Prediction of Mitochondrial Targeting Signals Using Hidden Markov Model

Yukiko Fujiwara[1] (fujiwara@ccm.cl.nec.co.jp)
Minoru Asogawa[1] (asogawa@ccm.cl.nec.co.jp)
Kenta Nakai[2] (nakai@imcb.osaka-u.ac.jp)

[1] Computational Engineering Technology Group, C&C Media Laboratories, NEC Corporation
4-1-1 Miyazaki, Miyamae-ku, Kawasaki, Kanagawa 216, Japan
[2] Institute of Molecular and Cellular Biology, Osaka University
1-3 Yamada-oka, Suita 565, Japan


Abstract

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 fully-connected 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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Japanese Society for Bioinformatics