Identification of a selective DDX3X inhibitor with newly developed quantitative high-throughput RNA helicase assays
Bibliographic record
Abstract
The DEAD-box family of RNA helicases plays essential roles in both transcriptional and translational mRNA degradation; they unwind short double-stranded RNA by breaking the RNA-RNA interactions. Two DEAD-box RNA helicases, eukaryotic translation initiation factor 4A3 (eIF4A3) and DEAD-box helicase 3 (DDX3X), show high homology in the ATP-binding region and are considered key molecules for cancer progression. Several small molecules that target eIF4A3 and DDX3X have been reported to inhibit cancer cell growth; however, more potent compounds are required for cancer therapeutics, and there is a critical need for high-throughput assays to screen for RNA helicase inhibitors. In this study, we developed novel fluorescence resonance energy transfer-based high-throughput RNA helicase assays for eIF4A3 and DDX3X. Using these assays, we identified several eIF4A3 allosteric inhibitors whose inhibitory effect on eIF4A3 ATPase showed a strong correlation with inhibitory effect on helicase activity. From 102 compounds that exhibited eIF4A3 ATPase inhibition, we identified a selective DDX3X inhibitor, C1, which showed stronger inhibition of DDX3X than of eIF4A3. Small-molecule helicase inhibitors can be valuable for clarifying the molecular machinery of DEAD-box RNA helicases. The high-throughput quantitative assays established here should facilitate the evaluation of the helicase inhibitory activity of compounds.
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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 | 0.001 |
| 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) | 0.001 | 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".