Effects of estradiol and 4-hydroxytamoxifen on the conformation, thermal stability, and DNA recognition of estrogen receptor β
Bibliographic record
Abstract
Estrogen receptors (ERalpha and ERbeta) are ligand-activated transcription factors. We examined the effects of estradiol (E2), 4-hydroxytamoxifen (HT), and the estrogen response element (ERE) on the helical content and thermal unfolding of ERbeta. A circular dichroism (CD) spectrum of ERbeta showed changes at 210 and 222 nm that were due to the presence of E2, which is indicative of partial unfolding. In contrast, HT did not alter the CD spectrum of ERbeta. The addition of E2 + ERE caused an increase in the alpha-helical content and an increase in the temperature midpoint of folding transition (TM) from 39 +/- 0.7 degrees C to 57.2 +/- 1 degrees C. The addition of E2 + mutant ERE, or E2 + control oligonucleotide, increased the TM of ERbeta to 45 +/- 2 degrees C only. In the presence of HT, ERbeta yielded similar TM values (55-58 degrees C) with ERE, mutant ERE, or control oligodeoxynucleotide. The binding affinity of ERbeta for ERE increased 125.7-fold as a result of the presence of E2, but only 4-fold as a result of HT. These results demonstrate coupled effects of E2 and ERE on ERbeta stability and binding affinity. The increased thermal stability of HT-ERbeta-ERE was associated with reduced specificity of ERbeta-ERE recognition, illustrating profound differences in conformational states of ERbeta induced by E2 and HT.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".