Protein interaction networks revealed by proteome coevolution
Open accessAbstract
Predicting protein pairs: Biological function is driven by interaction between proteins. High-throughput experimental techniques have provided large datasets of protein interactions in several organisms; however, much combinatorial space remains uncharted. Cong et al. predict protein interfaces by identifying coevolving residues in aligned protein sequences (see the Perspective by Vajda and Emili). In comparison with gold-standard and negative control sets, they show that the accuracy is higher than for proteome-wide two-hybrid and mass spectrometry screens. The approach predicts 1618 protein interactions in Escherichia coli , 682 of which were unanticipated, and 911 interacting pairs in Mycobacterium tuberculosis , most of which had not been previously described. With an expected false-positive rate of between 10 and 20%, the predicted interactions and networks provide an excellent starting point for further study. Science , this issue p. 185 ; see also p. 120
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The RoseTTAFold CERS1 model for residues 98 to 304 (Pfam TLC domain) includes six transmembrane helices that traverse the membrane in an up and down arrangement.
“The RoseTTAFold CERS1 model for residues 98 to 304 (Pfam TLC domain) (29) includes six TMH that traverse the membrane in an up and down arrangement (Fig. 3E).”
From Accurate prediction of protein structures and interactions using a 3-track network (2021) · cites this paper as [29]
The paper’s Abstract (page unavailable) describes coevolution-based protein interaction models in E. coli and M. tuberculosis, not a CERS1 model. Searches for CERS1, the Pfam TLC domain, RoseTTAFold, and six transmembrane helices found no passage in our copy addressing the cited domain identification or the stated membrane arrangement. (LLM, conf=0.76)
Experimental — accuracy not yet measuredCombining standard protein-protein docking methods with co-evolutionary signals or deep learning models has been carried out on full proteomes.
“Combining standard protein-protein docking methods with co-evolutionary signals28 or even with deep learning models29 has also been carried out on full proteomes.”
From AF2Complex predicts direct physical interactions in multimeric proteins with deep learning (2022) · cites this paper as [28]
The Direct Coupling Analysis (DCA) method could not effectively distinguish a gold standard set of 768 yeast protein pairs known to interact from a much larger set of 768,000 primarily non-interacting pairs (area under the curve: 0.016).
“In a first set of calculations, we found that even with the advantages of S. cerevisiae and improved ortholog identification, the statistical method (Direct Coupling Analysis, DCA) we had used in our previous coevolution-guided protein-protein interaction (PPI) screen in Prokaryotes (9) (the more accurate GREMLIN (11) method is too slow for this) could not effectively distinguish a “gold standard””
From Computed structures of core eukaryotic protein complexes (2021) · cites this paper as [9]
Amino acid coevolution has been used to guide modeling of complexes for cases in which the structures of the partners are known, and to systematically identify pairs of interacting proteins in Prokaryotes with accuracy higher than experimental screens.
“Such amino acid coevolution has been used to guide modeling of complexes for cases in which the structures of the partners are known (11,12), and to systematically identify pairs of interacting proteins in Prokaryotes with accuracy higher than experimental screens (9).”
From Computed structures of core eukaryotic protein complexes (2021) · cites this paper as [9]
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 [9]
Cell-cell similarity graphs in single cell omics, gene regulatory networks, and protein-protein interaction networks are fundamental tools to rationalize the complexity of biological systems such as the human body.
“Cell-cell similarity graphs in single cell omics [ 47 ], gene regulatory networks [ 48 ] and protein-protein interaction networks [ 49 ] are fundamental tools to rationalize the complexity of biological systems such as the human body.”
From igraph 1.0 enables fast and robust network analysis across programming languages (2026) · cites this paper as [49]
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- Authors
- Cong, Q., Anishchenko, I., Ovchinnikov, S., & Baker, D.
- Affiliations
- University of Washington (US)
- Harvard University (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Baker, David
- Venue
- Science
- Published
- 2019-07-12
- Volume
- 365
- Issue
- 6449
- Pages
- 185-189
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6948103
- OpenAlex
- W2957157683
- Cited by
- 241
- References
- 56
- Citations by year
- 2026: 19 · 2025: 13 · 2024: 28 · 2023: 41 · 2022: 48 · 2021: 44 · 2020: 33 · 2019: 14 · 2018: 1
- FWCI
- 11.67
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (DP5OD026389)
- Howard Hughes Medical Institute
- U.S. Department of Energy (DE-AC02-05CH11231)
- National Institute of General Medical Sciences (R01-GM092802-07)
- National Institute of Allergy and Infectious Diseases (HHSN272201700059C)
- Spark Therapeutics
- IPD Director’s Fund
- Washington Research Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- protein-protein interaction, protein-protein interaction network, coevolution, protein interface prediction, sequence alignment, Escherichia coli, Mycobacterium tuberculosis
- MeSH terms
- Biological Coevolution, Bacterial Proteins, Escherichia coli, Mycobacterium tuberculosis, Protein Conformation, Proteome, Escherichia coli Proteins, Protein Interaction Maps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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