Insights into <scp>RNA</scp> structure and dynamics from recent <scp>NMR</scp> and X‐ray studies of the <i>Neurospora</i> Varkud satellite ribozyme
Bibliographic record
Abstract
Despite the large number of noncoding RNAs and their importance in several biological processes, our understanding of RNA structure and dynamics at atomic resolution is still very limited. Like many other RNAs, the Neurospora Varkud satellite (VS) ribozyme performs its functions through dynamic exchange of multiple conformational states. More specifically, the VS ribozyme recognizes and cleaves its stem-loop substrate via a mechanism that involves several structural transitions within its stem-loop substrate. The recent publications of high-resolution structures of the VS ribozyme, obtained by NMR spectroscopy and X-ray crystallography, offer an opportunity to integrate the data and closely examine the structural and dynamic properties of this model RNA system. Notably, these investigations provide a valuable example of the divide-and-conquer strategy for structural and dynamic characterization of a large RNA, based on NMR structures of several individual subdomains. The success of this divide-and-conquer approach reflects the modularity of RNA architecture and the great care taken in identifying the independently-folding modules. Together with previous biochemical and biophysical characterizations, the recent NMR and X-ray studies provide a coherent picture into how the VS ribozyme recognizes its stem-loop substrate. Such in-depth characterization of this RNA enzyme will serve as a model for future structural and engineering studies of dynamic RNAs and may be particularly useful in planning divide-and-conquer investigations. WIREs RNA 2017, 8:e1421. doi: 10.1002/wrna.1421 For further resources related to this article, please visit the WIREs website.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".