In vitro selection of small RNA-cleaving deoxyribozymes that cleave pyrimidine–pyrimidine junctions
Bibliographic record
Abstract
Herein, we sought new or improved endoribonucleases based on catalytic DNA molecules known as deoxyribozymes. The current repertoire of RNA-cleaving deoxyribozymes can cleave nearly all of the 16 possible dinucleotide junctions with rates of at least 0.1/min, with the exception of pyrimidine-pyrimidine (pyr-pyr) junctions, which are cleaved 1-3 orders of magnitude slower. We conducted four separate in vitro selection experiments to target each pyr-pyr dinucleotide combination (i.e. CC, UC, CT and UT) within a chimeric RNA/DNA substrate. We used a library of DNA molecules containing only 20 random-sequence nucleotides, so that all possible sequence permutations could be sampled in each experiment. From a total of 245 clones, we identified 22 different sequence families, of which 21 represented novel deoxyribozyme motifs. The fastest deoxyribozymes exhibited k(obs) values (single-turnover, intermolecular format) of 0.12/min, 0.04/min, 0.13/min and 0.15/min against CC, UC, CT and UT junctions, respectively. These values represent a 6- to 8-fold improvement for CC and UC junctions, and a 1000- to 1600-fold improvement for CT and UT junctions, compared to the best rates reported previously under identical reaction conditions. The same deoxyribozymes exhibited approximately 1000-fold lower activity against all RNA substrates, but could potentially be improved through further in vitro evolution and engineering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".