Cited by 607Protein Structure and DynamicsGenomics and Phylogenetic StudiesMachine Learning in Bioinformatics

Emerging methods in protein co-evolution

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de Juan, David, Pazos, Florencio, Valencia, Alfonso·2013·Nature Reviews Genetics

Abstract

Co-evolution is a fundamental component of the theory of evolution and is essential for understanding the relationships between species in complex ecological networks. A wide range of co-evolution-inspired computational methods has been designed to predict molecular interactions, but it is only recently that important advances have been made. Breakthroughs in the handling of phylogenetic information and in disentangling indirect relationships have resulted in an improved capacity to predict interactions between proteins and contacts between different protein residues. Here, we review the main co-evolution-based computational approaches, their theoretical basis, potential applications and foreseeable developments.

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  • One of the major insights leveraged by AlphaFold comes from the use of evolutionary covariations that can be extracted from large-scale multiple sequence alignments.

    “One of the major insights leveraged by AlphaFold 1,2 comes from the use of evolutionary covariations that can be extracted from large-scale multiple sequence alignments.[49][50][51][52][53][54]”

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Authors
de Juan, D., Pazos, F., & Valencia, A.
Affiliations
  • Centro Nacional de Biotecnología (ES)
  • Spanish National Cancer Research Centre (ES)
Corresponding author
Valencia, Alfonso
Venue
Nature Reviews Genetics
Published
2013-03-05
Volume
14
Issue
4
Pages
249-261
ISSN
  • 1471-0056 (ISSN-L)
  • 1471-0064
OpenAlex
W2065921821
Cited by
607
References
112
Citations by year
2026: 15 · 2025: 30 · 2024: 24 · 2023: 34 · 2022: 43 · 2021: 64 · 2020: 43 · 2019: 56 · 2018: 49 · 2017: 56 · 2016: 65 · 2015: 56 · 2014: 50 · 2013: 22
FWCI
6.06
Type
journal article
Language
en
Open access
Locations
  • Nature Reviews Genetics (publishedVersion)
  • PubMed (publishedVersion)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, other-oa)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
protein-protein interaction, protein domain interactions, phylogenetic analysis, indirect interactions, molecular interaction prediction, ecological networks
MeSH terms
Animals, Humans, Models, Genetic, Models, Molecular, Mutation, Phylogeny, Proteins, Protein Structure, Tertiary, Evolution, Molecular, Computational Biology

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