Incorporating Backbone Flexibility in MedusaDock Improves Ligand-Binding Pose Prediction in the CSAR2011 Docking Benchmark

Abstract

Solution of the structures of ligand-receptor complexes via computational docking is an integral step in many structural modeling efforts as well as in rational drug discovery. A major challenge in ligand-receptor docking is the modeling of both receptor and ligand flexibilities in order to capture receptor conformational changes induced by ligand binding. In the molecular docking suite MedusaDock, both ligand and receptor side chain flexibilities are modeled simultaneously with sets of discrete rotamers, where the ligand rotamer library is generated "on the fly" in a stochastic manner. Here, we introduce backbone flexibility into MedusaDock by implementing ensemble docking in a sequential manner for a set of distinct receptor backbone conformations. We generate corresponding backbone ensembles to capture backbone changes upon binding to different ligands, as observed experimentally. We develop a simple clustering and ranking approach to select the top poses as blind predictions. We applied our method in the CSAR2011 benchmark exercise. In 28 out of 35 cases (80%) where the ligand-receptor complex structures were released, we were able to predict near-native poses (<2.5 Å RMSD), the highest success rate reported for CSAR2011. This result highlights the importance of modeling receptor backbone flexibility to the accurate docking of ligands to flexible targets. We expect a broad application of our fully flexible docking approach in biological studies as well as in rational drug design.