Complex dynamics under tension in a high-efficiency frameshift stimulatory structure
Bibliographic record
Abstract
Specific structures in mRNA can stimulate programmed ribosomal frameshifting (PRF). PRF efficiency can vary enormously between different stimulatory structures, but the features that lead to efficient PRF stimulation remain uncertain. To address this question, we studied the structural dynamics of the frameshift signal from West Nile virus (WNV), which stimulates -1 PRF at very high levels and has been proposed to form several different structures, including mutually incompatible pseudoknots and a double hairpin. Using optical tweezers to apply tension to single mRNA molecules, mimicking the tension applied by the ribosome during PRF, we found that the WNV frameshift signal formed an unusually large number of different metastable structures, including all of those previously proposed. From force-extension curve measurements, we mapped 2 mutually exclusive pathways for the folding, each encompassing multiple intermediates. We identified the intermediates in each pathway from length changes and the effects of antisense oligomers blocking formation of specific contacts. Intriguingly, the number of transitions between the different conformers of the WNV frameshift signal was maximal in the range of forces applied by the ribosome during -1 PRF. Furthermore, the occupancy of the pseudoknotted conformations was far too low for static pseudoknots to account for the high levels of -1 PRF. These results support the hypothesis that conformational heterogeneity plays a key role in frameshifting and suggest that transitions between different conformers under tension are linked to efficient PRF stimulation.
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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".