Transglutaminase-Catalyzed Bioconjugation Using One-Pot Metal-Free Bioorthogonal Chemistry
Bibliographic record
Abstract
General approaches for controlled protein modification are increasingly sought-after in the arena of chemical biology. Here, using bioorthogonal reactions, we present combinatorial chemoenzymatic strategies to effectuate protein labeling. A total of three metal-free conjugations were simultaneously or sequentially incorporated in a one-pot format with microbial transglutaminase (MTG) to effectuate protein labeling. MTG offers the particularity of conjugating residues within a protein sequence rather than at its extremities, providing a route to labeling the native protein. The reactions are rapid and circumvent the incompatibility posed by metal catalysts. We identify the tetrazine ligation as most-reactive for this purpose, as demonstrated by the fluorescent labeling of two proteins. The Staudinger ligation and strain-promoted azide-alkyne cycloaddition are alternatives. Owing to the breadth of labels that MTG can use as a substrate, our results demonstrate the versatility of this system, with the researcher being able to combine specific protein substrates with a variety of labels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".