Engineering of the <scp> <i>Neurospora</i> </scp> Varkud Satellite Ribozyme for Cleavage of Nonnatural Stem‐Loop Substrates
Bibliographic record
Abstract
The Neurospora VS ribozyme uses a unique substrate recognition mechanism to perform cleavage of a specific phosphodiester bond within the internal loop of its stem-loop I (SLI) substrate. Specific recognition and cleavage of the SLI substrate involves a bipartite tertiary interaction with the ribozyme. This includes a highly-stable kissing-loop interaction (KLI) of the SLI terminal loop with stem-loop V and an intimate association of the SLI cleavage-site internal loop (G638 loop) with the A 756 internal loop to form the active site. In this review, we summarize what has been learned from biochemical, biophysical and structural biology studies in terms of substrate recognition by the VS ribozyme. More specifically, we discuss in detail the topological and structural relation between the VS ribozyme and its substrate, the formation of the KLI and associated helix shift in SLI, the dynamics of the KLI, and the formation of the active site. In addition, we examine the substrate specificity of this ribozyme first by summarizing the sequence diversity of known substrate variants that can be cleaved by the wild-type trans ribozyme and then defining a consensus sequence and secondary structure. To explore the potential of the VS ribozyme for cleavage of a wider range of substrates, recent research efforts have focused on engineering derivatives of the VS ribozyme. The main conclusions from these investigations are presented, in order to highlight fundamental knowledge that has been gained. In particular, the recent RNA engineering work demonstrates the critical role of conformational exchange in RNA recognition and helps define basic principles of RNA engineering, thus providing valuable insights for future development of highly functional designer RNA molecules.
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 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) | 0.000 | 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 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".