Opposite Electron-Transfer Dissociation and Higher-Energy Collisional Dissociation Fragmentation Characteristics of Proteolytic K/R(X)<sub><i>n</i></sub> and (X)<i><sub>n</sub></i>K/R Peptides Provide Benefits for Peptide Sequencing in Proteomics and Phosphoproteomics
Bibliographic record
Abstract
A key step in shotgun proteomics is the digestion of proteins into peptides amenable for mass spectrometry. Tryptic peptides can be readily sequenced and identified by collision-induced dissociation (CID) or higher-energy collisional dissociation (HCD) because the fragmentation rules are well-understood. Here, we investigate LysargiNase, a perfect trypsin mirror protease, because it cleaves equally specific at arginine and lysine residues, albeit at the N-terminal end. LysargiNase peptides are therefore practically tryptic-like in length and sequence except that following ESI, the two protons are now both positioned at the N-terminus. Here, we compare side-by-side the chromatographic separation properties, gas-phase fragmentation characteristics, and (phospho)proteome sequence coverage of tryptic (i.e., (X) n K/R) and LysargiNase (i.e., K/R(X) n ) peptides using primarily electron-transfer dissociation (ETD) and, for comparison, HCD. We find that tryptic and LysargiNase peptides fragment nearly as mirror images. For LysargiNase predominantly N-terminal peptide ions (c-ions (ETD) and b-ions (HCD)) are formed, whereas for trypsin, C-terminal fragment ions dominate (z-ions (ETD) and y-ions (HCD)) in a homologous mixture of complementary ions. Especially during ETD, LysargiNase peptides fragment into low-complexity but information-rich sequence ladders. Trypsin and LysargiNase chart distinct parts of the proteome, and therefore, the combined use of these enzymes will benefit a more in-depth and reliable analysis of (phospho)proteomes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".