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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".