Hidden Markov models for detecting remote protein homologies

Hidden Markov models for detecting remote protein homologies (1998)

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by Kevin Karplus , Christian Barrett , Richard Hughey

Venue:	Bioinformatics
Citations:	229 - 12 self

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Datum	Value	Source
TITLE	Hidden Markov models for detecting remote protein homologies	INFERENCE
AUTHOR NAME	Kevin Karplus	SVM HeaderParse 0.2
AUTHOR AFFIL	Department ofComputer Engineering; Jack Baskin School of Engineering; University of California	SVM HeaderParse 0.2
AUTHOR ADDR	Santa Cruz, CA 95064	SVM HeaderParse 0.2
AUTHOR NAME	Christian Barrett	SVM HeaderParse 0.2
AUTHOR AFFIL	Department ofComputer Engineering; Jack Baskin School of Engineering; University of California	SVM HeaderParse 0.2
AUTHOR ADDR	Santa Cruz, CA 95064	SVM HeaderParse 0.2
AUTHOR NAME	Richard Hughey	SVM HeaderParse 0.2
AUTHOR AFFIL	Department ofComputer Engineering; Jack Baskin School of Engineering; University of California	SVM HeaderParse 0.2
AUTHOR ADDR	Santa Cruz, CA 95064	SVM HeaderParse 0.2
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	SVM HeaderParse 0.2
YEAR	1998	INFERENCE
VENUE	Bioinformatics	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	846--856	INFERENCE
VOLUME	14	INFERENCE
CITATIONS	34 found	ParsCit 1.0