Monitoring ligand-dependent assembly of receptor ternary complexes in live cells by BRETFect
Bibliographic record
Abstract
There is currently an unmet need for versatile techniques to monitor the assembly and dynamics of ternary complexes in live cells. Here we describe bioluminescence resonance energy transfer with fluorescence enhancement by combined transfer (BRETFect), a high-throughput technique that enables robust spectrometric detection of ternary protein complexes based on increased energy transfer from a luciferase to a fluorescent acceptor in the presence of a fluorescent intermediate. Its unique donor-intermediate-acceptor relay system is designed so that the acceptor can receive energy either directly from the donor or indirectly via the intermediate in a combined transfer, taking advantage of the entire luciferase emission spectrum. BRETFect was used to study the ligand-dependent cofactor interaction properties of the estrogen receptors ERα and ERβ, which form homo- or heterodimers whose distinctive regulatory properties are difficult to dissect using traditional methods. BRETFect uncovered the relative capacities of hetero- vs. homodimers to recruit receptor-specific cofactors and regulatory proteins, and to interact with common cofactors in the presence of receptor-specific ligands. BRETFect was also used to follow the assembly of ternary complexes between the V2R vasopressin receptor and two different intracellular effectors, illustrating its use for dissection of ternary protein-protein interactions engaged by G protein-coupled receptors. Our results indicate that BRETFect represents a powerful and versatile technique to monitor the dynamics of ternary interactions within multimeric complexes in live 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".