TOPTMH: Topology Predictor for Transmembrane α-Helices

Authors:
Rezwan Ahmed;Huzefa Rangwala;George Karypis
Affiliations:
Department of Computer Science & Engineering, University of Minnesota, Minneapolis MN 55455;Department of Computer Science & Engineering, University of Minnesota, Minneapolis MN 55455;Department of Computer Science & Engineering, University of Minnesota, Minneapolis MN 55455
Venue:
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Year:
2008

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Abstract

Alpha-helical transmembrane proteins mediate many key biological processes and represent 20%---30% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods to predict the location and orientation of transmembrane helix segments using sequence information are essential. We present, TOPTMHa new transmembrane helix topology prediction method that combines support vector machines, hidden Markov models, and a widely-used rule-based scheme. The contribution of this work is the development of a prediction approach that first uses a binary SVM classifier to predict the helix residues and then it employs a pair of HMM models that incorporate the SVM predictions and hydropathy-based features to identify the entire transmembrane helix segments by capturing the structural characteristics of these proteins. TOPTMHoutperforms state-of-the-art prediction methods and achieves the best performance on an independent static benchmark.