Bioinformatics Advance Access originally published online on July 26, 2006
Bioinformatics 2006 22(20):2466-2474; doi:10.1093/bioinformatics/btl411
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Bayesian search of functionally divergent protein subgroups and their function specific residues
1 Department of Mathematics and Statistics, PO Box 68, 00014 University of Helsinki Finland
2 Institute of Biotechnology, PO Box 56, 00014 University of Helsinki Finland
3 Department of Biological and Environmental Sciences, PO Box 56, 00014 University of Helsinki Finland
*To whom correspondence should be addressed.
Motivation: The rapid increase in the amount of protein sequence data has created a need for an automated identification of evolutionarily related subgroups from large datasets. The existing methods typically require a priori specification of the number of putative groups, which defines the resolution of the classification solution.
Results: We introduce a Bayesian model-based approach to simultaneous identification of evolutionary groups and conserved parts of the protein sequences. The model-based approach provides an intuitive and efficient way of determining the number of groups from the sequence data, in contrast to the ad hoc methods often exploited for similar purposes. Our model recognizes the areas in the sequences that are relevant for the clustering and regards other areas as noise. We have implemented the method using a fast stochastic optimization algorithm which yields a clustering associated with the estimated maximum posterior probability. The method has been shown to have high specificity and sensitivity in simulated and real clustering tasks. With real datasets the method also highlights the residues close to the active site.
Availability: Software ‘kPax’ is available at http://www.rni.helsinki.fi/jic/softa.html
Contact: pekka.marttinen{at}helsinki.fi
Supplementary information: http://www.rni.helsinki.fi/~jic/softa.html
Received on February 16, 2006; revised on July 23, 2006; accepted on July 24, 2006
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