Engineered, highly reactive substrates of microbial transglutaminase enable protein labeling within various secondary structure elements
Bibliographic record
Abstract
Microbial transglutaminase (MTG) is a practical tool to enzymatically form isopeptide bonds between peptide or protein substrates. This natural approach to crosslinking the side-chains of reactive glutamine and lysine residues is solidly rooted in food and textile processing. More recently, MTG's tolerance for various primary amines in lieu of lysine have revealed its potential for site-specific protein labeling with aminated compounds, including fluorophores. Importantly, MTG can label glutamines at accessible positions in the body of a target protein, setting it apart from most labeling enzymes that react exclusively at protein termini. To expand its applicability as a labeling tool, we engineered the B1 domain of Protein G (GB1) to probe the selectivity and enhance the reactivity of MTG toward its glutamine substrate. We built a GB1 library where each variant contained a single glutamine at positions covering all secondary structure elements. The most reactive and selective variants displayed a >100-fold increase in incorporation of a recently developed aminated benzo[a]imidazo[2,1,5-cd]indolizine-type fluorophore, relative to native GB1. None of the variants were destabilized. Our results demonstrate that MTG can react readily with glutamines in α-helical, β-sheet, and unstructured loop elements and does not favor one type of secondary structure. Introducing point mutations within MTG's active site further increased reactivity toward the most reactive substrate variant, I6Q-GB1, enhancing MTG's capacity to fluorescently label an engineered, highly reactive glutamine substrate. This work demonstrates that MTG-reactive glutamines can be readily introduced into a protein domain for fluorescent labeling.
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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) | 0.000 | 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 | 0.001 |
| 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 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".