Exploring the Backbone of Enkephalins To Adjust Their Pharmacological Profile for the δ-Opioid Receptor
Bibliographic record
Abstract
The role of each of the four amide bonds in Leu(5)-enkephalin was investigated by systematically and sequentially replacing each with its corresponding trans-alkene. Six Leu(5)-enkephalin analogs based on six dipeptide surrogates and two Met(5)-enkephalin analogs were synthesized and thoroughly tested using a δ-opioid receptor internalization assay, an ERK1/2 activation assay, and a competition binding assay to evaluate their biological properties. We observed that an E-alkene can efficiently replace the first amide bond of Leu(5)- and Met(5)-enkephalin without significantly affecting biological activity. By contrast, the second amide bond was found to be highly sensitive to the same modification, suggesting that it is involved in biologically essential intra- or intermolecular interactions. Finally, we observed that the affinity and activity of analogs containing an E-alkene at either the third or fourth position were partially reduced, indicating that these amide bonds are less important for these intra- or intermolecular interactions. Overall, our study demonstrates that the systematic and sequential replacement of amide bonds by E-alkene represents an efficient way to explore peptide backbones.
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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".