Glyco-Engineering Cell Surfaces by Exo-Enzymatic Installation of GlcNAz and LacNAz Motifs
Bibliographic record
Abstract
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. While the installation of monosaccharides and their derivatives using glycosyltransferase enzymes has rapidly evolved, similar strategies 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, and the donor and acceptor substrate tolerance of the human glycosyltransferases B3GNT2 and B4GalT1, respectively, to form derivatized LacNAc moieties. We also demonstrate that B3GNT2 can be used to exo-enzymatically install GlcNAc and GlcNAz onto cell-surface glycans. GlcNAc- or GlcNAz-engineered cells can be further extended by B4GalT1, producing LacNAc or LacNAz-engineered cells. Our 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 | 0.001 |
| 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".