Selection of the Most Potent Specific On/Off Adaptor-Hepatitis Delta Virus Ribozymes for Use in Gene Targeting
Bibliographic record
Abstract
The Hepatitis Delta Virus (HDV) ribozyme, which is well adapted to the environment of the human cell, is an excellent candidate for the future development of gene-inactivation systems. On top of this, a new generation of HDV ribozymes now exists that benefits from the addition of a specific on/off adaptor (specifically the SOFA-HDV ribozymes) which greatly increases both the ribozyme's specificity and its cleavage activity. Unlike RNAi and hammerhead ribozymes, the designing of SOFA-HDV ribozymes to cleave, in trans, given RNA species has never been the object of a systematic optimization study, even with their recent use for the gene knockdown of various targets. This report aims at both improving and clarifying the design process of SOFA-HDV ribozymes. Both the ribozyme and the targeted RNA substrate were analyzed in order to provide new criteria that are useful in the selection of the most potent SOFA-HDV ribozymes. The crucial features present in both the ribozyme's biosensor and blocker, as well as at the target site, were identified and characterized. Simple rules were derived and tested using hepatitis C virus NS5B RNA as a model target. Overall, this method should promote the use of the SOFA-HDV ribozymes in a plethora of applications in both functional genomics and gene therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".