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Open Access Highly Accessed Research article

Predicting conserved protein motifs with Sub-HMMs

Kevin Horan1, Christian R Shelton1 and Thomas Girke2*

Author Affiliations

1 Department of Computer Science and Engineering, University of California Riverside, Riverside, California, USA

2 Department of Botany and Plant Sciences, University of California Riverside, Riverside, California, USA

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BMC Bioinformatics 2010, 11:205  doi:10.1186/1471-2105-11-205

Published: 26 April 2010

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 residues. At the same time, 46,768 novel conserved regions could be predicted. The data set also allowed us to link at least 461 Pfam domains of known and unknown function by their common sub-HMMs. Finally, the sub-HMM method showed very promising results as an alternative search method for identifying proteins that share only short sequence similarities.

Conclusions

Sub-HMMs extend the application spectrum of profile HMMs to motif discovery. Their most interesting utility is the identification of the functionally relevant residues in proteins of known and unknown function. Additionally, sub-HMMs can be used for highly localized sequence similarity searches that focus on shorter conserved features rather than entire domains or global similarities. The motif data generated by this study is a valuable knowledge resource for characterizing protein functions in the future.