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Multi-species Conserved Sequences02:51

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Related Experiment Video

Updated: May 17, 2026

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

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Published on: February 10, 2023

Accurate simulation and detection of coevolution signals in multiple sequence alignments.

Sharon H Ackerman1, Elisabeth R Tillier, Domenico L Gatti

  • 1Department of Biochemistry and Molecular Biology, Wayne State University School of Medicine, Detroit, Michigan, United States of America.

Plos One
|October 24, 2012
PubMed
Summary

Global correlation methods outperform local ones for detecting coevolving residues in multiple sequence alignments (MSAs). Simulating MSAs helps select the best coevolution detection method for specific protein families.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Conserved positions in multiple sequence alignments (MSAs) are studied, but non-conserved positions can also be functionally significant.
  • Compensatory mutations at non-conserved sites can maintain protein stability and function.
  • Various methods exist to identify evolutionary relationships between amino acid sites, distinguishing functional dependencies from phylogenetic signals.

Purpose of the Study:

  • To evaluate the efficacy of different coevolution detection methods.
  • To compare methods using simulated and experimental MSAs.
  • To assess the ability of methods to predict residue contacts in proteins.

Main Methods:

  • Utilized a new program, MSAvolve, for in silico evolution of MSAs, recording coevolutionary histories.
  • Simulated over 1600 MSAs across 8 protein families to generate global coevolution matrices.
  • Compared coevolution matrices from simulations with those from various detection methods.
  • Evaluated method performance in predicting residue contacts from experimental MSAs of 150 protein families.

Main Results:

  • Methods identifying global correlations between residue pairs generally outperformed those using local correlations.
  • This superiority was observed for both simulated and experimental MSAs.
  • Performance varied significantly across different protein families and methods.

Conclusions:

  • Global correlation-based methods are more effective for identifying coevolving residues.
  • The variability in method performance highlights the utility of MSA simulations.
  • Simulating MSAs that match experimental properties aids in selecting the optimal coevolution detection method for specific proteins.