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.
Leak Proof PDBBind: A Reorganized Dataset of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction
Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley（加州大学伯克利分校皮策理论化学中心化学系）；Departments of Bioengineering and Chemical and Biomolecular Engineering, University of California, Berkeley（加州大学伯克利分校生物工程系和化学与生物工程系）
