Assay Development for the Determination of Phosphorylation Stoichiometry Using Multiple Reaction Monitoring Methods with and without Phosphatase Treatment: Application to Breast Cancer Signaling Pathways
Bibliographic record
Abstract
We have developed a phosphatase-based phosphopeptide quantitation (PPQ) method for determining phosphorylation stoichiometry in complex biological samples. This PPQ method is based on enzymatic dephosphorylation, combined with specific and accurate peptide identification and quantification by multiple reaction monitoring (MRM) with stable-isotope-labeled standard peptides. In contrast with classical MRM methods for the quantitation of phosphorylation stoichiometry, the PPQ-MRM method needs only one nonphosphorylated SIS (stable isotope-coded standard) and two analyses (one for the untreated sample and one for the phosphatase-treated sample), from which the expression and modification levels can accurately be determined. From these analyses, the percent phosphorylation can be determined. In this manuscript, we compare the PPQ-MRM method with an MRM method without phosphatase and demonstrate the application of these methods to the detection and quantitation of phosphorylation of the classic phosphorylated breast cancer biomarkers (ERalpha and HER2), and for phosphorylated RAF and ERK1, which also contain phosphorylation sites of biological importance. Using synthetic peptides spiked into a complex protein digest, we were able to use our PPQ-MRM method to accurately determine the total phosphorylation stoichiometry on specific peptides as well as the absolute amount of the peptide and phosphopeptide present. Analyses of samples containing ERalpha protein revealed that the PPQ-MRM method is capable of determining phosphorylation stoichiometry in proteins from cell lines, and is in good agreement with determinations obtained using the direct MRM approach in terms of phosphorylation and total protein amount.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".