Structural and functional conservation of the programmed −1 ribosomal frameshift signal of SARS-CoV-2
Bibliographic record
Abstract
17 years after the SARS-CoV epidemic, the world is facing the COVID-19 pandemic. COVID-19 is caused by a coronavirus named SARS-CoV-2. Given the most optimistic projections estimating that it will take over a year to develop a vaccine, the best short-term strategy may lie in identifying virus-specific targets for small molecule interventions. All coronaviruses utilize a molecular mechanism called -1 PRF to control the relative expression of their proteins. Prior analyses of SARS-CoV revealed that it employs a structurally unique three-stemmed mRNA pseudoknot to stimulate high rates of -1 PRF, and that it also harbors a -1 PRF attenuation element. Altering -1 PRF activity negatively impacts virus replication, suggesting that this molecular mechanism may be therapeutically targeted. Here we present a comparative analysis of the original SARS-CoV and SARS-CoV-2 frameshift signals. Structural and functional analyses revealed that both elements promote similar rates of -1 PRF and that silent coding mutations in the slippery sites and in all three stems of the pseudoknot strongly ablated -1 PRF activity. The upstream attenuator hairpin activity has also been functionally retained. Small-angle x-ray scattering indicated that the pseudoknots in SARS-CoV and SARS-CoV-2 had 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 provide promising lead compounds to counter the current pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".