[General Articles] Ribozyme-Based Gene-Inactivation Systems Require A Fine Comprehension of their Substrate Specificities; the Case of Delta Ribozyme
Bibliographic record
Abstract
The ability of ribozymes (i.e. RNA enzymes) to specifically recognize and subsequently catalyze the cleavage of an RNA substrate makes them attractive for the development of therapeutic tools for the inactivation of both viral RNAs and mRNAs associated with various diseases. Several applicable ribozyme models have been tested both in vitro and in a cellular environment, and have shown significant promise. However, several hurdles remain to be surpassed before we generate a useful gene-inactivation system based on a ribozyme. Among the most important requirements for further progress are a better understanding of the features that contribute to defining the substrate specificity for cleavage by a ribozyme, and the identification of the potential cleavage sites in a given target RNA. The goal of this review is to illustrate the importance of both of these factors at the RNA level in the development of any type of ribozyme based gene-therapy. This is achieved by reviewing the recent progress in both the structure-function relationships and the development of a gene-inactivation system of a model ribozyme, specifically delta ribozyme.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.034 |
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".