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Structure-based prediction of protein–protein interactions on a genome-wide scale
Open accessAbstract
The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms. Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification, as well as from manual curation of experiments on individual systems. A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of protein-protein interactions (PPIs). Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 high-confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.
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“More than 300 000 protein-protein interactions (PPIs) occur in human cells and they are involved in all physiological processes, including disease. 1”
From Chasing molecular glue degraders: screening approaches (2022) · cites this paper as [1]
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“Computational strategies include homology modeling 8,9 , rigid body docking 10 , and linking genomic data with structural information 11 .”
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Metadata
- Authors
- Zhang, Q. C., Petrey, D., Deng, L., Qiang, L., Shi, Y., Thu, C. A., Bisikirska, B., Lefebvre, C., Accili, D., Hunter, T., Maniatis, T., Califano, A., & Honig, B.
- Affiliations
- Howard Hughes Medical Institute (US)
- Tongji University (CN)
- Salk Institute for Biological Studies (US)
- Columbia University (US)
- Corresponding author
- Califano, Andrea
- Honig, Barry
- ORCID
- Venue
- Nature
- Published
- 2012-09-30
- Volume
- 490
- Issue
- 7421
- Pages
- 556-560
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3482288
- OpenAlex
- W2039473152
- Cited by
- 723
- References
- 45
- Citations by year
- 2026: 23 · 2025: 35 · 2024: 42 · 2023: 30 · 2022: 55 · 2021: 62 · 2020: 47 · 2019: 43 · 2018: 52 · 2017: 56 · 2016: 59 · 2015: 81 · 2014: 62 · 2013: 71 · 2012: 5
- FWCI
- 28.39
- Type
- journal article
- Language
- en
- Update notices
- correction (2013-03-06)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Cancer Institute (U54CA121852, CA082683, CA121852., R01CA082683)
- National Institute of General Medical Sciences (R01GM030518, GM030518, R37GM030518, GM094597, U54GM094597)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK057539, DK057539)
- National Institute of Neurological Disorders and Stroke (R01NS043915)
- 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 › Machine Learning in Bioinformatics
- Keywords
- protein-protein interaction, protein-protein interaction prediction, protein structure, protein-protein interaction network, homology model, remote homology, yeast, human interactome, cell regulation
- MeSH terms
- Algorithms, Animals, Bayes Theorem, Brain, Humans, Models, Molecular, Phylogeny, Protein Binding, Protein Conformation, Protein Kinases, Proteins, ROC Curve, Saccharomyces cerevisiae, Transcription Factors, Reproducibility of Results, Cadherins, Proteome, Protein Interaction Mapping, Matrix Attachment Region Binding Proteins, Proteomics, PPAR gamma, Suppressor of Cytokine Signaling Proteins, Mice, High-Throughput Screening Assays, 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.
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