Rational engineering of the<i>Neurospora</i>VS ribozyme to allow substrate recognition via different kissing-loop interactions
Bibliographic record
Abstract
The Neurospora VS ribozyme is a catalytic RNA that has the unique ability to specifically recognize and cleave a stem-loop substrate through formation of a highly stable kissing-loop interaction (KLI). In order to explore the engineering potential of the VS ribozyme to cleave alternate substrates, we substituted the wild-type KLI by other known KLIs using an innovative engineering method that combines rational and combinatorial approaches. A bioinformatic search of the protein data bank was initially performed to identify KLIs that are structurally similar to the one found in the VS ribozyme. Next, substrate/ribozyme (S/R) pairs that incorporate these alternative KLIs were kinetically and structurally characterized. Interestingly, several of the resulting S/R pairs allowed substrate cleavage with substantial catalytic efficiency, although with reduced activity compared to the reference S/R pair. Overall, this study describes an innovative approach for RNA engineering and establishes that the KLI of the trans VS ribozyme can be adapted to cleave other folded RNA substrates.
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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) | 0.000 | 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".