title: TOPTMH: topology predictor for transmembrane alpha-helices.
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 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.
authors.affiliation: Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA. ahmed@cs.umn.edu
authors.forename: Rezwan
authors.initials: R
authors.lastname: Ahmed
authors.forename: Huzefa
authors.initials: H
authors.lastname: Rangwala
authors.forename: George
authors.initials: G
authors.lastname: Karypis
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D030562:Databases, Protein; D057927:Hydrophobic and Hydrophilic Interactions; D008390:Markov Chains; D008565:Membrane Proteins / Q000737:chemistry*; D008958:Models, Molecular; D017433:Protein Structure, Secondary
chemical_list: D008565:Membrane Proteins