A genetic algorithm to enhance transmembrane helices prediction

Nazar Zaki, Salah Bouktif, Sanja Lazarova-Molnar

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

4 Citations (Scopus)

Abstract

A transmembrane helix (TMH) topology prediction is becoming a central problem in bioinformatics because the structure of TM proteins is difficult to determine by experimental means. Therefore, methods which could predict the TMHs topologies computationally are highly desired. In this paper we introduce TMHindex, a method for detecting TMH segments solely by the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index deduced from a combination of the difference in amino acid appearances in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, genetic algorithm was employed to find the optimal threshold value to separate TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in 70 testing protein sequences. The level of accuracy achieved using TMHindex in comparison to recent methods for predicting the topology of TM proteins is a strong argument in favor of our method.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computation Conference, GECCO'11
Pages347-354
Number of pages8
DOIs
Publication statusPublished - Aug 24 2011
Event13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin, Ireland
Duration: Jul 12 2011Jul 16 2011

Publication series

NameGenetic and Evolutionary Computation Conference, GECCO'11

Other

Other13th Annual Genetic and Evolutionary Computation Conference, GECCO'11
Country/TerritoryIreland
CityDublin
Period7/12/117/16/11

Keywords

  • Amino acid composition
  • Compositional index
  • Genetic algorithm
  • Membrane protein
  • Transmembrane helices

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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