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.
Highlights
Essential to life at the molecular level is the interplay of molecules and macromolecules
To maintain interaction in the course of evolution, proteins and their domains are required to coevolve: evolutionary changes in the interaction partners appear correlated across several scales, from correlated presence-absence patterns of proteins across species, up to correlations in the amino-acid usage
Our approach combines these different scales within a common mathematical-statistical inference framework, which is inspired by the so-called direct coupling analysis
Summary
Essential to life at the molecular level is the interplay of molecules and macromolecules. Proteins are no exceptions and many of them undergo concerted interactions to achieve their full potential. Many interactions have been described in detail, including inter- and intra-protein domain-domain interactions, which will be the focus of this work. Advances in sequencing technology and the subsequent accumulation of vast sequence databases have fueled the generation of mathematical frameworks which aim to identify protein-protein interactions [2, 3]. Some of these techniques rely on the correlated evolution of interacting proteins [4,5,6,7,8,9,10]. Whenever interactions are conserved across many organisms, sufficient sequence examples are in principle available to computationally identify novel interactions relying on sequences alone
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