Design and Synthesis of Fluorophore-Tagged Disparlure Enantiomers to Study Pheromone Enantiomer Discrimination in the Pheromone-Binding Proteins from the Gypsy Moth, Lymantria Dispar.
Bibliographic record
Abstract
Abstract Fluorescent analogues of the gypsy moth sex pheromone (+)-disparlure (1) and its enantiomer (-)-disparlure (ent-1) were designed, synthesized and characterized. The fluorescently labelled analogues 6-FAM (+)-disparlure 1a 6-FAM (-)-disparlure ent-1a were prepared by copper-catalyzed azide-alkyne cycloaddition (CuAAC) of disparlure alkyne and 6-FAM azide. These fluorescent disparlure analogues 1a ent-1a were used to measure the disparlure binding to two pheromone-binding proteins from the gypsy moth, LdisPBP1 and LdisPBP2. The fluorescence binding assay using 6-FAM disparlure enantiomers 1a and ent-1a showed that the LdisPBP1 and LdisPBP2 have different binding affinities with 1a and ent-1a. The LdisPBP1 has stronger affinity for 6-FAM (-)-disparlure ent-1a, whereas LdisPBP2 has stronger affinity for 6-FAM (+)-disparlure 1a, consistent with the findings from previous study with disparlure enantiomers. The 6-FAM disparlure enantiomers appeared to be much stronger ligands for LdisPBPs, with the binding constant (Kd) in nanomolar range, compared to the fluorescent reporter such as 1-NPN (which had Kd values in micromolar range). The fluorescence competitive binding assays were used to determine the displacement constant (Ki) for the disparlure enantiomers in competition with fluorescent disparlure analogues binding to LdisPBP1 and LdisPBP2. The Ki data showed that disparlure enantiomers can effectively displace the fluorescent disparlure from the binding pocket of LdisPBPs.
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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 | 0.001 |
| 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".