Identification of a Broad-Spectrum Viral Inhibitor Targeting a Novel Allosteric Site in the RNA-Dependent RNA Polymerases of Dengue Virus and Norovirus
Bibliographic record
Abstract
All RNA viruses encode the RNA-dependent RNA polymerase (RdRp) which replicates and transcribes viral RNA. This essential viral enzyme does not exist in mammalian cells, thus presents a main target for the development of antiviral drugs with potential pan-antiviral activity. In this study, we take advantage of the structurally equivalent site in the dengue virus (DENV) RdRp, the N-pocket, and in the human norovirus (hNV) RdRp, the B-site, and performed a parallel structure-based virtual screening to discover compounds that can inhibit the RdRps of both hNV and DENV. We successfully identified a small molecule called Entrectinib (RAI-13) as a potent inhibitor of both hNV and DENV infection. Specifically, RAI-13 binds directly to hNV and DENV RdRps, effectively inhibits the polymerase activity in the in vitro biochemical assays, and exhibits does-responsive inhibition of murine norovirus (MNV) and DENV2 infection with IC50 values of 2.01 μM and 2.43 μM, respectively. Most promisingly, RAI-13 inhibits hepatitis C virus (HCV) infection by 95% at the 2 μM concentration. We have therefore discovered a small molecule compound that targets an allosteric site that is shared by different viral RdRps and strongly inhibits multiple pathogenic RNA viruses, thus holding the potential of being developed into a broad-spectrum antiviral drug
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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) | 0.001 | 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".