Fragmentation of Peptide Radical Cations Containing a Tyrosine or Tryptophan Residue: Structural Features That Favor Formation of [<i>x</i><sub>(<i>n</i>–1)</sub> + H]<sup>•+</sup> and [<i>z</i><sub>(<i>n</i>–1)</sub> + H]<sup>•+</sup> Ions
Bibliographic record
Abstract
Peptide radical cations A(n)Y(•+) (where n = 3, 4, or 5) and A5W(•+) have been generated by collision-induced dissociation (CID) of [Cu(II)(tpy)(peptide)](•2+) complexes. Apart from the charge-driven fragmentation at the N-Cα bond of the hetero residue producing either [c + 2H](+) or [z - H](•+) ions and radical-driven fragmentation at the Cα-C bond to give a(+) ions, unusual product ions [x + H](•+) and [z + H](•+) are abundant in the CID spectra of the peptides with the hetero residue in the second or third position of the chain. The formation of these ions requires that both the charge and radical be located on the peptide backbone. Energy-resolved spectra established that the [z + H](•+) ion can be produced either directly from the peptide radical cation or via the fragment ion [x + H](•+). Additionally, backbone dissociation by loss of the C-terminal amino acid giving [b(n-1) - H](•+) increases in abundance with the length of the peptides. Mechanisms by which peptide radical cations dissociate have been modeled using density functional theory (B3LYP/6-31++G** level) on tetrapeptides AYAG(•+), AAYG(•+), and AWAG(•+).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".