Discovery of hit compounds for methyl-lysine reader proteins from a target class DNA-encoded library
Bibliographic record
Abstract
Methyl-lysine (Kme) reader domains are prevalent in chromatin regulatory proteins which bind post-translational modification sites to recruit repressive and activating factors; therefore, these proteins play crucial roles in cellular signaling and epigenetic regulation. Proteins that contain Kme domains are implicated in various diseases, including cancer, making them attractive therapeutic targets for drug and chemical probe discovery. Herein, we report on expanding the utility of a previously reported, Kme-focused DNA-encoded library (DEL), UNCDEL003, as a screening tool for hit discovery through the specific targeting of Kme reader proteins. As an efficient method for library generation, focused DELs are designed based on structural and functional features of a specific class of proteins with the intent of novel hit discovery. To broadly assess the applicability of our library, UNCDEL003 was screened against five diverse Kme reader protein domains (53BP1 TTD, KDM7B JmjC-PHD, CDYL2 CD, CBX2 CD, and LEDGF PWWP) with varying structures and functions. From these screening efforts, we identified hit compounds which contain unique chemical scaffolds distinct from previously reported ligands. The selected hit compounds were synthesized off-DNA and confirmed using primary and secondary assays and assessed for binding selectivity. Hit compounds from these efforts can serve as starting points for additional development and optimization into chemical probes to aid in further understanding the functionality of these therapeutically relevant 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".