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

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 displayed ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even displayed ligand enrichment 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.Author summary: Three-dimensional structures of proteins combined with computational methods have become widely used to identify starting-points for drug discovery. However, this powerful approach is limited by the lack of atomic resolution structures for many drug targets. G protein-coupled receptors (GPCRs) belong to the largest family of cell surface receptors and play roles in numerous physiological processes. As GPCRs are important therapeutic targets, there is significant interest in applying structure-based in silico screening to accelerate the drug discovery process. However, GPCRs have been notoriously difficult to crystallize and structures are lacking for >80% of the family. We assessed prediction of GPCR structure based on previously determined crystal structures as templates by using the homology modeling method. We explored strategies to identify models suitable for virtual screening with the molecular docking method and to further refine structures using molecular dynamics simulations. Our calculations revealed that the closest homologue of a target is not necessarily the best template and demonstrated how accurate binding site models with excellent ability to identify ligands can be obtained. The results highlight strengths and weaknesses of structure prediction methods and provide guidelines for successful application of virtual screening to proteins of unknown structure.