A multiplexed post‐translational modification monitoring approach on a matrix‐assisted laser desorption/ionization time‐of‐flight/time‐of‐flight mass spectrometer
Bibliographic record
Abstract
Post-translational modifications (PTMs) of proteins are essential for proper function, as they regulate many aspects of a protein's activity and interaction with substrates. When analyzing modified peptides derived from such proteins by mass spectrometry, these modifications can dissociate, producing either a marker ion or neutral loss characteristic of the modification, which have conventionally been monitored with a precursor ion scan or neutral loss scan, respectively. Although powerful, both precursor ion scans and neutral loss scans can only screen for one particular modification at a time. This has led to the development of multiple neutral loss monitoring (MNM) for neutral losses and multiple precursor ion monitoring (MPM) for marker ions on electrospray instruments. Here, we report their implementation on a matrix-assisted laser desorption/ionization (MALDI) instrument as well as the inception of a novel scan strategy termed targeted multiple precursor ion monitoring (tMPM). This latter scan strategy has been developed on a MALDI tandem time-of-flight (TOF/TOF) mass spectrometer for the identification of multiple PTMs via their associated marker ions by manipulating certain components of the instrument, notably the timed ion selector and the delayed extraction source 2. Targeted MPM combined with a second approach, multiple neutral loss monitoring (MNM), is shown to be a successful approach in the identification of PTMs, identifying multiple modified peptides in a complex sample matrix.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".