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A model-based approach for detecting coevolving positions in a molecule.

Julien Dutheil1, Tal Pupko, Alain Jean-Marie

  • 1CNRS UMR 5171 Laboratoire Génome, Populations, Interactions, Adaptation, Université Montpellier II, Montpellier Cedex, France. julien.dutheil@univ-montp2.fr

Molecular Biology and Evolution
|June 10, 2005
PubMed
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We developed a novel method to detect coevolving sites in molecules using evolutionary Markov models and phylogenetic trees. This approach accurately predicts interacting molecular sites, with over 95% accuracy in a bacterial RNA dataset.

Area of Science:

  • Molecular Evolution
  • Bioinformatics
  • Phylogenetics

Background:

  • Detecting coevolving sites is crucial for understanding molecular function and structure.
  • Existing methods may not fully account for evolutionary uncertainties like ancestral states and rate variation.

Purpose of the Study:

  • To introduce a new computational method for identifying coevolving sites in aligned molecular sequences.
  • To validate the method's accuracy using a well-characterized bacterial ribosomal RNA dataset.

Main Methods:

  • Utilizes Markov models of evolution to map substitutions onto a phylogenetic tree.
  • Accounts for uncertainty in ancestral states and among-site rate variation.
  • Calculates coevolution as Pearson correlation between site-specific substitution vectors.

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Main Results:

  • The method generates substitution vectors representing posterior estimates of branch-specific substitutions.
  • Pearson correlation of these vectors quantifies coevolutionary signal.
  • Applied to bacterial rRNA, the method predicted over 95% of known intramolecular interacting sites.

Conclusions:

  • The novel method accurately identifies coevolving sites by integrating evolutionary modeling and phylogenetic inference.
  • This approach offers a robust tool for predicting functional and structural interactions in molecules.
  • High concordance with known interactions in bacterial rRNA highlights the method's practical utility.