ExD vs EThcD: What’s Better for the Direct Sequencing of Endogenous Amphibian Disulfide Peptides
Bibliographic record
Abstract
Intact amphibian skin peptides, apart from their intrinsic interest, are a challenging model system to demonstrate direct sequencing, avoiding any preliminary derivatization steps. They are relatively long (up to 46-mer), contain an intramolecular disulfide bridge, and include a number of isomeric Leu/Ile residues. Sixteen intact peptides from the skin secretion of the Rostov (Russia) population of Pelophylax ridibundus were studied in the present research using EThcD, ExD, and ExciD fragmentation. Comparison of the efficiency of EThcD and ExD tandem mass spectrometry approaches demonstrated that both are appropriate for the direct sequencing of these peptides. Although the majority of the isomeric Leu/Ile residues could be differentiated using w -ions, the usefulness of d -ions, especially inside C-terminal disulfide rings, was also demonstrated. The d -ions arise more often in ExD/ExciD than in the EThcD mode. EThcD and ExciD are complementary methods and together distinguished more isomeric residues than either of them alone. While both methods provided similar sequence information within intact C-terminal S–S loops, their combined use consistently yielded 100% sequence coverage. ExciD demonstrated superior results in determining peptide sequences due to the higher yield of all fragment ion types, establishing complete sequences for all peptides, including that of the longest (46-mer) esculentins, compared to six with EThcD alone. The increased number of characteristic ions ( c/z and b/y ) in ExciD further enhanced confidence in the sequence assignments. Ultimately, the complementary use of ExciD and EThcD resulted in reliable 100% sequence coverage for all 16 intact disulfide peptides analyzed in this study.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".