Bibliographic record
Abstract
An automatic method for disulfide bond assignment using dimethyl labeling and computational screening of a(1) ions with customized software, RADAR, is developed. By utilization of the enhanced a(1) ions generated from labeled peptides, the N-terminal amino acids from disulfide-linked peptides can be determined. In this study, we applied this method for structural characterization of recombinant monoclonal antibodies, an important group of therapeutic proteins. In addition to a(1) ion screening and molecular weight match, new RADAR is capable of confirming the matched peptide pairs by further comparing the collision-induced dissociation (CID) fragment ions. With the N-terminal amino acid identities as a threshold, the identification of disulfide-linked peptide pairs can be achieved rapidly at a higher confidence level. Unlike most current approaches, prior knowledge of disulfide linkages or a high-end mass spectrometer is not required, and tedious work or deliberate interpretation can be avoided in this study. Our approach makes it possible to analyze unknown disulfide bonds of protein pharmaceuticals as well as their degraded forms without further protein separation. It can be used as a convenient quality examination tool during biopharmaceutical development and manufacturing processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".