Improving virtual screening of G protein-coupled receptors via ligand-directed modeling

Thomas Coudrat, John Simms, Arthur Christopoulos, Denise Wootten, Patrick M. Sexton

Research output: Contribution to journalArticle

Abstract

G protein-coupled receptors (GPCRs) play crucial roles in cell physiology and pathophysiology. There is increasing interest in using structural information for virtual screening (VS) of libraries and for structure-based drug design to identify novel agonist or antagonist leads. However, the sparse availability of experimentally determined GPCR/ligand complex structures with diverse ligands impedes the application of structure-based drug design (SBDD) programs directed to identifying new molecules with a select pharmacology. In this study, we apply ligand-directed modeling (LDM) to available GPCR X-ray structures to improve VS performance and selectivity towards molecules of specific pharmacological profile. The described method refines a GPCR binding pocket conformation using a single known ligand for that GPCR. The LDM method is a computationally efficient, iterative workflow consisting of protein sampling and ligand docking. We developed an extensive benchmark comparing LDM-refined binding pockets to GPCR X-ray crystal structures across seven different GPCRs bound to a range of ligands of different chemotypes and pharmacological profiles. LDM-refined models showed improvement in VS performance over origin X-ray crystal structures in 21 out of 24 cases. In all cases, the LDM-refined models had superior performance in enriching for the chemotype of the refinement ligand. This likely contributes to the LDM success in all cases of inhibitor-bound to agonist-bound binding pocket refinement, a key task for GPCR SBDD programs. Indeed, agonist ligands are required for a plethora of GPCRs for therapeutic intervention, however GPCR X-ray structures are mostly restricted to their inactive inhibitor-bound state.

Original languageEnglish
Article numbere1005819
JournalPLoS computational biology
Volume13
Issue number11
Early online date13 Nov 2017
DOIs
Publication statusPublished - 13 Nov 2017

Bibliographical note

Copyright: © 2017 Coudrat et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: Computational studies were supported by Melbourne Bioinformatics at the University of Melbourne. This research was supported in part by the Monash eResearch Centre and eSolutions-Research Support Services through the use of the Monash Campus HPC Cluster.

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