TRANSMEMBRANE HELIX AND TOPOLOGY PREDICTION USING HIERARCHICAL SVM CLASSIFIERS AND AN ALTERNATING GEOMETRIC SCORING FUNCTION

Allan Lo, Hua-Sheng Chiu, Ting-Yi Sung, Wen-Lian Hsu*

Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan, R.O.C. hsu@iis.sinica.edu.tw

Comput Syst Bioinformatics Conf. August, 2006. Vol. 5, p. 31-42. Full-Text PDF

*To whom correspondence should be addressed.

Motivation: A key class of membrane proteins contains one or more transmembrane (TM) helices, traversing the membrane lipid bilayer. Various properties such as the length, arrangement and topology or orientation of TM helices, are closely related to a protein's functions. Although a range of methods have been developed to predict TM helices and their topologies, no single method consistently outperforms the others. In addition, topology prediction has much lower accuracy than helix prediction, and thus requires continuous improvements. Results: We develop a method based on support vector machines (SVM) in a hierarchical framework to predict TM helices first, followed by their topology. By partitioning the prediction problem into two steps, specific input features can be selected and integrated in each step. We also propose a novel scoring function for topology models based on membrane protein folding process. When benchmarked against other methods in terms of performance, our approach achieves the highest scores at 86% in helix prediction (Q