COMP 108 |
| We present results of testing of the ability of eleven popular scoring functions to predict native docked positions using a recently developed method [1,2] for estimation the entropy contributions of relative and torsional motions to protein-ligand binding affinity. The method is based on the Monte-Carlo 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 [3] 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. 1. Ruvinsky A.M. submitted, 2006. 2. Ruvinsky AM, Kozincev AV. J Comp Chem 2005; 26: 1089-1095. 3. Wang R, Lu Y, Wang S. J Med Chem 2003; 46: 2287- 2303. |
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Emerging Technologies in Computational Chemistry, Sponsored by Schrodinger, Inc
1:00 PM-5:35 PM, Monday, 11 September 2006 Moscone Center -- Room 228/230, Oral
Division of Computers in Chemistry |