Screening for Inhibitors of Low-Affinity Epigenetic Peptide-Protein Interactions: An AlphaScreen™-Based Assay for Antagonists of Methyl-Lysine Binding Proteins
Bibliographic record
Abstract
The histone code comprises many posttranslational modifications that occur mainly in histone tail peptides. The identity and location of these marks are read by a variety of histone-binding proteins that are emerging as important regulators of cellular differentiation and development and are increasingly being implicated in numerous disease states. The authors describe the development of the first high-throughput screening assay for the discovery of inhibitors of methyl-lysine binding proteins that will be used to initiate a full-scale discovery effort for this broad target class. They focus on the development of an AlphaScreen-based assay for malignant brain tumor (MBT) domain-containing proteins, which bind to the lower methylation states of lysine residues present in histone tail peptides. This assay takes advantage of the avidity of the AlphaScreen beads to clear the hurdle to assay development presented by the low micromolar binding constants of the histone binding proteins for their cognate peptides. The assay is applicable to other families of methyl-lysine binding proteins, and it has the potential to be used in screening efforts toward the discovery of novel small molecules with utility as research tools for cellular reprogramming and ultimately drug discovery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".