Comparison of crystal structures of human androgen receptor ligand‐binding domain complexed with various agonists reveals molecular determinants responsible for binding affinity
Bibliographic record
Abstract
Androgens exert their effects by binding to the highly specific androgen receptor (AR). In addition to natural potent androgens, AR binds a variety of synthetic agonist or antagonist molecules with different affinities. To identify molecular determinants responsible for this selectivity, we have determined the crystal structure of the human androgen receptor ligand-binding domain (hARLBD) in complex with two natural androgens, testosterone (Testo) and dihydrotestosterone (DHT), and with an androgenic steroid used in sport doping, tetrahydrogestrinone (THG), at 1.64, 1.90, and 1.75 A resolution, respectively. Comparison of these structures first highlights the flexibility of several residues buried in the ligand-binding pocket that can accommodate a variety of ligand structures. As expected, the ligand structure itself (dimension, presence, and position of unsaturated bonds that influence the geometry of the steroidal nucleus or the electronic properties of the neighboring atoms, etc.) determines the number of interactions it can make with the hARLBD. Indeed, THG--which possesses the highest affinity--establishes more van der Waals contacts with the receptor than the other steroids, whereas the geometry of the atoms forming electrostatic interactions at both extremities of the steroid nucleus seems mainly responsible for the higher affinity measured experimentally for DHT over Testo. Moreover, estimation of the ligand-receptor interaction energy through modeling confirms that even minor modifications in ligand structure have a great impact on the strength of these interactions. Our crystallographic data combined with those obtained by modeling will be helpful in the design of novel molecules with stronger affinity for the AR.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".