Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/10113
Title: Predicting the accuracy of protein-ligand docking on homology models
Authors: Bordogna, A
Pandini, A
Bonati, L
Keywords: Drug discovery;Homology modeling;Model quality assessment;Model quality indices;Molecular docking
Issue Date: 2011
Citation: Journal of Computational Chemistry, 32 (1): 81 - 98, (January 2011)
Abstract: Ligand-protein docking is increasingly used in Drug Discovery. The initial limitations imposed by a reduced availability of target protein structures have been overcome by the use of theoretical models, especially those derived by homology modeling techniques. While this greatly extended the use of docking simulations, it also introduced the need for general and robust criteria to estimate the reliability of docking results given the model quality. To this end, a large-scale experiment was performed on a diverse set including experimental structures and homology models for a group of representative ligand-protein complexes. A wide spectrum of model quality was sampled using templates at different evolutionary distances and different strategies for target-template alignment and modeling. The obtained models were scored by a selection of the most used model quality indices. The binding geometries were generated using AutoDock, one of the most common docking programs. An important result of this study is that indeed quantitative and robust correlations exist between the accuracy of docking results and the model quality, especially in the binding site. Moreover, state-of-the-art indices for model quality assessment are already an effective tool for an a priori prediction of the accuracy of docking experiments in the context of groups of proteins with conserved structural characteristics.
URI: http://onlinelibrary.wiley.com/doi/10.1002/jcc.21601/abstract
http://bura.brunel.ac.uk/handle/2438/10113
DOI: http://dx.doi.org/10.1002/jcc.21601
ISSN: 0192-8651
1096-987X
Appears in Collections:Dept of Computer Science Research Papers

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