Anti-frameshifting Ligand Reduces the Conformational Plasticity of the SARS Virus Pseudoknot
Bibliographic record
Abstract
Programmed -1 ribosomal frameshifting (-1 PRF) stimulated by mRNA pseudoknots regulates gene expression in many viruses, making pseudoknots potential targets for anti-viral drugs. The mechanism by which pseudoknots trigger -1 PRF, however, remains controversial, with several competing models. Recent work showed that high -1 PRF efficiency was linked to high pseudoknot conformational plasticity via the formation of alternate conformers. We tested whether pseudoknots bound with an anti-frameshifting ligand exhibited a similar correlation between conformational plasticity and -1 PRF efficiency by measuring the effects of a ligand that was found to inhibit -1 PRF in the SARS coronavirus on the conformational dynamics of the SARS pseudoknot. Using single-molecule force spectroscopy to unfold pseudoknots mechanically, we found that the ligand binding effectively abolished the formation of alternate conformers. This result extends the connection between -1 PRF and conformational dynamics and, moreover, suggests that targeting the conformational dynamics of pseudoknots may be an effective strategy for anti-viral drug design.
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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".