ArticlePLoS computational biology2022
Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 20 citations in OpenAlex.
- Gaugefixer: overcoming parameter non-identifiability in models of sequence-function relationships.BMC bioinformatics · 2026Article
- GaugeFixer: overcoming parameter non-identifiability in models of sequence-function relationships.bioRxiv : the preprint server for biology · 2026Article
- Identification of coevolving positions by ancestral reconstruction.Communications biology · 2025Article
- Gauge fixing for sequence-function relationships.PLoS computational biology · 2025Article
- DiffPaSS-high-performance differentiable pairing of protein sequences using soft scores.Bioinformatics (Oxford, England) · 2024Article
- Impact of phylogeny on the inference of functional sectors from protein sequence data.PLoS computational biology · 2024Article
- Pairing interacting protein sequences using masked language modeling.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Gauge fixing for sequence-function relationships.bioRxiv : the preprint server for biology · 2024Article
- Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins.PLoS computational biology · 2023Article
- Article
- Impact of phylogeny on structural contact inference from protein sequence data.Journal of the Royal Society, Interface · 2023Article
- Symmetry, gauge freedoms, and the interpretability of sequence-function relationships.Physical review researchArticle
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3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
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.
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