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Paper abstract

TOPTMH: Topology Predictor for Transmembrane Alpha Helices

Rezwan Ahmed - University of Minnesota, Minneapolis, USA
Huzefa Rangwala - University of Minnesota, Minneapolis, USA
George Karypis - University of Minnesota, Minneapolis, USA

Session: Classification 1
Springer Link: http://dx.doi.org/10.1007/978-3-540-87479-9_20

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, TOPTMH a 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. TOPTMH outperforms state-of-the-art prediction methods and achieves the best performance on an independent static benchmark.