A First-in-class, Highly Selective and Cell-active Allosteric Inhibitor of Protein Arginine Methyltransferase 6 (PRMT6)
Bibliographic record
Abstract
Abstract PRMT6 catalyzes monomethylation and asymmetric dimethylation of arginine residues in various proteins, plays important roles in biological processes and is associated with multiple cancers. While there are several reported PRMT6 inhibitors, a highly selective PRMT6 inhibitor has not been reported to date. Furthermore, allosteric inhibitors of protein methyltransferases are rare. Here we report the discovery and characterization of a first-in-class, highly selective allosteric inhibitor of PRMT6, SGC6870. SGC6870 is a potent PRMT6 inhibitor (IC 50 = 77 ± 6 nM) with outstanding selectivity for PRMT6 over a broad panel of other methyltransferases and non-epigenetic targets. Notably, the crystal structure of the PRMT6–SGC6870 complex and kinetic studies revealed SGC6870 binds a unique, induced allosteric pocket. Additionally, SGC6870 engages PRMT6 and potently inhibits its methyltransferase activity in cells. Moreover, SGC6870’s enantiomer, SGC6870N, is inactive against PRMT6 and can be utilized as a negative control. Collectively, SGC6870 is a well-characterized PRMT6 chemical probe and valuable tool for further investigating PRMT6 functions in health and disease.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".