Highly selective inhibition of histone demethylases by de novo macrocyclic peptides
Bibliographic record
Abstract
The JmjC histone demethylases (KDMs) are linked to tumour cell proliferation and are current cancer targets; however, very few highly selective inhibitors for these are available. Here we report cyclic peptide inhibitors of the KDM4A-C with selectivity over other KDMs/2OG oxygenases, including closely related KDM4D/E isoforms. Crystal structures and biochemical analyses of one of the inhibitors (CP2) with KDM4A reveals that CP2 binds differently to, but competes with, histone substrates in the active site. Substitution of the active site binding arginine of CP2 to N-ɛ-trimethyl-lysine or methylated arginine results in cyclic peptide substrates, indicating that KDM4s may act on non-histone substrates. Targeted modifications to CP2 based on crystallographic and mass spectrometry analyses results in variants with greater proteolytic robustness. Peptide dosing in cells manifests KDM4A target stabilization. Although further development is required to optimize cellular activity, the results reveal the feasibility of highly selective non-metal chelating, substrate-competitive inhibitors of the JmjC KDMs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".