An atlas of substrate specificities for the human serine/threonine kinome
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Abstract
Protein phosphorylation is one of the most widespread post-translational modifications in biology 1,2 . With advances in mass-spectrometry-based phosphoproteomics, 90,000 sites of serine and threonine phosphorylation have so far been identified, and several thousand have been associated with human diseases and biological processes 3,4 . For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein serine/threonine (Ser/Thr) kinases encoded in the human genome are responsible 3 . Here we used synthetic peptide libraries to profile the substrate sequence specificity of 303 Ser/Thr kinases, comprising more than 84% of those predicted to be active in humans. Viewed in its entirety, the substrate specificity of the kinome was substantially more diverse than expected and was driven extensively by negative selectivity. We used our kinome-wide dataset to computationally annotate and identify the kinases capable of phosphorylating every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites for which the putative protein kinases involved have been previously reported, our predictions were in excellent agreement. When this approach was applied to examine the signalling response of tissues and cell lines to hormones, growth factors, targeted inhibitors and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the intrinsic substrate specificity of the human Ser/Thr kinome, illuminate cellular signalling responses and provide a resource to link phosphorylation events to biological pathways.
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An optogenetic-phosphoproteomic study reveals dynamic Akt1 signaling profiles in endothelial cells
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Substrate recognition principles for the PP2A-B55 protein phosphatase
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Structural basis of Cdk7 activation by dual T-loop phosphorylation
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Self-citation
- Authors
- Johnson, J. L., Yaron, T. M., Huntsman, E. M., Kerelsky, A., Song, J., Regev, A., Lin, T.-Y., Liberatore, K., Cizin, D. M., Cohen, B. M., Vasan, N., Ma, Y., Krismer, K., Robles, J. T., van de Kooij, B., van Vlimmeren, A. E., Andrée-Busch, N., Käufer, N. F., Dorovkov, M. V., Ryazanov, A. G., Takagi, Y., Kastenhuber, E. R., Goncalves, M. D., Hopkins, B. D., Elemento, O., Taatjes, D. J., Maucuer, A., Yamashita, A., Degterev, A., Uduman, M., Lu, J., Landry, S. D., Zhang, B., Cossentino, I., Linding, R., Blenis, J., Hornbeck, P. V., Turk, B. E., Yaffe, M. B., & Cantley, L. C.
- Affiliations
- Weill Cornell Medicine (US)
- Memorial Sloan Kettering Cancer Center (US)
- Cornell University (US)
- Columbia University Irving Medical Center (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Yale University (US)
- Technische Universität Braunschweig (DE)
- Rutgers, The State University of New Jersey (US)
- Indiana University School of Medicine (US)
- Icahn School of Medicine at Mount Sinai (US)
- University of Colorado Boulder (US)
- Inserm (FR)
- University of the Ryukyus (JP)
- Tufts University (US)
- Cell Signaling Technology (United States) (US)
- Humboldt-Universität zu Berlin (DE)
- National Institutes of Health (US)
- Corresponding author
- Turk, Benjamin E.
- Yaffe, Michael B.
- Cantley, Lewis C.
- ORCID
- Johnson, Jared L.
- Yaron, Tomer M.
- Huntsman, Emily M.
- Song, Junho
- Regev, Amit
- Lin, Ting-Yu
- Liberatore, Katarina
- Cohen, Benjamin M.
- Vasan, Neil
- Ma, Yilun
- Krismer, Konstantin
- Robles, Jaylissa Torres
- van Vlimmeren, Anne E.
- Andrée-Busch, Nicole
- Dorovkov, Maxim V.
- Takagi, Yuichiro
- Kastenhuber, Edward R.
- Goncalves, Marcus D.
- Hopkins, Benjamin D.
- Elemento, Olivier
- Taatjes, Dylan J.
- Maucuer, Alexandre
- Yamashita, Akio
- Degterev, Alexei
- Uduman, Mohamed
- Lu, Jingyi
- Landry, Sean D.
- Cossentino, Ian
- Linding, Rune
- Blenis, John
- Hornbeck, Peter V.
- Turk, Benjamin E.
- Yaffe, Michael B.
- Cantley, Lewis C.
- Venue
- Nature
- Published
- 2023-01-11
- Volume
- 613
- Issue
- 7945
- Pages
- 759-766
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC9876800
- OpenAlex
- W4315642941
- Cited by
- 728
- References
- 120
- Citations by year
- 2026: 206 · 2025: 223 · 2024: 193 · 2023: 102 · 2022: 4
- FWCI
- 67.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- IUScholarWorks (Indiana University) (submittedVersion, cc-by)
- University oh the Ryukyus Repository (submittedVersion, cc-by-nc-nd)
- Funders
- Cancer Research UK (A28596, C42454/A28596)
- National Cancer Institute (CA120964, CA14051, T32CA203702, CA197588, CA226898, R01CA226898, Grant P30-CA14051, R35CA197588, P01CA120964, P30CA014051, P01CA117969, P30-CA-14051)
- National Institute of General Medical Sciences (R01GM104047, R35GM139550)
- National Institute of Diabetes and Digestive and Kidney Diseases (U24DK116204)
- National Institute of Environmental Health Sciences (R35ES028374)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Kinase Regulation and GTPase Signaling
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Tyrosine Phosphatases
- Keywords
- serine/threonine kinase, kinase substrate specificity, protein phosphorylation, phosphoproteome, synthetic peptide libraries, kinase profiling
- MeSH terms
- Cell Line, Humans, Phosphoproteins, Phosphorylation, Phosphoserine, Phosphothreonine, Serine, Substrate Specificity, Threonine, Protein Serine-Threonine Kinases, Proteome, Datasets as Topic
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