TOPTMH: Topology predictor for transmembrane α-helices

Rezwan Ahmed, Huzefa Rangwala, George Karypis

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)39-57
Number of pages19
JournalJournal of Bioinformatics and Computational Biology
Issue number1
StatePublished - Feb 2010

Bibliographical note

Funding Information:
This work was supported by NSF IIS-0431135, NIH RLM008713A, and by the Digital Technology Center and Minnesota Supercomputing Institute.


  • Classification
  • Hidden Markov model
  • Membrane protein
  • Secondary structure
  • Support vector machines


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