TOPTMH: Topology predictor for transmembrane α-helices

Rezwan Ahmed, Huzefa Rangwala, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 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)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings
Number of pages16
EditionPART 1
StatePublished - 2008
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008 - Antwerp, Belgium
Duration: Sep 15 2008Sep 19 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5211 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008

Bibliographical note

Funding Information:
This work was supported by NSF EIA-9986042, ACI-0133464, IIS-0431135, NIH RLM008713A, NIH T32GM008347, the Digital Technology Center, University of Minnesota and the Minnesota Supercomputing Institute.


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