A benchmarking study on virtual ligand screening against homology models of human GPCRs

Victor Jun Yu Lim, Weina Du, Yu Zong Chen, Hao Fan
2018 Proteins: Structure, Function, and Bioinformatics  
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
more » ... 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. Introduction GPCRs, also commonly known as seven-transmembrane domain receptors, are responsible for many of our physiological responses and activities, including responses to hormone, neurotransmitter and even environmental stimulants such as taste, smell and vision 1 . GPCRs represent the largest and most successful class of druggable targets in the human genome, with over 27% of FDA-approved drugs targeting approximately 60 out of the total over 800 GPCRs 2-3 . However, majority of human GPCRs have not yet been explored in peer-reviewed)
doi:10.1002/prot.25533 pmid:30051928 fatcat:ktmqex4vxrh7hgjfa7ouwnr7ga