Anti-frameshifting ligand active against SARS coronavirus-2 is resistant to natural mutations of the frameshift-stimulatory pseudoknot
Bibliographic record
Abstract
The coronavirus SARS-CoV-2 causing the COVID-19 pandemic uses −1 programmed ribosomal frameshifting (−1 PRF) to control the expression levels of key viral proteins. Because modulating −1 PRF can attenuate viral propagation, ligands binding to the viral RNA pseudoknot that stimulates −1 PRF may prove useful as therapeutics. Mutations in the pseudoknot have been observed over the course of the pandemic, but how they affect −1 PRF and the activity of inhibitors is unknown. Cataloguing natural mutations in all parts of the SARS-CoV-2 pseudoknot, we studied a panel of 6 mutations in key structural regions. Most mutations left the −1 PRF efficiency unchanged, even when base-pairing was disrupted, but one led to a remarkable three-fold decrease, suggesting that SARS-CoV-2 propagation may be less sensitive to modulation of −1 PRF efficiency than some other viruses. Examining the effects of one of the few small-molecule ligands known to suppress −1 PRF significantly in SARS-CoV, we found that it did so by similar amounts in all SARS-CoV-2 mutants tested, regardless of the basal −1 PRF efficiency, indicating that the activity of anti-frameshifting ligands can be resistant to natural pseudoknot mutations. These results have important implications for therapeutic strategies targeting SARS-CoV-2 through modulation of −1 PRF.
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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".