A modified X10-23 DNAzyme that can better access large, structured RNA targets
Bibliographic record
Abstract
The 10-23 DNA enzyme is one of the most efficient RNA-cleaving enzymes reported, possessing substrate recognition arms that can be designed to target virtually any AU diribonucleotide junction. However, 10-23 often shows reduced activity for large, structured RNA (lsRNA) substrates like messenger RNA. Increasing arm length or adding antisense DNA oligonucleotides (ASOs) can improve accessibility to lsRNA but may also reduce the efficiency of product release. Xeno nucleic acids (XNAs), such as 2'-fluoro-arabinonucleic acid (FANA), have been substituted for DNA into the arms of 10-23 to improve activity, such as in the FANA-modified X10-23, but X10-23 also shows poor accessibility for lsRNA targets. To overcome this issue, we substituted patterns of various XNAs with high RNA binding strength into the substrate recognition arms of X10-23. We found that an X10-23 enzyme with a distinct 2'F-RNA-LNA-FANA arm pattern, denoted as XdZ-2, could gain access to several lsRNA targets from SARS-CoV-2, achieving cleavage rates up to 82-fold faster than X10-23 for one system. While the ASO strategy provided higher cleavage rates for two other lsRNA systems, XdZ-2 may be a more attractive alternative in low Mg2+ environments and in terms of improving the efficiency of product release and stability in biological samples.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| 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".