei Province, Wuhan, 430030, Hubei, China. chenhuitj@mails.tjmu.edu.cn. (4)Department of Pharmacology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China. rongxu@hust.edu.cn. (5)The Key Laboratory for Drug Target Researches and Pharmacodynamic Evaluation of Hubei Province, Wuhan, 430030, Hubei, China. rongxu@hust.edu.cn. BACKGROUND: Accumulating evidence suggests that cancer stem cells (CSCs) play a critical role in tumor initiation, progression and therapy, and recent studies have indicated that Forkhead box C1 (FOXC1) is strongly associated with CSCs. This study investigates the regulatory effects of FOXC1 on CSC-like properties in non-small cell lung cancer (NSCLC). METHODS: We analyzed FOXC1 expression in NSCLC using the Cancer Genome Atlas (TCGA) database on UALCANC and performed survival analyses of NSCLC patients on Human Protein Atlas. CSC-like properties were analyzed based on CSC marker-positive cell population, self-renewal ability, stemness-related gene expression, tumorigenicity and drug resistance. The percentage of CD133+ cells was analyzed by flow cytometric analysis. Self-renewal ability was detected by sphere-formation analysis. Real-time PCR, western blotting and immunohistochemical staining were employed to detect mRNA and protein levels. Tumorigenicity was determined based on a xenograft formation assay, and effects of FOXC1 on drug resistance were assessed by cell viability and apoptosis assays. Luciferase reporter and chromatin immunoprecipitation (ChIP) assays were used to investigate the binding of FOXC1 to beta-catenin promoter. RESULTS: FOXC1 expression was found to be elevated in NSCLC tissues and negatively correlated with patient survival. FOXC1 knockdown reduced CD133+ cell percentage, suppressed self-renewal ability, decreased expression of stemness-related genes (Oct4, NANOG, SOX2 and ABCG2) and inhibited NSCLC cell tumorigenicity in vivo. Moreover, FOXC1 knockdown increased cisplatin and docetaxel sensitivity and reduced gefitinib resistance, whereas FOXC1 overexpression enhanced CSC-like properties. Luciferase reporter and ChIP assays showed beta-catenin to be a direct transcriptional target of FOXC1. Furthermore, overexpression of beta-catenin reversed the CSC-like property inhibition induced by FOXC1 knockdown, and knockdown of beta-catenin attenuated the CSC-like properties induced by FOXC1 overexpression. CONCLUSIONS: This study demonstrates that FOXC1 induces CSC-like properties in NSCLC by promoting beta-catenin expression. The findings indicate that FOXC1 is a potential molecular target for anti-CSC-based therapies in NSCLC. DOI: 10.1186/s13046-018-0894-0 PMCID: PMC6127900 PMID: 30189871 [Indexed for MEDLINE] 615. J Ethnopharmacol. 2018 Jan 10;210:287-295. doi: 10.1016/j.jep.2017.08.041. Epub 2017 Sep 4. Network pharmacology analysis of the anti-cancer pharmacological mechanisms of Ganoderma lucidum extract with experimental support using Hepa1-6-bearing C57 BL/6 mice. Zhao RL(1), He YM(2). Author information: (1)School of Basic Medicine College, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China. Electronic address: 1954875843@qq.com. (2)School of Basic Medicine College, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China. Electronic address: heyumin109109@sina.com. ETHNOPHARMACOLOGICAL RELEVANCE: Ganoderma lucidum (GL) is an oriental medical fungus, which was used to prevent and treat many diseases. Previously, the effective compounds of Ganoderma lucidum extract (GLE) were extracted from two kinds of GL, [Ganoderma lucidum (Leyss. Ex Fr.) Karst.] and [Ganoderma sinense Zhao, Xu et Zhang], which have been used for adjuvant anti-cancer clinical therapy for more than 20 years. However, its concrete active compounds and its regulation mechanisms on tumor are unclear. AIM OF THE STUDY: In this study, we aimed to identify the main active compounds from GLE and to investigate its anti-cancer mechanisms via drug-target biological network construction and prediction. MATERIALS AND METHODS: The main active compounds of GLE were identified by HPLC, EI-MS and NMR, and the compounds related targets were predicted using docking program. To investigate the functions of GL holistically, the active compounds of GL and related targets were predicted based on four public databases. Subsequently, the Identified-Compound-Target network and Predicted-Compound-Target network were constructed respectively, and they were overlapped to detect the hub potential targets in both networks. Furthermore, the qRT-PCR and western-blot assays were used to validate the expression levels of target genes in GLE treated Hepa1-6-bearing C57 BL/6 mice. RESULTS: In our work, 12 active compounds of GLE were identified, including Ganoderic acid A, Ganoderenic acid A, Ganoderic acid B, Ganoderic acid H, Ganoderic acid C2, Ganoderenic acid D, Ganoderic acid D, Ganoderenic acid G, Ganoderic acid Y, Kaemferol, Genistein and Ergosterol. Using the docking program, 20 targets were mapped to 12 compounds of GLE. Furthermore, 122 effective active compounds of GL and 116 targets were holistically predicted using public databases. Compare with the Identified-Compound-Target network and Predicted-Compound-Target network, 6 hub targets were screened, including AR, CHRM2, ESR1, NR3C1, NR3C2 and PGR, which was considered as potential markers and might play important roles in the process of GLE treatment. GLE effectively inhibited tumor growth in Hepa1-6-bearing C57 BL/6 mice. Finally, consistent with the results of qRT-PCR data, the results of western-blot assay demonstrated the expression levels of PGR and ESR1 were up-regulated, as well as the expression levels of NR3C2 and AR were down-regulated, while the change of NR3C1 and CHRM2 had no statistical significance. CONCLUSIONS: The results indicated that these 4 hub target genes, including NR3C2, AR, ESR1 and PGR, might act as potential markers to evaluate the curative effect of GLE treatment in tumor. And, the combined data provide preliminary study of the pharmacological mechanisms of GLE, which may be a promising potential therapeutic and chemopreventative candidate for anti-cancer. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved. DOI: 10.1016/j.jep.2017.08.041 PMID: 28882624 [Indexed for MEDLINE] 616. Int J Mol Sci. 2016 Mar 16;17(3):389. doi: 10.3390/ijms17030389. Discovery of Dual ETA/ETB Receptor Antagonists from Traditional Chinese Herbs through in Silico and in Vitro Screening. Wang X(1), Zhang Y(2), Liu Q(3), Ai Z(4), Zhang Y(5), Xiang Y(6), Qiao Y(7). Author information: (1)Beijing Key Lab of Traditional Chinese Medicine (TCM) Collateral Disease Theory Research, School of Traditional Chinese Medicine, Capital Medical University, Beijing 100069, China. wangxing@ccmu.edu.cn. (2)Key Laboratory of TCM-Information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100102, China. zhangyuxinwjzy@163.com. (3)Key Laboratory of TCM-Information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100102, China. sdliuqing17@tom.com. (4)Beijing Key Lab of Traditional Chinese Medicine (TCM) Collateral Disease Theory Research, School of Traditional Chinese Medicine, Capital Medical University, Beijing 100069, China. azxccmu@163.com. (5)Key Laboratory of TCM-Information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100102, China. collean_zhang@163.com. (6)Department of Chemistry, Capital Normal University, Beijing 100069, China. cnuxiangyh@163.com. (7)Key Laboratory of TCM-Information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100102, China. yjqiao@bucm.edu.cn. Endothelin-1 receptors (ETAR and ETBR) act as a pivotal regulator in the biological effects of ET-1 and represent a potential drug target for the treatment of multiple cardiovascular diseases. The purpose of the study is to discover dual ETA/ETB receptor antagonists from traditional Chinese herbs. Ligand- and structure-based virtual screening was performed to screen an in-house database of traditional Chinese herbs, followed by a series of in vitro bioassay evaluation. Aristolochic acid A (AAA) was first confirmed to be a dual ETA/ETB receptor antagonist based intracellular calcium influx assay and impedance-based assay. Dose-response curves showed that AAA can block both ETAR and ETBR with IC50 of 7.91 and 7.40 μM, respectively. Target specificity and cytotoxicity bioassay proved that AAA is a selective dual ETA/ETB receptor antagonist and has no significant cytotoxicity on HEK293/ETAR and HEK293/ETBR cells within 24 h. It is a feasible and effective approach to discover bioactive compounds from traditional Chinese herbs using in silico screening combined with in vitro bioassay evaluation. The structural characteristic of AAA for its activity was especially interpreted, which could provide valuable reference for the further structural modification of AAA. DOI: 10.3390/ijms17030389 PMCID: PMC4813245 PMID: 26999111 [Indexed for MEDLINE] 617. Proc Natl Acad Sci U S A. 2016 Mar 29;113(13):3603-8. doi: 10.1073/pnas.1521251113. Epub 2016 Mar 15. Exploring the surfaceome of Ewing sarcoma identifies a new and unique therapeutic target. Town J(1), Pais H(1), Harrison S(2), Stead LF(2), Bataille C(3), Bunjobpol W(1), Zhang J(1), Rabbitts TH(4). Author information: (1)Medical Research Council Molecular Haematology Unit, Weatherall Institute for Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, United Kingdom; (2)Leeds Institute of Molecular Medicine, St. James's Hospital, Leeds LS9 7TF, United Kingdom; (3)Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3AT, United Kingdom. (4)Medical Research Council Molecular Haematology Unit, Weatherall Institute for Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, United Kingdom; terence.rabbitts@imm.ox.ac.uk. The cell surface proteome of tumors mediates the interface between the transformed cells and the general microenvironment, including interactions with stromal cells in the tumor niche and immune cells such as T cells. In addition, the cell surface proteome of individual cancers defines biomarkers for that tumor type and potential proteins that can be the target of antibody-mediated therapy. We have used next-generation deep RNA sequencing (RNA-seq) coupled to an in-house database of genes encoding cell surface proteins (herein referred to as the surfaceome) as a tool to define a cell surface proteome of Ewing sarcoma compared with progenitor mesenchymal stem cells. This subtractive RNA-seq analysis revealed a specific surfaceome of Ewing and showed unexpectedly that the leucine-rich repeat and Ig domain protein 1 (LINGO1) is expressed in over 90% of Ewing sarcoma tumors, but not expressed in any other somatic tissue apart from the brain. We found that the LINGO1 protein acts as a gateway protein internalizing into the tumor cells when engaged by antibody and can carry antibody conjugated with drugs to kill Ewing sarcoma cells. Therefore, LINGO1 is a new, unique, and specific biomarker and drug target for the treatment of Ewing sarcoma. DOI: 10.1073/pnas.1521251113 PMCID: PMC4822608 PMID: 26979953 [Indexed for MEDLINE] 618. Int J Mol Sci. 2016 Jul 15;17(7). pii: E1141. doi: 10.3390/ijms17071141. Ligand and Structure-Based Approaches for the Identification of Peptide Deformylase Inhibitors as Antibacterial Drugs. Gao J(1)(2), Liang L(3), Zhu Y(4), Qiu S(5), Wang T(6), Zhang L(7). Author information: (1)Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou 221004, Jiangsu, China. gaojian@xzhmu.edu.cn. (2)Jiangsu Center for the Collaboration and Innovation of Cancer Biotherapy, Xuzhou Medical University, Xuzhou 221004, Jiangsu, China. gaojian@xzhmu.edu.cn. (3)Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou 221004, Jiangsu, China. 18852143485@163.com. (4)Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou 221004, Jiangsu, China. 18151865223@163.com. (5)Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou 221004, Jiangsu, China. neoevens@hotmail.com. (6)Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou 221004, Jiangsu, China. lswangtao@163.com. (7)Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou 221004, Jiangsu, China. zhamgling1999@163.com. Peptide deformylase (PDF) is a metalloprotease catalyzing the removal of a formyl group from newly synthesized proteins, which makes it an important antibacterial drug target. Given the importance of PDF inhibitors like actinonin in antibacterial drug discovery, several reported potent PDF inhibitors were used to develop pharmacophore models using the Galahad module of Sybyl 7.1 software. Generated pharmacophore models were composed of two donor atom centers, four acceptor atom centers and two hydrophobic groups. Model-1 was screened against the Zinc database and several compounds were retrieved as hits. Compounds with Qfit values of more than 60 were employed to perform a molecular docking study with the receptor Escherichia coli PDF, then compounds with docking score values of more than 6 were used to predict the in silico pharmacokinetic and toxicity risk via OSIRIS property explorer. Two known PDF inhibitors were also used to perform a molecular docking study with E. coli PDF as reference molecules. The results of the molecular docking study were validated by reproducing the crystal structure of actinonin. Molecular docking and in silico pharmacokinetic and toxicity prediction studies suggested that ZINC08740166 has a relatively high docking score of 7.44 and a drug score of 0.78. DOI: 10.3390/ijms17071141 PMCID: PMC4964514 PMID: 27428963 [Indexed for MEDLINE] 619. Mol Immunol. 2015 May;65(1):189-204. doi: 10.1016/j.molimm.2014.12.013. Epub 2015 Feb 14. A comprehensive immunoinformatics and target site study revealed the corner-stone toward Chikungunya virus treatment. Hasan MA(1), Khan MA(2), Datta A(3), Mazumder MH(3), Hossain MU(2). Author information: (1)Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong-4331, Bangladesh. Electronic address: anayet_johny@yahoo.com. (2)Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, Bangladesh. (3)Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong-4331, Bangladesh. Recent concerning facts of Chikungunya virus (CHIKV); a Togaviridae family alphavirus has proved this as a worldwide emerging threat which causes Chikungunya fever and devitalizing arthritis. Despite severe outbreaks and lack of antiviral drug, a mere progress has been made regarding to an epitope-based vaccine designed for CHIKV. In this study, we aimed to design an epitope-based vaccine that can trigger a significant immune response as well as to prognosticate inhibitor that can bind with potential drug target sites by using various immunoinformatics and docking simulation tools. Initially, whole proteome of CHIKV was retrieved from database and perused to identify the most immunogenic protein. Structural properties of the selected protein were analyzed. The capacity to induce both humoral and cell-mediated immunity by T cell and B cell were checked for the selected protein. The peptide region spanning 9 amino acids from 397 to 405 and the sequence YYYELYPTM were found as the most potential B cell and T cell epitopes respectively. This peptide could interact with as many as 19 HLAs and showed high population coverage ranging from 69.50% to 84.94%. By using in silico docking techniques the epitope was further assessed for binding against HLA molecules to verify the binding cleft interaction. In addition with this, the allergenicity of the epitopes was also evaluated. In the post therapeutic strategy, three dimensional structure was predicted along with validation and verification that resulted in molecular docking study to identify the potential drug binding sites and suitable therapeutic inhibitor against targeted protein. Finally, pharmacophore study was also performed in quest of seeing potent drug activity. However, this computational epitope-based peptide vaccine designing and target site prediction against CHIKV opens up a new horizon which may be the prospective way in Chikungunya virus research; the results require validation by in vitro and in vivo experiments. Copyright © 2014 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.molimm.2014.12.013 PMID: 25682054 [Indexed for MEDLINE] 620. Biomed Res Int. 2014;2014:873010. doi: 10.1155/2014/873010. Epub 2014 Jul 1. Potential smoothened inhibitor from traditional Chinese medicine against the disease of diabetes, obesity, and cancer. Chen KC(1), Sun MF(2), Chen HY(3), Lee CC(4), Chen CY(5). Author information: (1)School of Pharmacy, China Medical University, Taichung 40402, Taiwan. (2)School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung 40402, Taiwan ; Department of Acupuncture, China Medical University Hospital, Taichung, Taiwan ; Research Center for Chinese Medicine & Acupuncture, China Medical University, Taichung, Taiwan. (3)Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan. (4)School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan. (5)Research Center for Chinese Medicine & Acupuncture, China Medical University, Taichung, Taiwan ; Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan ; School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan ; Human Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan. Nowadays, obesity becomes a serious global problem, which can induce a series of diseases such as type 2 diabetes mellitus, cancer, cardiovascular disease, metabolic syndrome, and stoke. For the mechanisms of diseases, the hedgehog signaling pathway plays an important role in body patterning during embryogenesis. For this reason, smoothened homologue (Smo) protein had been indicated as the drug target. In addition, the small-molecule Smo inhibitor had also been used in oncology clinical trials. To improve drug development of TCM compounds, we aim to investigate the potent lead compounds as Smo inhibitor from the TCM compounds in TCM Database@Taiwan. The top three TCM compounds, precatorine, labiatic acid, and 2,2'-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid), have displayed higher potent binding affinities than the positive control, LY2940680, in the docking simulation. After MD simulations, which can optimize the result of docking simulation and validate the stability of H-bonds between each ligand and Smo protein under dynamic conditions, top three TCM compounds maintain most of interactions with Smo protein, which keep the ligand binding stable in the binding domain. Hence, we propose precatorine, labiatic acid, and 2,2'-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid) as potential lead compounds for further study in drug development process with the Smo protein. DOI: 10.1155/2014/873010 PMCID: PMC4127221 PMID: 25136636 [Indexed for MEDLINE] 621. Rev Esp Salud Publica. 2017 Nov 24;91. pii: e201711043. [Description of postauthorisation observational prospective studies with drugs in the valencian region between 2010 and 2015. Factors associated with their authorisation.] [Article in Spanish; Abstract available in Spanish from the publisher] Grau Rubio MA(1)(2), Gómez-Pajares F(1)(3), Izquierdo María R(1)(4), Zapater Hernández P(1)(5), Fernández Martínez S(1)(6). Author information: (1)Comité Autonómico de Estudios Posautorización Observacionales de Medicamentos de la Comunitat Valenciana (CAEPO). Dirección General de Farmacia y Productos Sanitarios. Conselleria de Sanidad Universal y Salud Pública. (2)CAVIME. Servicio de Prestación Farmacéutica y Dietoterapéutica. Dirección General de Farmacia y Productos Sanitarios. Conselleria de Sanidad Universal y Salud Pública. (3)Servicio de Medicina Preventiva. Hospital Arnau de Vilanova. Valencia. (4)Servicio de Farmacia de Atención Primaria. Departamento La Plana. Vila-real (Castellón). (5)Unidad farmacología Clínica. Hospital general Universitario Alicante. (6)Servicio de Medicina Preventiva. Hospital de Sagunto. Valencia. OBJECTIVE: Postauthorisation observational studies are crucial source of information on drug effectiveness and safety. The objectives of this work were to describe the characteristics of the postauthorisation observational prospective studies (EPA-SP) for which authorisation was requested in the Valencian region, as well as to explore which factors influenced the aforementioned authorisation. METHODS: We performed a retrospective analytical study comprising all the EPA-SP for which authorisation was requested in the Valencian region between 2010 and 2015.The variables associated to the studies (e.g., objectives, studied drug, target disease) as well as those concerning the authorisation process itself (e.g., authorisation status, reason for authorisation refusal, current status of the study) were obtained from relevant databases. The analysis was divided into descriptive and analytical stages. RESULTS: We included a total of 249 studies, out of which 192 (77, 1%) aimed at estimating effectiveness or quality of life. The most frequent types of drugs involved in the studies were the antineoplastic and immunomodulating agents (42%). Only 57% of the studies were granted authorisation, with prescription induction and unusual practice being the most common causes for refusal (40.1% and 39.3%, respectively). The authorisation was linked to the diagnosis (circulatory system OR 10,7, IC95% 2,3 to 49,1), ATC L group (OR 4,2, IC95% 1,9 to 49,1) as well as to whether it had been advocated by the industry (OR 0,5, IC95% 0,3 to 0,9). CONCLUSIONS: Given the importance of having information on effectiveness and safety in usual practice, it is a priority for EPA-SP to be directed towards these aims and to promote independent research. Publisher: Los estudios posautorización observacionales son una fuente de información clave sobre efectividad y seguridad de los medicamentos. Los objetivos del estudio fueron describir las características de los estudios observacionales de seguimiento prospectivo (EPA-SP) que solicitaron autorización en la Comunitat Valenciana (CV) y explorar qué factores se asociaron con su autorización.Se realizó estudio observacional analítico retrospectivo, en el que se incluyeron todos los EPA-SP que solicitaron autorización en la CV desde 2010 hasta 2015. A partir de las bases de datos de la Dirección General de Farmacia y Productos Sanitarios y GESTO se obtuvieron variables referentes al estudio (objetivos, medicamento estudiado, enfermedad diana, etc) y referentes al procedimiento de autorización (autorización, motivo de no autorización y estado actual del estudio). El análisis se organizó en una fase descriptiva y otra analítica mediante regresión logística con variable dependiente la autorización.Fueron incluidos un total de 249 estudios, de los que 192 (77,1%) estaban diseñados para estimar efectividad o calidad de vida. Los medicamentos más frecuentemente estudiados fueron los agentes antineoplásicos e inmunomoduladores (42%). Sólo consiguieron la autorización el 57%, siendo las causas más frecuente de denegación la inducción a la prescripción (40,1%) y la práctica no habitual (39,3%). La autorización se asoció con el diagnóstico (aparato circulatorio OR 10,7, IC95% 2,3 a 49,1), grupo ATC L (OR 4,2, IC95% 1,9 a 49,1) y el haber sido promovidos por la industria (OR 0,5, IC95% 0,3 a 0,9).Dada la importancia de contar con información sobre efectividad y seguridad en la práctica habitual, es prioritario que los EPA-SP sean orientados a estos fines y que se potencie la investigación independiente. PMID: 29171462 [Indexed for MEDLINE] Conflict of interest statement: Disclosure The authors report no conflicts of interest in this work. 622. Front Microbiol. 2016 Jun 28;7:1008. doi: 10.3389/fmicb.2016.01008. eCollection 2016. Dissemination of Novel Antimicrobial Resistance Mechanisms through the Insertion Sequence Mediated Spread of Metabolic Genes. Furi L(1), Haigh R(2), Al Jabri ZJ(2), Morrissey I(3), Ou HY(4), León-Sampedro R(5), Martinez JL(6), Coque TM(7), Oggioni MR(1). Author information: (1)Department of Genetics, University of LeicesterLeicester, UK; Dipartimento di Biotecnologie Mediche, Universita di SienaSiena, Italy. (2)Department of Genetics, University of Leicester Leicester, UK. (3)IHMA Europe Sàrl Epalinges, Switzerland. (4)State Key Laboratory for Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiaotong University Shanghai, China. (5)Departamento de Microbiología, Instituto Ramón y Cajal de Investigación Sanitaria, Hospital Universitario Ramón y CajalMadrid, Spain; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)Spain. (6)Departamento de Biotecnología Microbiana, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones CientíficasMadrid, Spain; Unidad de Resistencia a Antibióticos y Virulencia Bacteriana (RYC-Consejo Superior de Investigaciones Científicas)Madrid, Spain. (7)Departamento de Microbiología, Instituto Ramón y Cajal de Investigación Sanitaria, Hospital Universitario Ramón y CajalMadrid, Spain; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)Spain; Unidad de Resistencia a Antibióticos y Virulencia Bacteriana (RYC-Consejo Superior de Investigaciones Científicas)Madrid, Spain. The widely used biocide triclosan selectively targets FabI, the NADH-dependent trans-2-enoyl-acyl carrier protein (ACP) reductase, which is also an important target for the development of narrow spectrum antibiotics. The analysis of triclosan resistant Staphylococcus aureus isolates had previously shown that in about half of the strains, the mechanism of triclosan resistance consists on the heterologous duplication of the triclosan target gene due to the acquisition of an additional fabI allele derived from Staphylococcus haemolyticus (sh-fabI). In the current work, the genomic sequencing of 10 of these strains allowed the characterization of two novel composite transposons TnSha1 and TnSha2 involved in the spread of sh-fabI. TnSha1 harbors one copy of IS1272, whereas TnSha2 is a 11.7 kb plasmid carrying TnSha1 present either as plasmid or in an integrated form generally flanked by two IS1272 elements. The target and mechanism of integration for IS1272 and TnSha1 are novel and include targeting of DNA secondary structures, generation of blunt-end deletions of the stem-loop and absence of target duplication. Database analyses showed widespread occurrence of these two elements in chromosomes and plasmids, with TnSha1 mainly in S. aureus and with TnSha2 mainly in S. haemolyticus and S. epidermidis. The acquisition of resistance by means of an insertion sequence-based mobilization and consequent duplication of drug-target metabolic genes, as observed here for sh-fabI, is highly reminiscent of the situation with the ileS2 gene conferring mupirocin resistance, and the dfrA and dfrG genes conferring trimethoprim resistance both of which are mobilized by IS257. These three examples, which show similar mechanisms and levels of spread of metabolic genes linked to IS elements, highlight the importance of this genetic strategy for recruitment and rapid distribution of novel resistance mechanisms in staphylococci. DOI: 10.3389/fmicb.2016.01008 PMCID: PMC4923244 PMID: 27446047 623. Genome Med. 2018 May 31;10(1):41. doi: 10.1186/s13073-018-0546-1. PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data. Piñeiro-Yáñez E(1), Reboiro-Jato M(2)(3), Gómez-López G(1), Perales-Patón J(1), Troulé K(1), Rodríguez JM(4), Tejero H(1), Shimamura T(5), López-Casas PP(1), Carretero J(6), Valencia A(1), Hidalgo M(1)(7), Glez-Peña D(2)(3), Al-Shahrour F(8). Author information: (1)Spanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain. (2)Computer Science Department - University of Vigo, Vigo, Spain. (3)Biomedical Research Centre (CINBIO), Vigo, Spain. (4)Spanish National Bioinformatics Institute (INB), Madrid, Spain. (5)Loyola University Chicago Stritch School of Medicine, Maywood, IL, USA. (6)Department of Physiology - University of Valencia, Valencia, Spain. (7)Beth Israel Deaconess Medical Center, Boston, USA. (8)Spanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain. falshahrour@cnio.es. BACKGROUND: Large-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decision-making. For this reason, the medical community agrees on the urgent need of methodologies to establish the relevance of tumor alterations, assisting in genomic profile interpretation, and, more importantly, to prioritize those that could be clinically actionable for cancer therapy. RESULTS: We present PanDrugs, a new computational methodology to guide the selection of personalized treatments in cancer patients using the variant lists provided by genome-wide sequencing analyses. PanDrugs offers the largest database of drug-target associations available from well-known targeted therapies to preclinical drugs. Scoring data-driven gene cancer relevance and drug feasibility PanDrugs interprets genomic alterations and provides a prioritized evidence-based list of anticancer therapies. Our tool represents the first drug prescription strategy applying a rational based on pathway context, multi-gene markers impact and information provided by functional experiments. Our approach has been systematically applied to TCGA patients and successfully validated in a cancer case study with a xenograft mouse model demonstrating its utility. CONCLUSIONS: PanDrugs is a feasible method to identify potentially druggable molecular alterations and prioritize drugs to facilitate the interpretation of genomic landscape and clinical decision-making in cancer patients. Our approach expands the search of druggable genomic alterations from the concept of cancer driver genes to the druggable pathway context extending anticancer therapeutic options beyond already known cancer genes. The methodology is public and easily integratable with custom pipelines through its programmatic API or its docker image. The PanDrugs webtool is freely accessible at http://www.pandrugs.org . DOI: 10.1186/s13073-018-0546-1 PMCID: PMC5977747 PMID: 29848362 [Indexed for MEDLINE] 624. J Ethnopharmacol. 2017 Feb 23;198:15-23. doi: 10.1016/j.jep.2016.12.041. Epub 2016 Dec 24. Esculentoside A suppresses lipopolysaccharide-induced pro-inflammatory molecule production partially by casein kinase 2. Li Y(1), Cao Y(1), Xu J(1), Qiu L(1), Xu W(1), Li J(1), Song Y(1), Lu B(1), Hu Z(2), Zhang J(3). Author information: (1)School of Pharmacy, Second Military Medical University, Shanghai 200433, China. (2)School of Pharmacy, Second Military Medical University, Shanghai 200433, China. Electronic address: zhenlinhu@hotmail.com. (3)School of Pharmacy, Second Military Medical University, Shanghai 200433, China. Electronic address: jpzhang08@163.com. ETHNOPHARMACOLOGICAL RELEVANCE: Esculentoside A (EsA) is a saponin isolated from the root of Phytolacca esculenta, an herb which has long been used in Traditional Chinese Medicine for various inflammatory diseases. EsA has been reported to have potent anti-inflammatory properties both in vitro and in vivo. AIM OF THE STUDY: The present study focused on the molecular mechanism of EsA for its anti-inflammatory effects in RAW264.7 cells stimulated with lipopolysaccharide (LPS). METHODS AND RESULTS: Enzyme Linked Immunosorbent Assay (ELISA) showed EsA dose dependently inhibited the production of tumor necrosis factor alpha (TNF-α), interleukin-6 (IL-6) and nitric oxide in RAW264.7 cells. Real-time quantitative reverse-transcription polymerase chain reaction (RT-PCR) assay further confirmed the suppression of LPS-induced TNF-α, IL-6 and iNOS gene expression by EsA on a transcriptional level. Moreover, EsA treatment markedly suppressed LPS-stimulated IκB phosphorylation and degradation as well as LPS-stimulated luciferase reporter construct driven by κB response elements in RAW264.7 cells. In addition, EsA significantly reduced LPS-induced stimulation of p38 and JNK, but not ERK1/2, phosphorylation. Furthermore, we used a computational method called "reverse docking" to search the possible binding proteins of EsA from the potential drug target database (PDTD), and focused on CK2 as the primary binding protein of EsA. Afterward, we further tested EsA directly interacts with recombinant CK2 using SPR assay. In CK2 kinase activity assay, EsA inhibited recombinant CK2 holoenzyme activity obviously in a dose-dependent manner. In addition, TBB (4, 5, 6, 7-tetrabromo-2-benzotriazole, a pharmacological inhibitor of CK2) blocked IL-6 release in a dose-dependent manner, whereas co-treatment of cells with EsA and TBB did not have an additive effect. CONCLUSIONS: Taken together, these results indicate that EsA blocks the LPS-induced pro-inflammatory molecules expression, at least in part, by impediment of LPS-triggered activation of NF-κB and p38/JNK MAPK pathways in macrophages. Furthermore, we discovered for the first time EsA as a ligand for CK2, which was involved in the inhibition of EsA to the expression of inflammatory cytokines. These findings extended our understanding on the cellular and molecular mechanisms responsible for the anti-inflammatory activity of EsA. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved. DOI: 10.1016/j.jep.2016.12.041 PMID: 28027904 [Indexed for MEDLINE] 625. Mol Biochem Parasitol. 2016 Mar-Apr;206(1-2):13-9. doi: 10.1016/j.molbiopara.2016.03.002. Epub 2016 Mar 11. Selenoproteins of African trypanosomes are dispensable for parasite survival in a mammalian host. Bonilla M(1), Krull E(1), Irigoín F(2), Salinas G(3), Comini MA(4). Author information: (1)Redox Biology of Trypanosomes Laboratory, Institut Pasteur de Montevideo, Uruguay. (2)Molecular Human Genetics Laboratory, Institut Pasteur de Montevideo, Uruguay; Departamento de Histología y Embriología, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay. (3)Worm Biology Laboratory, Institut Pasteur de Montevideo, Uruguay; Cátedra de Inmunología, Departamento de Biociencias, Facultad de Química, Universidad de la República, Montevideo, Uruguay. Electronic address: gsalin@fq.edu.uy. (4)Redox Biology of Trypanosomes Laboratory, Institut Pasteur de Montevideo, Uruguay. Electronic address: mcomini@pasteur.edu.uy. The trace element selenium is found in polypeptides as selenocysteine, the 21(st) amino acid that is co-translationally inserted into proteins at a UGA codon. In proteins, selenocysteine usually plays a role as an efficient redox catalyst. Trypanosomatids previously examined harbor a full set of genes encoding the machinery needed for selenocysteine biosynthesis and incorporation into three selenoproteins: SelK, SelT and, the parasite-specific, Seltryp. We investigated the selenoproteome of kinetoplastid species in recently sequenced genomes and assessed the in vivo relevance of selenoproteins for African trypanosomes. Database mining revealed that SelK, SelT and Seltryp genes are present in most kinetoplastids, including the free-living species Bodo saltans, and Seltryp was lost in the subgenus Viannia from the New World Leishmania. Homology and sinteny with bacterial sulfur dioxygenases and sulfur transferases suggest a putative role for Seltryp in sulfur metabolism. A Trypanosoma brucei selenocysteine synthase (SepSecS) null-mutant, in which selenoprotein synthesis is abolished, displayed similar sensitivity to oxidative stress induced by a short-term exposure to high concentrations of methylglyoxal or H2O2 to that of the parental wild-type cell line. Importantly, the infectivity of the SepSecS knockout cell line was not impaired when tested in a mouse infection model and compensatory effects via up-regulation of proteins involved in thiol-redox metabolism were not observed. Collectively, our data show that selenoproteins are not required for survival of African trypanosomes in a mammalian host and exclude a role for selenoproteins in parasite antioxidant defense and/or virulence. On this basis, selenoproteins can be disregarded as drug target candidates. Copyright © 2016 Elsevier B.V. All rights reserved. DOI: 10.1016/j.molbiopara.2016.03.002 PMID: 26975431 [Indexed for MEDLINE] 626. Sci Rep. 2015 Oct 16;5:15328. doi: 10.1038/srep15328. The identification of novel Mycobacterium tuberculosis DHFR inhibitors and the investigation of their binding preferences by using molecular modelling. Hong W(1), Wang Y(2), Chang Z(2), Yang Y(3), Pu J(4), Sun T(2), Kaur S(5), Sacchettini JC(6), Jung H(6), Lin Wong W(7), Fah Yap L(7), Fong Ngeow Y(5), Paterson IC(7), Wang H(2). Author information: (1)School of Chemistry and Chemical Engineering, Beifang University of Nationalities, Yinchuan, 750021, P. R. China. (2)School of Pharmacy, Ningxia Medical University, Yinchuan, 750004, P. R. China. (3)School of Basic Medicine, Ningxia Medical University, Yinchuan, 750004, P. R. China. (4)Key Laboratory of Fertility Preservation and Maintenance of Ministry of Education, Ningxia Medical University, Yinchuan, 750004, P.R. China. (5)Department of Pre-Clinical Sciences, Faculty of Medicine and Health Sciences, Universiti Tunku Abdul Rahman, Bandar Sungei Long, 43000, Malaysia. (6)Department of Biochemistry and Biophysics, Texas A &M University, College Station, TX 77843, USA. (7)Department of Oral Biology and Biomedical Sciences and Oral Cancer Research and Coordinating Centre, Faculty of Dentistry, University of Malaya, Kuala Lumpur, 50603, Malaysia. It is an urgent need to develop new drugs for Mycobacterium tuberculosis (Mtb), and the enzyme, dihydrofolate reductase (DHFR) is a recognised drug target. The crystal structures of methotrexate binding to mt- and h-DHFR separately indicate that the glycerol (GOL) binding site is likely to be critical for the function of mt-DHFR selective inhibitors. We have used in silico methods to screen NCI small molecule database and a group of related compounds were obtained that inhibit mt-DHFR activity and showed bactericidal effects against a test Mtb strain. The binding poses were then analysed and the influence of GOL binding site was studied by using molecular modelling. By comparing the chemical structures, 4 compounds that might be able to occupy the GOL binding site were identified. However, these compounds contain large hydrophobic side chains. As the GOL binding site is more hydrophilic, molecular modelling indicated that these compounds were failed to occupy the GOL site. The most potent inhibitor (compound 6) demonstrated limited selectivity for mt-DHFR, but did contain a novel central core (7H-pyrrolo[3,2-f]quinazoline-1,3-diamine), which may significantly expand the chemical space of novel mt-DHFR inhibitors. Collectively, these observations will inform future medicinal chemistry efforts to improve the selectivity of compounds against mt-DHFR. DOI: 10.1038/srep15328 PMCID: PMC4607890 PMID: 26471125 [Indexed for MEDLINE] 627. Proc Natl Acad Sci U S A. 2011 Apr 19;108(16):6597-602. doi: 10.1073/pnas.1007694108. Epub 2011 Apr 1. AMP kinase-related kinase NUAK2 affects tumor growth, migration, and clinical outcome of human melanoma. Namiki T(1), Tanemura A, Valencia JC, Coelho SG, Passeron T, Kawaguchi M, Vieira WD, Ishikawa M, Nishijima W, Izumo T, Kaneko Y, Katayama I, Yamaguchi Y, Yin L, Polley EC, Liu H, Kawakami Y, Eishi Y, Takahashi E, Yokozeki H, Hearing VJ. Author information: (1)Laboratory of Cell Biology, National Cancer Institute, Bethesda, MD 20814, USA. The identification of genes that participate in melanomagenesis should suggest strategies for developing therapeutic modalities. We used a public array comparative genomic hybridization (CGH) database and real-time quantitative PCR (qPCR) analyses to identify the AMP kinase (AMPK)-related kinase NUAK2 as a candidate gene for melanomagenesis, and we analyzed its functions in melanoma cells. Our analyses had identified a locus at 1q32 where genomic gain is strongly associated with tumor thickness, and we used real-time qPCR analyses and regression analyses to identify NUAK2 as a candidate gene at that locus. Associations of relapse-free survival and overall survival of 92 primary melanoma patients with NUAK2 expression measured using immunohistochemistry were investigated using Kaplan-Meier curves, log rank tests, and Cox regression models. Knockdown of NUAK2 induces senescence and reduces S-phase, decreases migration, and down-regulates expression of mammalian target of rapamycin (mTOR). In vivo analysis demonstrated that knockdown of NUAK2 suppresses melanoma tumor growth in mice. Survival analysis showed that the risk of relapse is greater in acral melanoma patients with high levels of NUAK2 expression than in acral melanoma patients with low levels of NUAK2 expression (hazard ratio = 3.88; 95% confidence interval = 1.44-10.50; P = 0.0075). These data demonstrate that NUAK2 expression is significantly associated with the oncogenic features of melanoma cells and with the survival of acral melanoma patients. NUAK2 may provide a drug target to suppress melanoma progression. This study further supports the importance of NUAK2 in cancer development and tumor progression, while AMPK has antioncogenic properties. DOI: 10.1073/pnas.1007694108 PMCID: PMC3081019 PMID: 21460252 [Indexed for MEDLINE] 628. Infect Immun. 2000 Jun;68(6):3491-501. Identification of potential vaccine and drug target candidates by expressed sequence tag analysis and immunoscreening of Onchocerca volvulus larval cDNA libraries. Lizotte-Waniewski M(1), Tawe W, Guiliano DB, Lu W, Liu J, Williams SA, Lustigman S. Author information: (1)Program in Molecular and Cellular Biology, University of Massachusetts, Amherst, Massachusetts, USA. The search for appropriate vaccine candidates and drug targets against onchocerciasis has so far been confronted with several limitations due to the unavailability of biological material, appropriate molecular resources, and knowledge of the parasite biology. To identify targets for vaccine or chemotherapy development we have undertaken two approaches. First, cDNA expression libraries were constructed from life cycle stages that are critical for establishment of Onchocerca volvulus infection, the third-stage larvae (L3) and the molting L3. A gene discovery effort was then initiated by random expressed sequence tag analysis of 5,506 cDNA clones. Cluster analyses showed that many of the transcripts were up-regulated and/or stage specific in either one or both of the cDNA libraries when compared to the microfilariae, L2, and both adult stages of the parasite. Homology searches against the GenBank database facilitated the identification of several genes of interest, such as proteinases, proteinase inhibitors, antioxidant or detoxification enzymes, and neurotransmitter receptors, as well as structural and housekeeping genes. Other O. volvulus genes showed homology only to predicted genes from the free-living nematode Caenorhabditis elegans or were entirely novel. Some of the novel proteins contain potential secretory leaders. Secondly, by immunoscreening the molting L3 cDNA library with a pool of human sera from putatively immune individuals, we identified six novel immunogenic proteins that otherwise would not have been identified as potential vaccinogens using the gene discovery effort. This study lays a solid foundation for a better understanding of the biology of O. volvulus as well as for the identification of novel targets for filaricidal agents and/or vaccines against onchocerciasis based on immunological and rational hypothesis-driven research. PMCID: PMC97634 PMID: 10816503 [Indexed for MEDLINE] 629. J Proteomics. 2017 Jan 16;151:162-173. doi: 10.1016/j.jprot.2016.05.016. Epub 2016 May 18. Neutrophil proteomic analysis reveals the participation of antioxidant enzymes, motility and ribosomal proteins in the prevention of ischemic effects by preconditioning. Arshid S(1), Tahir M(2), Fontes B(3), Montero EF(3), Castro MS(4), Sidoli S(5), Schwämmle V(6), Roepstorff P(6), Fontes W(7). Author information: (1)Laboratory of Biochemistry and Protein Chemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF, Brazil; Laboratory of Surgical Physiopathology (LIM-62), Faculty of Medicine, University of São Paulo, Brazil. (2)Laboratory of Biochemistry and Protein Chemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF, Brazil; Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense M, Denmark. (3)Laboratory of Surgical Physiopathology (LIM-62), Faculty of Medicine, University of São Paulo, Brazil. (4)Laboratory of Biochemistry and Protein Chemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF, Brazil. (5)Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense M, Denmark; Epigenetics Program, Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. (6)Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense M, Denmark. (7)Laboratory of Biochemistry and Protein Chemistry, Department of Cell Biology, Institute of Biology, University of Brasilia, Brasília, DF, Brazil. Electronic address: wagnerf@unb.br. Intestinal ischemia and reperfusion injury are widely used models, which result into tissue injury and multiple organ failure also observed after trauma and surgery. Ischemic preconditioning (IPC) preceding ischemia and reperfusion (IR) was shown to attenuate this injury and has a potential therapeutic application; however the exact underlying mechanism is not clear. Neutrophils play an important role in the mechanism of injuries caused by ischemia and reperfusion while IPC led to a decrease in neutrophil stimulation and activation. The effect of preconditioning on the neutrophil proteome is unclear. Proteomic analysis has been ratified as an appropriate tool for studying complex systems. In order to evaluate the effect of IPC preceding 45min of ischemia on the proteome of neutrophils we used Wistar rats divided in four experimental groups: Control, sham laparotomy, intestinal ischemia reperfusion and ischemic preconditioning. After neutrophil separation, proteins were extracted, trypsin digested and the resulting peptides were iTRAQ labeled followed by HILIC fractionation and nLC-MS/MS analysis. After database searches, normalization and statistical analysis our proteomic analysis resulted in the identification of 2437 protein groups that were assigned to five different clusters based on the relative abundance profiles among the experimental groups. The clustering followed by statistical analysis led to the identification of significantly up and downregulated proteins in IR and IPC. Cluster based KEGG pathways analysis revealed up- regulation of actin cytoskeleton, metabolism, Fc gamma R mediated phagocytosis, chemokine signaling, focal adhesion and leukocyte transendothelial migration whereas downregulation in ribosome, spliceosome, RNA transport, protein processing in endoplasmic reticulum and proteasome, after intestinal ischemic preconditioning. Furthermore, enzyme prediction analysis revealed the regulation of some important antioxidant enzymes and having their role in reactive oxygen species production. To our knowledge, this work describes the most comprehensive and detailed quantitative proteomic study of the neutrophil showing the beneficial role of ischemic preconditioning and its effects on the neutrophil proteome. This data will be helpful to understand the effect of underlying protective mechanisms modulating the role of PMNs after IPC and provide a trustworthy basis for future studies.BIOLOGICAL SIGNIFICANCE: Preconditioning is a relevant strategy to overcome clinical implications from ischemia and reperfusion. Such implications have the neutrophil as a major player. Although many publications describe specific biochemical and physiological roles of the neutrophil in such conditions, there is no report of a proteomic study providing a broader view of this scenario. Here we describe a group of proteins significantly regulated by ischemia and reperfusion being such regulation prevented by preconditioning. Such finding may provide relevant information for a deeper understanding of the mechanisms involved, as well as serve as basis for future biomarker or drug target assays. Copyright © 2016 Elsevier B.V. All rights reserved. DOI: 10.1016/j.jprot.2016.05.016 PMID: 27208787 [Indexed for MEDLINE] 630. Drug Des Devel Ther. 2015 Apr 13;9:2149-57. doi: 10.2147/DDDT.S75429. eCollection 2015. The clinicopathological significance of CDH1 in gastric cancer: a meta-analysis and systematic review. Zeng W(1), Zhu J(2), Shan L(3), Han Z(3), Aerxiding P(3), Quhai A(3), Zeng F(4), Wang Z(5), Li H(6). Author information: (1)College of Public Health, Xinjiang Medical University, Xinjiang, People's Republic of China ; First Department of Lung Cancer Chemotherapy, The Affiliated Cancer Hospital of Xinjiang Medical University, Xinjiang, People's Republic of China. (2)Department of Gastrointestinal Surgery, The Affiliated Cancer Hospital of Xinjiang Medical University, Xinjiang, People's Republic of China. (3)First Department of Lung Cancer Chemotherapy, The Affiliated Cancer Hospital of Xinjiang Medical University, Xinjiang, People's Republic of China. (4)Department of Oncology, Traditional Chinese Medical Hospital Affiliated to Xinjiang Medical University, Xinjiang, People's Republic of China. (5)Department of Gastrointestinal Surgery, First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China. (6)School of Basic Medicine, Xinjiang Medical University, Xinjiang, People's Republic of China. Comment in Drug Des Devel Ther. 2016;10:1159-60. BACKGROUND: CDH1 is a protein encoded by the CDH1 gene in humans. Loss of CDH1 function contributes to cancer progression by increasing proliferation, invasion, and/or metastasis. However, the association and clinicopathological significance between CDH1 hypermethylation and gastric cancer (GC) remains unclear. In this study, we systematically reviewed the studies of CDH1 hypermethylation and GC, and evaluated the association between CDH1 hypermethylation and GC using meta-analysis methods. METHODS: A comprehensive search of the PubMed and Embase databases was performed for publications up to July 2014. Methodological quality of the studies was also evaluated. The data were extracted and assessed by two reviewers independently. Analyses of pooled data were performed. Odds ratios (ORs) were calculated and summarized. RESULTS: A final analysis of 1,079 GC patients from 14 eligible studies was performed. CDH1 hypermethylation level in the cancer group was significantly higher compared to the normal gastric mucosa (OR =8.55, 95% confidence interval [CI]: 2.39-33.51, Z=5.47, P<0.00001). CDH1 hypermethylation was not significantly higher in GC than in adjacent gastric mucosa (OR =3.68, 95% CI: 0.96-14.18, Z=1.90, P=0.06). However, CDH1 hypermethylation was higher in adjacent gastric mucosa compared to that in normal gastric mucosa (OR =2.55, 95% CI: 1.22-5.32, Z=2.49, P<0.01). In addition, CDH1 hypermethylation was correlated with Helicobacter pylori (HP) status in GC. The pooled OR from six studies including 280 HP-positive GCs and 193 HP-negative GCs is 1.72 (95% CI: 1.13-2.61, Z=2.55, P=0.01). CONCLUSION: The results of this meta-analysis reveal that CDH1 hypermethylation levels in cancer and adjacent gastric mucosa are significantly higher compared to normal gastric mucosa. Thus, CDH1 hypermethylation is significantly correlated with GC risk. CDH1 hypermethylation is correlated with HP status, indicating that it plays a more important role in the pathogenesis of HP-positive GC and might be an interesting potential drug target for GC patients. DOI: 10.2147/DDDT.S75429 PMCID: PMC4403748 PMID: 25926721 [Indexed for MEDLINE] 631. BMC Complement Altern Med. 2015 Mar 5;15:41. doi: 10.1186/s12906-015-0579-6. In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics. Yin L(1), Zheng L(2), Xu L(3), Dong D(4), Han X(5), Qi Y(6), Zhao Y(7), Xu Y(8), Peng J(9)(10). Author information: (1)College of Pharmacy, Dalian Medical University, Western 9 Lvshun South Road, Dalian, 116044, China. yinlianhong1015@163.com. (2)The First Affiliated Hospital of Dalian Medical University, Dalian, 116022, China. zheng_ll2009@126.com. (3)College of Pharmacy, Dalian Medical University, Western 9 Lvshun South Road, Dalian, 116044, China. Linaxu_632@126.com. (4)The First Affiliated Hospital of Dalian Medical University, Dalian, 116022, China. Jihongyao_361@163.com. (5)College of Pharmacy, Dalian Medical University, Western 9 Lvshun South Road, Dalian, 116044, China. Xuhan2002zs@163.com. (6)College of Pharmacy, Dalian Medical University, Western 9 Lvshun South Road, Dalian, 116044, China. Yanqi_1976@163.com. (7)College of Pharmacy, Dalian Medical University, Western 9 Lvshun South Road, Dalian, 116044, China. Yanyanzhao_2009@126.com. (8)College of Pharmacy, Dalian Medical University, Western 9 Lvshun South Road, Dalian, 116044, China. Youweixu_1964@163.com. (9)College of Pharmacy, Dalian Medical University, Western 9 Lvshun South Road, Dalian, 116044, China. jinyongpeng2005@163.com. (10)Research Institute of Integrated Traditional and Western Medicine of Dalian Medical University, Dalian, 116011, China. jinyongpeng2005@163.com. BACKGROUND: Inverse docking technology has been a trend of drug discovery, and bioinformatics approaches have been used to predict target proteins, biological activities, signal pathways and molecular regulating networks affected by drugs for further pharmacodynamic and mechanism studies. METHODS: In the present paper, inverse docking technology was applied to screen potential targets from potential drug target database (PDTD). Then, the corresponding gene information of the obtained drug-targets was applied to predict the related biological activities, signal pathways and processes networks of the compound by using MetaCore platform. After that, some most relevant regulating networks were considered, which included the nodes and relevant pathways of dioscin. RESULTS: 71 potential targets of dioscin from humans, 7 from rats and 8 from mice were screened, and the prediction results showed that the most likely targets of dioscin were cyclin A2, calmodulin, hemoglobin subunit beta, DNA topoisomerase I, DNA polymerase lambda, nitric oxide synthase and UDP-N-acetylhexosamine pyrophosphorylase, etc. Many diseases including experimental autoimmune encephalomyelitis of human, temporal lobe epilepsy of rat and ankylosing spondylitis of mouse, may be inhibited by dioscin through regulating immune response alternative complement pathway, G-protein signaling RhoB regulation pathway and immune response antiviral actions of interferons, etc. The most relevant networks (5 from human, 3 from rat and 5 from mouse) indicated that dioscin may be a TOP1 inhibitor, which can treat cancer though the cell cycle- transition and termination of DNA replication pathway. Dioscin can down regulate EGFR and EGF to inhibit cancer, and also has anti-inflammation activity by regulating JNK signaling pathway. CONCLUSIONS: The predictions of the possible targets, biological activities, signal pathways and relevant regulating networks of dioscin provide valuable information to guide further investigation of dioscin on pharmacodynamics and molecular mechanisms, which also suggests a practical and effective method for studies on the mechanism of other chemicals. DOI: 10.1186/s12906-015-0579-6 PMCID: PMC4354738 PMID: 25879470 [Indexed for MEDLINE] 632. Expert Opin Pharmacother. 2006 Aug;7(12):1583-90. Pharmacogenetics of antiarrhythmic therapy. Darbar D(1), Roden DM. Author information: (1)Vanderbilt Arrhythmia Service, Vanderbilt University School of Medicine, Room 1285A, MRB IV, Nashville, TN 37323-6602, USA. dawood.darbar@vanderbilt.edu Individuals vary widely in their responses to therapy with most drugs. Indeed, responses to antiarrhythmic drugs are so highly variable that study of the underlying mechanisms has elucidated important lessons for understanding variable responses to drug therapy in general. Variability in drug response may reflect variability in the relationship between a drug dose and the concentrations of the drug and metabolite(s) at relevant target sites; this is termed pharmacokinetic variability. Another mechanism is that individuals vary in their response to identical exposures to a drug (pharmacodynamic variability). In this case, there may be variability in the target molecule(s) with which a drug interacts or, more generally, in the broad biological context in which the drug-target interaction occurs. Variants (polymorphisms and mutations) in the genes that encode proteins that are important for pharmacokinetics or for pharmacodynamics have now been described as important contributors to variable drug actions, including proarrhythmia, and these are described in this review. However, the translation of pharmacogenetics into clinical practice has been slow. To this end, the creation of large, well-characterised DNA databases and appropriate control groups, as well as large prospective trials to evaluate the impact of genetic variation on drug therapy, may hasten the impact of pharmacogenetics and pharmacogenomics in terms of delivering personalised drug therapy and to avoid therapeutic failure and serious side effects. DOI: 10.1517/14656566.7.12.1583 PMID: 16872261 [Indexed for MEDLINE] 633. ACS Chem Biol. 2012 May 18;7(5):856-62. doi: 10.1021/cb200408a. Epub 2012 Mar 5. Rationally designed small molecules targeting the RNA that causes myotonic dystrophy type 1 are potently bioactive. Childs-Disney JL(1), Hoskins J, Rzuczek SG, Thornton CA, Disney MD. Author information: (1)The Kellogg School of Science and Engineering, Department of Chemistry, The Scripps Research Institute, Scripps Florida, 130 Scripps Way #3A1, Jupiter, FL 33458, USA. RNA is an important drug target, but it is difficult to design or discover small molecules that modulate RNA function. In the present study, we report that rationally designed, modularly assembled small molecules that bind the RNA that causes myotonic dystrophy type 1 (DM1) are potently bioactive in cell culture models. DM1 is caused when an expansion of r(CUG) repeats, or r(CUG)(exp), is present in the 3' untranslated region (UTR) of the dystrophia myotonica protein kinase (DMPK) mRNA. r(CUG)(exp) folds into a hairpin with regularly repeating 5'CUG/3'GUC motifs and sequesters muscleblind-like 1 protein (MBNL1). A variety of defects are associated with DM1, including (i) formation of nuclear foci, (ii) decreased translation of DMPK mRNA due to its nuclear retention, and (iii) pre-mRNA splicing defects due to inactivation of MBNL1, which controls the alternative splicing of various pre-mRNAs. Previously, modularly assembled ligands targeting r(CUG)(exp) were designed using information in an RNA motif-ligand database. These studies showed that a bis-benzimidazole (H) binds the 5'CUG/3'GUC motif in r(CUG)(exp.) Therefore, we designed multivalent ligands to bind simultaneously multiple copies of this motif in r(CUG)(exp). Herein, we report that the designed compounds improve DM1-associated defects including improvement of translational and pre-mRNA splicing defects and the disruption of nuclear foci. These studies may establish a foundation to exploit other RNA targets in genomic sequence. DOI: 10.1021/cb200408a PMCID: PMC3356481 PMID: 22332923 [Indexed for MEDLINE] 634. Drug Des Devel Ther. 2015 Jul 15;9:3625-33. doi: 10.2147/DDDT.S86032. eCollection 2015. A meta-analysis for C-X-C chemokine receptor type 4 as a prognostic marker and potential drug target in hepatocellular carcinoma. Hu F(1), Miao L(1), Zhao Y(1), Xiao YY(1), Xu Q(1). Author information: (1)Department of Medical Oncology, Shanghai Tenth People's Hospital, Tongji University, School of Medicine, Shanghai, People's Republic of China. Chemokines (CKs), small proinflammatory chemoattractant cytokines that bind to specific G-protein coupled seven-span transmembrane receptors, are major regulators of cell trafficking and adhesion. C-X-C chemokine receptor type 4 (CXCR4) has gained tremendous attention over the last decade, since it was found to be upregulated in a wide variety of cancer types, including hepatocellular carcinoma (HCC). The clinical relevance of expression of CXCR4 in HCC remains controversial; our aim was to identify the precise relationship of CXCR4 to prognosis and clinicopathological features. We searched the database from MEDLINE, PubMed, Web of Science, Scopus and Embase and then conducted a meta-analysis from publications met the inclusion criteria for the qualitative study. Our data showed that 1) CXCR4 is overexpressed in HCC tissues but not in normal hepatic tissue, OR = 84.26, 95% confidence interval (CI) = 11.86-598.98, P < 0.0001. CXCR4 expression is higher in HCC than those in cirrhosis as well, OR = 20.71, 95% CI = 7.61-56.34, P < 0.00001. 2) The expression levels of CXCR4 does not increase during local progression, however, CXCR4 expression increases the risk of distant metastases in HCC, OR = 5.84, 95% CI = 2.84-12.00, P < 0.00001. 3) High levels of CXCR4 gene expression are associated with worse survival in HCC, HR = 0.18, 95% CI = 0.10-0.32, Z = 5.77, P < 0.00001. These data indicate that CXCR4 expression correlates with an increased risk and worse survival in HCC patients. The aberrant CXCR4 expression plays an important role in the carcinogenesis and metastasis of HCC. Our conclusion also supports that the promise of CXCR4 signaling pathway blockade as a potential strategy for HCC patients. DOI: 10.2147/DDDT.S86032 PMCID: PMC4507792 PMID: 26203228 [Indexed for MEDLINE] 635. Int J Mol Sci. 2019 Feb 16;20(4). pii: E860. doi: 10.3390/ijms20040860. Shedding Light on the Interaction of Human Anti-Apoptotic Bcl-2 Protein with Ligands through Biophysical and in Silico Studies. Ramos J(1), Muthukumaran J(2), Freire F(3), Paquete-Ferreira J(4), Otrelo-Cardoso AR(5), Svergun D(6), Panjkovich A(7), Santos-Silva T(8). Author information: (1)UCIBIO-NOVA, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal. jc.ramos@campus.fct.unl.pt. (2)UCIBIO-NOVA, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal. muthu@fct.unl.pt. (3)UCIBIO-NOVA, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal. f.freire@campus.fct.unl.pt. (4)UCIBIO-NOVA, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal. jcp.ferreira@campus.fct.unl.pt. (5)UCIBIO-NOVA, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal. a.cardoso@campus.fct.unl.pt. (6)European Molecular Biology Laboratory (EMBL), Hamburg Outstation, c/o DESY, 22067 Hamburg, Germany. svergun@embl-hamburg.de. (7)European Molecular Biology Laboratory (EMBL), Hamburg Outstation, c/o DESY, 22067 Hamburg, Germany. alejandro.panjkovich@embl-hamburg.de. (8)UCIBIO-NOVA, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal. tsss@fct.unl.pt. Bcl-2 protein is involved in cell apoptosis and is considered an interesting target for anti-cancer therapy. The present study aims to understand the stability and conformational changes of Bcl-2 upon interaction with the inhibitor venetoclax, and to explore other drug-target regions. We combined biophysical and in silico approaches to understand the mechanism of ligand binding to Bcl-2. Thermal shift assay (TSA) and urea electrophoresis showed a significant increase in protein stability upon venetoclax incubation, which is corroborated by molecular docking and molecular dynamics simulations. An 18 °C shift in Bcl-2 melting temperature was observed in the TSA, corresponding to a binding affinity multiple times higher than that of any other reported Bcl-2 inhibitor. This protein-ligand interaction does not implicate alternations in protein conformation, as suggested by SAXS. Additionally, bioinformatics approaches were used to identify deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) of Bcl-2 and their impact on venetoclax binding, suggesting that venetoclax interaction is generally favored against these deleterious nsSNPs. Apart from the BH3 binding groove of Bcl-2, the flexible loop domain (FLD) also plays an important role in regulating the apoptotic process. High-throughput virtual screening (HTVS) identified 5 putative FLD inhibitors from the Zinc database, showing nanomolar affinity toward the FLD of Bcl-2. DOI: 10.3390/ijms20040860 PMID: 30781512 636. Eur J Med Chem. 2018 Sep 5;157:1005-1016. doi: 10.1016/j.ejmech.2018.08.007. Epub 2018 Aug 22. Novel selective thiadiazine DYRK1A inhibitor lead scaffold with human pancreatic β-cell proliferation activity. Kumar K(1), Man-Un Ung P(2), Wang P(3), Wang H(1), Li H(1), Andrews MK(1), Stewart AF(3), Schlessinger A(4), DeVita RJ(5). Author information: (1)Drug Discovery Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. (2)Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. (3)Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. (4)Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. Electronic address: avner.schlessinger@mssm.edu. (5)Drug Discovery Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. Electronic address: robert.devita@mssm.edu. The Dual-Specificity Tyrosine Phosphorylation-Regulated Kinase 1A (DYRK1A) is an enzyme that has been implicated as an important drug target in various therapeutic areas, including neurological disorders (Down syndrome, Alzheimer's disease), oncology, and diabetes (pancreatic β-cell expansion). Current small molecule DYRK1A inhibitors are ATP-competitive inhibitors that bind to the kinase in an active conformation. As a result, these inhibitors are promiscuous, resulting in pharmacological side effects that limit their therapeutic applications. None are in clinical trials at this time. In order to identify a new DYRK1A inhibitor scaffold, we constructed a homology model of DYRK1A in an inactive, DFG-out conformation. Virtual screening of 2.2 million lead-like compounds from the ZINC database, followed by in vitro testing of selected 68 compounds revealed 8 hits representing 5 different chemical classes. We chose to focus on one of the hits from the computational screen, thiadiazine 1 which was found to inhibit DYRK1A with IC50 of 9.41 μM (Kd = 7.3 μM). Optimization of the hit compound 1, using structure-activity relationship (SAR) analysis and in vitro testing led to the identification of potent thiadiazine analogs with significantly improved binding as compared to the initial hit (Kd = 71-185 nM). Compound 3-5 induced human β-cell proliferation at 5 μM while showing selectivity for DYRK1A over DYRK1B and DYRK2 at 10 μM. This newly developed DYRK1A inhibitor scaffold with unique kinase selectivity profiles has potential to be further optimized as novel therapeutics for diabetes. Copyright © 2018. Published by Elsevier Masson SAS. DOI: 10.1016/j.ejmech.2018.08.007 PMCID: PMC6396881 PMID: 30170319 [Indexed for MEDLINE] 637. BMC Cancer. 2018 Mar 7;18(1):264. doi: 10.1186/s12885-018-4050-1. Exploration for novel inhibitors showing back-to-front approach against VEGFR-2 kinase domain (4AG8) employing molecular docking mechanism and molecular dynamics simulations. Rampogu S(1), Baek A(1), Zeb A(1), Lee KW(2). Author information: (1)Division of Applied Life Science (BK21 Plus Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju, 52828, Republic of Korea. (2)Division of Applied Life Science (BK21 Plus Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju, 52828, Republic of Korea. kwlee@gnu.ac.kr. BACKGROUND: Angiogenesis is a process of formation of new blood vessels and is an important criteria demonstrated by cancer cells. Over a period of time, these cancer cells infect the other parts of the healthy body by a process called progression. The objective of the present article is to identify a drug molecule that inhibits angiogenesis and progression. METHODS: In this pursuit, ligand based pharmacophore virtual screening was employed, generating a pharmacophore model, Hypo1 consisting of four features. Furthermore, this Hypo1 was validated recruiting, Fischer's randomization, test set method and decoy set method. Later, Hypo1 was allowed to screen databases such as Maybridge, Chembridge, Asinex and NCI and were further filtered by ADMET filters and Lipinski's Rule of Five. A total of 699 molecules that passed the above criteria, were challenged against 4AG8, an angiogenic drug target employing GOLD v5.2.2. RESULTS: The results rendered by molecular docking, DFT and the MD simulations showed only one molecule (Hit) obeyed the back-to-front approach. This molecule displayed a dock score of 89.77, involving the amino acids, Glu885 and Cys919, Asp1046, respectively and additionally formed several important hydrophobic interactions. Furthermore, the identified lead molecule showed interactions with key residues when challenged with CDK2 protein, 1URW. CONCLUSION: The lead candidate showed several interactions with the crucial residues of both the targets. Furthermore, we speculate that the residues Cys919 and Leu83 are important in the development of dual inhibitor. Therefore, the identified lead molecule can act as a potential inhibitor for angiogenesis and progression. DOI: 10.1186/s12885-018-4050-1 PMCID: PMC5842552 PMID: 29514608 [Indexed for MEDLINE] 638. N-[2-(N-(2-mercaptoethyl)) amino ethyl]-N-(2-mercaptoethyl)-3,5-dimethylacetamide amantadine-technetium. Shan L(1). In: Molecular Imaging and Contrast Agent Database (MICAD) [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2004-2013. 2012 Jun 12. Author information: (1)National Center for Biotechnology Information, NLM, NIH N-[2-(N-(2-mercaptoethyl)) amino ethyl]-N-(2-mercaptoethyl)-3,5-dimethylacetamide amantadine-technetium (99mTc-NCAM) and 1-[N-[N-(2-mercaptoethyl)]-N-[2-[N-(2-mercaptoethyl) amino] ethyl] aminoethyl] amino-3,5-dimethyladmantane-technetium (99mTc-NHAM) are two memantine derivatives synthesized by Zhou et al. for imaging of N-methyl-d-aspartate receptors (NMDARs) (1). NMDARs are oligomeric ligand-gated, voltage-dependent ion channels formed by the assembly of a NR1 subunit and various NR2 subunits. NR1 has eight subtypes and encodes the ion channel, while NR2 has four subtypes (NR2A, NR2B, NR2C and NR2D) and mediates the fast excitatory neurotransmission in combination with NR1 (2). Recently the NR3 subunit (two subtypes, NR3A and NR3B) has been shown to combine with NR1 and NR2 to functional heterotrimers (3). In terms of agonist requirement and channel operation, the three subunit families exhibit distinct properties; NR1 and NR3 require glycine as the agonist and have no binding site for glutamate, whereas NR2 is activated by glutamate. Opening of the ion channel is dependent on the voltage state of the cell membrane as well as the binding of dual ligands; glutamate binds to a site on the NR2 subunit, and glycine or d-serine binds to a modulatory site on the NR1 subunit (2, 4). NMDARs present in both intra- and extra-synaptic areas, with a higher density within the synapse. The extra-synaptic NMDARs have been proposed to mediate excitotoxicity, while intra-synaptic NMDARs appear to be neuroprotective (5). NMDARs have been a drug target for >25 years for neurological and psychiatric indications (2). A large number of mediators have been developed by targeting different modulatory sites on the NMDARs. With better understanding of the NMDAR pathophysiology, the therapeutic concept with channel mediators has changed over the years. As reviewed by Koller and Urwyler, the most important recent strategies aiming for inhibition of NMDAR-mediated neurotransmission is to avoid full receptor blockade while allowing a low degree of normal receptor function for safety reasons (2). To this aim, approaches include blocking the channel with compounds of low affinity, antagonizing receptor activity with highly potent NR2B ligands, partial agonism at the glutamate or glycine binding site, and improvement of pharmacokinetic properties of well established, safe antagonists by deuteration (2, 6). Molecular imaging with radiotracers has been used to map the changes in the density of NMDARs in specific regions of brain and to establish receptor occupancy of a drug that may facilitate drug development and dose optimization. Because of the considerable number of binding sites (especially the channel pore, the glycine site, and the NR2B subunit), the NMDAR complex offers opportunities to develop imaging probes by targeting different sites (1, 7, 8). In general, most probes either have unfavorable pharmacokinetics or exhibit less specific binding, failing to reflect the distribution and density of NMDARs in different regions of brain. Memantine is a partial NMDAR antagonist approved in the United States and Europe for the treatment of moderate to severe Alzheimer’s disease. By acting at the channel pore, memantine preferentially blocks excessive NMDAR activity without disrupting its normal activity. Zhou et al. synthesized 99mTc-NCAM and 99mTc-NHAM with memantine as the lead compound (1). This chapter summarizes the data obtained with 99mTc-NCAM and 99mTc-NHAM. PMID: 22812023 639. Biochem Biophys Res Commun. 2016 Jul 1;475(3):295-300. doi: 10.1016/j.bbrc.2016.04.149. Epub 2016 May 18. A bitter pill for type 2 diabetes? The activation of bitter taste receptor TAS2R38 can stimulate GLP-1 release from enteroendocrine L-cells. Pham H(1), Hui H(2), Morvaridi S(1), Cai J(3), Zhang S(4), Tan J(5), Wu V(6), Levin N(7), Knudsen B(8), Goddard WA 3rd(9), Pandol SJ(10), Abrol R(11). Author information: (1)Departments of Biomedical Sciences and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. (2)David Geffen School of Medicine, University of California, Los Angeles, CA, USA; International Center for Metabolic Diseases, Southern Medical University, Guangzhou, China. (3)International Center for Metabolic Diseases, Southern Medical University, Guangzhou, China. (4)Department of Medicinal Chemistry, Xi'an Jiaotong University, 710061, China. (5)Materials and Process Simulation Center, California Institute of Technology, Pasadena, CA, USA; Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400030, China. (6)Veterans Affairs Greater Los Angeles Healthcare System, University of California, Los Angeles, CA, USA. (7)GIRx Metabolics Inc., Los Angeles, CA, USA. (8)Departments of Biomedical Sciences and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA; David Geffen School of Medicine, University of California, Los Angeles, CA, USA. (9)Materials and Process Simulation Center, California Institute of Technology, Pasadena, CA, USA. (10)Departments of Biomedical Sciences and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA; David Geffen School of Medicine, University of California, Los Angeles, CA, USA; Veterans Affairs Greater Los Angeles Healthcare System, University of California, Los Angeles, CA, USA; GIRx Metabolics Inc., Los Angeles, CA, USA. (11)Departments of Biomedical Sciences and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA; David Geffen School of Medicine, University of California, Los Angeles, CA, USA; Materials and Process Simulation Center, California Institute of Technology, Pasadena, CA, USA; GIRx Metabolics Inc., Los Angeles, CA, USA. Electronic address: abrolr@csmc.edu. The bitter taste receptor TAS2R38 is a G protein coupled receptor (GPCR) that has been found in many extra-oral locations like the gastrointestinal (GI) system, respiratory system, and brain, though its function at these locations is only beginning to be understood. To probe the receptor's potential metabolic role, immunohistochemistry of human ileum tissues was performed, which showed that the receptor was co-localized with glucagon-like peptide 1 (GLP-1) in L-cells. In a previous study, we had modeled the structure of this receptor for its many taste-variant haplotypes (Tan et al. 2011), including the taster haplotype PAV. The structure of this haplotype was then used in a virtual ligand screening pipeline using a collection of ∼2.5 million purchasable molecules from the ZINC database. Three compounds (Z7, Z3, Z1) were purchased from the top hits and tested along with PTU (known TAS2R38 agonist) in in vitro and in vivo assays. The dose-response study of the effect of PTU and Z7 on GLP-1 release using wild-type and TAS2R38 knockout HuTu-80 cells showed that the receptor TAS2R38 plays a major role in GLP-1 release due to these molecules. In vivo studies of PTU and the three compounds showed that they each increase GLP-1 release. PTU was also chemical linked to cellulose to slow its absorption and when tested in vivo, it showed an enhanced and prolonged GLP-1 release. These results suggest that the GI lumen location of TAS2R38 on the L-cell makes it a relatively safe drug target as systemic absorption is not needed for a TAS2R38 agonist drug to effect GLP-1 release. Copyright © 2016 Elsevier Inc. All rights reserved. DOI: 10.1016/j.bbrc.2016.04.149 PMCID: PMC4918516 PMID: 27208775 [Indexed for MEDLINE] 640. Biosystems. 2015 Jun;132-133:20-34. doi: 10.1016/j.biosystems.2015.04.007. Epub 2015 Apr 24. Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties. Herrera-Ibatá DM(1), Pazos A(2), Orbegozo-Medina RA(3), Romero-Durán FJ(4), González-Díaz H(5). Author information: (1)Department of Information and Communication Technologies, University of A Coruña (UDC), 15071 A Coruña, Spain. Electronic address: diana.herrera@udc.es. (2)Department of Information and Communication Technologies, University of A Coruña (UDC), 15071 A Coruña, Spain. (3)Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain. (4)Department of Organic Chemistry (USC), 15782 Santiago de Compostela, Spain. (5)Department of Organic Chemistry II, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain; IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain. Electronic address: humberto.gonzalezdiaz@ehu.es. Using computational algorithms to design tailored drug cocktails for highly active antiretroviral therapy (HAART) on specific populations is a goal of major importance for both pharmaceutical industry and public health policy institutions. New combinations of compounds need to be predicted in order to design HAART cocktails. On the one hand, there are the biomolecular factors related to the drugs in the cocktail (experimental measure, chemical structure, drug target, assay organisms, etc.); on the other hand, there are the socioeconomic factors of the specific population (income inequalities, employment levels, fiscal pressure, education, migration, population structure, etc.) to study the relationship between the socioeconomic status and the disease. In this context, machine learning algorithms, able to seek models for problems with multi-source data, have to be used. In this work, the first artificial neural network (ANN) model is proposed for the prediction of HAART cocktails, to halt AIDS on epidemic networks of U.S. counties using information indices that codify both biomolecular and several socioeconomic factors. The data was obtained from at least three major sources. The first dataset included assays of anti-HIV chemical compounds released to ChEMBL. The second dataset is the AIDSVu database of Emory University. AIDSVu compiled AIDS prevalence for >2300 U.S. counties. The third data set included socioeconomic data from the U.S. Census Bureau. Three scales or levels were employed to group the counties according to the location or population structure codes: state, rural urban continuum code (RUCC) and urban influence code (UIC). An analysis of >130,000 pairs (network links) was performed, corresponding to AIDS prevalence in 2310 counties in U.S. vs. drug cocktails made up of combinations of ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4856 protocols, and 10 possible experimental measures. The best model found with the original data was a linear neural network (LNN) with AUROC>0.80 and accuracy, specificity, and sensitivity≈77% in training and external validation series. The change of the spatial and population structure scale (State, UIC, or RUCC codes) does not affect the quality of the model. Unbalance was detected in all the models found comparing positive/negative cases and linear/non-linear model accuracy ratios. Using synthetic minority over-sampling technique (SMOTE), data pre-processing and machine-learning algorithms implemented into the WEKA software, more balanced models were found. In particular, a multilayer perceptron (MLP) with AUROC=97.4% and precision, recall, and F-measure >90% was found. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved. DOI: 10.1016/j.biosystems.2015.04.007 PMID: 25916548 [Indexed for MEDLINE] 641. Proc Natl Acad Sci U S A. 2008 Nov 11;105(45):17278-83. doi: 10.1073/pnas.0805820105. Epub 2008 Nov 3. Discovery of drug-like inhibitors of an essential RNA-editing ligase in Trypanosoma brucei. Amaro RE(1), Schnaufer A, Interthal H, Hol W, Stuart KD, McCammon JA. Author information: (1)Department of Chemistry and Biochemistry and Center for Theoretical Biological Physics, University of California at San Diego, La Jolla, CA 92093-0365, USA. ramaro@mccammon.ucsd.edu Trypanosomatid RNA editing is a unique process and essential for these organisms. It therefore represents a drug target for a group of protozoa that includes the causative agents for African sleeping sickness and other devastating tropical and subtropical diseases. Here, we present drug-like inhibitors of a key enzyme in the editing machinery, RNA-editing ligase 1 (REL1). These inhibitors were identified through a strategy employing molecular dynamics to account for protein flexibility. A virtual screen of the REL1 crystal structure against the National Cancer Institute Diversity Set was performed by using AutoDock4. The top 30 compounds, predicted to interact with REL1's ATP-binding pocket, were further refined by using the relaxed complex scheme (RCS), which redocks the compounds to receptor structures extracted from an explicitly solvated molecular dynamics trajectory. The resulting reordering of the ligands and filtering based on drug-like properties resulted in an initial recommended set of 8 ligands, 2 of which exhibited micromolar activity against REL1. A subsequent hierarchical similarity search with the most active compound over the full National Cancer Institute database and RCS rescoring resulted in an additional set of 6 ligands, 2 of which were confirmed as REL1 inhibitors with IC(50) values of approximately 1 microM. Tests of the 3 most promising compounds against the most closely related bacteriophage T4 RNA ligase 2, as well as against human DNA ligase IIIbeta, indicated a considerable degree of selectivity for RNA ligases. These compounds are promising scaffolds for future drug design and discovery efforts against these important pathogens. DOI: 10.1073/pnas.0805820105 PMCID: PMC2577703 PMID: 18981420 [Indexed for MEDLINE] 642. BMC Bioinformatics. 2017 Dec 28;18(Suppl 14):532. doi: 10.1186/s12859-017-1889-0. Repositioning drugs by targeting network modules: a Parkinson's disease case study. Yue Z(1)(2), Arora I(2), Zhang EY(2), Laufer V(3), Bridges SL(3), Chen JY(4)(5)(6). Author information: (1)Center for Biomedical Big Data, Wenzhou Medical University First Affiliated Hospital, Wenzhou, Zhejiang Province, China. (2)Informatics Institute in School of Medicine, University of Alabama at Birmingham, Birmingham, 35233, AL, USA. (3)Division of Clinical Immunology and Rheumatology in School of Medicine, University of Alabama at Birmingham, Birmingham, 35233, AL, USA. (4)Center for Biomedical Big Data, Wenzhou Medical University First Affiliated Hospital, Wenzhou, Zhejiang Province, China. jakechen@uab.edu. (5)Informatics Institute in School of Medicine, University of Alabama at Birmingham, Birmingham, 35233, AL, USA. jakechen@uab.edu. (6)Wenzhou Yuekang InfoTech, Ltd., Wenzhou, Zhejiang Province, China. jakechen@uab.edu. BACKGROUND: Much effort has been devoted to the discovery of specific mechanisms between drugs and single targets to date. However, as biological systems maintain homeostasis at the level of functional networks robustly controlling the internal environment, such networks commonly contain multiple redundant mechanisms designed to counteract loss or perturbation of a single member of the network. As such, investigation of therapeutics that target dysregulated pathways or processes, rather than single targets, may identify agents that function at a level of the biological organization more relevant to the pathology of complex diseases such as Parkinson's Disease (PD). Genome-wide association studies (GWAS) in PD have identified common variants underlying disease susceptibility, while gene expression microarray data provide genome-wide transcriptional profiles. These genomic studies can illustrate upstream perturbations causing the dysfunction in signaling pathways and downstream biochemical mechanisms leading to the PD phenotype. We hypothesize that drugs acting at the level of a gene expression module specific to PD can overcome the lack of efficacy associated with targeting a single gene in polygenic diseases. Thus, this approach represents a promising new direction for module-based drug discovery in human diseases such as PD. RESULTS: We built a framework that integrates GWAS data with gene co-expression modules from tissues representing three brain regions-the frontal gyrus, the lateral substantia, and the medial substantia in PD patients. Using weighted gene correlation network analysis (WGCNA) software package in R, we conducted enrichment analysis of data from a GWAS of PD. This led to the identification of two over-represented PD-specific gene co-expression network modules: the Brown Module (Br) containing 449 genes and the Turquoise module (T) containing 905 genes. Further enrichment analysis identified four functional pathways within the Br module (cellular respiration, intracellular transport, energy coupled proton transport against the electrochemical gradient, and microtubule-based movement), and one functional pathway within the T module (M-phase). Next, we utilized drug-protein regulatory relationship databases (DMAP) and developed a Drug Effect Sum Score (DESS) to evaluate all candidate drugs that might restore gene expression to normal level across the Br and T modules. Among the drugs with the 12 highest DESS scores, 5 had been reported as potential treatments for PD and 6 hold potential repositioning applications. CONCLUSION: In this study, we present a systems pharmacology framework which draws on genetic data from GWAS and gene expression microarray data to reposition drugs for PD. Our innovative approach integrates gene co-expression modules with biomolecular interaction network analysis to identify network modules critical to the PD pathway and disease mechanism. We quantify the positive effects of drugs in a DESS score that is based on known drug-target activity profiles. Our results illustrate that this modular approach is promising for repositioning drugs for use in polygenic diseases such as PD, and is capable of addressing challenges of the hindered gene target in drug repositioning approaches to date. DOI: 10.1186/s12859-017-1889-0 PMCID: PMC5751600 PMID: 29297292 [Indexed for MEDLINE] 643. Gene. 2015 Feb 10;556(2):213-26. doi: 10.1016/j.gene.2014.11.056. Epub 2014 Nov 28. Metabolic pathway analysis approach: identification of novel therapeutic target against methicillin resistant Staphylococcus aureus. Uddin R(1), Saeed K(2), Khan W(3), Azam SS(4), Wadood A(5). Author information: (1)Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi-75270, Pakistan. Electronic address: mriazuddin@iccs.edu. (2)Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi-75270, Pakistan. (3)Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi-75270, Pakistan; Jamil-ur-Rahman Center for Genome Research, PCMD Ext., International Center for Chemical and Biological Sciences, University of Karachi, Karachi-75270, Pakistan. (4)National Centre for Bioinformatics, Quaid-i-Azam University, Islamabad, Pakistan. (5)Department of Biochemistry, Abdul Wali Khan University, Mardan, Pakistan. Comment in Gene. 2015 Jun 15;564(2):233-5. Multiple Drug Resistant (MDR) bacteria are no more inhibited by the front line antibiotics due to extreme resistance. Methicillin Resistant Staphylococcus aureus (MRSA) is one of the MDR pathogens notorious for its widespread infection around the world. The high resistance acquired by MRSA needs a serious concern and efforts should be carried out for the discovery of better therapeutics. With this aim, we designed a comparison of the metabolic pathways of the pathogen, MRSA strain 252 (MRSA252) with the human host (i.e., Homo sapiens) by using well-established in silico methods. We identified several metabolic pathways unique to MRSA (i.e., absent in the human host). Furthermore, a subtractive genomics analysis approach was applied for retrieval of proteins only from the unique metabolic pathways. Subsequently, proteins of unique MRSA pathways were compared with the host proteins. As a result, we have shortlisted few unique and essential proteins that could act as drug targets against MRSA. We further assessed the druggability potential of the shortlisted targets by comparing them with the DrugBank Database (DBD). The identified drug targets could be useful for an effective drug discovery phase. We also searched the sequences of unique as well as essential enzymes from MRSA in Protein Data Bank (PDB). We shortlisted at least 12 enzymes for which there was no corresponding deposition in PDB, reflecting that their crystal structures are yet to be solved! We selected Glutamate synthase out of those 12 enzymes owing to its participation in significant metabolic pathways of the pathogen e.g., Alanine, Aspartate, Glutamate and Nitrogen metabolism and its evident suitability as drug target among other MDR bacteria e.g., Mycobacteria. Due to the unavailability of any crystal structure of Glutamate synthase in PDB, we generated the 3D structure by homology modeling. The modeled structure was validated by multiple analysis tools. The active site of Glutamate synthase was identified by not only superimposing the template structure (PDB ID: 1E0A) over each other but also by the Parallel-ProBiS algorithm. The identified active site was further validated by cross-docking the co-crystallized ligand (2-oxoglutaric acid; AKG) of PDB ID: 1LLW. It was concluded that the comparative metabolic in silico analysis together with structure-based methods provides an effective approach for the identification of novel antibiotic targets against MRSA. Copyright © 2014 Elsevier B.V. All rights reserved. DOI: 10.1016/j.gene.2014.11.056 PMID: 25436466 [Indexed for MEDLINE] 644. Anticancer Res. 2014 Apr;34(4):1873-84. Impact of S100A8 expression on kidney cancer progression and molecular docking studies for kidney cancer therapeutics. Mirza Z(1), Schulten HJ, Farsi HM, Al-Maghrabi JA, Gari MA, Chaudhary AG, Abuzenadah AM, Al-Qahtani MH, Karim S. Author information: (1)King Abdulaziz University, PO BOX 80216, Jeddah 21589, Kingdom of Saudi Arabia. Tel: +1 96626401000 ext. 25123, skarim1@kau.edu.sa; sajjad_k_2000@yahoo.com. BACKGROUND/AIM: The proinflammatory protein S100A8, which is expressed in myeloid cells under physiological conditions, is strongly expressed in human cancer tissues. Its role in tumor cell differentiation and tumor progression is largely unclear and virtually unstudied in kidney cancer. In the present study, we investigated whether S100A8 could be a potential anticancer drug target and therapeutic biomarker for kidney cancer, and the underlying molecular mechanisms by exploiting its interaction profile with drugs. MATERIALS AND METHODS: Microarray-based transcriptomics experiments using Affymetrix HuGene 1.0 ST arrays were applied to renal cell carcinoma specimens from Saudi patients for identification of significant genes associated with kidney cancer. In addition, we retrieved selected expression data from the National Center for Biotechnology Information Gene Expression Omnibus database for comparative analysis and confirmation of S100A8 expression. Ingenuity Pathway Analysis (IPA) was used to elucidate significant molecular networks and pathways associated with kidney cancer. The probable polar and non-polar interactions of possible S100A8 inhibitors (aspirin, celecoxib, dexamethasone and diclofenac) were examined by performing molecular docking and binding free energy calculations. Detailed analysis of bound structures and their binding free energies was carried out for S100A8, its known partner (S100A9), and S100A8-S100A9 complex (calprotectin). RESULTS: In our microarray experiments, we identified 1,335 significantly differentially expressed genes, including S100A8, in kidney cancer using a cut-off of p<0.05 and fold-change of 2. Functional analysis of kidney cancer-associated genes showed overexpression of genes involved in cell-cycle progression, DNA repair, cell death, tumor morphology and tissue development. Pathway analysis showed significant disruption of pathways of atherosclerosis signaling, liver X receptor/retinoid X receptor (LXR/RXR) activation, notch signaling, and interleukin-12 (IL-12) signaling. We identified S100A8 as a prospective biomarker for kidney cancer and in silico analysis showed that aspirin, celecoxib, dexamethasone and diclofenac binds to S100A8 and may inhibit downstream signaling in kidney cancer. CONCLUSION: The present study provides an initial overview of differentially expressed genes in kidney cancer of Saudi Arabian patients using whole-transcript, high-density expression arrays. Our analysis suggests distinct transcriptomic signatures, with significantly high levels of S100A8, and underlying molecular mechanisms contributing to kidney cancer progression. Our docking-based findings shed insight into S100A8 protein as an attractive anticancer target for therapeutic intervention in kidney cancer. To our knowledge, this is the first structure-based docking study for the selected protein targets using the chosen ligands. PMID: 24692722 [Indexed for MEDLINE] 645. ACS Chem Biol. 2009 Jan 16;4(1):29-40. doi: 10.1021/cb8002804. Exploiting structural analysis, in silico screening, and serendipity to identify novel inhibitors of drug-resistant falciparum malaria. Dasgupta T(1), Chitnumsub P, Kamchonwongpaisan S, Maneeruttanarungroj C, Nichols SE, Lyons TM, Tirado-Rives J, Jorgensen WL, Yuthavong Y, Anderson KS. Author information: (1)Department of Pharmacology, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA. Plasmodium falciparum thymidylate synthase-dihydrofolate reductase (TS-DHFR) is an essential enzyme in folate biosynthesis and a major malarial drug target. This bifunctional enzyme thus presents different design approaches for developing novel inhibitors against drug-resistant mutants. We performed a high-throughput in silico screen of a database of diverse, drug-like molecules against a non-active-site pocket of TS-DHFR. The top compounds from this virtual screen were evaluated by in vitro enzymatic and cellular culture studies. Three compounds active to 20 microM IC(50)'s in both wildtype and antifolate-resistant P. falciparum parasites were identified; moreover, no inhibition of human DHFR enzyme was observed, indicating that the inhibitory effects appeared to be parasite-specific. Notably, all three compounds had a biguanide scaffold. However, relative free energy of binding calculations suggested that the compounds might preferentially interact with the active site over the screened non-active-site region. To resolve the two possible modes of binding, co-crystallization studies of the compounds complexed with TS-DHFR enzyme were performed. Surprisingly, the structural analysis revealed that these novel, biguanide compounds do indeed bind at the active site of DHFR and additionally revealed the molecular basis by which they overcome drug resistance. To our knowledge, these are the first co-crystal structures of novel, biguanide, non-WR99210 compounds that are active against folate-resistant malaria parasites in cell culture. DOI: 10.1021/cb8002804 PMCID: PMC2711878 PMID: 19146480 [Indexed for MEDLINE] 646. J Comput Aided Mol Des. 2017 Oct;31(10):877-889. doi: 10.1007/s10822-017-0052-3. Epub 2017 Sep 6. In silico probing and biological evaluation of SETDB1/ESET-targeted novel compounds that reduce tri-methylated histone H3K9 (H3K9me3) level. Park I(1)(2), Hwang YJ(1), Kim T(1)(3), Viswanath ANI(1)(3), Londhe AM(1)(3), Jung SY(1), Sim KM(1)(4), Min SJ(5), Lee JE(6), Seong J(1)(3), Kim YK(1)(3), No KT(2), Ryu H(7)(8), Pae AN(9)(10). Author information: (1)Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea. (2)Department of Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea. (3)Division of Bio-Medical Science &Technology, KIST School, Korea University of Science and Technology, Daejeon, 34113, Republic of Korea. (4)Department of Integrated Biomedical and Life Science, Korea University, Seoul, 02841, Republic of Korea. (5)Department of Applied Chemistry, Hanyang University, Ansan, Gyeonggi-do, 15888, Republic of Korea. (6)Center for Theragnosis, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea. (7)Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea. hoonryu@bu.edu. (8)Department of Neurology and Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, 02118, USA. hoonryu@bu.edu. (9)Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea. anpae@kist.re.kr. (10)Division of Bio-Medical Science &Technology, KIST School, Korea University of Science and Technology, Daejeon, 34113, Republic of Korea. anpae@kist.re.kr. ERG-associated protein with the SET domain (ESET/SET domain bifurcated 1/SETDB1/KMT1E) is a histone lysine methyltransferase (HKMT) and it preferentially tri-methylates lysine 9 of histone H3 (H3K9me3). SETDB1/ESET leads to heterochromatin condensation and epigenetic gene silencing. These functional changes are reported to correlate with Huntington's disease (HD) progression and mood-related disorders which make SETDB1/ESET a viable drug target. In this context, the present investigation was performed to identify novel peptide-competitive small molecule inhibitors of the SETDB1/ESET by a combined in silico-in vitro approach. A ligand-based pharmacophore model was built and employed for the virtual screening of ChemDiv and Asinex database. Also, a human SETDB1/ESET homology model was constructed to supplement the data further. Biological evaluation of the selected 21 candidates singled out 5 compounds exhibiting a notable reduction of the H3K9me3 level via inhibitory potential of SETDB1/ESET activity in SETDB1/ESET-inducible cell line and HD striatal cells. Later on, we identified two compounds as final hits that appear to have neuronal effects without cytotoxicity based on the result from MTT assay. These compounds hold the calibre to become the future lead compounds and can provide structural insights into more SETDB1/ESET-focused drug discovery research. Moreover, these SETDB1/ESET inhibitors may be applicable for the preclinical study to ameliorate neurodegenerative disorders via epigenetic regulation. DOI: 10.1007/s10822-017-0052-3 PMID: 28879500 [Indexed for MEDLINE]