Predicting protein-ligand binding site using support vector machine with protein properties

Ginny Y. Wong, Hung Fat Frank Leung, S. H. Ling

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)

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.
Original languageEnglish
Article number6636315
Pages (from-to)1517-1529
Number of pages13
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume10
Issue number6
DOIs
Publication statusPublished - 1 Nov 2013

Keywords

  • Binding sites predication
  • Bioinformatics
  • Protein-ligand binding sites
  • Structure-based drug design
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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