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MBE Advance Access published online on May 3, 2006
Molecular Biology and Evolution, doi:10.1093/molbev/msl002
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© The Author 2006. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Accepted April 27, 2006

Research Article

Prediction of Function Divergence in Protein Families Using the Substitution Rate Variation Parameter Alpha

Saraswathi Abhiman 1 *, Carsten O. Daub 2, and Erik L.L. Sonnhammer 2

1 Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, SWEDEN; Present adress: Stockholm Bioinformatics Center, AlbaNova University Center, Stockholm University, S-106 91 Stockholm, Sweden
2 Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, SWEDEN

* To whom correspondence should be addressed.
Saraswathi Abhiman, E-mail: Abhiman.Saraswathi{at}cgb.ki.se


  Abstract

Protein families typically embody a range of related functions and may thus be decomposed into subfamilies with e.g. distinct substrate specificities. Detection of functionally divergent subfamilies is possible by methods for recognizing branches of adaptive evolution in a gene tree. As the number of genome sequences is growing rapidly, it is highly desirable to automatically detect subfamily function divergence.

To this end, we here introduce a method for large-scale prediction of function divergence within protein families. It is called the Alpha Shift Measure (ASM) as it is based on detecting a shift in the shape parameter (alpha) of the substitution rate gamma distribution. Four different methods for estimating alpha were investigated. We benchmarked the accuracy of ASM using function annotation from Enzyme Commission numbers within Pfam protein families divided into subfamilies by the automatic treebased method BETE.

In a test using 563 subfamily pairs in 162 families, ASM outperformed functional site based methods using rate or conservation shifting (RSM and CSM). The best results were obtained using the "GZ-Gamma" method for estimating alpha. By combining ASM with RSM and CSM using linear discriminant analysis the prediction accuracy was further improved.

Keywords: Protein evolution; Adaptive evolution; Enzyme; Protein function; Protein Subfamily; Substitution rates; Gamma distribution; Alpha parameter; Function shift.
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