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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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Cites
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
Inference of Macromolecular Assemblies from Crystalline State
Krissinel, E., & Henrick, K. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.05.022 · Cited by 10,007
UniProt: the Universal Protein knowledgebase
Apweiler, R. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh131 · Cited by 7,593
Comparative assessment of large-scale data sets of protein–protein interactions
von Mering, C., Krause, R., Snel, ., Cornell, M. J., Oliver, S. G., Fields, S., & Peer Bork, B. (2002) · Nature · doi:10.1038/nature750 · Cited by 2,339
Interactome Networks and Human Disease
Vidal, M., Cusick, M. E., & Barabási, A.-L. (2011) · Cell · doi:10.1016/j.cell.2011.02.016 · Cited by 1,771
von Mering, C. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki005 · Cited by 1,768
High-Quality Binary Protein Interaction Map of the Yeast Interactome Network
Yu, H., Braun, P., Yıldırım, M. A., Lemmens, I., Venkatesan, K., Sahalie, J., Hirozane-Kishikawa, T., Gebreab, F., Li, N., Simonis, N., et al. (2008) · Science · doi:10.1126/science.1158684 · Cited by 1,392
A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data
Jansen, R., Yu, H., Greenbaum, D. S., Kluger, Y., Krogan, N. J., Chung, S., Emili, A., Snyder, M. P., Greenblatt, J. F., & Gerstein, M. (2003) · Science · doi:10.1126/science.1087361 · Cited by 1,254
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Giese, M. (2016) · Springer eBooks · doi:10.1007/978-3-319-25832-4_6 · Cited by 1
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Zhan, X., You, Z., Yu, C.-Q., Pan, J., & Li, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60802-6_12 · Cited by 1
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Du, T. (2024) · Library, Museums and Press - UDSpace (University of Delaware) · doi:10.58088/tzbm-5g14 · Cited by 1
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Luck, K., Jailkhani, N., Cusick, M. E., Rolland, T., Calderwood, M. A., Charloteaux, B., & Vidal, M. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-821618-7.40037-4 · Cited by 1
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Zhang, Q. C., Petrey, D. S., & Honig, B. H. (2013) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2013.786422
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Maleki, M., Hall, M., & Rueda, L. G. (2015) · doi:10.1002/9781119078845.ch16
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Webb, S. (2013) · BioTechniques · doi:10.2144/000114072
Villoutreix, B. O., Kuenemann, M. A., Lagorce, D., Spérandio, O., & Miteva, M. A. (2015) · CRC Press eBooks · doi:10.1201/b18799-21
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Russell, R. B., Apic, G., Kalinina, O., Trabuco, L. G., Betts, M. J., & Lu, Q. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-405926-9.00002-2
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Rodgers‐Melnick, E. (2013) · The Research Repository @ WVU (West Virginia University) · doi:10.33915/etd.3662
Zhao, C., & Saçan, A. (2016) · doi:10.17918/etd-6775
Development of protein-protein docking methodology and benchmarking environment
Anishchenko, I. V. (2016) · KU ScholarWorks (University of Kansas)
Sutherland, R. D. (2016) · Research Portal (King's College London)
Template Based Modeling and Structural Refinement of Protein-Protein Interactions.
Govindarajoo, B. (2016) · Deep Blue (University of Michigan)
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Eid, F. E. (2017) · VTechWorks (Virginia Tech)
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Liu, J., & Yue, W. (2017) · Modeling and optimization in science and technologies · doi:10.1007/978-3-319-50688-3_11
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Srihari, S., Yong, C. H., & Wong, L. (2017) · doi:10.1145/3064650.3064658
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Srihari, S., Yong, C. H., & Wong, L. (2017) · doi:10.1145/3064650.3064654
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Koon, Y. L. (2018) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/73360
Peng, Z. (2014) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3x34n103
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Gui, Y., Wang, R., Wei, Y., & Xue, W. (2018) · International Workshop on Bioinformatics, Biochemistry, Biomedical Sciences (BBBS) · doi:10.2991/bbbs-18.2018.47
Han, L., & Wang, R. (2018) · Springer eBooks · doi:10.1007/978-981-13-0773-7_4
Ohmuro‐Matsuyama, Y., & Ueda, H. (2018) · InTech eBooks · doi:10.5772/intechopen.75644
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Zhang, C., Guo, S., Zhang, J., Xi-zi, J., Li, Y., Du, N., Sun, P., & Jiang, B. (2018) · Letters in Organic Chemistry · doi:10.2174/1570178615666180802122253
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Lasso, G., Mayer, S. V., Winkelmann, E., Chu, T., Elliott, O., Patiño-Galindo, J. Á., Park, K., Rabadán, R., Honig, B. H., & Shapira, S. D. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3346979
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Califano, A. (2015) · Elsevier eBooks · doi:10.1016/b978-1-4557-4066-6.00020-2
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Ma, W., Bao, W., Cao, Y., & Chen, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60802-6_39
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Burnside, D. J. (2019) · Carleton University's Institutional Repository (MacOdrum Library, Carleton University) · doi:10.22215/etd/2019-13862
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Shah, S. U., Hameed, A., Ahmad, J., Fatima, H. U. R. S., & Amin, M. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120977
Saha, S., Soliman, A. T., & Rajasekaran, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.19.465019
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Srihari, S., Yong, C. H., & Wong, L. (2017) · doi:10.1145/3064650.3064660
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Srihari, S., Yong, C. H., & Wong, L. (2017) · doi:10.1145/3064650.3064651
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Qiu, A., Zhang, H., Ramsey, J., Andrews, B., Sun, B., Ren, S., Lu, M., Zhang, K., Cooper, G. F., Lu, B., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag139
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Zhu, N., Swietlik, E. M., Welch, C. L., Pauciulo, M. W., Hagen, J. J., Zhou, X., Guo, Y., Karten, J., Pandya, D., Tilly, T., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14569987
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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
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