An evolutionary space-time model with varying among-site dependencies.

Area of Science:

• Evolutionary biology
• Computational biology
• Biophysics

Background:

• Protein evolution is often studied assuming independent evolutionary rates at different sites.
• However, proteins exhibit conserved and variable domains, suggesting correlated evolutionary rates between neighboring sites.
• Existing autocorrelation models assume a uniform correlation level across the entire protein sequence.

Purpose of the Study:

• To develop and evaluate a novel autocorrelation model for protein evolutionary rates.
• To investigate a model where the correlation between adjacent sites varies across different protein regions.
• To compare this new model against existing models of rate independence and fixed autocorrelation.

Main Methods:

• Development of a hidden Markov model (HMM) incorporating spatially varying autocorrelation of evolutionary rates.
• Implementation of the HMM to allow for both autocorrelated and independent rate regions within a protein.
• Testing the model's performance on diverse protein sequence datasets.

Main Results:

• The novel HMM-based model demonstrates a better fit to most tested protein datasets compared to models assuming complete independence or fixed autocorrelation.
• Analysis of the potassium-channel protein family reveals a correlation between the dependence of adjacent site rates and the protein's tertiary structure.
• The study highlights the importance of considering spatially varying evolutionary rate correlations.

Conclusions:

• Protein evolutionary rates exhibit complex patterns of autocorrelation that are not uniform across the sequence.
• The developed HMM provides a more accurate framework for modeling protein evolution by accounting for regional variations in rate dependence.
• Understanding these varying correlations offers insights into protein structure-function relationships and evolutionary dynamics.