Programmed −1 frameshifting efficiency correlates with RNA pseudoknot conformational plasticity, not resistance to mechanical unfolding
Bibliographic record
Abstract
Programmed -1 frameshifting, whereby the reading frame of a ribosome on messenger RNA is shifted in order to generate an alternate gene product, is often triggered by a pseudoknot structure in the mRNA in combination with an upstream slippery sequence. The efficiency of frameshifting varies widely for different sites, but the factors that determine frameshifting efficiency are not yet fully understood. Previous work has suggested that frameshifting efficiency is related to the resistance of the pseudoknot against mechanical unfolding. We tested this hypothesis by studying the mechanical properties of a panel of pseudoknots with frameshifting efficiencies ranging from 2% to 30%: four pseudoknots from retroviruses, two from luteoviruses, one from a coronavirus, and a nonframeshifting bacteriophage pseudoknot. Using optical tweezers to apply tension across the RNA, we measured the distribution of forces required to unfold each pseudoknot. We found that neither the average unfolding force, nor the unfolding kinetics, nor the parameters describing the energy landscape for mechanical unfolding of the pseudoknot (energy barrier height and distance to the transition state) could be correlated to frameshifting efficiency. These results indicate that the resistance of pseudoknots to mechanical unfolding is not a primary determinant of frameshifting efficiency. However, increased frameshifting efficiency was correlated with an increased tendency to form alternate, incompletely folded structures, suggesting a more complex picture of the role of the pseudoknot involving the conformational dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".