Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening

Recent benchmark studies have demonstrated the difficulties in obtaining accurate predictions of ligand binding conformations to comparative models of G-protein-coupled receptors. We have developed a data-driven optimization protocol, which integrates mutational data and structural information from multiple X-ray receptor structures in combination with a fully flexible ligand docking protocol to determine the binding conformation of AR231453, a small-molecule agonist, in the GPR119 receptor. Resulting models converge to one conformation that explains the majority of data from mutation studies and is consistent with the structure-activity relationship for a large number of AR231453 analogs. Another key property of the refined models is their success in separating active ligands from decoys in a large-scale virtual screening. These results demonstrate that mutation-guided receptor modeling can provide predictions of practical value for describing receptor-ligand interactions and drug discovery.