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Activity Number:
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272
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 14, 2002 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing*
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| Abstract - #301374 |
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Title:
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HMMSTR: A Hidden Markov Model for Protein Local Structure Motifs
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Author(s):
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Christopher Bystroff*+ and Vesteinn Thorsson and David Baker
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Affiliation(s):
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Rensselaer Polytechnic Institute and University of Washington and University of Washington
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Address:
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, Troy, New York, 12180, USA
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Keywords:
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protein folding ; machine learning ; peptide structure ; bioinformatics ; sequence grammar ; gene finding
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Abstract:
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HMMSTR is a hidden Markov model for general protein sequence based on the I-sites library of sequence-structure motifs. Unlike profile HMMs, used to model individual protein families, HMMSTR has a highly branched topology. Markov state pathways capture 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 database, and achieves a great reduction in parameters by representing overlapping motifs in a much more compact form. The HMM attributes a considerably higher probability to coding sequence than does an equivalent dipeptide model; predicts secondary structure with an accuracy of 74.6% and backbone torsion angles better than any previously reported method; and predicts the structural context of beta strands and turns with an accuracy that should be useful for tertiary structure prediction.
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