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Detecting coevolving amino acid sites using Bayesian mutational mapping.

Matthew W Dimmic1, Melissa J Hubisz, Carlos D Bustamante

  • 1Department of Biological Statistics and Computational Biology, Cornell University Ithaca, NY 13101, USA. matt@dimmic.net

Bioinformatics (Oxford, England)
|June 18, 2005
PubMed
Summary
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Bayesian mutational mapping (BMM) detects coevolving residues in protein families by analyzing evolutionary trees. This method accurately identifies interacting sites, outperforming non-parametric approaches.

Area of Science:

  • Evolutionary biology
  • Bioinformatics
  • Computational biology

Background:

  • Protein sequence evolution is shaped by residue interactions, leading to coevolution.
  • Harmful mutations can be compensated by neighboring site substitutions.
  • Detecting coevolving residues is crucial for understanding protein function and evolution.

Purpose of the Study:

  • To develop and validate a Bayesian phylogenetic approach for detecting coevolving residues in protein families.
  • To assess the performance of the Bayesian mutational mapping (BMM) method using simulated and real biological data.
  • To investigate the impact of evolutionary rate heterogeneity on coevolution detection.

Main Methods:

  • Bayesian mutational mapping (BMM) assigns mutations to evolutionary tree branches stochastically.

Related Experiment Videos

  • Test statistics are calculated to identify coevolutionary signals.
  • Posterior predictive P-values estimate significance, with specificity maintained by integrating over tree topology and rate uncertainties.
  • A coevolutionary Markov model for codon substitution is employed.

    • Main Results:

    • BMM successfully detects most coevolving sites in simulated data when the model is correctly specified.
    • Parametric statistics are more powerful than non-parametric methods like mutual information.
    • In the phosphoglycerate kinase (PGK) family, interdomain contacts show a stronger coevolutionary signal than non-contacts.
    • Ignoring rate heterogeneity across sites in PGK reduced test discrimination and increased false positives.

    Conclusions:

    • BMM is an effective method for detecting coevolving residues and identifying interacting sites in protein families.
    • Accurate modeling of evolutionary processes, including rate heterogeneity, is essential for reliable coevolution detection.
    • The findings provide insights into the structural and functional constraints governing protein evolution.