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Predicting protein structure using hidden Markov models
Author(s) -
Karplus Kevin,
Sjölander Kimmen,
Barrett Christian,
Cline Melissa,
Haussler David,
Hughey Richard,
Holm Liisa,
Sander Chris
Publication year - 1997
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/(sici)1097-0134(1997)1+<134::aid-prot18>3.0.co;2-p
Subject(s) - hidden markov model , protein data bank (rcsb pdb) , markov chain , maximum entropy markov model , protein data bank , computer science , pattern recognition (psychology) , protein structure prediction , artificial intelligence , sequence alignment , markov model , sequence (biology) , set (abstract data type) , protein structure , computational biology , variable order markov model , machine learning , biology , peptide sequence , genetics , biochemistry , gene , programming language
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. Proteins, Suppl. 1:134–139, 1997. © 1998 Wiley‐Liss, Inc.