Applications of evolutionary SVM to prediction of membrane alpha-helices

Abstract

This paper is in the area of membrane proteins. Membrane proteins make up about 75% of possible targets for novel drugs discovery. However, membrane proteins are one of the most understudied groups of proteins in biochemical research because of technical difficulties of attaining structural information about transmembrane regions or domains. Structural determination of TM regions is an important priority in pharmaceutical industry, as it paves the way for structure based drug design. This research presents a novel evolutionary support vector machine (SVM) based alpha-helix transmembrane region prediction algorithm to solve the membrane helices in amino acid sequences. The SVM-genetic algorithm (GA) methodology is based on the optimisation of sliding window size, evolutionary encoding selection and SVM parameter optimisation. In this research average hydrophobicity and propensity based on skew statistics are used to encode the one letter representation of amino acid sequences datasets. The computer simulation results demonstrate that the proposed SVM-GA methodology performs better than most conventional techniques producing an accuracy of 86.71% for cross-validation and 86.43% for jack-knife for randomly selected proteins containing single and multiple transmembrane regions. Furthermore, for the amino acid sequence 3LVG, the proposed SVM-GA produces better alpha-helix region identification than PRED-TMR2, MEMSATSVM/MEMSAT3 and PSIPRED V3.0.