Structural dynamics of the SARS-CoV-2 frameshift-stimulatory pseudoknot reveal topologically distinct conformers
Bibliographic record
Abstract
The RNA pseudoknot that stimulates −1 programmed ribosomal frameshifting in SARS coronavirus-2 (SARS-CoV-2) is a possible drug target. To understand how this 3-stemmed pseudoknot responds to the mechanical tension applied by ribosomes during translation, which is thought to play a key role during frame-shifting, we probed its structural dynamics under tension using optical tweezers. Unfolding curves revealed that the frameshift signal formed multiple different structures: at least two distinct pseudoknotted conformers with different unfolding forces and energy barriers, as well as alternative stem-loop structures. Refolding curves showed that stem 1 formed first in the pseudoknotted conformers, followed by stem 3 and then stem 2. By extending the handle holding the RNA to occlude the 5′ end of stem 1, the proportion of the different pseudoknot conformers could be altered systematically, consistent with structures observed in cryo-EM images and computational simulations that had distinct topologies: the 5′ end of the RNA threaded through the 3-helix junction to form a ring-knot, or unthreaded as in more standard H-type pseudoknots. These results resolve the folding mechanism of the frameshift signal in SARS-CoV-2 and highlight the dynamic conformational heterogeneity of this RNA, with important implications for structure-based drug-discovery efforts.
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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) | 0.001 | 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".