Unknown

Dataset Information

0

A multi-scale coevolutionary approach to predict interactions between protein domains.


ABSTRACT: 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.

SUBMITTER: Croce G 

PROVIDER: S-EPMC6822775 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

altmetric image

Publications

A multi-scale coevolutionary approach to predict interactions between protein domains.

Croce Giancarlo G   Gueudré Thomas T   Ruiz Cuevas Maria Virginia MV   Keidel Victoria V   Figliuzzi Matteo M   Szurmant Hendrik H   Weigt Martin M  

PLoS computational biology 20191021 10


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 predic  ...[more]

Similar Datasets

| S-EPMC2936518 | biostudies-other
| S-EPMC1919395 | biostudies-literature
| S-EPMC5519331 | biostudies-literature
| S-EPMC8793231 | biostudies-literature
| S-EPMC6191966 | biostudies-other
| S-EPMC3615344 | biostudies-literature
2006-02-08 | GSE3758 | GEO
| S-EPMC5167203 | biostudies-literature
| S-EPMC2246258 | biostudies-literature
| S-EPMC2896152 | biostudies-literature