Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8208
Title: Predicting protein-ligand binding site using support vector machine with protein properties
Authors: Wong, GY
Leung, FHF 
Ling, SH
Keywords: Binding sites predication
Bioinformatics
Protein-ligand binding sites
Structure-based drug design
Support vector machine (SVM)
Issue Date: 2013
Publisher: IEEE Computer Soc
Source: IEEE/acm transactions on computational biology and bioinformatics, 2013, v. 10, no. 6, 6636315, p. 1517-1529 How to cite?
Journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics 
Abstract: Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In the past two decades, different approaches have been developed to predict the binding site, such as the geometric, energetic, and sequence-based methods. When scores are calculated from these methods, the algorithm for doing classification becomes very important and can affect the prediction results greatly. In this paper, the support vector machine (SVM) is used to cluster the pockets that are most likely to bind ligands with the attributes of geometric characteristics, interaction potential, offset from protein, conservation score, and properties surrounding the pockets. Our approach is compared to LIGSITE, LIGSITEcsc, SURFNET, Fpocket, PocketFinder, Q-SiteFinder, ConCavity, and MetaPocket on the data set LigASite and 198 drug-target protein complexes. The results show that our approach improves the success rate from 60 to 80 percent at AUC measure and from 61 to 66 percent at top 1 prediction. Our method also provides more comprehensive results than the others.
URI: http://hdl.handle.net/10397/8208
ISSN: 1545-5963
DOI: 10.1109/TCBB.2013.126
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