A lipidated peptide derived from the C-terminal tail of the vasopressin 2 receptor shows promise as a new $β$-arrestin inhibitor
Bibliographic record
Abstract
$β$-arrestins play pivotal roles in seven transmembrane receptor (7TMR) signalling and trafficking. To study their functional role in the regulation of specific receptor systems, current research relies mainly on genetic tools, as few pharmacological options are available. To address this issue, we designed and synthesised a novel lipidated phosphomimetic peptide inhibitor targeting $β$-arrestins, called ARIP, which was developed based on the C-terminal tail (A343-S371) of the vasopressin V2 receptor. As the V2R sequence has been shown to bind $β$-arrestins with high affinity and stability, we added an N-terminal palmitate residue to allow membrane tethering and subsequent cell entry. Here, using BRET2-based biosensors, we demonstrated the ability of ARIP to inhibit agonist-induced $β$-arrestin recruitment on a series of 7TMRs belonging to class A (low stable associations with arrestins) or class B (high stability), with efficiencies that dependent on receptor type. In addition, we showed that ARIP was unable to recruit $β$-arrestins to the cell membrane by itself, and that it did not interfere with canonical G protein signalling. Molecular modelling studies also revealed that ARIP binds $β$-arrestins in the same way as V2Rpp, the phosphorylated peptide derived from the V2R C-terminal domain, and that replacing the p-Ser and p-Thr residues of V2Rpp with Glu residues does not alter the inhibitory activity of ARIP on $β$-arrestin recruitment. Importantly, ARIP exerted an opioid-sparing effect in vivo, as intrathecal injection of ARIP potentiated the analgesic effect of morphine in the tail-flick nociceptive model, a behavioural response consistent with $β$-arrestin genetic inhibition. ARIP therefore represents a promising pharmacological tool for investigating the fine-tuning roles of $β$-arrestins in 7TMR-driven pathophysiological processes.
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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 | 0.001 |
| 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".