Synthesis of Gly-ψ[(<i>Z</i>)CF═CH]-Phe, a Fluoroalkene Dipeptide Isostere, and Its Incorporation into a Leu-enkephalin Peptidomimetic
Bibliographic record
Abstract
A new Leu-enkephalin peptidomimetic designed to explore the hydrogen bond acceptor ability of the third peptide bond has been prepared and studied. This new analog is produced by replacing the third amide of Leu-enkephalin with a fluoroalkene. An efficient and innovative synthesis of the corresponding dipeptide surrogate Fmoc-Gly-ψ[( Z )CF═CH]-Phe-OH is described. The key step involves the alkylation of a tin dienolate from the less hindered face of its chiral sulfonamide auxiliary derived from camphor. Once its synthesis was complete, its incorporation into the peptidomimetic sequence was achieved on a solid support with chlorotrityl resin following the Fmoc strategy. The peptidomimetic was characterized using competition binding with [ 125 I]-deltorphin I on membrane extracts of HEK293 cells expressing the mouse delta opioid receptor (DOPr) and based on its abilities to inhibit the electrically induced contractions of the mouse vas deferens and to activate the ERK1/2 signaling pathway in DRGF11/DOPr-GFP cells. Together with our previous observations, our findings strongly suggest that the third amide bond of Leu-enkephalin primarily acts as a hydrogen bond acceptor in DOPr. Consequently, this amide bond can be successfully replaced by an ester, a thioamide, or a fluoroalkene without greatly impacting the binding or biological activity of the corresponding analogs. The lipophilicity (LogD 7.4 ) of the active analog was also measured. It appears that fluoroalkenes are almost as efficient at increasing the lipophilicity as normal alkenes.
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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".