Bioinformatics, Vol 14, 846-856, Copyright © 1998 by Oxford University Press
K Karplus, C Barrett and R Hughey
MOTIVATION: A new hidden Markov model method (SAM-T98) for finding 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. METHODS: 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 (Structural Classification of Proteins)-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 find
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 Modeling (SAM) software suite, can be found at
http://www.cse.ucsc.edu/research/compbi o/ CONTACT: karplus@cse.ucsc.edu;
http://www.cse.ucsc.edu/~karplus
ARTICLES
Hidden Markov models for detecting remote protein homologies
Department of Computer Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA.
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