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Detecting amino acid preference shifts with codon-level mutation-selection mixture models.

S Omar Kazmi1, Nicolas Rodrigue2,3

  • 1Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, Canada.

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|February 28, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a fast, codon-level method to detect amino acid profile shifts in protein evolution. The approach accurately identifies pronounced shifts but struggles with subtle changes between similar profiles.

Keywords:
Mixture modelsMonte Carlo methodsSubstitution models

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Molecular evolution

Background:

  • Phylogeny-based statistical methods are crucial for understanding amino acid fitness changes over evolutionary time.
  • Existing random-effects models often lack mechanistic codon-level detail or are computationally intensive.

Purpose of the Study:

  • To develop a simple, fast, and computationally efficient method for detecting site-specific amino acid profile shifts.
  • To evaluate the method's performance using simulations and real data within a codon-level substitution framework.

Main Methods:

  • Developed a new method integrating existing ideas into a finite mixture model of amino acid profiles.
  • Utilized a mutation-selection formulation within a codon-level substitution model.
  • Tested the method's reliability and accuracy using simulations and applied it to real biological data.

Main Results:

  • Simulations demonstrated varying reliability based on the complexity of finite mixture models.
  • The method showed overlap with previous approaches in identifying significant sites but also revealed unique findings.
  • Performance was highly dependent on the distinctness of amino acid profiles involved in the shift.

Conclusions:

  • The developed method excels at detecting strongly pronounced, site-specific shifts in amino acid profiles.
  • Identifying subtle shifts between profiles with shared characteristics remains a significant challenge.
  • The findings highlight the method's utility and limitations in evolutionary sequence analysis.