TOPTMH: topology predictor for transmembrane alpha-helices.
Publication Type:Journal Article
Source:J Bioinform Comput Biol, Volume 8, Number 1, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA. email@example.com, p.39–57 (2010)
Keywords:*Algorithms, *Models, *Protein Structure, Artificial Intelligence, Computational Biology, Databases, Hydrophobic and Hydrophilic Interactions, Markov Chains, Membrane Proteins/*chemistry, Molecular, Protein, Secondary
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.