Exo-Enzymatic Cell-Surface Glycan Labeling for Capturing Glycan–Protein Interactions through Photo-Cross-Linking
Bibliographic record
Abstract
Tools to interrogate glycoconjugate-protein interactions in the context of living cells are highly attractive for the identification of critically important functional binding partners of glycan-binding proteins. These interactions are challenging to study due to the low affinity and rapid dissociation rates of glycan-protein binding events. The use of photo-cross-linkers to capture glycan-protein interaction complexes has shown great promise for identifying binding partners involved in these interactions. Current methodologies use metabolic oligosaccharide engineering (MOE) to incorporate photo-cross-linking sugars. However, these MOE strategies are not amenable to all cell types and can result in low incorporation and cell-surface display of the photo-cross-linking probe, limiting their utility for studying many types of interactions. We describe here an exo-enzymatic strategy for selectively introducing photo-cross-linking probes into cell-surface glycoconjugates using the recombinant human sialyltransferase ST6GAL1 and a diazirine-linked CMP-Neu5Ac derivative. Probe introduction is highly efficient, amenable to different cell types, and resulted in improved cross-linking when compared to MOE. This exo-enzymatic labeling approach can selectively introduce the photo-cross-linking sugar onto specific glycan epitopes and subclasses by harnessing the specificity of the sialyltransferase employed, underscoring its potential as a tool to interrogate and identify glycoconjugate ligands for diverse glycan-binding proteins.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".