Predicting conserved protein motifs with Sub-HMMs.

ABSTRACT:

Background

Profile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is the difficulty to pinpoint their much shorter functional sub-features, such as catalytically relevant sequence motifs in enzymes or ligand binding signatures of receptor proteins.

Results

To identify these conserved motifs efficiently, we propose a method for extracting the most information-rich regions in protein families from their profile HMMs. The method was used here to predict a comprehensive set of sub-HMMs from the Pfam domain database. Cross-validations with the PROSITE and CSA databases confirmed the efficiency of the method in predicting most of the known functionally relevant motifs and res

SUBMITTER: Horan K 

PROVIDER: S-EPMC2879284 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature