Using microenvironments to identify allosteric binding sites

Christopher Eric Foley, Sana Mohammad M Al Azwari, Mark Dufton, John Wilson

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)

Abstract

Protein amino acid residues can be classified by their chemical properties and data mining can be used to make predictions about their structure and function. However, the properties of the surrounding residues contribute to the overall chemical context. This paper defines microenvironments as the spherical volume around a point in space and uses these volumes to determine average properties of the encompassed residues. The approach to index generation rapidly constructs microenvironment data. The averaged chemical properties are then employed in allosteric site prediction using support vector machines and neural networks. The results show that index generation performs best when microenvironment radius matches the granularity of the index and that microenvironments improve the classification accuracy.
Original languageEnglish
Title of host publication2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2012)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages411-415
Number of pages5
ISBN (Print)9781467325592
DOIs
Publication statusPublished - 4 Oct 2012
Event2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2012) - Philadelphia, United States
Duration: 4 Oct 20127 Oct 2012

Conference

Conference2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2012)
Country/TerritoryUnited States
CityPhiladelphia
Period4/10/127/10/12

Keywords

  • neural networks
  • microenvironments
  • allosteric binding sites
  • bioinformatics

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