THE BRET<sup>2</sup>/ARRESTIN ASSAY IN STABLE RECOMBINANT CELLS: A PLATFORM TO SCREEN FOR COMPOUNDS THAT INTERACT WITH G PROTEIN-COUPLED RECEPTORS (GPCRS)*
Bibliographic record
Abstract
In BRET2 (Bioluminescence Resonance Energy Transfer), a Renilla luciferase (RLuc) is used as the donor protein, while a Green Fluorescent Protein (GFP2) is used as the acceptor protein. In the presence of the cell permeable substrate DeepBlueC, RLuc emits blue light at 395 nm. If the GFP2 is brought into close proximity to RLuc via a specific biomolecular interaction, the GFP2 will absorb the blue light energy and reemit green light at 510nm. BRET2 signals are therefore easily determined by measuring the ratio of green over blue light (510/395nm) using appropriate dual channel luminometry instruments (e.g., Fusion Universal Microplate Analyzer, Packard BioScience). Since no light source is required for BRET2 assays, the technology does not suffer from high fluorescent background or photobleaching, the common problems associated with standard FRET-based assays. Using BRET2, we developed a generic G Protein-Coupled Receptor (GPCR) assay based on the observation that activation of the majority of GPCRs by agonists leads to the interaction of beta-arrestin (a protein that is involved in receptor desensitization and sequestration) with the receptor. We established a cell line stably expressing the GFP2:beta-arrestin 2 fusion protein, and showed that it can be used to monitor the activation of various transiently expressed GPCRs, in BRET2/arrestin assays. In addition, using the HEK 293/GFP2:beta-arrestin 2 cell line as a recipient, we generated a double-stable line co-expressing the vasopressin 2 receptor (V2R) fused to RLuc (V2R:RLuc) and used it for the pharmacological characterization of compounds in BRET2/arrestin assays. This approach yields genuine pharmacology and supports the BRET2/arrestin assay as a tool that can be used with recombinant cell lines to characterize ligand-GPCR interactions which can be applied to ligand identification for orphan receptors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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".