Characterization of non-8–17 sequences uncovers structurally diverse RNA-cleaving deoxyribozymes
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.
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".