( <i>R</i> )-PFI-2 is a potent and selective inhibitor of SETD7 methyltransferase activity in cells
Bibliographic record
Abstract
SET domain containing (lysine methyltransferase) 7 (SETD7) is implicated in multiple signaling and disease related pathways with a broad diversity of reported substrates. Here, we report the discovery of (R)-PFI-2-a first-in-class, potent (Ki (app) = 0.33 nM), selective, and cell-active inhibitor of the methyltransferase activity of human SETD7-and its 500-fold less active enantiomer, (S)-PFI-2. (R)-PFI-2 exhibits an unusual cofactor-dependent and substrate-competitive inhibitory mechanism by occupying the substrate peptide binding groove of SETD7, including the catalytic lysine-binding channel, and by making direct contact with the donor methyl group of the cofactor, S-adenosylmethionine. Chemoproteomics experiments using a biotinylated derivative of (R)-PFI-2 demonstrated dose-dependent competition for binding to endogenous SETD7 in MCF7 cells pretreated with (R)-PFI-2. In murine embryonic fibroblasts, (R)-PFI-2 treatment phenocopied the effects of Setd7 deficiency on Hippo pathway signaling, via modulation of the transcriptional coactivator Yes-associated protein (YAP) and regulation of YAP target genes. In confluent MCF7 cells, (R)-PFI-2 rapidly altered YAP localization, suggesting continuous and dynamic regulation of YAP by the methyltransferase activity of SETD7. These data establish (R)-PFI-2 and related compounds as a valuable tool-kit for the study of the diverse roles of SETD7 in cells and further validate protein methyltransferases as a druggable target class.
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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) | 0.002 | 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".