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Population Genetics Based Phylogenetics Under Stabilizing Selection for an Optimal Amino Acid Sequence: A Nested

Jeremy M Beaulieu1,2,3, Brian C O'Meara2,3, Russell Zaretzki4

  • 1Department of Biological Sciences, University of Arkansas, Fayetteville, AR.

Molecular Biology and Evolution
|December 7, 2018
PubMed
Summary

We developed a new phylogenetic model, selection on amino acids and codons (SelAC), that better explains protein-coding DNA evolution. This model links protein expression to population genetics, improving biological realism in evolutionary studies.

Keywords:
Wright–Fisherallele substitutiongene expressionprotein functionstabilizing selection

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Area of Science:

  • Evolutionary Biology
  • Computational Biology
  • Genomics

Background:

  • Phylogenetic models often simplify the evolutionary dynamics of protein-coding DNA.
  • Existing codon models assume uniform substitution rates, neglecting biological factors like protein expression levels.

Purpose of the Study:

  • Introduce a novel phylogenetic approach, selection on amino acids and codons (SelAC), for more realistic modeling of protein-coding DNA evolution.
  • Develop a nested model linking protein expression, population genetics, and evolutionary substitution rates.

Main Methods:

  • Developed SelAC, a nested model using a cost-benefit approach to link stabilizing selection strength with protein synthesis levels.
  • Generated 20 amino acid-specific matrix families from a few parameters.
  • Applied SelAC to a yeast dataset of 100 orthologs across 6 taxa.

Main Results:

  • SelAC demonstrated superior fit to yeast data compared to popular models (AICc difference of 10^4-10^5).
  • The model accurately predicted gene-specific protein synthesis rates, correlating well with empirical and theoretical predictions.
  • SelAC identified optimal amino acid predictions at each site.

Conclusions:

  • Nested, mechanistic models like SelAC offer improved biological realism in studying amino acid sequence evolution.
  • SelAC provides biologically meaningful insights beyond traditional phylogenetic inference, including protein synthesis rates.
  • The SelAC framework allows for future extensions, such as modeling shifts in optimal amino acid sequences.