Identification of protein-ligand binding site using multi-clustering and support vector machine

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Multi-clustering has been widely used. It acts as a pre-training process for identifying protein-ligand binding in structure-based drug design. Then, the Support Vector Machine (SVM) is employed to classify the sites most likely for binding ligands. Three types of attributes are used, namely geometry-based, energy-based, and sequence conservation. Comparison is made on 198 drug-target protein complexes with LIGSITECSC, SURFNET, Fpocket, Q-SiteFinder, ConCavity, and MetaPocket. The results show an improved success rate of up to 86%.
Original languageEnglish
Title of host publicationProceedings of the IECON 2016 - 42nd Annual Conference of the Industrial Electronics Society
PublisherIEEE Computer Society
Pages939-944
Number of pages6
ISBN (Electronic)9781509034741
DOIs
Publication statusPublished - 21 Dec 2016
Event42nd Conference of the Industrial Electronics Society, IECON 2016 - Palazzo dei Congressi, Florence, Italy
Duration: 24 Oct 201627 Oct 2016

Conference

Conference42nd Conference of the Industrial Electronics Society, IECON 2016
Country/TerritoryItaly
CityFlorence
Period24/10/1627/10/16

Keywords

  • Multi-clustering
  • Protein-ligand binding site
  • SVM

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this