Dehydroepiandrosterone and Dihydrotestosterone Recognition by Human Estrogenic 17β-Hydroxysteroid Dehydrogenase
Bibliographic record
Abstract
Steroid hormones share a very similar structure, but they behave distinctly. We present structures of human estrogenic 17beta-hydroxysteroid dehydrogenase (17beta-HSD1) complexes with dehydroepiandrosterone (DHEA) and dihydrotestosterone (DHT), providing the first pictures to date of DHEA and DHT bound to a protein. Comparisons of these structures with that of the enzyme complexed with the most potent estrogen, estradiol, revealed the structural basis and general model for sex hormone recognition and discrimination. Although the binding cavity is almost entirely composed of hydrophobic residues that can make only nonspecific interactions, the arrangement of residues is highly complementary to that of the estrogenic substrate. Relatively small changes in the shape of the steroid hormone can significantly affect the binding affinity and specificity. The K(m) of estrone is more than 1000-fold lower than that of DHEA and the K(m) of estradiol is about 10 times lower than that of DHT. The structures suggest that Leu-149 is the primary contributor to the discrimination of C-19 steroids and estrogens by 17beta-HSD1. The critical role of Leu-149 has been well confirmed by site-directed mutagenesis experiments, as the Leu-149 --> Val variant showed a significantly decreased K(m) for C-19 steroids while losing discrimination between estrogens and C-19 steroids. The electron density of DHEA also revealed a distortion of its 17-ketone toward a beta-oriented form, which approaches the transition-state conformation for DHEA reduction.
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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 | 0.001 |
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".