Demonstration of Improvements to the Bioluminescence Resonance Energy Transfer (BRET) Technology for the Monitoring of G Protein–Coupled Receptors in Live Cells
Bibliographic record
Abstract
The bioluminescence resonance energy transfer (BRET) technique has become extremely popular for studying protein-protein interactions in living cells and real time. Of particular interest is the ability to monitor interactions between G protein-coupled receptors, such as the thyrotropin-releasing hormone receptor (TRHR), and proteins critical for regulating their function, such as beta-arrestin. Using TRHR/beta-arrestin interactions, we have demonstrated improvements to all 3 generations of BRET (BRET(1), BRET(2), and eBRET) by using the novel forms of luciferase, Rluc2 and Rluc8, developed by the Gambhir laboratory. Furthermore, for the 1st time it was possible to use the BRET2 system to detect ligand-induced G protein-coupled receptor/beta-arrestin interactions over prolonged periods (on the scale of hours rather than seconds) with a very stable signal. As demonstrated by our Z'-factor data, these luciferases increase the sensitivity of BRET to such an extent that they substantially increase the potential applicability of this technology for effective drug discovery high-throughput screening.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".