Enzyme-Sialylation-Controlled Chemical Sulfation of Glycan Epitopes for Decoding the Binding of Siglec Ligands
Bibliographic record
Abstract
Widely distributed in nature, sulfated glycan epitopes play important roles in diverse pathophysiological processes. However, due to their structural complexity, the preparation of glycan epitopes with structurally defined sulfation patterns is challenging, which significantly hampers the detailed elucidation of their biological functions at the molecular level. Here, we introduce a strategy for site-specific chemical sulfation of glycan epitopes, leveraging enzymatic sialylation and desialylation processes to precisely control the regio-specificity of sulfation of disaccharide or trisaccharide glycan backbones. Using this method, a sulfated glycan library covering the most common sialylated glycan epitopes was prepared in high yield and efficiency. By screening a microarray prepared with this glycan library, we systematically probed their binding specificity with human Siglecs (sialic acid-binding immunoglobulin-type lectins), many of which function as glyco-immune checkpoints to suppress immune system activation. Our investigation revealed that sulfation and sialylation patterns serve as important determinants of Siglec binding affinity and specificity. Thus, these findings offer new insights for the development of research tools and potential therapeutic agents targeting glyco-immune checkpoints by modulating the Siglec signaling pathway.
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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".