Structural impact of synonymous mutations in six SARS-CoV-2 Variants of Concern
Bibliographic record
Abstract
SARS-CoV-2 continues to spread and infect people worldwide. While most effort into characterizing variants of this virus have focused on non-synonymous changes, accumulation of synonymous mutations in different viral variants has also occurred. Here we characterize six Variants of Concern in terms of their mutational content, and make predictions regarding the impact of those mutations on potential genomic RNA secondary structure and stability. Our hypothesis is that if non-protein changing, yet RNA structure-changing mutations impact viral fitness by imposing deleterious change to predicted RNA structure, we would expect to those mutations to be less abundant, while if those synonymous mutations do not impact viral fitness through influence of RNA structure, we would see them more frequently than non-synonymous mutations. We find that synonymous mutations typically have no or modest impact to RNA secondary structure. As synonymous mutations are free from the selective pressure imposed on protein-altering mutations, the impact of synonymous mutations is largely limited to RNA secondary structure considerations. The absence of major, structure-altering synonymous mutations emphasize the importance of RNA structure, including within coding regions, to viral fitness. Synonymous mutations should be included in the characterization of emerging RNA viruses as these mutations may confer effects to viral fitness via RNA secondary structural modifications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".