Pituitary Adenylate Cyclase-Activating Polypeptide: Focus on Structure- Activity Relationships of a Neuroprotective Peptide
Bibliographic record
Abstract
Pituitary adenylate cyclase-activating polypeptide (PACAP) is a 38-amino acid peptide that was initially isolated from hypothalamus extracts on the basis of its ability to stimulate the production of cAMP in cultured pituitary cells. Recent studies have shown that PACAP exerts potent neuroprotective effects not only in vitro but also in in vivo models of Parkinson's disease, Huntington's disease, traumatic brain injury and stroke. The protective effects of PACAP are based on its capacity to prevent neuronal apoptosis by acting directly on neurons or indirectly through the release of neuroprotective factors by astrocytes. These biological activities are mainly mediated through activation of the PAC1 receptor which is currently considered as a potential target for the treatment of neurodegenerative diseases. However, the use of native PACAP, the endogenous ligand of PAC1, as an efficient neuroprotective drug is actually limited by its rapid degradation. Moreover, injection of PACAP to human induces peripheral side effects which are mainly mediated through VPAC1 and VPAC2 receptors. Strategies to overcome these compromising conditions include the development of metabolically stable analogs of PACAP acting as selective agonists of the PAC1 receptor. This review presents an overview of the structure-activity relationships of PACAP and summarizes the molecular and conformational requirements for activation of PAC1 receptor. The applicability of PACAP analogs as therapeutic agents for treatment of neurodegenerative diseases is also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".