Systematic identification of engineered methionines and oxaziridines for efficient, stable, and site-specific antibody bioconjugation
Bibliographic record
Abstract
Abstract Chemical modification of antibodies is one of the most important bioconjugations utilized by biologists and biotechnology. To date, the field has been dominated by random modification of lysines or more site-specific labeling of cysteines, each with attendant challenges. Recently we have developed oxaziridine chemistry for highly selective and efficient sulfimide modification of methionine called redox-activated chemical tagging (ReACT). Here, we systematically scanned methionines throughout one of the most popular antibody scaffolds, trastuzumab, for antibody engineering and drug conjugation. We tested the expression, reactivities, and stabilities of 123 single engineered methionines distributed over the surface of the antibody when reacted with oxaziridine. We found uniformly high expression for these mutants and generally good reaction efficiencies with the panel of oxaziridines. Remarkably, the stability to hydrolysis of the sulfimide varied more than ten-fold depending on temperature and the site of the engineered methionine. Interestingly, the most stable and reactive sites were those that were partially buried, likely because of their reduced access to water. There was also a ten-fold variation in stability depending on the nature of the oxaziridine, which we determined was inversely correlated with the electrophilic nature of the sulfimide. Importantly, the stabilities of the best analogs and antibody drug conjugate potencies were comparable to those reported for cysteine-maleimide modifications of trastuzumab. We also found our antibody drug conjugates to be potent in a breast cancer mouse xenograft model. These studies provide a roadmap for broad application of ReACT for efficient, stable, and site-specific antibody and protein bioconjugation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".