A Quantitative Chemical Proteomic Strategy for Profiling Phosphoprotein Phosphatases from Yeast to Humans
Bibliographic record
Abstract
A "tug-of-war" between kinases and phosphatases establishes the phosphorylation states of proteins. While serine and threonine phosphorylation can be catalyzed by more than 400 protein kinases, the majority of serine and threonine dephosphorylation is carried out by seven phosphoprotein phosphatases (PPPs). The PPP family consists of protein phosphatases 1 (PP1), 2A (PP2A), 2B (PP2B), 4 (PP4), 5 (PP5), 6 (PP6), and 7 (PP7). The imbalance in numbers between serine- and threonine-directed kinases and phosphatases led to the early belief that PPPs are unspecific and that kinases are the primary determinants of protein phosphorylation. However, it is now clear that PPPs achieve specificity through association with noncatalytic subunits to form multimeric holoenzymes, which expands the number of functionally distinct signaling entities to several hundred. Although there has been great progress in deciphering signaling by kinases, much less is known about phosphatases.We have developed a chemical proteomic strategy for the systematic interrogation of endogenous PPP catalytic subunits and their interacting proteins, including regulatory and scaffolding subunits (the "PPPome"). PP1, PP2A, PP4, PP5, and PP6 were captured using an immobilized, specific but nonselective PPP inhibitor microcystin-LR (MCLR), followed by protein identification by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a single analysis. Here, we combine this approach of phosphatase inhibitor bead profiling and mass spectrometry (PIB-MS) with label-free and tandem mass tag (TMT) quantification to map the PPPome in human cancer cell lines, mouse tissues, and yeast species, through which we identify cell- and tissue-type-specific PPP expression patterns and discover new PPP interacting proteins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".