Toward the Combinatorial Selection of Chemically Modified DNAzyme RNase A Mimics Active Against all-RNA Substrates
Bibliographic record
Abstract
The convenient use of SELEX and related combinatorial methods of in vitro selection provides a formidable gateway for the generation of DNA enzymes, especially in the context of improving their potential as gene therapeutic agents. Here, we report on the selection of DNAzyme 12-91, a modified nucleic acid catalyst adorned with imidazole, ammonium, and guanidinium groups that provide for efficient M(2+)-independent cleavage of an all-RNA target sequence (kobs = 0.06 min(-1)). While Dz12-91 was selected for intramolecular cleavage of an all-RNA target, it surprisingly cleaves a target containing a lone ribocytosine unit with even greater efficiency (kobs = 0.27 min(-1)) than Dz9-86 (kobs = 0.13 min(-1)). The sequence composition of Dz12-91 bears a marked resemblance to that of Dz9-86 (kobs = 0.0014 min(-1) with an all-RNA substrate) that was selected from the same library to cleave a target containing a single ribonucleotide. However, small alterations in the sequence composition have a profound impact on the substrate preference and catalytic properties. Indeed, Dz12-91 displays the highest known rate enhancement for the M(2+)-independent cleavage of all-RNA targets. Hence, Dz12-91 represents a step toward the generation of potentially therapeutically active DNAzymes and further underscores the usefulness of modified triphosphates in selection experiments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".