Calcitonin Gene-Related Peptide Analogues with Aza and Indolizidinone Amino Acid Residues Reveal Conformational Requirements for Antagonist Activity at the Human Calcitonin Gene-Related Peptide 1 Receptor
Bibliographic record
Abstract
Calcitonin gene-related peptide antagonists have potential for the treatment and prevention of disease states such as non-insulin-dependent diabetes mellitus, migraine headache, pain, and inflammation. To gain insight into the spatial requirements for CGRP antagonism, three strategies were employed to restrict the conformation of the potent undecapeptide antagonist, [D31,P34,F35]CGRP27-37. First, aza-amino acid scanning was performed, and ten aza-peptide analogues were synthesized and examined for biological activity. Second, (3S,6S,9S)-2-oxo-3-amino-indolizidin-2-one amino acid (I2aa) and (2S,6S,8S)-9-oxo-8-amino-indolizidin-9-one amino acid (I9aa) both were introduced at positions 31-32, 32-33, 33-34, and 34-35, regions of the backbone expected to adopt turns. Finally, the conformation of the backbone and side-chain of the C-terminal residue, Phe35-Ala36-Phe37-NH2, was explored employing (2S,4R,6R,8S)-9-oxo-8-amino-4-phenyl-indolizidin-9-one amino acid (4-Ph-I9aa) as a constrained phenylalanine mimic. The structure-activity relationships exhibited by our 26 analogues illustrate conformational requirements important for designing CGRP antagonists and highlight the importance of beta-turns centered at Gly33-Pro34 for potency.
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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".