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BioMed Research International
Volume 2013 (2013), Article ID 409658, 7 pages
http://dx.doi.org/10.1155/2013/409658
Research Article

An Accurate Method for Prediction of Protein-Ligand Binding Site on Protein Surface Using SVM and Statistical Depth Function

1College of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
2Division of Experimental Cancer, Cross Cancer Institute, 115660 University Avenue, Edmonton, AB, Canada T6G 2V4

Received 17 May 2013; Revised 15 August 2013; Accepted 29 August 2013

Academic Editor: Bing Niu

Copyright © 2013 Kui Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Since proteins carry out their functions through interactions with other molecules, accurately identifying the protein-ligand binding site plays an important role in protein functional annotation and rational drug discovery. In the past two decades, a lot of algorithms were present to predict the protein-ligand binding site. In this paper, we introduce statistical depth function to define negative samples and propose an SVM-based method which integrates sequence and structural information to predict binding site. The results show that the present method performs better than the existent ones. The accuracy, sensitivity, and specificity on training set are 77.55%, 56.15%, and 87.96%, respectively; on the independent test set, the accuracy, sensitivity, and specificity are 80.36%, 53.53%, and 92.38%, respectively.