Engineered, highly reactive substrates of microbial transglutaminase enable protein labeling within various secondary structure elements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it