ArticlePLoS computational biology2023
Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- Systematic analysis of CDR contacts and pairing constraints between T cell receptor αβ chains.Bioinformatics (Oxford, England) · 2026Article
- PHACE: Phylogeny-Aware Detection of Molecular Coevolution.Molecular biology and evolution · 2025Article
- DiffPaSS-high-performance differentiable pairing of protein sequences using soft scores.Bioinformatics (Oxford, England) · 2024Article
- Predicting the involvement of polyQ- and polyA in protein-protein interactions by their amino acid context.Heliyon · 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
- Impact of phylogeny on structural contact inference from protein sequence data.Journal of the Royal Society, Interface · 2023Article
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4 authors.
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Abstract
Predicting protein-protein interactions from sequences is an important goal of computational biology. Various sources of information can be used to this end. Starting from the sequences of two interacting protein families, one can use phylogeny or residue coevolution to infer which paralogs are specific interaction partners within each species. We show that these two signals can be combined to improve the performance of the inference of interaction partners among paralogs. For this, we first align the sequence-similarity graphs of the two families through simulated annealing, yielding a robust partial pairing. We next use this partial pairing to seed a coevolution-based iterative pairing algorithm. This combined method improves performance over either separate method. The improvement obtained is striking in the difficult cases where the average number of paralogs per species is large or where the total number of sequences is modest.
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