A neurotensin receptor type 1-derived pepducin acts as a biased allosteric modulator to regulate target receptor function
Bibliographic record
Abstract
Abstract Pepducins are synthetic membrane-tethered lipopeptides designed to allosterically modulate G protein-coupled receptor (GPCR) signaling. Here, we characterize a series of pepducins targeting the neurotensin receptor type 1 (NTS1), revealing multifaceted modulation of this receptor class. Using BRET-based biosensors, we show that PP-001, a pepducin derived from NTS1’s first intracellular loop, preferentially activates G protein over β-arrestin signaling while inhibiting NT binding, NT-induced β-arrestin recruitment, and NTS1 receptor internalization, thereby acting as biased allosteric agonist and negative allosteric modulator. PP-001 also promotes the formation of both homo- and heteromeric multi-receptor units. In vivo, PP-001 elicits potent, sustained hypotensive effects, reversible by the NTS1 antagonist SR48692. Finally, although the mechanism of pepducin-receptor interaction remains unclear, this study identifies a critical N-terminal RKK motif for PP-001’s biological activity. Thermodenaturation assays with purified NTS1 and mutagenesis further provide evidence for the role of NTS1’s H8 domain in direct pepducin-receptor interaction. This work highlights pepducins’ modulatory potential as pharmacological tools for GPCR-targeted drug development.
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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".