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 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) | 0.000 | 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) | 0.001 | 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".