In Silico Identification and Experimental Validation of (−)-Muqubilin A, a Marine Norterpene Peroxide, as PPARα/γ-RXRα Agonist and RARα Positive Allosteric Modulator
Bibliographic record
Abstract
The nuclear receptors (NRs) RARα, RXRα, PPARα, and PPARγ represent promising pharmacological targets for the treatment of neurodegenerative diseases. In the search for molecules able to simultaneously target all the above-mentioned NRs, we screened an in-house developed molecular database using a ligand-based approach, identifying (-)-Muqubilin (Muq), a cyclic peroxide norterpene from a marine sponge, as a potential hit. The ability of this compound to stably and effectively bind these NRs was assessed by molecular docking and molecular dynamics simulations. Muq recapitulated all the main interactions of a canonical full agonist for RXRα and both PPARα and PPARγ, whereas the binding mode toward RARα showed peculiar features potentially impairing its activity as full agonist. Luciferase assays confirmed that Muq acts as a full agonist for RXRα, PPARα, and PPARγ with an activity in the low- to sub-micromolar range. On the other hand, in the case of RAR, a very weak agonist activity was observed in the micromolar range. Quite surprisingly, we found that Muq is a positive allosteric modulator for RARα, as both luciferase assays and in vivo analysis using a zebrafish transgenic retinoic acid (RA) reporter line showed that co-administration of Muq with RA produced a potent synergistic enhancement of RARα activation and RA signaling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".