Structural determinants of pure antiestrogenicity
Bibliographic record
Abstract
Abstract Inhibitory activities of antiestrogens on estrogen receptor alpha (ERα) range from mixed antagonism/agonism (selective ER modulators; SERMs) to complete antiestrogenicity associated with accelerated ERα turnover (selective ER degraders; SERDs). Here, we show using a panel of SERMs, SERDs and a PROTAC that efficient induction of ERα SUMOylation better accounts for complete transcriptional repression than increased suppression of coactivator recruitment or accelerated ERα degradation. Antiestrogen-induced ERα SUMOylation depends on the hydrophobicity of N-terminal residues of ligand binding domain (LBD) helix 12 (H12). L536 mutations, including those occurring in endocrine therapy-resistant breast cancer, abolished SUMOylation with all antiestrogens. Structures of the L536S human ERα LBD bound to fulvestrant analogs and molecular dynamics simulations predict dynamic side chain interactions with wild-type ERα H12 in the coactivator-binding groove and model the impact of L536 mutations. Together, our results show that induced SUMOylation contributes to the complete suppression of ERα activity and provide insights into its structural determinants.
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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".