Finding Inspiration in the Protein Data Bank to Chemically Antagonize Readers of the Histone Code
Bibliographic record
Abstract
Members of the Royal family of proteins are readers of the histone code that contain aromatic cages capable of recognizing specific sequences and lysine methylation states on histone tails. These binding modules play a key role in epigenetic signalling, and are part of a larger group of epigenetic targets that are becoming increasingly attractive for drug discovery. In the current study, pharmacophore representations of the aromatic cages forming the methyl-lysine (Me-Lys) recognition site were used to search the Protein Data Bank (PDB) for ligand binding pockets possessing similar chemical and geometrical features in unrelated proteins. The small molecules bound to these sites were then extracted from the PDB, and clustered based on fragments binding to the aromatic cages. The compounds collected are numerous and structurally diverse, but point to a limited set of preferred chemotypes; these include quaternary ammonium, sulfonium, and primary, secondary and tertiary amine moieties, as well as aromatic, aliphatic or orthogonal rings, and bicyclic systems. The chemical tool-kit identified can be used to design antagonists of the Royal family and related proteins.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".