ArticleProceedings of the National Academy of Sciences of the United States of America2016
Inferring interaction partners from protein sequences.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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73 citing papers in PubMed.
- Systematic analysis of CDR contacts and pairing constraints between T cell receptor αβ chains.Bioinformatics (Oxford, England) · 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
- Accurate protein-protein interactions modeling through physics-informed geometric invariant learning.Communications biology · 2026Article
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- High-accuracy protein complex structure modeling based on sequence-derived structure complementarity.Nature communications · 2025Article
- Inference and visualization of complex genotype-phenotype maps withbioRxiv : the preprint server for biology · 2025Article
- Investigating Statistical Conditions of Coevolutionary Signals that Enable Algorithmic Predictions of Protein Partners.Journal of chemical information and modeling · 2025Article
- Direct coupling analysis and the attention mechanism.BMC bioinformatics · 2025Article
- DiffPaSS-high-performance differentiable pairing of protein sequences using soft scores.Bioinformatics (Oxford, England) · 2024Article
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
- 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
- Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information.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
- Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.Cold Spring Harbor perspectives in biology · 2024Review
- Enhancing coevolutionary signals in protein-protein interaction prediction through clade-wise alignment integration.Scientific reports · 2024Article
- Evolution shapes interaction patterns for epistasis and specific protein binding in a two-component signaling system.Communications chemistry · 2024Article
- Predicted models and CCP4.Acta crystallographica. Section D, Structural biology · 2023Article
- Generating interacting protein sequences using domain-to-domain translation.Bioinformatics (Oxford, England) · 2023Article
- Integrative structure determination of histones H3 and H4 using genetic interactions.The FEBS journal · 2023Article
13 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multiprotein complexes and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners, causing their sequences to be correlated. Here we exploit these correlations to accurately identify, from sequence data alone, which proteins are specific interaction partners. Our general approach, which employs a pairwise maximum entropy model to infer couplings between residues, has been successfully used to predict the 3D structures of proteins from sequences. Thus inspired, we introduce an iterative algorithm to predict specific interaction partners from two protein families whose members are known to interact. We first assess the algorithm's performance on histidine kinases and response regulators from bacterial two-component signaling systems. We obtain a striking 0.93 true positive fraction on our complete dataset without any a priori knowledge of interaction partners, and we uncover the origin of this success. We then apply the algorithm to proteins from ATP-binding cassette (ABC) transporter complexes, and obtain accurate predictions in these systems as well. Finally, we present two metrics that accurately distinguish interacting protein families from noninteracting ones, using only sequence data.
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