A First-in-Class, Highly Selective and Cell-Active Allosteric Inhibitor of Protein Arginine Methyltransferase 6
Bibliographic record
Abstract
Protein arginine methyltransferase 6 (PRMT6) catalyzes monomethylation and asymmetric dimethylation of arginine residues in various proteins, plays important roles in biological processes, and is associated with multiple cancers. To date, a highly selective PRMT6 inhibitor has not been reported. Here we report the discovery and characterization of a first-in-class, highly selective allosteric inhibitor of PRMT6, (R)- 2 (SGC6870). (R)- 2 is a potent PRMT6 inhibitor (IC 50 = 77 ± 6 nM) with outstanding selectivity for PRMT6 over a broad panel of other methyltransferases and nonepigenetic targets. Notably, the crystal structure of the PRMT6– (R)- 2 complex and kinetic studies revealed (R)- 2 binds a unique, induced allosteric pocket. Additionally, (R)- 2 engages PRMT6 and potently inhibits its methyltransferase activity in cells. Moreover, (R)- 2 ’s enantiomer, (S)- 2 (SGC6870N), is inactive against PRMT6 and can be utilized as a negative control. Collectively, (R) - 2 is a well-characterized PRMT6 chemical probe and a valuable tool for further investigating PRMT6 functions in health and disease.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".