Published online 7 August 2006
Nucleic Acids Research, 2006, Vol. 34, No. 13 3698-3707
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commerical use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Protein binding site prediction using an empirical scoring function
Howard Hughes Medical Institute Center for Single Molecule Biophysics, Department of Physiology and Biophysics, State University of New York at Buffalo 124 Sherman Hall, Buffalo, NY 14214, USA
*To whom correspondence should be addressed. Tel: +1 716 829 2985; Fax: +1 716 829 2344; Email: yqzhou{at}buffalo.edu
Received January 13, 2006. Revised June 12, 2006. Accepted June 13, 2006.
Most biological processes are mediated by interactions between proteins and their interacting partners including proteins, nucleic acids and small molecules. This work establishes a method called PINUP for binding site prediction of monomeric proteins. With only two weight parameters to optimize, PINUP produces not only 42.2% coverage of actual interfaces (percentage of correctly predicted interface residues in actual interface residues) but also 44.5% accuracy in predicted interfaces (percentage of correctly predicted interface residues in the predicted interface residues) in a cross validation using a 57-protein dataset. By comparison, the expected accuracy via random prediction (percentage of actual interface residues in surface residues) is only 15%. The binding sites of the 57-protein set are found to be easier to predict than that of an independent test set of 68 proteins. The average coverage and accuracy for this independent test set are 30.5 and 29.4%, respectively. The significant gain of PINUP over expected random prediction is attributed to (i) effective residue-energy score and accessible-surface-area-dependent interface-propensity, (ii) isolation of functional constraints contained in the conservation score from the structural constraints through the combination of residue-energy score (for structural constraints) and conservation score and (iii) a consensus region built on top-ranked initial patches.
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