Multiple reaction monitoring as a method for identifying protein posttranslational modifications.
Bibliographic record
Abstract
The activity of many transcriptional regulators is significantly altered by posttranslational modifications of specific sites. For example, the activity of the muscle-restricted transcription factor family myocyte enhancer factor 2 (MEF2) is tightly controlled by phosphorylation. This modification is responsible for either an increase or a decrease in transcriptional activity, depending on the specific amino acid residues that are phosphorylated by signal-dependent kinases. Although mass spectrometry-based methods, such as precursor ion and neutral loss scans, are extremely useful for identifying unknown phosphopeptides from a complex mixture, they do not take advantage of any prior knowledge about the protein being investigated. Quite often a significant amount of information is available. This may include the primary sequence, type of phosphorylation (serine/threonine vs. tyrosine), or predicted phosphoacceptor sites (consensus peptide that is targeted by a kinase). This information can be used to predict precursor and fragment ion m/z values for a multiple reaction monitoring (MRM) experiment. By using these highly sensitive MRM experiments to trigger dependent product ion scans on a hybrid quadrupole linear ion-trap instrument, we were able to identify low levels of phosphorylation of MEF2A (a member of the MEF2 family), and alpha-casein. This method of monitoring protein phosphorylation at specific phosphoacceptor sites may prove useful in understanding the physiological regulation of protein function.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".