SARS-CoV-2 Ribosomal Frameshifting Pseudoknot: Detection of Inter-viral Structural Similarity
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the COVID-19 pandemic. After over 160 million cases and 3.3 million deaths worldwide as of May 2021, it is still unclear when this crisis will end. Thus, there is an urgent need for treatments that improve the prognosis of seriously affected patients. Multiple viruses including HIV, MERS-CoV (coronavirus responsible for Middle East Respiratory Syndrome, MERS), SARS-CoV (coronavirus responsible for SARS) and SARS-CoV-2 use a mechanism known as -1 programmed ribosomal frameshifting (-1 PRF) to successfully replicate. SARS-CoV and SARS-CoV-2 possess a unique RNA pseudoknotted structure that stimulates -1 PRF. Recent experiments identified small molecules as antiviral agents that can bind to the pseudoknot and disrupt its stimulation of -1 PRF. Targeting -1 PRF in SARS-CoV-2 can be an excellent strategy to impair viral replication and improve patients’ prognoses. Crucial to developing these successful therapies is modeling the structure of the SARS-CoV-2 -1 PRF pseudoknot. Following a structural alignment approach, we identify similarities in -1 PRF pseudoknots of SARS-CoV-2, SARS-CoV, and MERS-CoV. In addition, we provide a better understanding of the SARS-CoV-2 -1 PRF pseudoknot by investigating the structural landscape using a hierarchical folding approach. We provide in-depth analysis on alternative structure prediction methods based on SARS-CoV-2 structural reactivity (SHAPE) data to contextualize and motivate future RNA structure-function research. Since understanding the impact of mutations is vital to long-term success of treatments based on predicted RNA functional structures, we provide insight on SARS-CoV-2 -1 PRF pseudoknot sequence mutations and their effect on the resulting structure.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".