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 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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".