Affinity-based profiling of endogenous phosphoprotein phosphatases by mass spectrometry.
Affinity-based profiling of endogenous phosphoprotein phosphatases by mass spectrometry.
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DOI:
10.1038/s41596-021-00604-3
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发表时间:
2021-10
期刊:
影响因子:
14.8
通讯作者:
Kettenbach AN
中科院分区:
文献类型:
--
作者:
Brauer BL;Wiredu K;Mitchell S;Moorhead GB;Gerber SA;Kettenbach AN
Phosphoprotein Phosphatases (PPPs) execute over 90% of serine/threonine dephosphorylation in cells and tissues. While the role of PPPs in cell biology and diseases such as cancer, cardiac hypertrophy, and Alzheimer’s disease is well established, the molecular mechanisms governing and governed by PPPs still await discovery. Here we describe a chemical proteomic strategy, Phosphatase-Inhibitor-Beads and Mass Spectrometry (PIB-MS), that enables the identification and quantification of PPPs and their post-translational modifications in as little as 12 hours. Using a specific but non-selective PPP inhibitor immobilized on beads, PIB-MS enables the efficient affinity-capture, identification, and quantification of endogenous PPPs and associated proteins (“PPPome”) from cells and tissues. PIB-MS captures functional, endogenous PPP subunit interactions and allows for discovering new binding partners. It performs PPP enrichment without exogenous expression of tagged proteins or specific antibodies. Because PPPs are among the most conserved proteins across evolution, PIB-MS can be employed in any cell line, tissue, or organism. This protocol describes a proteomic approach for efficient affinity-capture, identification, and quantification of endogenous phosphoprotein phosphatases and associated proteins from cells and tissues. New protocol for affinity capture and proteomic analysis of endogenous phosphoprotein phosphatases and associated proteins from cells and tissues. Profiling endogenous phosphoprotein phosphatases Key references using this protocol Lyons, S. P. et al. MCP 17, 2448–2461 (2018): https://doi.org/10.1074/mcp.RA118.000822 Nasa, I. et al. Science Signaling 13, eaba7823 (2020): https://doi.org/10.1126/scisignal.aba7823
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