High-Throughput Screening of G Protein-Coupled Receptor Antagonists Using a Bioluminescence Resonance Energy Transfer 1-Based β-Arrestin2 Recruitment Assay
Bibliographic record
Abstract
In this study, the authors developed HEK293 cell lines that stably coexpressed optimal amounts of beta-arrestin2-Rluc and VENUS fusions of G protein-coupled receptors (GPCRs) belonging to both class A and class B receptors, which include receptors that interact transiently or stably with beta-arrestins. This allowed the use of a bioluminescence resonance energy transfer (BRET) 1- beta-arrestin2 translocation assay to quantify receptor activation or inhibition. One of the developed cell lines coexpressing CCR5-VENUS and beta-arrestin2- Renilla luciferase was then used for high-throughput screening (HTS) for antagonists of the chemokine receptor CCR5, the primary co-receptor for HIV. A total of 26,000 compounds were screened for inhibition of the agonist-promoted beta-arrestin2 recruitment to CCR5, and 12 compounds were found to specifically inhibit the agonist-induced beta-arrestin2 recruitment to CCR5. Three of the potential hits were further tested using other functional assays, and their abilities to inhibit CCR5 agonist-promoted signaling were confirmed. This is the 1st study describing a BRET1-beta-arrestin recruitment assay in stable mammalian cells and its successful application in HTS for GPCRs antagonists.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".