ArticlePloS one2016
Inter-Protein Sequence Co-Evolution Predicts Known Physical Interactions in Bacterial Ribosomes and the Trp Operon.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 65 citations in OpenAlex.
- Extending protein language models to a viral genomic scale using biologically induced sparse attention.GigaScience · 2026Article
- EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals.Nature communications · 2025Article
- 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
- Enhancing coevolutionary signals in protein-protein interaction prediction through clade-wise alignment integration.Scientific reports · 2024Article
- Coevolution combined with molecular dynamics simulations provides structural and mechanistic insights into the interactions between the integrator complex subunits.Computational and structural biotechnology journal · 2023Article
- Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences.PLoS computational biology · 2022Article
- Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences.Nature communications · 2021Article
- Inter-protein residue covariation information unravels physically interacting protein dimers.BMC bioinformatics · 2020Article
- A multi-scale coevolutionary approach to predict interactions between protein domains.PLoS computational biology · 2019Article
- Phylogenetic correlations can suffice to infer protein partners from sequences.PLoS computational biology · 2019Article
- Article
- Statistical investigations of protein residue direct couplings.PLoS computational biology · 2018Article
- Inferring interaction partners from protein sequences using mutual information.PLoS computational biology · 2018Article
- ComplexContact: a web server for inter-protein contact prediction using deep learning.Nucleic acids research · 2018Article
- Inter-residue, inter-protein and inter-family coevolution: bridging the scales.Current opinion in structural biology · 2018Review
- Evolution of an intricate J-protein network driving protein disaggregation in eukaryotes.eLife · 2017Article
- Modeling Hsp70/Hsp40 interaction by multi-scale molecular simulations and coevolutionary sequence analysis.eLife · 2017Article
- Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness.Current opinion in structural biology · 2017Review
- Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Co-evolution techniques are reshaping the way we do structural bioinformatics.F1000Research · 2017Review
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
Abstract
Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of protein-protein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a true-positive rate of 70% resp. 90% within the first 10 predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all predictions. In the trp operon, it assigns the two largest interaction scores to the only two interactions experimentally known. On the level of residue interactions we show that for both the small and the large ribosomal subunit our approach predicts interacting residues in the system with a true positive rate of 60% and 85% in the first 20 predictions. We use artificial data to show that the performance of our approach depends crucially on the size of the joint multiple sequence alignments and analyze how many sequences would be necessary for a perfect prediction if the sequences were sampled from the same model that we use for prediction. Given the performance of our approach on the test data we speculate that it can be used to detect new interactions, especially in the light of the rapid growth of available sequence data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.