Classification of heterodimer interfaces using docking models and construction of scoring functions for the complex structure prediction

ssification of heterodimer interfaces using docking models and construction of scoring functions for the complex structure prediction Original Research (5572) Total Article Views Authors: Yuko Tsuchiya, Eiji Kanamori, Haruki Nakamura, et al Published Date September 2009 Volume 2009:2 Pages 79 - 100 DOI: http://dx.doi.org/10.2147/AABC.S6347 Yuko Tsuchiya1, Eiji Kanamori2,3, Haruki Nakamura4, Kengo Kinoshita1,5 1Institute of Medical Science, University of Tokyo, Tokyo, Japan; 2Biomedicinal Information Research Center, Japan Biological Informatics Consortium, Tokyo, Japan; 3Hitachi Software Engineering Co., Ltd., Yokohama, Japan; 4Institute for Protein Research, Osaka University, Osaka, Japan; 5Bioinformatics Research and Development, JST Saitama, Japan Abstract: Protein–protein docking simulations can provide the predicted complex structural models. In a docking simulation, several putative structural models are selected by scoring functions from an ensemble of many complex models. Scoring functions based on statistical analyses of heterodimers are usually designed to select the complex model with the most abundant interaction mode found among the known complexes, as the correct model. However, because the formation schemes of heterodimers are extremely diverse, a single scoring function does not seem to be sufficient to describe the fitness of the predicted models other than the most abundant interaction mode. Thus, it is necessary to classify the heterodimers in terms of their individual interaction modes, and then to construct multiple scoring functions for each heterodimer type. In this study, we constructed the classification method of heterodimers based on the discriminative characters between near-native and decoy models, which were found in the comparison of the interfaces in terms of the complementarities for the hydrophobicity, the electrostatic potential and the shape. Consequently, we found four heterodimer clusters, and then constructed the multiple scoring functions, each of which was optimized for each cluster. Our multiple scoring functions were applied to the predictions in the unbound docking.