Molecular insights into human transmembrane protease serine-2 (TMPS2) inhibitors against SARS-CoV2: homology modelling, molecular dynamics, and docking studies

Kishk, Safaa M., Kishk, Rania M., Yassen, Asmaa S. A., Nafie, Mohamed S., Nemr, Nader A., ElMasry, Gamal, Al-Rejaie, Salim and Simons, Claire ORCID: https://orcid.org/0000-0002-9487-1100 2020. Molecular insights into human transmembrane protease serine-2 (TMPS2) inhibitors against SARS-CoV2: homology modelling, molecular dynamics, and docking studies. Molecules 25 (21) , 5007. 10.3390/molecules25215007

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Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), which caused novel corona virus disease-2019 (COVID-19) pandemic, necessitated a global demand for studies related to genes and enzymes of SARS-CoV2. SARS-CoV2 infection depends on the host cell Angiotensin-Converting Enzyme-2 (ACE2) and Transmembrane Serine Protease-2 (TMPRSS2), where the virus uses ACE2 for entry and TMPRSS2 for S protein priming. The TMPRSS2 gene encodes a Transmembrane Protease Serine-2 protein (TMPS2) that belongs to the serine protease family. There is no crystal structure available for TMPS2, therefore, a homology model was required to establish a putative 3D structure for the enzyme. A homology model was constructed using SWISS-MODEL and evaluations were performed through Ramachandran plots, Verify 3D and Protein Statistical Analysis (ProSA). Molecular dynamics simulations were employed to investigate the stability of the constructed model. Docking of TMPS2 inhibitors, camostat, nafamostat, gabexate, and sivelestat, using Molecular Operating Environment (MOE) software, into the constructed model was performed and the protein-ligand complexes were subjected to MD simulations and computational binding affinity calculations. These in silico studies determined the tertiary structure of TMPS2 amino acid sequence and predicted how ligands bind to the model, which is important for drug development for the prevention and treatment of COVID-19.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Pharmacy
Additional Information:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Publisher:	MDPI
ISSN:	1420-3049
Date of First Compliant Deposit:	11 November 2020
Date of Acceptance:	24 October 2020
Last Modified:	12 Jul 2024 16:06
URI:	https://orca.cardiff.ac.uk/id/eprint/136288

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