Fluorinated Carbohydrates as Lectin Ligands: Biorelevant Sensors with Capacity to Monitor Anomer Affinity in <sup>19</sup>F‐NMR‐Based Inhibitor Screening
Bibliographic record
Abstract
Abstract Our recent description of pulse sequences for the intramolecular relay of 1H polarization to the 19F reporter of a carbohydrate ligand after saturation transfer from a cognate lectin prompted us to test the applicability of this technique for inhibitor screening. By strategically combining synthetic organic chemistry and cell assays with 19F‐NMR‐based competition experiments, we document the validity of this approach. Two mannose‐specific leguminous agglutinins as receptors, the α‐methyl derivative of 2‐deoxy‐2‐fluoro‐D‐mannopyranoside as sensor and synthetic mannosides selected to represent different inhibitory capacities were used to establish a test panel. Signal amplitudes were found to vary among the two related lectins, and their precipitation by glycodendrimers enabled inherent limits to be set. The obtained experimental basis was then broadened by assaying a galactopyranoside‐binding plant toxin, using 6‐deoxy‐6‐fluoro‐D‐galactopyranose as a sensor molecule. The easy identification of the two anomeric signals by 19F NMR spectroscopy enabled ready detection of the preference of this lectin for the α‐anomer and allowed the two individual inhibition profiles to be deduced. These 19F NMR spectroscopic data were in accord with the activities of inhibitors used to protect cells from toxicity. Our results therefore provide an experimental basis for 19F‐NMR‐based inhibitor screening.
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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".