Nucleotides -1 to -4 of Hepatitis Delta Ribozyme Substrate Increase the Specificity of Ribozyme Cleavage
Bibliographic record
Abstract
In the past, the use of delta ribozyme as a therapeutic tool was limited because substrate specificity was thought to be determined by only 8 nucleotides. Recently, we have accumulated evidence suggesting that the substrate sequence upstream of the cleavage site, which is not involved in the binding with the delta ribozyme, appears to be essential in the selection of an appropriate cleavage site. To understand the role of this region in efficient cleavage, we synthesized a collection of small substrates that possessed single and multiple mutations in positions -1 to -4 and determined the kinetic parameters of their cleavage using a model antigenomic delta ribozyme. Some substrates were found to be uncleavage, whereas others showed >60-fold difference in relative specificity between the least and most efficiently cleaved substrates. The base at each position from -1 to -4 contributes differently to the ability of a substrate to be cleaved. An optimal sequence for positions -1 to -4 was determined to be -1HRHY(-4) (H = U, C, or A). These results shed light on new features that contribute to the substrate requirement of delta ribozyme cleavage and should increase interest in the use of this unique ribozyme.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".