Discovery and Characterization of a Eukaryotic Initiation Factor 4A-3-Selective Inhibitor That Suppresses Nonsense-Mediated mRNA Decay
Bibliographic record
Abstract
Eukaryotic initiation factor 4A-3 (eIF4A3) is an Asp-Glu-Ala-Asp (DEAD) box-family adenosine triphosphate (ATP)-dependent RNA helicase. Subtypes eIF4A1 and eIF4A2 are required for translation initiation, but eIF4A3 participates in the exon junction complex (EJC) and functions in RNA metabolism including nonsense-mediated RNA decay (NMD). No small molecules for NMD inhibition via selective inhibition of eIF4A3 have been discovered. Here, we identified allosteric eIF4A3 inhibitors from a high-throughput screening campaign. Chemical optimization of the lead compounds based on ATPase activity yielded compound 2, which exhibited noncompetitive inhibition with ATP or RNA and high selectivity for eIF4A3 over other helicases. The optimized compounds suppressed the helicase activity of eIF4A3 in an ATPase-dependent manner. Hydrogen/deuterium exchange mass spectrometry demonstrated that the deuterium-incorporation pattern of compound 2 overlapped with that of an allosteric pan-eIF4A inhibitor, hippuristanol, suggesting that compound 2 binds to an allosteric region on eIF4A3. We examined NMD activity using a luciferase-based cellular reporter system and a quantitative real-time polymerase chain-reaction-based cellular system to monitor levels of endogenous NMD substrates. NMD suppression by the compounds correlated positively with their ATPase-inhibitory activity. In conclusion, we developed a novel eIF4A3 inhibitor that targets the EJC. The optimized chemical probes represent useful tools for understanding the functions of eIF4A3 in RNA homeostasis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".