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MBE Advance Access published online on November 2, 2005
Molecular Biology and Evolution, doi:10.1093/molbev/msj044
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© The Author 2005. 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 October 19, 2005

Research Article

An Evolutionary Space-Time Model with Varying Among Site Dependencies

Adi Stern 1 and Tal Pupko 1*

1 Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel

* To whom correspondence should be addressed.
Tal Pupko, E-mail: talp{at}post.tau.ac.il


  Abstract

It is now widely accepted that sites in a protein do not undergo independent evolutionary processes. The underlying assumption is that proteins are composed of conserved and variable linear domains, and thus rates at neighboring sites are correlated. In this paper we comprehensively examine the performance of an auto-correlation model of evolutionary rates in protein sequences. We further develop a model in which the level of correlation between rates at adjacent sites is not equal at all sites of the protein. High correlation is expected, for example, in linear functional domains. On the other hand when we consider non-linear functional regions (e.g., active sites), low correlation is expected, since the interaction between distant sites imposes independence of rates in the linear sequence. Our model is based on a Hidden Markov Model, which accounts for auto-correlation at certain regions of the protein and rate independence at others. We study the differences between the novel model and models which assume either independence or a fixed level of dependence throughout the protein. Using a diverse set of protein datasets we show that the novel model better fits most datasets. We further analyze the K-channel protein family and illustrate the relationship between the dependence of rates at adjacent sites and the tertiary structure of the protein.

Keywords: evolutionary models; hidden Markov models; site-specific evolutionary rates.
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