Structural and functional conservation of the programmed −1 ribosomal frameshift signal of SARS coronavirus 2 (SARS-CoV-2)
Bibliographic record
Abstract
Approximately 17 years after the severe acute respiratory syndrome coronavirus (SARS-CoV) epidemic, the world is currently facing the COVID-19 pandemic caused by SARS corona virus 2 (SARS-CoV-2). According to the most optimistic projections, it will take more than a year to develop a vaccine, so the best short-term strategy may lie in identifying virus-specific targets for small molecule-based interventions. All coronaviruses utilize a molecular mechanism called programmed -1 ribosomal frameshift (-1 PRF) to control the relative expression of their proteins. Previous analyses of SARS-CoV have revealed that it employs a structurally unique three-stemmed mRNA pseudoknot that stimulates high -1 PRF rates and that it also harbors a -1 PRF attenuation element. Altering -1 PRF activity impairs virus replication, suggesting that this activity may be therapeutically targeted. Here, we comparatively analyzed the SARS-CoV and SARS-CoV-2 frameshift signals. Structural and functional analyses revealed that both elements promote similar -1 PRF rates and that silent coding mutations in the slippery sites and in all three stems of the pseudoknot strongly ablate -1 PRF activity. We noted that the upstream attenuator hairpin activity is also functionally retained in both viruses, despite differences in the primary sequence in this region. Small-angle X-ray scattering analyses indicated that the pseudoknots in SARS-CoV and SARS-CoV-2 have the same conformation. Finally, a small molecule previously shown to bind the SARS-CoV pseudoknot and inhibit -1 PRF was similarly effective against -1 PRF in SARS-CoV-2, suggesting that such frameshift inhibitors may be promising lead compounds to combat the current COVID-19 pandemic.
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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) | 0.000 | 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".