Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences
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Increasing numbers of protein interactions have been identified in high-throughput experiments, but only a small proportion have solved structures. Recently, sequence coevolution-based approaches have led to a breakthrough in predicting monomer protein structures and protein interaction interfaces. Here, we address the challenges of large-scale interaction prediction at residue resolution with a fast alignment concatenation method and a probabilistic score for the interaction of residues. Importantly, this method (EVcomplex2) is able to assess the likelihood of a protein interaction, as we show here applied to large-scale experimental datasets where the pairwise interactions are unknown. We predict 504 interactions de novo in the E. coli membrane proteome, including 243 that are newly discovered. While EVcomplex2 does not require available structures, coevolving residue pairs can be used to produce structural models of protein interactions, as done here for membrane complexes including the Flagellar Hook-Filament Junction and the Tol/Pal complex.
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Because residues at protein-protein interfaces are expected to coevolve, the likelihood that any two proteins interact can be assessed by identifying and aligning ortholog sequences, joining them to create paired multiple sequence alignments, and determining the extent to which changes in sequences of orthologs for the first protein covary with ortholog sequence changes for the second.
“Because residues at protein-protein interfaces are expected to coevolve, the likelihood that any two proteins interact can be assessed by identifying and aligning the ortholog sequences of the two proteins in many different species, joining them to create paired multiple sequence alignments (pMSA), and then determining the extent to which changes in the sequences of orthologs for the first protein”
From Computed structures of core eukaryotic protein complexes (2021) · cites this paper as [10]
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Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure
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Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes
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Impact of phylogeny on structural contact inference from protein sequence data
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GSScore: a novel Graphormer-based shell-like scoring method for protein–ligand docking
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Deep learning methods for proteome-scale interaction prediction
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African Swine Fever Virus Protein–Protein Interaction Prediction
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ProBID-Net: a deep learning model for protein–protein binding interface design
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Interactomes in the era of deep learning
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KnotFold: Improving Peptide Structure Predictions with Simulated Coevolution
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What the protein!? Computational methods for predicting microbial protein functionsPreprint
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High-throughput machine learning-aided antibody discovery for cell surface antigens
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DiffPaSS—high-performance differentiable pairing of protein sequences using soft scores
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Computational Docking Reveals Co-Evolution of C4 Carbon Delivery Enzymes in Diverse Plants
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Modeling Protein Complexes and Molecular Assemblies Using Computational Methods
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- Authors
- Green, A. G., Elhabashy, H., Brock, K. P., Maddamsetti, R., Kohlbacher, O., & Marks, D. S.
- Affiliations
- Harvard University (US)
- Max Planck Institute for Developmental Biology (DE)
- Universitätsklinikum Tübingen (DE)
- Broad Institute (US)
- Corresponding author
- Kohlbacher, Oliver
- Marks, Debora S.
- ORCID
- Venue
- Nature Communications
- Published
- 2021-03-02
- Volume
- 12
- Issue
- 1
- Pages
- 1396-1396
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7925567
- OpenAlex
- W3133793061
- Cited by
- 114
- References
- 57
- Citations by year
- 2026: 14 · 2025: 17 · 2024: 24 · 2023: 25 · 2022: 21 · 2021: 13
- FWCI
- 6.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (DGE1144152)
- U.S. Department of Health & Human Services | National Institutes of Health (T15LM007092)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- protein-protein interaction, membrane protein complexes
- MeSH terms
- Amino Acids, Bacterial Proteins, Base Sequence, Escherichia coli, Eukaryotic Cells, Membrane Proteins, Protein Binding, Genome, Bacterial, Evolution, Molecular, Proteome, Protein Interaction Mapping, Molecular Docking Simulation
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