Cited by 13Bioinformatics and Genomic NetworksGenomics and Phylogenetic StudiesMachine Learning in Bioinformatics

Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins

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Gandarilla-Pérez, Carlos A., Pinilla, Sergio, Bitbol, Anne-Florence, Weigt, Martin·2023·PLOS Computational Biology

Published version of a preprint

Abstract

Predicting protein-protein interactions from sequences is an important goal of computational biology. Various sources of information can be used to this end. Starting from the sequences of two interacting protein families, one can use phylogeny or residue coevolution to infer which paralogs are specific interaction partners within each species. We show that these two signals can be combined to improve the performance of the inference of interaction partners among paralogs. For this, we first align the sequence-similarity graphs of the two families through simulated annealing, yielding a robust partial pairing. We next use this partial pairing to seed a coevolution-based iterative pairing algorithm. This combined method improves performance over either separate method. The improvement obtained is striking in the difficult cases where the average number of paralogs per species is large or where the total number of sequences is modest.

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Crossref · 10.1371/journal.pcbi.1011010

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References 49 · Cited by 13

Authors
Gandarilla-Pérez, C. A., Pinilla, S., Bitbol, A.-F., & Weigt, M.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • SIB Swiss Institute of Bioinformatics (CH)
Corresponding author
  • Bitbol, Anne-Florence
  • Weigt, Martin
Venue
PLOS Computational Biology
Published
2023-03-30
Volume
19
Issue
3
Pages
e1011010
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
OpenAlex
W4361267150
Cited by
13
References
49
Citations by year
2026: 4 · 2025: 2 · 2024: 6 · 2023: 1
FWCI
1.16
Type
journal article
Language
en
Open access
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Funders
  • H2020 Marie Skłodowska-Curie Actions (734439 InferNet)
  • European Research Council (851173)
  • Institut de Biologie Paris Seine (Action Intitative)
  • National Science Foundation (PHY-1607611)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • 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 prediction, paralogous proteins, phylogeny, residue coevolution, simulated annealing, protein families
MeSH terms
Algorithms, Phylogeny, Protein Binding, Proteins, Computational Biology

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