A Cyclic Tetrapeptide (“Cyclodal”) and Its Mirror-Image Isomer Are Both High-Affinity μ Opioid Receptor Antagonists
Bibliographic record
Abstract
Head-to-tail cyclization of the μ opioid receptor (MOR) agonist [Dmt 1 ]DALDA (H-Dmt- d -Arg-Phe-Lys-NH 2 ( 9; Dmt = 2′,6′-dimethyltyrosine) resulted in a highly active, selective MOR antagonist, c[- d -Arg-Phe-Lys-Dmt-] ( 1 ) (“cyclodal”), with subnanomolar binding affinity. A docking study of cyclodal using the crystal structure of MOR in the inactive form showed a unique binding mode with the two basic residues of the ligand forming salt bridges with the Asp 127 and Glu 229 receptor residues. Cyclodal showed high plasma stability and was able to cross the blood–brain barrier to reverse morphine-induced, centrally mediated analgesia when given intravenously. Surprisingly, the mirror-image isomer (optical antipode) of cyclodal, c[-Arg- d -Phe- d -Lys- d -Dmt-] ( 2 ), also turned out to be a selective MOR antagonist with 1 nM binding affinity, and thus, these two compounds represent the first example of mirror image opioid receptor ligands with both optical antipodes having high binding affinity. Reduction of the Lys-Dmt peptide bond in cyclodal resulted in an analogue, c[- d -Arg-Phe-LysΨ[CH 2 NH]Dmt-] ( 8 ), with MOR agonist activity.
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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) | 0.002 | 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".