SHREC2020 track: multi-domain protein shape retrieval challenge

Langenfeld, Florent, Peng, Yuxu, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680, Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884, Aderinwale, Tunde, Terashi, Genki, Christoffer, Charles, Kihara, Daisuke, Benhabiles, Halim, Hammoudi, Karim, Cabani, Adnane, Windal, Feryal, Melkemi, Mahmoud, Giachetti, Andrea, Mylonas, Stelios, Axenopoulos, Apostolos, Daras, Petros, Otu, Ekpo, Zwiggelaar, Reyer, Hunter, David, Liu, Yonghuai and Montès, Matthieu 2020. SHREC2020 track: multi-domain protein shape retrieval challenge. Computers and Graphics 91 , pp. 189-198. 10.1016/j.cag.2020.07.013

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Abstract

Proteins are natural modular objects usually composed of several domains, each domain bearing a specific function that is mediated through its surface, which is accessible to vicinal molecules. This draws attention to an understudied characteristic of protein structures: surface, that is mostly unexploited by protein structure comparison methods. In the present work, we evaluated the performance of six shape comparison methods, among which three are based on machine learning, to distinguish between 588 multi-domain proteins and to recreate the evolutionary relationships at the proteinand species levels of the SCOPe database. The six groups that participated in the challenge submitted a total of 15 sets of results. We observed that the performance of all the methods significantly decreases at the species level, suggesting that shape-only protein comparison is challenging for closely related proteins. Even if the dataset is limited in size (only 588 proteins are considered whereas more than 160,000 protein structures are experimentally solved), we think that this work provides useful insights into the current shape comparison methods performance, and highlights possible limitations to large-scale applications due to the computational cost.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Elsevier
ISSN:	0097-8493
Date of First Compliant Deposit:	14 August 2020
Date of Acceptance:	28 July 2020
Last Modified:	25 Oct 2024 15:31
URI:	https://orca.cardiff.ac.uk/id/eprint/134215

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