Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Abstract

Inferring protein-protein interactions from sequences is an important task in computational biology. Recent methods based on Direct Coupling Analysis (DCA) or Mutual Information (MI) allow to find interaction partners among paralogs of two protein families. Does successful inference mainly rely on correlations from structural contacts or from phylogeny, or both? Do these two types of signal combine constructively or hinder each other? To address these questions, we generate and analyze synthetic data produced using a minimal model that allows us to control the amounts of structural constraints and phylogeny. We show that correlations from these two sources combine constructively to increase the performance of partner inference by DCA or MI. Furthermore, signal from phylogeny can rescue partner inference when signal from contacts becomes less informative, including in the realistic case where inter-protein contacts are restricted to a small subset of sites. We also demonstrate that DCA-inferred couplings between non-contact pairs of sites improve partner inference in the presence of strong phylogeny, while deteriorating it otherwise. Moreover, restricting to non-contact pairs of sites preserves inference performance in the presence of strong phylogeny. In a natural data set, as well as in realistic synthetic data based on it, we find that non-contact pairs of sites contribute positively to partner inference performance, and that restricting to them preserves performance, evidencing an important role of phylogeny.

33 pages, 20 figures

References in corpus (4)

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Cited by in corpus (5)

• Generative power of a protein language model trained on multiple sequence alignments
• Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins
• Impact of phylogeny on structural contact inference from protein sequence data
• DiffPaSS -- High-performance differentiable pairing of protein sequences using soft scores
• Impact of phylogeny on the inference of functional sectors from protein sequence data