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.

Keywords

Hidden Markov modelProtein Data Bank (RCSB PDB)Markov chainMaximum-entropy Markov modelProtein Data BankComputer sciencePattern recognition (psychology)Protein structure predictionArtificial intelligenceSequence alignmentMarkov modelSequence (biology)Set (abstract data type)Protein structureComputational biologyVariable-order Markov modelMachine learningBiologyPeptide sequenceGenetics

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Publication Info

Year
1997
Type
article
Volume
29
Issue
S1
Pages
134-139
Citations
153
Access
Closed

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Kevin Karplus, Kimmen Sjölander, Curtis L. Barrett et al. (1997). Predicting protein structure using hidden Markov models. Proteins Structure Function and Bioinformatics , 29 (S1) , 134-139. https://doi.org/10.1002/(sici)1097-0134(1997)1+<134::aid-prot18>3.0.co;2-p

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DOI
10.1002/(sici)1097-0134(1997)1+<134::aid-prot18>3.0.co;2-p