Transmembrane helix prediction in proteins using hydrophobicity properties and higher-order statistics

  • Authors:

  • Ilias K. Kitsas;Leontios J. Hadjileontiadis;Stavros M. Panas

  • Affiliations:

  • Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki GR-54124, Greece;Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki GR-54124, Greece;Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki GR-54124, Greece

  • Venue:
  • Computers in Biology and Medicine
  • Year:
  • 2008

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Abstract

Prediction of the transmembrane (TM) helices is important in the study of membrane proteins. A novel method to predict the location and length of both single and multiple TM helices in human proteins is presented. The proposed method is based on a combination of hydrophobicity and higher-order statistics, resulting in a TM prediction tool, namely K"4HTM. A training dataset of 117 human single TM proteins and two test-datasets containing 499 and 484 human single and multiple TM proteins, respectively, were drawn from the SWISS-PROT public database and used for the optimisation and evaluation of K"4HTM. Validation results showed that K"4HTM correctly predicts the entire topology for 99.68% and 93.08% of the sequences in the single and multiple test-datasets, respectively. These results compare favourably with existing methods, such as SPLIT4, TMHMM2, WAVETM and SOSUI, constituting an alternative approach to the TM helix prediction problem.