Sinefungin Derivatives as Inhibitors and Structure Probes of Protein Lysine Methyltransferase SETD2
Bibliographic record
Abstract
Epigenetic regulation is involved in numerous physiological and pathogenic processes. Among the key regulators that orchestrate epigenetic signaling are over 50 human protein lysine methyltransferases (PKMTs). Interrogation of the functions of individual PKMTs can be facilitated by target-specific PKMT inhibitors. Given the emerging need for such small molecules, we envisioned an approach to identify target-specific methyltransferase inhibitors by screening privileged small-molecule scaffolds against diverse methyltransferases. In this work, we demonstrated the feasibility of such an approach by identifying the inhibitors of SETD2. N-propyl sinefungin (Pr-SNF) was shown to interact preferentially with SETD2 by matching the distinct transition-state features of SETD2's catalytically active conformer. With Pr-SNF as a structure probe, we further revealed the dual roles of SETD2's post-SET loop in regulating substrate access through a distinct topological reconfiguration. Privileged sinefungin scaffolds are expected to have broad use as structure and chemical probes of methyltransferases.
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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) | 0.001 | 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".