Small-molecule ligands can inhibit −1 programmed ribosomal frameshifting in a broad spectrum of coronaviruses
Bibliographic record
Abstract
Recurrent outbreaks of novel zoonotic coronavirus (CoV) diseases since 2000 have high-lighted the importance of developing therapeutics with broad-spectrum activity against CoVs. Because all CoVs use −1 programmed ribosomal frameshifting (−1 PRF) to control expression of key viral proteins, the frameshift signal in viral mRNA that stimulates −1 PRF provides a promising potential target for such therapeutics. To test the viability of this strategy, we explored a group of 6 small-molecule ligands, evaluating their activity against the frameshift signals from a panel of representative bat CoVs—the most likely source of future zoonoses—as well as SARS-CoV-2 and MERS-CoV. We found that whereas some ligands had notable activity against only a few of the frameshift signals, the serine protease inhibitor nafamostat suppressed −1 PRF significantly in several of them, while having limited to no effect on −1 PRF caused by frameshift signals from other viruses used as negative controls. These results suggest it is possible to find small-molecule ligands that inhibit −1 PRF specifically in a broad spectrum of CoVs, establishing the frameshift signal as a viable target for developing pan-coronaviral therapeutics.
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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 | 0.000 | 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 | 0.000 |
| 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".