Fluorogenic Photo‐Crosslinking of Glycan‐Binding Protein Recognition Using a Fluorinated Azido‐Coumarin Fucoside
Bibliographic record
Abstract
Glycan recognition by glycan-binding proteins is central to the biology of all living organisms. The efficient capture and characterization of relatively weak non-covalent interactions remains an important challenge in various fields of research. Photoaffinity labeling strategies can create covalent bonds between interacting partners, and photoactive scaffolds such as benzophenone, diazirines and aryl azides have proved widely useful. Since their first introduction, relatively few improvements have been advanced and products of photoaffinity labeling remain difficult to detect. We report a fluorinated azido-coumarin scaffold which enables photolabeling under fast and mild activation, and which can leave a fluorescent tag on crosslinked species. Coupling this scaffold to an α-fucoside, we demonstrate fluorogenic photolabeling of glycan-protein interactions over a wide range of affinities. We expect this strategy to be broadly applicable to other chromophores and we envision that such "fluoro-crosslinkers" could become important tools for the traceable capture of non-covalent binding events.
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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".