Membrane-tethered peptides derived from intracellular loops 2 and 3 of the urotensin II receptor act as allosteric biased ligands
Bibliographic record
Abstract
Abstract Over the last decade, the urotensinergic system has garnered significant attention as a promising new target for the treatment of various cardiovascular diseases and also cancer. Significant investment toward the development of clinically relevant UT ligands for therapeutic intervention has been made but have met little to no success to date. The UT system, which has yet to be effectively targeted, therefore remains to be therapeutically exploited. The discovery of allosteric sites that allow modulation of receptor activity will increase the searchable chemical space against a disease-relevant target. Pepducins and other lipidated peptides have been used as both mechanistic probes and potential therapeutics. Therefore, pepducins derived from the human urotensin II receptor might represent unique tools to generate signaling bias and study UT signaling networks. Two hUT-derived pepducins, derived from the second and the third intracellular loop of UT, respectively, have been synthesized and pharmacologically characterized. Our results demonstrated that hUT-Pep2 and [Trp 1 , Leu 2 ]hUT-Pep3 acted as biased ago-allosteric modulators, triggered ERK 1/2 phosphorylation and to a lesser extent, IP 1 production, stimulated cell proliferation yet were devoid of contractile activity. Interestingly, both hUT-derived pepducins were able to modulate hUII- and URP-mediated contraction albeit to different extents. These new derivatives represent unique tools to reveal the intricacies of hUT signaling and also a novel avenue to design allosteric ligands selectively targeting UT signaling that could prove to be useful for the treatment of hUT-associated diseases.
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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".