Potent Opioid Peptide Agonists Containing 4′‐[<i>N</i>‐((4′‐phenyl)‐phenethyl)carboxamido]phenylalanine (Bcp) in Place of Tyr
Bibliographic record
Abstract
Analogues of the opioid peptides H-Tyr-c[D-Cys-Gly-Phe(pNO2)-D-Cys]NH2 (non-selective), H-Tyr-D-Arg-Phe-Lys-NH2 (mu-selective) and dynorphin A(1-11)-NH2 (kappa-selective) containing 4'-[N-((4'-phenyl)-phenethyl)carboxamido]phenylalanine (Bcp) in place of Tyr1 were synthesized. All three Bcp1-opioid peptides retained high mu opioid receptor binding affinity, but showed very significant differences in the opioid receptor selectivity profiles as compared with the corresponding Tyr1-containing parent peptides. The cyclic peptide HBcp-c[D-Cys-Gly-Phe(pNO2)-D-Cys]NH2 turned out to be an extraordinarily potent, mu-selective opioid agonist, whereas the Bcp1-analogue of dynorphin A(1-11)-NH2 displayed partial agonism at the mu receptor. The obtained results suggest that the large biphenylethyl substituent contained in these compounds may engage in a hydrophobic interaction with a receptor subsite and thereby may play a role in the ligand's ability to induce a specific receptor conformation or to bind to a distinct receptor conformation in a situation of conformational receptor heterogeneity.
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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".