Towards Understanding the Tandem Mass Spectra of Protonated Oligopeptides. 2: The Proline Effect in Collision-Induced Dissociation of Protonated Ala-Ala-Xxx-Pro-Ala (Xxx = Ala, Ser, Leu, Val, Phe, and Trp)
Bibliographic record
Abstract
The product ion spectra of proline-containing peptides are commonly dominated by y(n) ions generated by cleavage at the N-terminal side of proline residues. This proline effect is investigated in the current work by collision-induced dissociation (CID) of protonated Ala-Ala-Xxx-Pro-Ala (Xxx includes Ala, Ser, Leu, Val, Phe, and Trp) in an electrospray/quadrupole/time-of-flight (QqTOF) mass spectrometer and by quantum chemical calculations on protonated Ala-Ala-Ala-Pro-Ala. The CID spectra of all investigated peptides show a dominant y(2) ion (Pro-Ala sequence). Our computational results show that the proline effect mainly arises from the particularly low threshold energy for the amide bond cleavage N-terminal to the proline residue, and from the high proton affinity of the proline-containing C-terminal fragment produced by this cleavage. These theoretical results are qualitatively supported by the experimentally observed y(2)/b(3) abundance ratios for protonated Ala-Ala-Xxx-Pro-Ala (Xxx = Ala, Ser, Leu, Val, Phe, and Trp). In the post-cleavage phase of fragmentation the N-terminal oxazolone fragment with the Ala-Ala-Xxx sequence and Pro-Ala compete for the ionizing proton for these peptides. As the proton affinity of the oxazolone fragment increases, the y(2)/b(3) abundance ratio decreases.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".