Enzymatic generation of peptides flanked by basic amino acids to obtain MS/MS spectra with 2× sequence coverage
Bibliographic record
Abstract
RATIONALE: Tandem mass (MS/MS) spectra generated by collision-induced dissociation (CID) typically lack redundant peptide sequence information in the form of e.g. b- and y-ion series due to frequent use of sequence-specific endopeptidases cleaving C- or N-terminal to Arg or Lys residues. METHODS: Here we introduce arginyl-tRNA protein transferase (ATE, EC 2.3.2.8) for proteomics. ATE recognizes acidic amino acids or oxidized Cys at the N-terminus of a substrate peptide and conjugates an arginine from an aminoacylated tRNA(Arg) onto the N-terminus of the substrate peptide. This enzymatic reaction is carried out under physiological conditions and, in combination with Lys-C/Asp-N double digest, results in arginylated peptides with basic amino acids on both termini. RESULTS: We demonstrate that in vitro arginylation of peptides using yeast arginyl tRNA protein transferase 1 (yATE1) is a robust enzymatic reaction, specific to only modifying N-terminal acidic amino acids. Precursors originating from arginylated peptides generally have an increased protonation state compared with their non-arginylated forms. Furthermore, the product ion spectra of arginylated peptides show near complete 2× fragment ladders within the same MS/MS spectrum using commonly available electrospray ionization peptide fragmentation modes. Unexpectedly, arginylated peptides generate complete y- and c-ion series using electron transfer dissociation (ETD) despite having an internal proline residue. CONCLUSIONS: We introduce a rapid enzymatic method to generate peptides flanked on either terminus by basic amino acids, resulting in a rich, redundant MS/MS fragment pattern.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".