Hidden Markov models for detecting remote protein homologies

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Hidden Markov models for detecting remote protein homologies (1998)

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by Kevin Karplus , Christian Barrett , Richard Hughey

Venue:	Bioinformatics
Citations:	459 - 15 self

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@ARTICLE{Karplus98hiddenmarkov,
    author = {Kevin Karplus and Christian Barrett and Richard Hughey},
    title = {Hidden Markov models for detecting remote protein homologies},
    journal = {Bioinformatics},
    year = {1998},
    volume = {14},
    pages = {846--856}
}

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Abstract

A new hidden Markov model method (SAM-T98) for nding remote homologs of protein sequences is described and evaluated. The method begins with a single target sequence and iteratively builds a hidden Markov model (hmm) from the sequence and homologs found using the hmm for database search. SAM-T98 is also used to construct model libraries automatically from sequences in structural databases. We evaluate the SAM-T98 method with four datasets. Three of the test sets are fold-recognition tests, where the correct answers are determined by structural similarity. The fourth uses a curated database. The method is compared against wu-blastp and against double-blast, a two-step method similar to ISS, but using blast instead of fasta. Results SAM-T98 had the fewest errors in all tests| dramatically so for the fold-recognition tests. At the minimum-error point on the SCOP-domains test, SAM-T98 got 880 true positives and 68 false positives, double-blast got 533 true positives with 71 false positives, and wu-blastp got 353 true positives with 24 false positives. The method is optimized to recognize superfamilies, and would require parameter adjustment to be used to nd family or fold relationships. One key to the performance of the hmm method is a new score-normalization technique that compares the score to the score with a reversed model rather than to a uniform null model. Availability A World Wide Web server, as well as information on obtaining the Sequence Alignment and PREPRINT to appear in Bioinformatics, 1999

Keyphrases

hidden markov model remote protein homology false positive true positive fold-recognition test sequence alignment remote homologs model library single target sequence structural similarity test set uniform null model scop-domains test fold relationship structural database parameter adjustment two-step method protein sequence reversed model minimum-error point availability world wide web hmm method new hidden markov model method sam-t98 method correct answer new score-normalization technique curated database database search

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