Mapping of Steroids Binding to 17β-Hydroxysteroid Dehydrogenase Type 1 Using Monte Carlo Energy Minimization Reveals Alternative Binding Modes
Bibliographic record
Abstract
Crystallographic studies of ligand-protein complexes reveal most preferable ligand binding modes, but do not show less populated modes that may contribute to measurable biochemical and biophysical characteristics of the complexes. In some cases, a ligand may bind a protein in essentially different modes. An example is 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1), a steroidogenic enzyme that catalyzes reduction of estrone to estradiol in gonadal and peripheral tissues. The enzyme exhibits a high specificity for estrogens which bind with their C17 atom in the proximity of the NADP(H) cofactor. 17Beta-HSD1 can also bind androgens, but in a reverse binding mode, in which the steroid C3 atom is the closest carbon atom to the cofactor. Here we map the interaction energy of estradiol and dihydrotestosterone binding to 17beta-HSD1. Positions and orientations of the steroids in the ligand-binding tunnel were sampled systematically, and at each combination of these generalized coordinates, the energy was Monte Carlo minimized. The computed maps show energy minima corresponding to the X-ray structures and predict alternative binding modes, in particular, an upside-down orientation in which steroidal face alpha is exposed to protein residues that normally interact with face beta. The methodology can be used for mapping ligand-receptor interactions in various systems, for example, in ion channels and G-protein-coupled receptors that bind elongated ligands in confined space between transmembrane helices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".