Inferring interaction partners from protein sequences using mutual information

Abstract

Functional protein-protein interactions are crucial in most cellular processes. They enable multi-protein complexes to assemble and to remain stable, and they allow signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interacting partners, and thus in correlations between their sequences. Pairwise maximum-entropy based models have enabled successful inference of pairs of amino-acid residues that are in contact in the three-dimensional structure of multi-protein complexes, starting from the correlations in the sequence data of known interaction partners. Recently, algorithms inspired by these methods have been developed to identify which proteins are functional interaction partners among the paralogous proteins of two families, starting from sequence data alone. Here, we demonstrate that a slightly higher performance for partner identification can be reached by an approximate maximization of the mutual information between the sequence alignments of the two protein families. Our mutual information-based method also provides signatures of the existence of interactions between protein families. These results stand in contrast with structure prediction of proteins and of multi-protein complexes from sequence data, where pairwise maximum-entropy based global statistical models substantially improve performance compared to mutual information. Our findings entail that the statistical dependences allowing interaction partner prediction from sequence data are not restricted to the residue pairs that are in direct contact at the interface between the partner proteins.

26 pages, 11 figures, published version

References in corpus (8)

• Identification of direct residue contacts in protein-protein interaction by message passing
• Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models
• Inferring interaction partners from protein sequences
• Fast and accurate multivariate Gaussian modeling of protein families: Predicting residue contacts and protein-interaction partners
• Simultaneous identification of specifically interacting paralogs and inter-protein contacts by Direct-Coupling Analysis
• Architecture and Co-Evolution of Allosteric Materials
• Phylogenetic correlations can suffice to infer protein partners from sequences
• Revealing evolutionary constraints on proteins through sequence analysis

Cited by in corpus (7)

• Phylogenetic correlations can suffice to infer protein partners from sequences
• Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences
• Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins
• Statistical physics of interacting proteins: impact of dataset size and quality assessed in synthetic sequences
• Impact of phylogeny on structural contact inference from protein sequence data
• DiffPaSS -- High-performance differentiable pairing of protein sequences using soft scores
• Correlated evolution: models and methods