Molecular and Conformational Determinants of Pituitary Adenylate Cyclase-Activating Polypeptide (PACAP) for Activation of the PAC1 Receptor
Bibliographic record
Abstract
PAC1 receptor is abundant in the CNS and plays an important role in neuronal survival. To identify the molecular determinants and the conformational components responsible for the activation of the PAC1 receptor, we performed a SAR study focusing on the N-terminal domain of its endogenous ligand, PACAP. This approach revealed that residues Asp(3) and Phe(6) are key elements of the pharmacophore of the PAC1 receptor. This study, supported by NMR structural analyses, suggests that the N-terminal tail of PACAP (residues 1 to 4) adopts a specific conformation similar to a turn when it activates the PAC1 receptor. Moreover, the integrity of the alpha-helix conformation observed at positions 5 to 7 appears crucial to allow the binding of PACAP. Characterization of analogues led to the identification of several superagonists, such as [Bip(6)]PACAP27, and of a new potent PAC1 receptor antagonist, [Sar(4)]PACAP38. The bioactive conformation inferred from this SAR study could constitute an appropriate molecular scaffold supporting the design of nonpeptidic PAC1 receptor agonists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".