Title: Predicting protein–protein interfaces as clusters of Optimal Docking Area points

Authors: Yasir Arafat, Joarder Kamruzzaman, Gour C. Karmakar, Juan Fernandez-Recio

Addresses: Faculty of Medicine, Department of Biochemistry and Molecular Biology, Monash University, VIC 3800, Australia. ' Faculty of Information Technology, Gippsland School of Information Technology, Monash University, VIC 3800, Australia. ' Faculty of Information Technology, Gippsland School of Information Technology, Monash University, VIC 3800, Australia. ' Barcelona Supercomputing Center, Life Sciences Department, 08034 Barcelona, Spain

Abstract: Desolvation property is used here to predict protein-protein binding sites exploiting the fact that lower-valued |optimal docking area| ODA (Fernandez-Recio et al., 2005) points form cluster at the interface. The proposed method involves two steps; clustering the ODA points and representing ODA points by average ODA values. On 51 nonredundant proteins, results show the success rate improved considerably. Considering only significant ODA, the previous ODA method has obtained a success rate of 65% with overall success rate of 39%. The proposed method improved the overall success rate to 61%. Further, comparable results were found for X-ray and NMR structures.

Keywords: desolvation energy; protein–protein binding; average ODA value; data mining; bioinformatics; protein–protein interfaces; optimal docking area; clustering; nonredundant proteins.

DOI: 10.1504/IJDMB.2009.023884

International Journal of Data Mining and Bioinformatics, 2009 Vol.3 No.1, pp.55 - 67

Published online: 17 Mar 2009 *

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