Characterization of non-8–17 sequences uncovers structurally diverse RNA-cleaving deoxyribozymes
Why this work is in the frame
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Bibliographic record
Abstract
RNA-cleaving deoxyribozymes (DNAzymes) can be isolated from random-sequence DNA pools via the process of in vitro selection. However, small and simple catalytic motifs, such as the 8-17 DNAzyme, are commonly observed in sequence space, presenting a challenge in discovering large and complex DNAzymes. In an effort to investigate underrepresented molecular species derived from in vitro selection, in this study we sought to characterize non-8-17 sequences obtained from a previous in vitro selection experiment wherein the 8-17 deoxyribozyme was the dominant motif. We examined 9 sequence families from 21 motifs by characterizing their structural and functional features. We discovered 9 novel deoxyribozyme classes with large catalytic domains (>40 nucleotides) utilizing three-way or four-way junction structural frameworks. Kinetic studies revealed that these deoxyribozymes exhibit moderate to excellent catalytic rates (k(obs) from 0.003 to 1 min(-1)), compared to other known RNA-cleaving DNAzymes. Although chemical probing experiments, site-directed mutational analyses, and metal cofactor dependency tests suggest unique catalytic cores for each deoxyribozyme, common dinucleotide junction selectivity was observed between DNAzymes with similar secondary structural features. Together, our findings indicate that larger, structurally more complex, and diverse catalytic motifs are able to survive the process of in vitro selection despite a sequence space dominated by smaller and structurally simpler catalysts.
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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 | 0.000 | 0.000 |
| 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 it