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Journal of Biochemistry Advance Access published online on August 31, 2006
Journal of Biochemistry, doi:10.1093/jb/mvj184
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© 2006 The Japanese Biochemical Society.
Received August 1, 2006
Accepted August 26, 2006

Regular Paper

A Monte Carlo Sampling Method of Amino Acid Sequences Adaptable to Given Main-Chain Atoms in the Proteins

Koji Ogata 1, Kenji Soejima 2 *, and Junichi Higo 3

1 Centre for Computational Biology, The Hospital for Sick Children, 555 University Avenue, Toronot Ontario M5G 1X8, Canada
2 Research Department 1, The Chemo-Sero-Therapeutic Research Institute, Kyokushikawabe, Kikuchi, Kumamoto 869-1298, Japan
3 Laboratory of Bioinformatics, School of Life Science, Tokyo University of Pharmacy and Life Science, 1432-1 Horinouchi, Hachioji, Tokyo 192-0392, Japan

* To whom correspondence should be addressed.
Kenji Soejima, E-mail: soejima{at}kaketsuken.or.jp


  Abstract

We have developed a computational method of protein design to detect amino acid sequences that are adaptable to given main-chain coordinates of a protein. In this method, the selection of amino acid types employs a Metropolis Monte Carlo method with a scoring function in conjunction with the approximation of free energies computed from 3D structures. To compute the scoring function, a side-chain prediction using another Metropolis Monte Carlo method was performed to select structurally suitable side-chain conformations from a side-chain library. In total, two layers of Monte Carlo procedures were performed, first to select amino acid types (1st layer Monte Carlo) and then to predict side-chain conformations (2nd layers Monte Carlo). We applied this method to sequence design for the entire sequence on the SH3 domain, Protein G, and BPTI. The predicted sequences were similar to those of the wild-type proteins. We compared the results of the predictions with and without the 2nd layer Monte Carlo method. The results revealed that the two-layer Monte Carlo method produced better sequence similarity to the wild-type proteins than the one-layer method. Finally, we applied this method to neuraminidase of influenza virus. The results were consistent with the sequences identified from the isolated viruses.

Keywords: free energy; Monte Carlo method; protein design; sequence prediction; side-chain prediction.
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