Predicting Interacting Protein Pairs by Coevolutionary Paralog Matching.

Coevolution Direct coupling analysis Paralog matching Predicting interacting paralogs Protein–protein interaction

Journal

Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969

Informations de publication

Date de publication:
2020
Historique:
entrez: 5 10 2019
pubmed: 5 10 2019
medline: 12 1 2021
Statut: ppublish

Résumé

Even if we know that two families of homologous proteins interact, we do not necessarily know, which specific proteins interact inside each species. The reason is that most families contain paralogs, i.e., more than one homologous sequence per species. We have developed a tool to predict interacting paralogs between the two protein families, which is based on the idea of inter-protein coevolution: our algorithm matches those members of the two protein families, which belong to the same species and collectively maximize the detectable coevolutionary signal. It is applicable even in cases, where simpler methods based, e.g., on genomic co-localization of genes coding for interacting proteins or orthology-based methods fail. In this method paper, we present an efficient implementation of this idea based on freely available software.

Identifiants

pubmed: 31583630
doi: 10.1007/978-1-4939-9873-9_5
doi:

Substances chimiques

Proteins 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

57-65

Auteurs

Thomas Gueudré (T)

Italian Institute for Genomic Medicine, Turin, Italy.

Carlo Baldassi (C)

Bocconi Institute for Data Science and Analytics, Bocconi University, Milan, Italy.
INFN, Sezione di Torino, Torino, Italy.

Andrea Pagnani (A)

Italian Institute for Genomic Medicine, Turin, Italy.
INFN, Sezione di Torino, Torino, Italy.
Dipartimento di Scienza Applicata e Tecnologia, Politecnico di Torino, Torino, Italy.

Martin Weigt (M)

Sorbonne Université, CNRS, Institut de Biologie Paris Seine, Biologie Computationnelle et Quantitative-LCQB, Paris, France. martin.weigt@upmc.fr.

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Classifications MeSH