Study of the impact of the T877A mutation on ligand‐induced helix‐12 positioning of the androgen receptor resulted in design and synthesis of novel antiandrogens
Bibliographic record
Abstract
Antiandrogen flutamide, an antagonist of the wild-type androgen receptor (AR), is used in the clinics for treating metastatic prostate cancer. However, the T877A mutated AR is paradoxically activated by hydroxyflutamide, an active form of flutamide. Despite of crystallographic studies, how the T877A mutation results in antagonist-agonist conversion of hydroxyflutamide remains a puzzle. Here, started from a structural model of the apo form of AR ligand-binding domain (AR-LBD), we have investigated the impact of the T877A mutation on ligand-induced helix-12 positioning by replica-exchange molecular dynamics (REMD) simulations with an unique protocol, which is capable of simulating the H12 dynamics and keeping the main body of AR-LBD unchanged. Specifically, (i) we have computationally demonstrated that on the binding of hydroxyflutamide, the apo form of H12 rearranges into the agonistic form in the T877A mutant, but into the antagonistic forms in the wild-type receptor, shedding light on hydroxyflutamide agonism/antagonism; (ii) By REMD simulations, we have predicted antiandrogen SC184 is a non-agonist of the T877A mutant. This was confirmed by luciferase assays; and (iii) on the basis of the binding modes of hydroxyflutamide and SC184 from the simulations, we designed a novel flutamide derivative called SC333, which was subsequently predicted to be a pure antagonist of the T877A mutant. We then synthesized and experimentally confirmed SC333 is a pan-antiandrogen effective against the wild-type and the T877A and W741C mutated ARs, showing low micromolar cytotoxicity in LNCaP cells. Importantly, we demonstrated that distribution of the H12 conformations from REMD simulations is correlated with ligand agonist/antagonist activity.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".