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MBE Advance Access originally published online on November 2, 2005
Molecular Biology and Evolution 2006 23(2):392-400; 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

Research Article

An Evolutionary Space-Time Model with Varying Among-Site Dependencies

Adi Stern and Tal Pupko

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

E-mail: talp{at}post.tau.ac.il.

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 autocorrelation 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 nonlinear functional regions (e.g., active sites), low correlation is expected because 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 autocorrelation 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 data sets we show that the novel model better fits most data sets. We further analyze the potassium-channel protein family and illustrate the relationship between the dependence of rates at adjacent sites and the tertiary structure of the protein.

Key Words: evolutionary models • hidden Markov models • site-specific evolutionary rates


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