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The development of an affinity evaluation and prediction system by using protein–protein docking simulations and parameter tuning

Author(s): Koki Tsukamoto | Tatsuya Yoshikawa | Kiyonobu Yokota | Yuichiro Hourai | Kazuhiko Fukui

Journal: Advances and Applications in Bioinformatics and Chemistry
ISSN 1178-6949

Volume: 2009;
Issue: default;
Start page: 1;
Date: 2009;
Original page

Koki Tsukamoto1, Tatsuya Yoshikawa1,2, Kiyonobu Yokota1, Yuichiro Hourai1, Kazuhiko Fukui11Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo, Japan; 2Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Toyonaka, Osaka, JapanAbstract: A system was developed to evaluate and predict the interaction between protein pairs by using the widely used shape complementarity search method as the algorithm for docking simulations between the proteins. We used this system, which we call the affinity evaluation and prediction (AEP) system, to evaluate the interaction between 20 protein pairs. The system first executes a “round robin” shape complementarity search of the target protein group, and evaluates the interaction between the complex structures obtained by the search. These complex structures are selected by using a statistical procedure that we developed called ‘grouping’. At a prevalence of 5.0%, our AEP system predicted protein–protein interactions with a 50.0% recall, 55.6% precision, 95.5% accuracy, and an F-measure of 0.526. By optimizing the grouping process, our AEP system successfully predicted 10 protein pairs (among 20 pairs) that were biologically relevant combinations. Our ultimate goal is to construct an affinity database that will provide cell biologists and drug designers with crucial information obtained using our AEP system.Keywords: protein–protein interaction, affinity analysis, protein–protein docking, FFT, massive parallel computing

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