Design of a glutamine substrate tag enabling protein labelling mediated by Bacillus subtilis transglutaminase
Bibliographic record
Abstract
Transglutaminases (TGases) are enzymes that catalyse protein cross-linking through a transamidation reaction between the side chain of a glutamine residue on one protein and the side chain of a lysine residue on another. Generally, TGases show low substrate specificity with respect to their amine substrate, such that a wide variety of primary amines can participate in the modification of specific glutamine residue. Although a number of different TGases have been used to mediate these bioconjugation reactions, the TGase from Bacillus subtilis (bTG) may be particularly suited to this application. It is smaller than most TGases, can be expressed in a soluble active form, and lacks the calcium dependence of its mammalian counterparts. However, little is known regarding this enzyme and its glutamine substrate specificity, limiting the scope of its application. In this work, we designed a FRET-based ligation assay to monitor the bTG-mediated conjugation of the fluorescent proteins Clover and mRuby2. This assay allowed us to screen a library of random heptapeptide glutamine sequences for their reactivity with recombinant bTG in bacterial cells, using fluorescence assisted cell sorting. From this library, several reactive sequences were identified and kinetically characterized, with the most reactive sequence (YAHQAHY) having a kcat/KM value of 19 ± 3 μM-1 min-1. This sequence was then genetically appended onto a test protein as a reactive 'Q-tag' and fluorescently labelled with dansyl-cadaverine, in the first demonstration of protein labelling mediated by bTG.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".