Identifying the interactions conferring functional mechanical rigidity on RNase-resistant RNA from Zika virus
Bibliographic record
Abstract
Some viruses counter host-cell efforts to digest invading viral RNA by using special structures resistant to host RNases, known as exoribonuclease-resistant RNAs (xrRNAs). xrRNAs typically form an unusual fold with the 5'-end threaded through a ring consisting of a multihelix junction closed by a pseudoknot. By using single-molecule force spectroscopy (SMFS), we previously showed that a Zika virus xrRNA is extremely rigid mechanically, withstanding very high forces, and that this mechanical resistance-not simply the knot-like fold topology-is essential for RNase resistance. Here, we have determined which interactions are most important for generating mechanical rigidity in the Zika virus xrRNA, by systematically mutating tertiary contacts. We found that removing any of the tertiary contacts involving the threaded 5' end was sufficient to abrogate mechanical resistance. In contrast, breaking a single pseudoknot base pair was not sufficient to do so: Two broken pairs were needed. This hierarchy of interaction importance for mechanical rigidity was supported by simulations mapping how mechanical tension was distributed within the xrRNA. For all mutants, RNase resistance varied in lock-step with mechanical resistance, confirming the primary role of mechanical rigidity in xrRNA function. This work reveals which interactions are most important for Zika xrRNA function, with implications for targeting the xrRNA therapeutically.
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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".