A Potent, Selective, and Cell-Active Inhibitor of Human Type I Protein Arginine Methyltransferases
Bibliographic record
Abstract
Protein arginine methyltransferases (PRMTs) play a crucial role in a variety of biological processes. Overexpression of PRMTs has been implicated in various human diseases including cancer. Consequently, selective small-molecule inhibitors of PRMTs have been pursued by both academia and the pharmaceutical industry as chemical tools for testing biological and therapeutic hypotheses. PRMTs are divided into three categories: type I PRMTs which catalyze mono- and asymmetric dimethylation of arginine residues, type II PRMTs which catalyze mono- and symmetric dimethylation of arginine residues, and type III PRMT which catalyzes only monomethylation of arginine residues. Here, we report the discovery of a potent, selective, and cell-active inhibitor of human type I PRMTs, MS023, and characterization of this inhibitor in a battery of biochemical, biophysical, and cellular assays. MS023 displayed high potency for type I PRMTs including PRMT1, -3, -4, -6, and -8 but was completely inactive against type II and type III PRMTs, protein lysine methyltransferases and DNA methyltransferases. A crystal structure of PRMT6 in complex with MS023 revealed that MS023 binds the substrate binding site. MS023 potently decreased cellular levels of histone arginine asymmetric dimethylation. It also reduced global levels of arginine asymmetric dimethylation and concurrently increased levels of arginine monomethylation and symmetric dimethylation in cells. We also developed MS094, a close analog of MS023, which was inactive in biochemical and cellular assays, as a negative control for chemical biology studies. MS023 and MS094 are useful chemical tools for investigating the role of type I PRMTs 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) | 0.001 | 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 | 0.000 | 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".