Recent advances in photoaffinity labeling strategies to capture Glycan–Protein interactions
Bibliographic record
Abstract
Glycans decorate all cells and are critical mediators of cellular processes through recognition by glycan-binding proteins (GBPs). While targeting glycan-protein interactions has great therapeutic potential, these interactions are challenging to study as they are generally transient and exhibit low binding affinities. Glycan-based photo-crosslinkable probes have enabled covalent capture and identification of unknown GBP receptors and glycoconjugate ligands. Here, we review recent progress in photo-crosslinking approaches targeting glycan-mediated interactions. We discuss two prominent emerging strategies: 1) development of photo-crosslinkable oligosaccharide ligands to identify GBP receptors; and 2) cell-surface glyco-engineering to identify glycoconjugate ligands of GBPs. Overall, photoaffinity labeling affords valuable insights into complex glycan-protein networks and is poised to help elucidate the glycan-protein interactome, providing novel targets for therapeutic intervention.
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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 | 0.000 | 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) | 0.000 | 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".