Introduction

The evolution of proteins cannot be fully understood without taking into account the coevolutionary linkages entangling them. From a practical point of view, coevolution between protein families has been used as a way of detecting protein interactions and functional relationships from genomic information. The most common approach to inferring protein coevolution involves the quantification of phylogenetic tree similarity using a family of methodologies termed mirrortree. In spite of their success, a fundamental problem of these approaches is the lack of an adequate statistical framework to assess the significance of a given coevolutionary score (tree similarity). As a consequence, a number of ad hoc filters and arbitrary thresholds are required in an attempt to obtain a final set of confident coevolutionary signals.In this work, we developed a method for associating confidence estimators (P values) to the tree-similarity scores, using a null model specifically designed for the tree comparison problem. We show how this approach largely improves the quality and coverage (number of pairs that can be evaluated) of the detected coevolution in all the stages of the mirrortree workflow, independently of the starting genomic information. This not only leads to a better understanding of protein coevolution and its biological implications, but also to obtain a highly reliable and comprehensive network of predicted interactions, as well as information on the substructure of macromolecular complexes using only genomic information.The software and datasets used in this work are freely available at: http://csbg.cnb.csic.es/pMT/.pazos@cnb.csic.esSupplementary data are available at Bioinformatics online.

Publications

  1. Detection of significant protein coevolution.
    Cite this
    Ochoa D, Juan D, Valencia A, Pazos F, 2015-07-01 - Bioinformatics (Oxford, England)

Credits

  1. David Ochoa
    Developer

    Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Spain

  2. David Juan
    Developer

    Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Spain

  3. Alfonso Valencia
    Developer

    Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Spain

  4. Florencio Pazos
    Investigator

    Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Spain

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Summary
AccessionBT000016
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Country/RegionSpain
Submitted ByFlorencio Pazos