Protein-ligand docking programs are
indispensable tools for predicting the binding pose of a ligand to the receptor
protein in current structure-based drug design. In this paper, we evaluate the
performance of grey wolf optimization (GWO) in protein-ligand docking. Two
versions of the GWO docking program – the original GWO and the modified one
with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments
show that the GWO programs have enhanced exploration capability leading to
significant speedup in the search while maintaining comparable binding pose
prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO
methods are competitive in pose ranking but lower in success rates than
AutoDockFR. Successful redocking of all the flexible cases to their holo
structures reveals that inaccurate scoring function and lack of proper
treatment of backbone are the major causes of docking failures.
