Hidden Markov models for detecting remote protein homologies

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 simple target sequence and iteratively builds a hidden Markov model (HMM) from the sequence and homologs found using die 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 foul 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 flue positives and 68 false positives, DOUBLE-BLAST got 533 true positives with 71 false positives, ann 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.