Conformational Profiling of the AT1 Angiotensin II Receptor Reflects Biased Agonism, G Protein Coupling, and Cellular Context
Bibliographic record
Abstract
Here, we report the design and use of G protein-coupled receptor-based biosensors to monitor ligand-mediated conformational changes in receptors in intact cells. These biosensors use bioluminescence resonance energy transfer with Renilla luciferase (RlucII) as an energy donor, placed at the distal end of the receptor C-tail, and the small fluorescent molecule FlAsH as an energy acceptor, its binding site inserted at different positions throughout the intracellular loops and C-terminal tail of the angiotensin II type I receptor. We verified that the modifications did not compromise receptor localization or function before proceeding further. Our biosensors were able to capture effects of both canonical and biased ligands, even to the extent of discriminating between different biased ligands. Using a combination of G protein inhibitors and HEK 293 cell lines that were CRISPR/Cas9-engineered to delete Gα q , Gα 11 , Gα 12 , and Gα 13 or β-arrestins, we showed that Gα q and Gα 11 are required for functional responses in conformational sensors in ICL3 but not ICL2. Loss of β-arrestin did not alter biased ligand effects on ICL2P2. We also demonstrate that such biosensors are portable between different cell types and yield context-dependent readouts of G protein-coupled receptor conformation. Our study provides mechanistic insights into signaling events that depend on either G proteins or β-arrestin.
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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".