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The functional landscape of the human phosphoproteome
Open accessPublished version of a preprint
Abstract
Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. Although tens of thousands of phosphorylation sites have been identified in human cells, approaches to determine the functional importance of each phosphosite are lacking. Here, we manually curated 112 datasets of phospho-enriched proteins, generated from 104 different human cell types or tissues. We re-analyzed the 6,801 proteomics experiments that passed our quality control criteria, creating a reference phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, we used machine learning to identify 59 features indicative of proteomic, structural, regulatory or evolutionary relevance and integrate them into a single functional score. Our approach identifies regulatory phosphosites across different molecular mechanisms, processes and diseases, and reveals genetic susceptibilities at a genomic scale. Several regulatory phosphosites were experimentally validated, including identifying a role in neuronal differentiation for phosphosites in SMARCC2, a member of the SWI/SNF chromatin-remodeling complex.
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“The inferred transcription factors and kinases indicate the well-annotated players in the DNA damage response, whereas proteins with altered thermal stability reflect less annotated parts of the signaling space with strong links to the phosphoproteomic measurements which aligns with observations linking functionality of phosphosites with thermal stability (Ochoa et al, 2020; Potel et al, 2021).”
From Network integration of thermal proteome profiling with multi-omics data decodes PARP inhibition (2024) · cites this paper as Ochoa et al, 2020
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Metadata
- Authors
- Ochoa, D., Jarnuczak, A. F., Viéitez, C., Gehre, M., Soucheray, M., Mateus, A., Kleefeldt, A. A., Hill, A., Garcia-Alonso, L., Stein, F., Krogan, N. J., Savitski, M. M., Swaney, D. L., Vizcaíno, J. A., Noh, K.-M., & Beltrao, P.
- Affiliations
- European Bioinformatics Institute (GB)
- European Molecular Biology Laboratory (DE)
- Gladstone Institutes (US)
- Corresponding author
- Ochoa, David
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2019-12-09
- Volume
- 38
- Issue
- 3
- Pages
- 365-373
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC7100915
- OpenAlex
- W2994464657
- Cited by
- 491
- References
- 106
- Citations by year
- 2026: 83 · 2025: 85 · 2024: 80 · 2023: 74 · 2022: 83 · 2021: 54 · 2020: 25 · 2019: 6
- FWCI
- 16.77
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Wellcome Trust (WT101477MA, 101477, 208391/Z/17/Z, Z/17/Z, 208391)
- 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 › Molecular Biology › Protein Tyrosine Phosphatases
- Keywords
- protein phosphorylation, phosphosites, post-translational modification, machine learning feature selection, genetic susceptibility, SMARCC2, SWI/SNF chromatin remodeling, neuronal differentiation
- MeSH terms
- Machine Learning, Binding Sites, Cell Line, DNA-Binding Proteins, HeLa Cells, Humans, Phosphoproteins, Protein Processing, Post-Translational, Mass Spectrometry, Transcription Factors, Computational Biology, Databases, Protein, Proteomics, Neurogenesis, Data Curation
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