Molecular interactions between sex hormone–binding globulin and nonsteroidal ligands that enhance androgen activity
Bibliographic record
Abstract
Sex hormone–binding globulin (SHBG) determines the equilibrium between free and protein-bound androgens and estrogens in the blood and regulates their access to target tissues. Using crystallographic approaches and radiolabeled competitive binding-capacity assays, we report here how two nonsteroidal compounds bind to human SHBG, and how they influence androgen activity in cell culture. We found that one of these compounds, (−)3,4-divanillyltetrahydrofuran (DVT), present in stinging nettle root extracts and used as a nutraceutical, binds SHBG with relatively low affinity. By contrast, a synthetic compound, 3-(1H-imidazol-1-ylmethyl)-2phenyl-1H-indole (IPI), bound SHBG with an affinity similar to that of testosterone and estradiol. Crystal structures of SHBG in complex with DVT or IPI at 1.71–1.80 Å resolutions revealed their unique orientations in the SHBG ligand-binding pocket and suggested opportunities for the design of other nonsteroidal ligands of SHBG. As observed for estradiol but not testosterone, IPI binding to SHBG was reduced by ∼20-fold in the presence of zinc, whereas DVT binding was almost completely lost. Estradiol-dependent fibulin-2 interactions with SHBG similarly occurred for IPI-bound SHBG, but not with DVT-bound SHBG. Both DVT and IPI increased the activity of testosterone in a cell culture androgen reporter system by competitively displacing testosterone from SHBG. These findings indicate how nonsteroidal ligands of SHBG maybe designed to modulate the bioavailability of sex steroids.
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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".