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A benchmarking study on virtual ligand screening against homology models of human GPCRs
Author(s) -
Lim Victor Jun Yu,
Du Weina,
Chen Yu Zong,
Fan Hao
Publication year - 2018
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25533
Subject(s) - g protein coupled receptor , virtual screening , homology modeling , computational biology , homology (biology) , benchmarking , docking (animal) , drug discovery , sequence homology , bioinformatics , biology , computer science , receptor , medicine , gene , genetics , biochemistry , peptide sequence , nursing , marketing , business , enzyme
Abstract G‐protein‐coupled receptor (GPCR) is an important target class of proteins for drug discovery, with over 27% of FDA‐approved drugs targeting GPCRs. However, being a membrane protein, it is difficult to obtain the 3D crystal structures of GPCRs for virtual screening of ligands by molecular docking. Thus, we evaluated the virtual screening performance of homology models of human GPCRs with respect to the corresponding crystal structures. Among the 19 GPCRs involved in this study, we observed that 10 GPCRs have homology models that have better or comparable performance with respect to the corresponding X‐ray structures, making homology models a viable choice for virtual screening. For a small subset of GPCRs, we also explored how certain methods like consensus enrichment and sidechain perturbation affect the utility of homology models in virtual screening, as well as the selectivity between agonists and antagonists. Most notably, consensus enrichment across multiple homology models often yields results comparable to the best performing model, suggesting that ligand candidates predicted with consensus scores from multiple models can be the optimal option in practical applications where the performance of each model cannot be estimated.