  Determining which proteins interact together is crucial to a systems-level
understanding of the cell. Recently, algorithms based on Direct Coupling
Analysis (DCA) pairwise maximum-entropy models have allowed to identify
interaction partners among paralogous proteins from sequence data. This success
of DCA at predicting protein-protein interactions could be mainly based on its
known ability to identify pairs of residues that are in contact in the
three-dimensional structure of protein complexes and that coevolve to remain
physicochemically complementary. However, interacting proteins possess similar
evolutionary histories. What is the role of purely phylogenetic correlations in
the performance of DCA-based methods to infer interaction partners? To address
this question, we employ controlled synthetic data that only involve phylogeny
and no interactions or contacts. We find that DCA accurately identifies the
pairs of synthetic sequences that share evolutionary history. While
phylogenetic correlations confound the identification of contacting residues by
DCA, they are thus useful to predict interacting partners among paralogs. We
find that DCA performs as well as phylogenetic methods to this end, and
slightly better than them with large and accurate training sets. Employing DCA
or phylogenetic methods within an Iterative Pairing Algorithm (IPA) allows to
predict pairs of evolutionary partners without a training set. We demonstrate
the ability of these various methods to correctly predict pairings among real
paralogous proteins with genome proximity but no known physical interaction,
illustrating the importance of phylogenetic correlations in natural data.
However, for physically interacting and strongly coevolving proteins, DCA and
mutual information outperform phylogenetic methods. We discuss how to
distinguish physically interacting proteins from those only sharing
evolutionary history.
