Genetic code expansion and photocross-linking identify different β-arrestin binding modes to the angiotensin II type 1 receptor
Bibliographic record
Abstract
The angiotensin II (AngII) type 1 receptor (AT1R) is a member of the G protein–coupled receptor (GPCR) family and binds β-arrestins (β-arrs), which regulate AT1R signaling and trafficking. These processes can be biased by different ligands or mutations in the AGTR1 gene. As for many GPCRs, the exact details for AT1R–β-arr interactions driven by AngII or β-arr–biased ligands remain largely unknown. Here, we used the amber-suppression technology to site-specifically introduce the unnatural amino acid (UAA) p -azido-l-phenylalanine (azF) into the intracellular loops (ICLs) and the C-tail of AT1R. Our goal was to generate competent photoreactive receptors that can be cross-linked to β-arrs in cells. We performed UV-mediated photolysis of 25 different azF-labeled AT1Rs to cross-link β-arr1 to AngII-bound receptors, enabling us to map important contact sites in the C-tail and in the ICL2 and ICL3 of the receptor. The extent of AT1R–β-arr1 cross-linking among azF-labeled receptors differed, revealing variability in β-arr's contact mode with the different AT1R domains. Moreover, the signature of ligated AT1R–β-arr complexes from a subset of azF-labeled receptors also differed between AngII and β-arr–biased ligand stimulation of receptors and between azF-labeled AT1R bearing and that lacking a bias signaling mutation. These observations further implied distinct interaction modalities of the AT1R–β-arr1 complex in biased signaling conditions. Our findings demonstrate that this photocross-linking approach is useful for understanding GPCR–β-arr complexes in different activation states and could be extended to study other protein–protein interactions in cells.
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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".