Using the Protein Chip to Screen Agonists and Antagonists of the Androgen Receptor
Bibliographic record
Abstract
Based on its important physical and pathological function, the androgen receptor (AR) is regarded as a significant drug target. In this report, the authors describe a novel strategy of protein chip technology to screen agonists and antagonists of AR. First, the AR ligand binding domain (AR-LBD) was expressed in Escherichia coli, purified, and then immobilized on a silane-polysaccharide surface of a protein chip. Second, the affinities of methyltestosterone (MT) and fluorescent-labeled testosterone for the AR-LBD protein chip were determined. Third, a converse strategy of the protein chip was tested to evaluate its reliability as a drug screening method. Fourth, a 10,067-compound library was screened to find new ligands of AR. From the results, the K(d) of testosterone and the IC(50) of MT are consistent with the literature (0.61 vs. 0.49 nM 2.88 vs. 3.90 nM, respectively). The Z' factor of the high-throughput screening (HTS) method was 0.76, which meets the requirement of drug screening (>0.4). Finally, 3 active ligands of AR were identified with their IC( 50) values of 3.63, 2.19, and 1.71 microM, respectively. In summary, the novel strategy of the AR-LBD protein chip was suitable for HTS at the molecular level.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".