title: Transmembrane helix prediction in proteins using hydrophobicity properties and higher-order statistics.
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(4)HTM. 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(4)HTM. Validation results showed that K(4)HTM 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.
authors.affiliation: Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki GR-54124, Greece.
authors.forename: Ilias K
authors.initials: IK
authors.lastname: Kitsas
authors.forename: Leontios J
authors.initials: LJ
authors.lastname: Hadjileontiadis
authors.forename: Stavros M
authors.initials: SM
authors.lastname: Panas
mesh_terms: D057927:Hydrophobic and Hydrophilic Interactions; D008565:Membrane Proteins / Q000737:chemistry*; D015233:Models, Statistical
chemical_list: D008565:Membrane Proteins