Performances and Critical Limitations of Docking and Protein–Ligand Interactions for Efficacy-Driven Virtual Screening Targeting Class A GPCRs
TL;DR
A comprehensive assessment based on systematic benchmarking using a diverse set of class A G-protein-coupled receptors and different approaches to represent protein–ligand demonstrates that the combined approach overall improves the efficacy bias of selected ligands as compared to docking alone.
Abstract
Structure-based virtual screening of chemical libraries is an established and widely used strategy for identifying novel ligands for G-protein-coupled receptors. An enhancement based on integrating protein–ligand interaction with docking has previously been proposed, but its actual impact on improving screening outcomes has remained unclear. Here, we present a comprehensive assessment based on systematic benchmarking using a diverse set of class A G-protein-coupled receptors and different approaches to represent protein–ligand, including dynamic patterns extracted from molecular dynamics simulations. Our results demonstrate that the combined approach overall improves the efficacy bias of selected ligands as compared to docking alone (ranking by scoring function). All tested variations prove broadly functional; however, the most sophisticated one─incorporating simulation and a learning model─emerges as the most robust alternative for a prospective setting. The analysis of two prospective cases, the design of both agonists and antagonists of CNR1 and the more challenging search for CXCR4 nonpeptidic agonist, reveals both great potential and inherent structural limitations, highlighting the need for an accurate and suitable three-dimensional structure.