Measurement of Protein Phosphorylation Stoichiometry by Selected Reaction Monitoring Mass Spectrometry
Bibliographic record
Abstract
The stoichiometry of protein phosphorylation at specific amino acid sites may be used to infer on the significance of the modification, and its biological function in the cell. However, detection and quantification of phosphorylation stoichiometry in tissue remain a significant challenge. Here we describe a strategy for highly sensitive, label-free quantification of protein phosphorylation stoichiometry. Method development included the analysis of synthetic peptides in order to determine constants to relate the mass spectrometry signals of cognate peptide/phosphopeptide pairs, and the detection of the cognate peptides by using high resolution Fourier Transform mass spectrometry (FTMS) and selected reaction monitoring mass spectrometry (SRM). By analyzing extracted ion currents by FTMS, the phosphorylation stoichiometries of two tyrosine residues (tyrosine-194 and tyrosine-397) in the protein tyrosine kinase Lyn were determined in transfected human HEK293T cells and two cultured human multiple myeloma strains. To achieve high sensitivity to measure phosphorylation stoichiometry in tissue, SRM methods were developed and applied for the analysis of phosphorylation stoichiometries of Lyn phospho-sites in multiple myeloma xenograft tumors. Western immuno-blotting was used to verify mass spectrometry findings. The SRM method has potential applications in analyzing clinical samples wherein protein phosphorylation stoichiometries may represent important pharmacodynamic biomarkers.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".