  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.
