Studying protein-protein interaction through side-chain modeling method OPUS-Mut.

protein side-chain modeling protein–protein docking protein–protein interaction scoring protein–protein docking poses

Journal

Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837

Informations de publication

Date de publication:
20 09 2022
Historique:
received: 15 05 2022
revised: 17 07 2022
accepted: 20 07 2022
pubmed: 13 8 2022
medline: 28 9 2022
entrez: 12 8 2022
Statut: ppublish

Résumé

Protein side chains are vitally important to many biological processes such as protein-protein interaction. In this study, we evaluate the performance of our previous released side-chain modeling method OPUS-Mut, together with some other methods, on three oligomer datasets, CASP14 (11), CAMEO-Homo (65) and CAMEO-Hetero (21). The results show that OPUS-Mut outperforms other methods measured by all residues or by the interfacial residues. We also demonstrate our method on evaluating protein-protein docking pose on a dataset Oligomer-Dock (75) created using the top 10 predictions from ZDOCK 3.0.2. Our scoring function correctly identifies the native pose as the top-1 in 45 out of 75 targets. Different from traditional scoring functions, our method is based on the overall side-chain packing favorableness in accordance with the local packing environment. It emphasizes the significance of side chains and provides a new and effective scoring term for studying protein-protein interaction.

Identifiants

pubmed: 35959990
pii: 6663639
doi: 10.1093/bib/bbac330
pii:
doi:

Substances chimiques

Proteins 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Auteurs

Gang Xu (G)

Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China.
Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China.
Shanghai AI Laboratory, Shanghai 200030, China.

Yilin Wang (Y)

Georgetown Preparatory School, North Bethesda, MD 20852, USA.

Qinghua Wang (Q)

Center for Biomolecular Innovation, Harcam Biomedicines, Shanghai, China.

Jianpeng Ma (J)

Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China.
Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China.
Shanghai AI Laboratory, Shanghai 200030, China.

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Classifications MeSH