Pseudo‐symmetry of C19‐steroids, alternative binding orientations and multispecificity in human estrogenic 17β‐hydroxysteroid dehydrogenase
Bibliographic record
Abstract
Steroids are implicated in many physiological processes, such as reproduction, aging, metabolism, and cancer. To understand the molecular basis for steroid recognition and discrimination, we studied the human estrogenic 17beta-hydroxysteroid dehydrogenase (17beta-HSD1) responsible for the last step in the bioactivation of all estrogens. Here we report the first observation of the conversion of dihydrotestosterone (DHT) into 3beta,17beta-androstanediol (3beta-diol) by 17beta-HSD1, an estrogenic enzyme studied for more than half a century. Kinetic observations demonstrate that both the 3beta-reduction of DHT into 3beta-diol (kcat = 0.040 s(-1)1; Km = 32 +/- 9 microM) and the 17beta-oxidation of DHT into androstandione (A-dione) (kcat = 0.19 s(-1); Km = 26 +/-6 microM) are catalyzed by 17beta-HSD1 via alternative binding orientation of the steroid. The reduction of DHT was also observed in intact cells by using HEK-293 cells stably transformed with 17beta-HSD1. The high-resolution structure of a 17beta-HSD1-C19-steroid (testosterone) complex solved at 1.54 A demonstrates that the steroid is reversibly oriented in the active site, which strongly supports the existence of alternative binding mode. Such a phenomenon can be explained by the pseudo-symmetric structure of C19-steroids. Our results confirm the role of the Leu149 residue in C18/C19-steroid discrimination and suggest a possible mechanism of 17beta-HSD1 in the modulation of DHT levels in tissues, such as the breast, where both the enzyme and DHT are present.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".