ArticlePLoS computational biology2019
A multi-scale coevolutionary approach to predict interactions between protein domains.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Two D-loop resolution systems enable natural genetic transformation in bacteria.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- 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
- Impact of phylogeny on the inference of functional sectors from protein sequence data.PLoS computational biology · 2024Article
- Sequence-Based Protein Design: A Review of Using Statistical Models to Characterize Coevolutionary Traits for Developing Hybrid Proteins as Genetic Sensors.International journal of molecular sciences · 2024Review
- PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces.Nature communications · 2023Article
- Analysis of CheW-like domains provides insights into organization of prokaryotic chemotaxis systems.Proteins · 2023Article
- Deep Antimicrobial Activity and Stability Analysis Inform Lysin Sequence-Function Mapping.ACS synthetic biology · 2023Article
- General strategies for using amino acid sequence data to guide biochemical investigation of protein function.Biochemical Society transactions · 2022Review
- Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes.eLife · 2022Article
- Machine-learning of complex evolutionary signals improves classification of SNVs.NAR genomics and bioinformatics · 2022Article
- Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences.PLoS computational biology · 2022Article
- Inverse Potts model improves accuracy of phylogenetic profiling.Bioinformatics (Oxford, England) · 2022Article
- Sequence coevolution and structure stabilization modulate olfactory receptor expression.Biophysical journal · 2022Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions.PLoS computational biology · 2021Article
- Inter-residue, inter-protein and inter-family coevolution: bridging the scales.Current opinion in structural biology · 2018Review
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7 authors.
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Abstract
Interacting proteins and protein domains coevolve on multiple scales, from their correlated presence across species, to correlations in amino-acid usage. Genomic databases provide rapidly growing data for variability in genomic protein content and in protein sequences, calling for computational predictions of unknown interactions. We first introduce the concept of direct phyletic couplings, based on global statistical models of phylogenetic profiles. They strongly increase the accuracy of predicting pairs of related protein domains beyond simpler correlation-based approaches like phylogenetic profiling (80% vs. 30-50% positives out of the 1000 highest-scoring pairs). Combined with the direct coupling analysis of inter-protein residue-residue coevolution, we provide multi-scale evidence for direct but unknown interaction between protein families. An in-depth discussion shows these to be biologically sensible and directly experimentally testable. Negative phyletic couplings highlight alternative solutions for the same functionality, including documented cases of convergent evolution. Thereby our work proves the strong potential of global statistical modeling approaches to genome-wide coevolutionary analysis, far beyond the established use for individual protein complexes and domain-domain interactions.
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