Deducing disulfide patterns of cysteine-rich proteins using signature fragments produced by top-down mass spectrometry
Bibliographic record
Abstract
Direct mapping of protein disulfide patterns using top-down mass spectrometry (MS) is often hampered by inadequate fragmentation at the disulfide-enclosing region, and insufficient structural information provided by the fragments. Here we used electron-transfer/high energy collision dissociation (EThcD) to improve the fragmentation efficiency, and developed strategies that minimize the false positive identification of fragments and deconvolute the signals representing specific modifications made to the disulfide-cleavage-induced fragments. We observed clear correlations between unique modification (attachment or removal of H or SH) patterns and the number of disulfide bonds that enclose the corresponding region. Using the characteristic signature fragments, we in part localized the Cys-bridging sites in disulfide-scrambled lysozymes, and reduced the number of putative disulfide patterns from 104 to 6. The results demonstrated the feasibility of direct analysis of complex disulfide patterns using top-down MS.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".