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Protein evolution with dependence among codons due to tertiary structure.

Douglas M Robinson1, David T Jones, Hirohisa Kishino

  • 1Bioinformatics Research Center, North Carolina State University, USA.

Molecular Biology and Evolution
|July 30, 2003
PubMed
Summary
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Protein evolution models now account for how protein structure influences changes in DNA sequences. This approach refines the detection of positive selection by linking evolutionary rates to protein stability and solvent accessibility.

Area of Science:

  • Evolutionary biology
  • Computational biology
  • Biophysics

Background:

  • Markovian models of protein evolution typically assume independent changes among codons.
  • Protein tertiary structure can influence evolutionary rates, suggesting a dependence among sites that is not captured by simpler models.

Purpose of the Study:

  • To develop and apply an evolutionary model that incorporates protein structure effects, such as solvent accessibility and residue interactions.
  • To quantify the impact of protein structure on evolutionary rates and refine the detection of positive selection.

Main Methods:

  • Developed a Markovian model of protein evolution relaxing the independence assumption among codons.
  • Incorporated protein structure attributes (solvent accessibility, pairwise interactions) into the evolutionary model.

Related Experiment Videos

  • Analyzed simulated and empirical protein-coding DNA sequence pairs (lysozyme c, annexin V).

    • Main Results:

    • The evolutionary model, incorporating structural effects, can accurately estimate parameters.
    • Amino acid replacement rates are higher for energetically favorable protein changes than for destabilizing ones.
    • The model successfully links nonsynonymous substitution rates to protein structure.

    Conclusions:

    • Protein structure significantly impacts protein evolution, influencing amino acid substitution rates.
    • This structure-aware evolutionary model enhances the detection and characterization of positive selection.
    • The statistical framework is generalizable to other scenarios of evolutionary dependence where fitness can be modeled.