Unbiased in vitro selection reveals the unique character of the self-cleaving antigenomic HDV RNA sequence
Bibliographic record
Abstract
In order to revisit the architecture of the catalytic center of the antigenomic hepatitis delta virus (HDV) ribozyme we developed an unbiased in vitro selection procedure that efficiently selected novel variants from a relatively small set of sequences. Using this procedure we examined all possible variants from a pool of HDV ribozymes that had been randomized at 25 positions (4(25)). The isolated set of sequences shows more variability than do the natural variants. Nucleotide variations were found at all randomized positions, even at positions when the general belief was that the specific base was absolutely required for catalytic activity. Covariation analysis supports the presence of several base pairs, although it failed to propose any new tertiary contacts. HDV ribozyme appears to possess a greater number of constraints, in terms of sequences capable of supporting the catalysed cleavage, than do other catalytic RNAs. This supports the idea that the appearance of this catalytic RNA structure has a low probability (i.e. is a rare event), which may explain why to date it has been found in nature only in the HDV. These contrasts with the hammerhead self-cleaving motif that is proposed to have multiple origins, and that is widespread among different organisms. Thus, just because a self-cleaving RNA motif is small does not imply that it occurs easily.
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 | 0.000 |
| 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".