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Related Concept Videos

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Related Experiment Video

Updated: Apr 16, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Detection of significant protein coevolution.

David Ochoa1, David Juan1, Alfonso Valencia1

  • 1Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid and Structural Bioinformatics Group, Spanish National Cancer Research Centre (CNIO), C/ Melchor Fernández Almagro 3, 28029 Madrid, Spain.

Bioinformatics (Oxford, England)
|February 27, 2015
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Summary

This study introduces a novel statistical framework to assess protein coevolutionary scores, improving the reliability of detecting protein interactions and functional relationships from genomic data. The new method enhances both the quality and coverage of coevolutionary signal detection.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Protein evolution is intricately linked with coevolutionary relationships.
  • Coevolution analysis aids in identifying protein interactions and functional links using genomic data.
  • Current methods like mirrortree lack robust statistical frameworks for assessing coevolutionary scores.

Purpose of the Study:

  • To develop a statistically sound method for assigning confidence estimators (P-values) to protein tree similarity scores.
  • To improve the reliability and coverage of coevolutionary signal detection in protein family analysis.

Main Methods:

  • Developed a null model specifically for tree comparison problems.
  • Integrated P-value estimation into the mirrortree workflow.
  • Utilized genomic information for coevolutionary analysis.

Main Results:

  • The new method significantly enhances the quality and coverage of detected coevolution.
  • The approach provides a more reliable and comprehensive network of predicted protein interactions.
  • Enables insights into the substructure of macromolecular complexes solely from genomic data.

Conclusions:

  • The developed statistical framework provides a robust way to evaluate coevolutionary signals.
  • This advancement leads to a better understanding of protein coevolution and its biological implications.
  • Facilitates the construction of high-confidence protein interaction networks and the study of complex structures.