Open AccessA multi-scale coevolutionary approach to predict interactions between protein domains
Author(s) -
Giancarlo Croce,
Thomas Gueudré,
Maria Virginia Ruiz Cuevas,
Victoria Keidel,
Matteo Figliuzzi,
Hendrik Szurmant,
Martin Weigt
Publication year - 2019
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1006891
Subject(s) - phyletic gradualism , phylogenetic tree , computational biology , protein domain , false positive paradox , coevolution , protein–protein interaction , biology , sequence alignment , multiple sequence alignment , genomics , computer science , genome , evolutionary biology , genetics , machine learning , peptide sequence , gene
Interacting proteins and protein domains coevolve on multiple scales, from their correlated presence across species, to correlations in amino-acid usage. Genomic databases provide rapidly growing data for variability in genomic protein content and in protein sequences, calling for computational predictions of unknown interactions. We first introduce the concept of direct phyletic couplings , based on global statistical models of phylogenetic profiles. They strongly increase the accuracy of predicting pairs of related protein domains beyond simpler correlation-based approaches like phylogenetic profiling (80% vs. 30–50% positives out of the 1000 highest-scoring pairs). Combined with the direct coupling analysis of inter-protein residue-residue coevolution, we provide multi-scale evidence for direct but unknown interaction between protein families. An in-depth discussion shows these to be biologically sensible and directly experimentally testable. Negative phyletic couplings highlight alternative solutions for the same functionality, including documented cases of convergent evolution. Thereby our work proves the strong potential of global statistical modeling approaches to genome-wide coevolutionary analysis, far beyond the established use for individual protein complexes and domain-domain interactions.