Predicting protein-ligand binding site with support vector machine

Ginny Y. Wong; Hung Fat Frank Leung

doi:10.1109/CEC.2010.5586110

Predicting protein-ligand binding site with support vector machine

Ginny Y. Wong
, Hung Fat Frank Leung

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

3 Citations (Scopus)

Abstract

Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In these two decades, many different approaches have been developed to predict the binding site, such as geometric, energetic and sequence-based methods. We present the binding site prediction algorithm that takes advantage of both sequence conservation and geometric methods for pocket finding (LIGSITE and SURFNET). SVM is used to cluster the pockets, which are most likely to bind ligands with the attributes of grid value, interaction potential and offset from protein. We compare our algorithm to four other approaches: LIGSITE, SURFNET, PocketFinder and Concavity. Our algorithm is found to provide the highest success rate.

Original language	English
Title of host publication	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
DOIs	https://doi.org/10.1109/CEC.2010.5586110
Publication status	Published - 1 Dec 2010
Event	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 - Barcelona, Spain Duration: 18 Jul 2010 → 23 Jul 2010

Conference

Conference	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Country/Territory	Spain
City	Barcelona
Period	18/07/10 → 23/07/10

ASJC Scopus subject areas

Computational Theory and Mathematics
Applied Mathematics

More information

10.1109/CEC.2010.5586110

Cite this

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title = "Predicting protein-ligand binding site with support vector machine",

abstract = "Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In these two decades, many different approaches have been developed to predict the binding site, such as geometric, energetic and sequence-based methods. We present the binding site prediction algorithm that takes advantage of both sequence conservation and geometric methods for pocket finding (LIGSITE and SURFNET). SVM is used to cluster the pockets, which are most likely to bind ligands with the attributes of grid value, interaction potential and offset from protein. We compare our algorithm to four other approaches: LIGSITE, SURFNET, PocketFinder and Concavity. Our algorithm is found to provide the highest success rate.",

author = "Wong, \{Ginny Y.\} and Leung, \{Hung Fat Frank\}",

year = "2010",

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doi = "10.1109/CEC.2010.5586110",

language = "English",

isbn = "9781424469109",

booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010",

note = "2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 ; Conference date: 18-07-2010 Through 23-07-2010",

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Wong, GY & Leung, HFF 2010, Predicting protein-ligand binding site with support vector machine. in 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010., 5586110, 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18/07/10. https://doi.org/10.1109/CEC.2010.5586110

Predicting protein-ligand binding site with support vector machine. / Wong, Ginny Y.; Leung, Hung Fat Frank.
2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. 2010. 5586110.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Leung, Hung Fat Frank

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N2 - Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In these two decades, many different approaches have been developed to predict the binding site, such as geometric, energetic and sequence-based methods. We present the binding site prediction algorithm that takes advantage of both sequence conservation and geometric methods for pocket finding (LIGSITE and SURFNET). SVM is used to cluster the pockets, which are most likely to bind ligands with the attributes of grid value, interaction potential and offset from protein. We compare our algorithm to four other approaches: LIGSITE, SURFNET, PocketFinder and Concavity. Our algorithm is found to provide the highest success rate.

AB - Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In these two decades, many different approaches have been developed to predict the binding site, such as geometric, energetic and sequence-based methods. We present the binding site prediction algorithm that takes advantage of both sequence conservation and geometric methods for pocket finding (LIGSITE and SURFNET). SVM is used to cluster the pockets, which are most likely to bind ligands with the attributes of grid value, interaction potential and offset from protein. We compare our algorithm to four other approaches: LIGSITE, SURFNET, PocketFinder and Concavity. Our algorithm is found to provide the highest success rate.

UR - https://www.scopus.com/pages/publications/79959449600

U2 - 10.1109/CEC.2010.5586110

DO - 10.1109/CEC.2010.5586110

M3 - Conference article published in proceeding or book

SN - 9781424469109

BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

Y2 - 18 July 2010 through 23 July 2010

ER -

Predicting protein-ligand binding site with support vector machine

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