Role of binding entropy in the refinement of protein-ligand docking predictions: Analysis based on the use of 11 scoring functions

We present results of testing the ability of eleven popular scoring functions to predict native docked positions using a recently developed method (Ruvinsky and Kozintsev, J Comput Chem 2005, 26, 1089) 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. J Med Chem 2003, 46, 2287) for each ligand in the test set. To test the suggested method we compared 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 compared the performance of the suggested method with the method based on ranking by cluster occupancy only. We analyze how the choice of a clustering‐RMSD and a low bound of dense clusters impacts on docking accuracy of the scoring methods. We derive optimal intervals of the clustering‐RMSD for 11 scoring functions. © 2007 Wiley Periodicals, Inc. J Comput Chem 2007