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| About Anatoly Ruvinsky (University of Kansas) |
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Anatoly Ruvinsky was born in Ivano-Frankivsk, Ukraine. He received his M.Sc. in Engineering/Material Sciences from the Moscow Institute for Steel and Alloys in 1995. Dr. Ruvinsky pursued graduate studies at Alma Mater under the direction of Professor Yurii E. Lozovik. His graduate work focused on magnetic and optical properties of multi-layered nanostructures. He received his Ph.D. in Physics and Mathematics from the Moscow Institute for Steel and Alloys in 1998.
His postgraduate work at the Laboratory of Nanostructure Spectroscopy, Institute of Spectroscopy of Russian Academy of Sciences focused on excitonic properties of quantum wells and dots. Between 2001 and 2006, Dr. Ruvinsky worked for Algodign, LLC, a first biotech-company in Moscow, Russia. He then started as a researcher at Force Field Lab and was promoted to a project manager. In 2006 he joined the Center of Bioinformatics of the University of Kansas as a postdoctoral researcher. He was the recipient of the Soros Science Foundation Graduate Scholarship and the Scholarship from the International Center of Fundamental Physics. He received a HP Award from Hewlett-Packard and the ACS Division of Computers in Chemistry in 2006.
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How important is binding entropy of relative motions in protein-ligand docking and virtual screening? Analysis based on the use of 11 scoring functions.
Anatoly M. Ruvinsky, Center for Bioinformatics, University of Kansas, 2030 Becker Drive, Lawrence, KS 66047
In the context of virtual database screening, calculations of protein-ligand binding entropy of relative and overall molecular motions are challenging, owing to the inherent structural complexity of the ligand binding well in the energy landscape of protein-ligand interactions and computing time limitations. We describe a fast statistical thermodynamic method for estimation of the binding entropy to address the challenges [1, 2]. 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 [2] 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.
Then we focus on the characterization of protein-ligand energy landscapes described by the 11 scoring functions and on the assessment of binding entropy [3]. We apply the method in conjunction with 11 popular scoring functions to evaluate the binding entropy of 100 protein-ligand complexes. The averaged values of binding entropy contribution vary from -9.1 to -6.2 kcal/mol, showing good agreement with the literature. We calculate positional sizes and the angular volume of the native ligand wells. The averaged geometric mean of positional sizes in principal directions varies from 0.8 to 1.4 Angstroms. The calculated range of angular volumes is 3.3-11.8 rad2. Then we demonstrate that the averaged six-dimensional volume of the native well is larger than the volume of the most populated non-native well in energy landscapes described by all of 11 scoring functions. We believe our results and methodology provide a promising starting point for routine incorporation of entropy calculations in screening applications.
References
1. Ruvinsky A.M. and Kozintsev A.V. J Comput Chem 2005, 26: 1089-1095.
2. Ruvinsky A.M. J Comput Chem 2007, 28: 1364–1372.
3. Ruvinsky A.M. J Comp-Aided Mol Des, 2007, in print.
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