Accurate and Fast Protein Acylation Identification by Eliminating Position Effects of Cyclic Immonium Ions with Stepped HCD
Bibliographic record
Abstract
Abstract Mass spectrometry (MS)-based proteomics is indispensable for studying post-translational modifications (PTMs). Cyclic immonium (CycIm) ions serve as invaluable diagnostic markers for lysine acylations, yet the principles governing their generation efficiency are poorly understood. Here, we systematically investigate this question and uncover a robust “position effect”: the generation of immonium ions is strongly favored when the modified residue is located near the N-terminus of a tryptic peptide. Utilizing LysargiNase digestion and isotope-labeled synthetic peptides, we demonstrate that this effect is likely driven by the inherent instability of b-type fragment ions during collision-induced dissociation. Furthermore, we show that a stepped HCD strategy enables enhanced sequence coverage and robust CycIm ion detection (∼99%), thereby improving the depth, reliability and speed of PTM identification. Collectively, this work provides fundamental understanding of immonium ion formation and establishes an optimized acquisition and analysis strategy that enhances the efficiency and confidence of PTM analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".