Context-Specific Independence Mixture Modelling for Protein Families

Authors:
Benjamin Georgi;Jörg Schultz;Alexander Schliep
Affiliations:
Max Planck Institute for Molecular Genetics, Dept. of Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany;Universität Würzburg, Dept. of Bioinformatics, 97074 Wuerzburg, Germany;Max Planck Institute for Molecular Genetics, Dept. of Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany
Venue:
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2007

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Abstract

Protein families can be divided into subgroups with functional differences. The analysis of these subgroups and the determination of which residues convey substrate specificity is a central question in the study of these families. We present a clustering procedure using the context-specific independencemixture framework using a Dirichlet mixture prior for simultaneous inference of subgroups and prediction of specificity determining residues based on multiple sequence alignments of protein families. Application of the method on several well studied families revealed a good clustering performance and ample biological support for the predicted positions. The software we developed to carry out this analysis PyMix - the Python mixture packageis available from http://www.algorithmics.molgen.mpg.de/pymix.html.