Deviation from the mobile proton model in amino‐modified peptides: implications for multiple reaction monitoring analysis of peptides
Bibliographic record
Abstract
The study of peptide fragmentation is important to the understanding of chemical processes occurring in the gas phase and the more practical concern of peptide identification for proteomic analysis. Using the mobile proton model as a framework, we explore the effect of amino-group modifications on peptide fragmentation. Three aldehydes are used to transform the peptides' primary amino groups into either a dimethylamino or a heterocyclic structure (five- or six-membered). The observed fragmentation patterns deviate strongly from those observed for the analogous underivatised peptides. In particular, the a1 ion is the base peak in most tandem mass spectra of the derivatised peptides. The a1 ion intensity depends strongly on the N-terminal amino acid, with tyrosine and phenylalanine having the strongest enhancement. Despite the change in fragmentation patterns of the derivatised peptides, they still provide high-quality tandem mass spectra that, in many cases, are more amenable to database searching than the spectra of underivatised peptides. In addition, the reliable presence of the a1 ion facilitates rapid quantitative measurements using the multiple reaction monitoring approach.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".