SVMtm: Support vector machines to predict transmembrane segments

Authors

  • Zheng Yuan,

    Corresponding author
    1. ARC Centre in Bioinformatics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia, 4072, Australia
    • ARC Centre in Bioinformatics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia, 4072, Australia
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  • John S. Mattick,

    1. ARC Special Research Centre for Functional and Applied Genomics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia, 4072, Australia
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  • Rohan D. Teasdale

    1. ARC Centre in Bioinformatics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia, 4072, Australia
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Abstract

A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with sensitivity of 93.4% and precision of 92.0%. For each predicted transmembrane segment, a score is given to show the strength of transmembrane signal and the prediction reliability. In particular, this method can distinguish transmembrane proteins from soluble proteins with an accuracy of ∼99%. This method can be used to complement current transmembrane helix prediction methods and can be used for consensus analysis of entire proteomes. The predictor is located at http://genet.imb.uq.edu.au/predictors/SVMtm. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 632–636, 2004

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