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.
Original language | English (US) |
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Title of host publication | Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings |
Pages | 23-38 |
Number of pages | 16 |
Edition | PART 1 |
DOIs | |
State | Published - 2008 |
Event | European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008 - Antwerp, Belgium Duration: Sep 15 2008 → Sep 19 2008 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Number | PART 1 |
Volume | 5211 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008 |
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Country/Territory | Belgium |
City | Antwerp |
Period | 9/15/08 → 9/19/08 |
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.