HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins - PubMed

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• PMID: 10926500
• DOI: 10.1006/jmbi.2000.3837

HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins

C Bystroff et al. J Mol Biol. 2000.

Abstract

We describe a hidden Markov model, HMMSTR, for general protein sequence based on the I-sites library of sequence-structure motifs. Unlike the linear hidden Markov models used to model individual protein families, HMMSTR has a highly branched topology and captures recurrent local features of protein sequences and structures that transcend protein family boundaries. The model extends the I-sites library by describing the adjacencies of different sequence-structure motifs as observed in the protein database and, by representing overlapping motifs in a much more compact form, achieves a great reduction in parameters. The HMM attributes a considerably higher probability to coding sequence than does an equivalent dipeptide model, predicts secondary structure with an accuracy of 74.3 %, backbone torsion angles better than any previously reported method and the structural context of beta strands and turns with an accuracy that should be useful for tertiary structure prediction.

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