Production and Purification of Histidine-Tagged Dihydrotestosterone-Bound Full-Length Human Androgen Receptor
Bibliographic record
Abstract
Protein purification and characterization is required for a full understanding of structure-function relationships. Because proteins have complex structures and can be present at low concentrations, efficient purification protocols are needed. Purification of full-length androgen receptor (AR) is complicated by its low abundance, instability in the absence of androgen, and size and charge similarities with other nuclear proteins. Previous approaches to steroid hormone receptor purification have included traditional chromatography, such as ion exchange, gel filtration, isoelectric focusing chromatography ( 1 , 2 ), and hormone, DNA, and antibody affinity chromatography ( 3 - 5 ), but with low yield and purity. Overexpression of recombinant nuclear receptors or their domains in insect cells ( 6 ), Escherichia coli ( 7 ), or mammalian cells ( 8 ), has facilitated their purification. Purification with histidine (His)-tagged proteins is advantageous, because, unlike protein tags, such as glutathione S -transferase, short His sequences can have minimal effects on protein structure and function, efficiently bind metal-chelating columns mostly independent of protein conformation, and may not require the use of a cleavage step ( 9 , 10 ). This chapter details a procedure for the isolation of nondenatured, recombinant human AR with more than 95% purity using four-step chromatography with milligram yields ( see Notes 1 - Notes 4 ). Purified AR may be used in physical and biochemical studies, such as monoclonal antibody development ( 11 ), crystallography and nuclear magnetic resonance studies, DNA binding, and solution dimerization ( 6 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".