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

By Anatoly M. Ruvinsky. 2006

57 citations recorded by the source corpus.

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

Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Chemistry & Drug Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

In Computational Chemistry & Drug Discovery

Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, background): “Issues such as desolvation and entropic effects are examples of the challenges to be overcome by the current docking algorithms [71,72].”
Docking and scoring with ICM: the benchmarking results and strategies for improvement (2012, background): “The reasons behind this scoring problem have been well described in recent reviews [2, 3], and typically pointed out to protein flexibility and induced-fit upon ligand binding [4], oversimplification of energy terms commonly used in the scoring functions such as solvation [5] and entropy contributio”
Protein-ligand docking in the new millennium--a retrospective of 10 years in the field. (2013, background): “The authors subsequently showed important improvements when applied in conjunction with other wellknown scoring functions [129], namely by 2–25% when used with G-Score, 7–41% with D-Score, 0–8% with LigScore, 1– 6% with PLP, 0–12% with LUDI, 2–8% with F-Score, 7– 29% with ChemScore, 0–9% with X-Scor”
Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go (2008, background): “A more advanced approach has recently been proposed that evaluates the restriction of the ligand conformational space by considering a large number of poses (Ruvinsky, 2007) at the expense of CPU time.”