Shapify: Pathways to SARS-CoV-2 Frameshifting Pseudoknot
Bibliographic record
Abstract
Abstract Background: Multiple viruses including HIV, MERS-CoV (coronavirus responsible for Middle East Respiratory Syndrome, MERS), SARS-CoV (coronavirus responsible for SARS) and SARS-CoV-2 (coronavirus responsible for COVID-19) use a mechanism known as -1 programmed ribosomal frameshifting (-1 PRF) to successfully replicate. SARS-CoV-2 possesses 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 to impair viral replication can improve patients' prognoses.Crucial to developing these successful therapies is modeling the structure of the SARS-CoV-2 -1 PRF pseudoknot.Our goal is to expand knowledge of possible pseudoknot conformations. Results: Following a structural alignment approach, we identify similarities in -1 PRF pseudoknots of SARS-CoV-2, SARS-CoV, and MERS-CoV. We introduce Shapify, a novel algorithm that given an RNA sequence incorporates structural reactivity (SHAPE) data and partial structure information to output an RNA secondary structure prediction within a biologically sound hierarchical folding approach. Shapify helps us to better understand non-native SARS-CoV-2 -1 PRF pseudoknot conformations that are relevant to structure function and may correlate with -1 PRF efficiency. We provide in-depth analysis by investigating the structural landscape for the SARS-CoV-2 -1 PRF pseudoknot, including reference and mutated sequences. To better understand the impact of mutations, we provide insight on SARS-CoV-2 -1 PRF pseudoknot sequence mutations and their effect on the resulting structure. Conclusion: We identify the consensus structure for SARS-CoV, SARS-CoV-2, and MERS-CoV -1 PRF pseudoknots; this similarity in functional RNA structures aids treatment preparation for existing and emergent viruses. Shapify predictions are guided both by SHAPE data and partial structure information. Applied to the SARS-CoV-2 -1 PRF pseudoknot, Shapify unveiled previously unknown pathways from initial stems to pseudoknotted secondary structures. Where SHAPE data is unavailable we provide predictions for noteworthy SARS-CoV-2 -1 PRF mutated pseudoknot sequences. By contextualizing our work with available experimental data, our structure predictions motivate future RNA structure-function research and can aid 3-D modeling of pseudoknots.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".