Glyco-Engineering Cell Surfaces by Exo-Enzymatic Installation of GlcNAz and LacNAz Motifs
Bibliographic record
Abstract
Exo-enzymatic glyco-engineering of cell-surface glycoconjugates enables the selective display of well-defined glyco-motifs bearing bioorthogonal functional groups, which can be used to study glycans and their interactions with glycan-binding proteins. In recent years, strategies to edit cellular glycans by installing monosaccharides and their derivatives using glycosyltransferase enzymes have rapidly expanded. However, analogous methods to introduce chemical reporter-functionalized type 2 LacNAc motifs have not been reported. Herein, we report the chemo-enzymatic synthesis of unnatural UDP-GlcNAc and UDP-GalNAc nucleotide-sugars bearing azide, alkyne, and diazirine functionalities on the C2-acetamido group using the mutant uridylyltransferase AGX1 F383A . The unnatural UDP-GlcNAc derivatives were examined as substrates for the human GlcNAc-transferase B3GNT2, where it was found that modified donors were tolerated for transfer, albeit to a lesser extent than the natural UDP-GlcNAc substrate. When the GlcNAc derivatives were examined as acceptor substrates for the human Gal-transferase B4GalT1, all derivatives were well tolerated and the enzyme could successfully form derivatized LacNAcs. B3GNT2 was also used to exo-enzymatically install GlcNAc and unnatural GlcNAc derivatives on cell-surface glycans. GlcNAc- or GlcNAz-engineered cells were further extended by B4GalT1 and UDP-Gal, producing LacNAc- or LacNAz-engineered cells. Our proof-of-concept glyco-engineering labeling strategy is amenable to different cell types and our work expands the exo-enzymatic glycan editing toolbox to selectively introduce unnatural type 2 LacNAc motifs.
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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 | 0.000 | 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 | 0.000 |
| 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".