HHsvm: fast and accurate classification of profile-profile matches identified by HHsearch.
BACKGROUND
Recently developed profile-profile methods rival structural comparisons in their ability to detect homology between distantly related proteins. Despite this tremendous progress, many genuine relationships between protein families cannot be recognized as comparisons of their profiles result in scores that are statistically insignificant.


RESULTS
Using known evolutionary relationships among protein superfamilies in SCOP database, support vector machines were trained on four sets of discriminatory features derived from the output of HHsearch. Upon validation, it was shown that the automatic classification of all profile-profile matches was superior to fixed threshold-based annotation in terms of sensitivity and specificity. The effectiveness of this approach was demonstrated by annotating several domains of unknown function from the Pfam database.


BACKGROUND
Programs and scripts implementing the methods described in this manuscript are freely available from http://hhsvm.dlakiclab.org/.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Microbiology, Montana State University, Bozeman, MT 59717-3520, USA. mdlakic@montana.edu
Mensur
M
Dlakić
D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D017386:Sequence Homology, Amino Acid; D012984:Software
D011506:Proteins