Characterization of long RNA-cleaving deoxyribozymes with short catalytic cores: the effect of excess sequence elements on the outcome of in vitro selection
Bibliographic record
Abstract
We previously conducted an in vitro selection experiment for RNA-cleaving deoxyribozymes, using a combinatorial DNA library containing 80 random nucleotides. Ultimately, 110 different sequence classes were isolated, but the vast majority contained a short14-15 nt catalytic DNA motif commonly known as 8-17. Herein, we report extensive truncation experiments conducted on multiple sequence classes to confirm the suspected catalytic role played by 8-17 and to determine the effect of excess sequence elements on the activity of this motif and the outcome of selection. Although we observed beneficial, detrimental and neutral consequences for activity, the magnitude of the effect rarely exceeded 2-fold. These deoxyribozymes appear to have survived increasing selection pressure despite the presence of additional sequence elements, rather than because of them. A new deoxyribozyme with comparable activity, called G15-30, was approximately 2.5-fold larger and experienced a approximately 4-fold greater inhibitory effect from excess sequence elements than the average 8-17 motif. Our results suggest that 8-17 may be less susceptible to the potential inhibitory effects of excess arbitrary sequence than larger motifs, which represents a previously unappreciated selective advantage that may contribute to its widespread recurrence.
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 | 0.001 | 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".