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Predicting protein structure using hidden Markov models.

Authors
  • K, Karplus
  • K, Sjölander
  • C, Barrett
  • M, Cline
  • David Haussler
  • R, Hughey
  • L, Holm
  • C, Sander
Type
Published Article
Journal
Proteins Structure Function and Bioinformatics
Publisher
Wiley (John Wiley & Sons)
Volume
Suppl 1
Pages
134–139
Source
UCSC Neuro biomedical-ucsc
License
Unknown

Abstract

We discuss how methods based on hidden Markov models performed in the fold-recognition section of the CASP2 experiment. Hidden Markov models were built for a representative set of just over 1,000 structures from the Protein Data Bank (PDB). Each CASP2 target sequence was scored against this library of HMMs. In addition, an HMM was built for each of the target sequences and all of the sequences in PDB were scored against that target model, with a good score on both methods indicating a high probability that the target sequence is homologous to the structure. The method worked well in comparison to other methods used at CASP2 for targets of moderate difficulty, where the closest structure in PDB could be aligned to the target with at least 15% residue identity.

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