A Multipronged Screening Approach Targeting Inhibition of ETV6 PNT Domain Polymerization
Bibliographic record
Abstract
ETV6 is an ETS family transcriptional repressor for which head-to-tail polymerization of its PNT domain facilitates cooperative binding to DNA by its ETS domain. Chromosomal translocations frequently fuse the ETV6 PNT domain to one of several protein tyrosine kinases. The resulting chimeric oncoproteins undergo ligand-independent self-association, autophosphorylation, and aberrant stimulation of downstream signaling pathways, leading to a variety of cancers. Currently, no small-molecule inhibitors of ETV6 PNT domain polymerization are known and no assays targeting PNT domain polymerization have been described. In this study, we developed complementary experimental and computational approaches for identifying such inhibitory compounds. One mammalian cellular approach utilized a mutant PNT domain heterodimer system covalently attached to split Gaussia luciferase fragments. In this protein–fragment complementation assay, inhibition of PNT domain heterodimerization reduces luminescence. A yeast assay took advantage of activation of the reporter HIS3 gene upon heterodimerization of mutant PNT domains fused to DNA-binding and transactivation domains. In this two-hybrid screen, inhibition of PNT domain heterodimerization prevents cell growth in medium lacking histidine. The Bristol University Docking Engine (BUDE) was used to identify virtual ligands from the ZINC8 library predicted to bind the PNT domain polymerization interfaces. More than 75 hits from these three assays were tested by nuclear magnetic resonance spectroscopy for binding to the purified ETV6 PNT domain. Although none were found to bind, the lessons learned from this study may facilitate future approaches for developing therapeutics that act against ETV6 oncoproteins by disrupting PNT domain polymerization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".