Exploration of the fifth position of leu‐enkephalin and its role in binding and activating delta (DOP) and mu (MOP) opioid receptors
Bibliographic record
Abstract
Abstract Enkephalins are pentapeptidic endogenous ligands that regulate nociception by binding to mu (MOP) and delta (DOP) opioid receptors. To further explore the role of the leucine residue of Leu‐enkephalin, 12 peptidomimetic analogs were synthesized by systematically replacing this residue with non‐natural amino acids. The analogs were tested for their ability to bind DOP and MOP. We also investigated the potency of these analogs to inhibit cAMP production and to recruit β‐arrestin 2 via both receptors. We found that replacement of the leucine residue by substituted non‐natural amino acid derivatives of alanine, cycloleucine, or isoleucine was generally well tolerated. By contrast, substituting leucine with homoproline greatly reduced the affinity for DOP and, to a lesser extent, for MOP. Interestingly, when compared to Leu‐enkephalin, analogs containing either aza‐β‐homoleucine or cycloleucine showed a bias toward inhibition of cAMP production through the activation of DOP but not MOP. By contrast, derivatives containing 4,5‐dehydroleucine or d‐allo‐isoleucine conferred a bias toward β‐arrestin 2 at MOP, but not DOP. Our results suggest that position 5 in Leu‐enkephalin analogs can be further exploited to develop compounds with the potential to produce bias toward G protein or β‐arrestin 2.
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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".