Development of a Versatile Platform for Nuclear Receptor Screening Using AlphaScreen™
Bibliographic record
Abstract
The interaction between nuclear receptors (NRs) and their coactivators, a key step in transcription regulation, requires a short consensus sequence called the LXXLL motif found in the coactivators' structure. Using the AlphaScreen technology, the authors have taken advantage of this receptor-coactivator interaction to develop a highly sensitive assay to identify and characterize compounds modulating NR activity. Estrogen and retinoic acid receptors were chosen as models to demonstrate the versatility of the AlphaScreen technology: (1) the assay can be designed using different antibodies to capture either full-length receptors or receptor domains that have been tagged, (2) the assay can differentiate between ligands that act as agonists or antagonists because only agonists will allow recruitment of the coactivator sequence-derived peptide, and (3) the assay gives the opportunity to screen for antagonists targeting the ligand-binding site or the dimerization interface between the receptor and the coactivator. Titration of the receptor and biotinylated peptide indicates that AlphaScreen is highly sensitive, requiring nanomolar concentration of reagents. Competition isotherms performed with known receptor antagonists demonstrate that the assay is a useful tool to rank the antagonists according to their order of potency. Overall, the results presented here indicate that the versatility, sensitivity, robustness, and ease of execution of the AlphaScreen NR assay will allow for efficient screening of NR modulators.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".