Chemoenzymatic Synthesis of Sulfated <i>N</i>-Glycans Recognized by Siglecs and Other Glycan-Binding Proteins
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Sulfated N -glycans are present in many glycoproteins, which are implicated in playing important roles in biological recognition processes. Here, we report the systematic chemoenzymatic synthesis of a library of sulfated and sialylated biantennary N -glycans and assess their binding to Siglecs and glycan-specific antibodies that recognize them as glycan ligands. The combined use of three human sulfotransferases, GlcNAc-6- O -sulfotransferase (CHST2), Gal-3- O -sulfotransferase (Gal3ST1), and keratan sulfate Gal-6- O -sulfotransferase (CHST1), resulted in asymmetric and symmetric branch-selective sulfation of the GlcNAc and/or Gal moieties of N -glycans. The extension of the sugar chain using α-2,3- and α-2,6-sialyltransferases afforded the sulfated and sialylated N -glycans. These synthetic glycans with different patterns of sulfation and sialylation were evaluated for binding to selected Siglecs and sulfoglycan-specific antibodies using glycan microarrays. The results confirm previously documented glycan-recognizing properties and further reveal novel specificities for these glycan-binding proteins, demonstrating the utility of the library for assessing the specificity of glycan-binding proteins recognizing sulfated and sialylated glycans.
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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".