TY - JOUR
T1 - An evolutionary space-time model with varying among-site dependencies
AU - Stern, Adi
AU - Pupko, Tal
PY - 2006/2
Y1 - 2006/2
N2 - 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.
AB - 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.
KW - Evolutionary models
KW - Hidden Markov models
KW - Site-specific evolutionary rates
UR - http://www.scopus.com/inward/record.url?scp=30744435826&partnerID=8YFLogxK
U2 - 10.1093/molbev/msj044
DO - 10.1093/molbev/msj044
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AN - SCOPUS:30744435826
SN - 0737-4038
VL - 23
SP - 392
EP - 400
JO - Molecular Biology and Evolution
JF - Molecular Biology and Evolution
IS - 2
ER -