Potent and selective SETDB1 covalent negative allosteric modulator reduces methyltransferase activity in cells
Bibliographic record
Abstract
A promising drug target, SETDB1, is a dual methyl-lysine (Kme) reader and methyltransferase implicated in cancer and neurodegenerative disease progression. To help understand the role of the triple Tudor domain (3TD) of SETDB1, its Kme reader, we first identify a low micromolar potency small molecule ligand, UNC6535, which occupies simultaneously both the TD2 and TD3 reader binding sites. Further optimization leads to the discovery of UNC10013, a covalent 3TD ligand targeting Cys385 of SETDB1. UNC10013 is potent with a kinact/KI of 1.0 × 106 M−1s−1 and demonstrates proteome-wide selectivity. In cells, negative allosteric modulation of SETDB1-mediated Akt methylation occurs after treatment with UNC10013. Therefore, UNC10013 is a potent, selective, and cell-active covalent ligand for the 3TD of SETDB1, demonstrating negative allosteric modulator properties and making it a promising tool to study the biological role of SETDB1 in disease progression. Design of cysteine-targeting analogs of a reversible SETDB1 triple Tudor domain (3TD) ligand, UNC6535, led to UNC10013, a potent covalent ligand with high selectivity. UNC10013 demonstrated allosteric inhibition of SETDB1-mediated Akt methylation in cells, a promising approach to SETDB1 therapeutics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".