Detection and quantification of free sulfhydryls in monoclonal antibodies using maleimide labeling and mass spectrometry
Bibliographic record
Abstract
The detection of free sulfhydryls in proteins can reveal incomplete disulfide bond formation, indicate cysteine residues available for conjugation, and offer insights into protein stability and structure. Traditional spectroscopic methods of free sulfhydryl detection, such as Ellman’s reagent, generally require a relatively large amount of sample, preventing their use for the analysis of biotherapeutics early in the development cycle. These spectroscopic methods also cannot accurately determine the location of the free sulfhydryl, further limiting their utility. Mass spectrometry was used to detect free sulfhydryl residues in intact proteins after labeling with Maleimide-PEG2-Biotin. As little as 2% cysteine residues with free sulfhydryls (0.02 mol SH per mol protein) could be detected by this method. Following reduction, the free sulfhydryl abundance on antibody heavy and light chains could be measured. To determine free sulfhydryl location at peptide-level resolution, free sulfhydryls and cysteines involved in disulfide bonds were differentially labeled with N-ethylmaleimide and d5-N-ethylmaleimide, respectively. Following enzymatic digestion and nanoLC-MS, the abundance of free sulfhydryls at individual cysteine residues was quantified down to 2%. The method was optimized to avoid non-specific labeling, disulfide bond scrambling, and maleimide exchange and hydrolysis. This new workflow for free sulfhydryl analysis was used to measure the abundance and location of free sulfhydryls in 3 commercially available monoclonal antibody standards (NIST Monoclonal Antibody Reference Material (NIST), SILu™Lite SigmaMAb Universal Antibody Standard (Sigma-Aldrich) and Intact mAb Mass Check Standard (Waters)) and 1 small protein standard (β-Lactoglobulin A).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".