BRET biosensors to study GPCR biology, pharmacology, and signal transduction
Bibliographic record
Abstract
Bioluminescence resonance energy transfer (BRET)-based biosensors have been extensively used over the last decade to study protein-protein interactions and intracellular signal transduction in living cells. In this review, we discuss the various BRET biosensors that have been developed to investigate biology, pharmacology, and signaling of G protein-coupled receptors (GPCRs). GPCRs form two distinct types of multiprotein signal transduction complexes based upon their inclusion of G proteins or β-arrestins that can be differentially affected by drugs that exhibit functional selectivity toward G protein or β-arrestin signaling. BRET has been especially adept at illuminating the dynamics of protein-protein interactions between receptors, G proteins, β-arrestins, and their many binding partners in living cells; as well as measuring the formation and accumulation of second messengers following receptor activation. Specifically, we discuss in detail the application of BRET to study dopamine and trace amine receptors signaling, presenting examples of an exchange protein activated by cAMP biosensor to measure cAMP, β-arrestin biosensors to determine β-arrestin recruitment to the receptor, and dopamine D2 receptor and trace amine-associated receptor 1 biosensors to investigate heterodimerization between them. As the biochemical spectrum of BRET biosensors expands, the number of signaling pathways that can be measured will concomitantly increase. This will be particularly useful for the evaluation of functional selectivity in which the real-time BRET capability to measure distinct signaling modalities will dramatically shorten the time to characterize new generation of biased drugs. These emerging approaches will further expand the growing application of BRET in the screening for novel pharmacologically active compounds.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".