Development of a NanoBRET assay for evaluation of 14-3-3σ molecular glues
Bibliographic record
Abstract
We report the development of a 384-well formatted NanoBRET assay to characterize molecular glues of 14-3-3/client interactions in living cells. The seven isoforms of 14-3-3 are dimeric hub proteins with diverse roles including transcription factor regulation and signal transduction. 14-3-3 interacts with hundreds of client proteins to regulate their function and is therefore an ideal therapeutic target when client selectivity can be achieved. We have developed the NanoBRET system for three 14-3-3σ client proteins CRAF, TAZ, and estrogen receptor α (ERα), which represent three specific binding modes. We have measured stabilization of 14-3-3σ/client complexes by molecular glues with EC 50 values between 100 nM and 1 μM in cells, which align with the EC 50 values calculated by fluorescence anisotropy in vitro. Developing this NanoBRET system for the hub protein 14-3-3σ allows for a streamlined approach, bypassing multiple optimization steps in the assay development process for other 14-3-3σ clients. The NanoBRET system allows for an assessment of PPI stabilization in a more physiologically relevant, cell-based environment using full-length proteins. The method is applicable to diverse protein-protein interactions (PPIs) and offers a robust platform to explore libraries of compounds for both PPI stabilizers and inhibitors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.002 |
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".