Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity.

Ismael Rodríguez-Espigares,Mariama Jaiteh,Alexander Mackerell,Jens Carlsson,Jana Selent

doi:10.1371/journal.pcbi.1007680

Abstract

Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.

Highlights

Gprotein-coupled receptors (GPCRs) constitute a large superfamily of membrane proteins and play key roles in cellular signaling
Three-dimensional structures of proteins combined with computational methods have become widely used to identify starting-points for drug discovery
We assessed prediction of G protein-coupled receptors (GPCRs) structure based on previously determined crystal structures as templates by using the homology modeling method

Summary

Introduction

Gprotein-coupled receptors (GPCRs) constitute a large superfamily of membrane proteins and play key roles in cellular signaling. GPCRs recognize chemically diverse molecules and binding of their endogenous ligands leads to activation of intracellular signaling pathways [1]. Structurebased virtual screens of large chemical libraries against GPCRs, followed by experimental testing of top-scoring compounds, have successfully identified leads of many therapeutic targets, including biogenic amine [3,4,5,6,7,8], purine [9], peptide [10,11] and protein-binding receptors [12]. The fact that molecular docking can be used to search chemical libraries with >100 million compounds [8] and identify ligands with hit rates as high as 73% [4] suggests that virtual screening can make important contributions to drug discovery

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Discussion

Conclusion