The role of binding entropy in the refinement of protein-ligand docking predictions: analysis based on the use of 11 scoring functions

Abstract

We present results of testing of the ability of eleven popular scoring functions to predict native docked positions using a recently developed method [1] for estimation the entropy contributions of relative motions to protein-ligand binding affinity. The method is based on the integration of the configurational integral over clusters obtained from multiple docked positions. We use a test set of 100 PDB protein-ligand complexes and ensembles of 101 docked positions generated by Wang et al [2] for each ligand in the test set. To test the suggested method we compare the averaged root-mean square deviations (RMSD) of the top-scored ligand docked positions, accounting and not accounting for entropy contributions, relative to the experimentally determined positions. We demonstrate that the method increases docking accuracy by 10-21% when used in conjunction with the AutoDock scoring function, by 2-25% with G-Score, by 7-41% with D-Score, by 0-8% with LigScore, by 1-6% with PLP, by 0-12% with LUDI, by 2-8% with F-Score, by 7-29% with ChemScore, by 0-9% with X-Score, by 2-19% with PMF, and by 1-7% with DrugScore. We also compare the performance of the suggested method with the method based on ranking by cluster occupancy only. We analyze how the choice of a RMSD-tolerance and a low bound of dense clusters impacts on docking accuracy of the scoring methods. We derive optimal intervals of the RMSD-tolerance for 11 scoring functions.