Measurement of Protein Phosphorylation Stoichiometry by SRM‐MS
Bibliographic record
Abstract
Abstract Protein phosphorylation is a major post‐translational modification involved in most biological processes and numerous diseases. Much biological relevance lies in the knowledge of the extent of phosphorylation at any given protein phosphorylation site, i.e., protein phosphorylation stoichiometry. Major progress in quantitative mass spectrometry has allowed the establishment of protocols that permit accurate determination of protein phosphorylation stoichiometry. The Basic Protocol presented herein describes a label‐free mass spectrometry approach for determining phosphorylation stoichiometry using electrospray nano‐liquid chromatography coupled to selected reaction monitoring (SRM). This entails the calculation of the respective response rates of the phosphorylated and unphosphorylated cognate peptide species using a peptide dephosphorylation reaction. The ratio of these response rates is then applied as a correction factor to the LC‐SRM‐MS signal intensity ratio of the corresponding endogenous phosphopeptide/nonphosphopeptide pair in a biological sample of interest. An Alternate Protocol is also presented that describes how heavy isotope‐labeled synthetic peptides can be used as internal standards to determine the stoichiometry of phosphorylation. Curr. Protoc. Chem. Biol. 4:65‐81 © 2012 by John Wiley & Sons, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".