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Identification of Protein Interactions Involved in Cellular Signaling
Open accessAbstract
Protein-protein interactions drive biological processes. They are critical for all intra- and extracellular functions, and the technologies to analyze them are widely applied throughout the various fields of biological sciences. This study takes an in-depth view of some common principles of cellular regulation and provides a detailed account of approaches required to comprehensively map signaling protein-protein interactions in any particular cellular system or condition. We provide a critical review of the benefits and disadvantages of the yeast two-hybrid method and affinity purification coupled with mass spectrometric procedures for identification of signaling protein-protein interactions. In particular, we emphasize the quantitative and qualitative differences between tandem affinity and one-step purification (such as FLAG and Strep tag) methods. Although applicable to all types of interaction studies, a special section is devoted in this review to aspects that should be considered when attempting to identify signaling protein interactions that often are transient and weak by nature. Finally, we discuss shotgun and quantitative information that can be gleaned by MS-coupled methods for analysis of multiprotein complexes.
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“suggesting that they may be functionally related69–71 or operate together72,73 to mediate phenotypic features associated with UDN-P9’s disease and hypomyelinating leukodystropy-20”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [72]
Citations
Cites
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
A novel genetic system to detect protein–protein interactions
Fields, S., & Song, O.-K. (1989) · Nature · doi:10.1038/340245a0 · Cited by 6,152
Ong, S.-E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., & Mann, M. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m200025-mcp200 · Cited by 5,574
Jaakkola, P., Mole, D. R., Tian, Y.-M., Wilson, M. I., Gielbert, J., Gaskell, S. J., Kriegsheim, A. V., Hebestreit, H. F., Mukherji, M., Schofield, C. J., et al. (2001) · Science · doi:10.1126/science.1059796 · Cited by 5,535
Functional organization of the yeast proteome by systematic analysis of protein complexes
Gavin, A., Böesche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A., Schultz, J., Rick, J. M., Michon, A., Cruciat, C., et al. (2002) · Nature · doi:10.1038/415141a · Cited by 4,729
A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae
Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., et al. (2000) · Nature · doi:10.1038/35001009 · Cited by 4,706
Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., et al. (2004) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m400129-mcp200 · Cited by 4,401
Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry
Ho, Y., Gruhler, A., Heilbut, A. M., Bader, G. D., Moore, L., Adams, S., Millar, A., Taylor, P. P., Bennett, K. L., Boutilier, K., et al. (2002) · Nature · doi:10.1038/415180a · Cited by 3,573
A comprehensive two-hybrid analysis to explore the yeast protein interactome
Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., & Sakaki, Y. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.061034498 · Cited by 3,566
Global landscape of protein complexes in the yeast Saccharomyces cerevisiae
Krogan, N. J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A., Li, J., Pu, S., Datta, N., Tikuisis, A., et al. (2006) · Nature · doi:10.1038/nature04670 · Cited by 2,964
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Metadata
- Authors
- Westermarck, J., Ivaska, J., & Corthals, G. L.
- Affiliations
- Åbo Akademi University (FI)
- Corresponding author
- Corthals, Garry L.
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2013-07
- Volume
- 12
- Issue
- 7
- Pages
- 1752-1763
- ISSN
- 1535-9476 (ISSN-L)
- PMC
- PMC3708163
- OpenAlex
- W2147130215
- Cited by
- 143
- References
- 101
- Citations by year
- 2026: 21 · 2025: 19 · 2024: 15 · 2023: 8 · 2022: 13 · 2021: 12 · 2020: 8 · 2019: 12 · 2018: 7 · 2017: 5 · 2016: 7 · 2015: 4 · 2014: 9 · 2013: 1
- FWCI
- 0.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Molecular & Cellular Proteomics (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Biotin and Related Studies
- Keywords
- protein-protein interaction, signal transduction, yeast two-hybrid, affinity purification, tandem affinity purification, FLAG tag, Strep-tag, protein complexes, protein interaction mapping
- MeSH terms
- Humans, Mass Spectrometry, Signal Transduction, Two-Hybrid System Techniques, Protein Interaction Mapping
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