Hidden Markov models in computational biology: applications to protein modeling

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Hidden Markov models in computational biology: applications to protein modeling (1994)

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by Anders Krogh , Michael Brown , I. Saira Mian , Kimmen Sjölander , David Haussler

Venue:	JOURNAL OF MOLECULAR BIOLOGY
Citations:	655 - 39 self

BibTeX

@ARTICLE{Krogh94hiddenmarkov,
    author = {Anders Krogh and Michael Brown and I. Saira Mian and Kimmen Sjölander and David Haussler},
    title = { Hidden Markov models in computational biology: applications to protein modeling},
    journal = {JOURNAL OF MOLECULAR BIOLOGY},
    year = {1994},
    volume = {235},
    pages = {1501--1531}
}

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Abstract

Hidden.Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated the on globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the. SWISS-PROT 22 database for other sequences. that are members of the given protein family, or contain the given domain. The Hi " produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate three-dimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EF-hand HMMs), the '\ HMM is able to distinguish members of these families from non-members with a high degree of accuracy. Both the HMM and PROFILESEARCH (a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appecvs to have a slight advantage over PROFILESEARCH in terms of lower rates of false

Keyphrases

computational biology hidden markov model multiple alignment protein family ef-hand calcium good quality slight advantage globin family database fit high degree protein sequence database searching protein domain statistical modeling discrimination test unaligned sequence threedimensional structural information training set ef-hand hmms multiple sequence alignment training sequence multiply aligned sequence catalytic domain hmm appecvs markov model

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