ArticlePLoS computational biology2018
Inferring interaction partners from protein sequences using mutual information.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 62 citations in OpenAlex.
- Systematic analysis of CDR contacts and pairing constraints between T cell receptor αβ chains.Bioinformatics (Oxford, England) · 2026Article
- Epistasis and co-adaptation in bacterial genome evolution.Nature reviews. Genetics · 2026Review
- ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Pan-Angiosperm Analysis of the CLE Signaling Peptide Gene Family Unveils Paths, Patterns, and Predictions of Paralog Diversification.Molecular biology and evolution · 2025Article
- Investigating Statistical Conditions of Coevolutionary Signals that Enable Algorithmic Predictions of Protein Partners.Journal of chemical information and modeling · 2025Article
- EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals.Nature communications · 2025Article
- DiffPaSS-high-performance differentiable pairing of protein sequences using soft scores.Bioinformatics (Oxford, England) · 2024Article
- Pairing interacting protein sequences using masked language modeling.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Enhancing coevolutionary signals in protein-protein interaction prediction through clade-wise alignment integration.Scientific reports · 2024Article
- Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins.PLoS computational biology · 2023Article
- Coevolution-based prediction of key allosteric residues for protein function regulation.eLife · 2023Article
- Impact of phylogeny on structural contact inference from protein sequence data.Journal of the Royal Society, Interface · 2023Article
- Article
- Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences.PLoS computational biology · 2022Article
- Extracting phylogenetic dimensions of coevolution reveals hidden functional signals.Scientific reports · 2022Article
- Coevolutionary methods enable robust design of modular repressors by reestablishing intra-protein interactions.Nature communications · 2021Article
- Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction.International journal of molecular sciences · 2021Review
- Information Theory in Molecular Evolution: From Models to Structures and Dynamics.Entropy (Basel, Switzerland) · 2021Article
- Trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches.Scientific reports · 2021Article
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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