Prediction of Mitochondrial Targeting Signals Using Hidden Markov Models

  • Fujiwara Yukiko
    Computational Engineering Technology Group, C & C Media Laboratories, NEC Corporation
  • Asogawa Minoru
    Computational Engineering Technology Group, C & C Media Laboratories, NEC Corporation
  • Nakai Kenta
    Institute of Molecular and Cellular Biology, Osaka University

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Description

The mitochondrial targeting signal (MTS) is the presequence that directs nascent proteins bearing it to mitochondria. We have developed a hidden Markov model (HMM) that represents various known sequence characteristics of MTSs, such as the length variation, amino acid composition, amphiphilicity, and consensus pattern around the cleavage site. The topology and parameters of this model are automatically determined by the iterative duplication method, in which a small fullyconnected HMM is gradually expanded by state splitting. The model can be used to predict the existence of MTSs for given amino acid sequences. Its prediction accuracy was estimated to be 86.9% using the cross validation test. Furthermore, a higher correlation was observed between the HMM score and the in vitro ATPase activity of MSF, which can be regarded as an experimental measure of signal strength, for various synthetic peptides than was observed with other methods.

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Details 詳細情報について

  • CRID
    1390282679467797760
  • NII Article ID
    130003812464
  • DOI
    10.11234/gi1990.8.53
  • ISSN
    2185842X
    09199454
  • PubMed
    11072305
  • Text Lang
    en
  • Article Type
    journal article
  • Data Source
    • JaLC
    • PubMed
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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