Characterization of pH3DZ1 — An RNA-cleaving deoxyribozyme with optimal activity at pH 3
Bibliographic record
Abstract
We previously described a cis-acting RNA-cleaving deoxyribozyme known as pH3DZ1 that exhibits optimal catalytic activity at pH 3.0 (Zhongjie Liu, Shirley H. Mei, John D. Brennan, and Yingfu Li. J. Am. Chem. Soc. 125, 7539 (2003)). This DNA catalyst was made of a 99-nucleotide (nt) catalytic domain covalently linked to a 23-nt DNA–RNA chimeric substrate containing a single ribonucleotide as the cleavage site. In the present work, we conducted an extensive sequence examination of this deoxyribozyme via nucleotide truncation and reselection experiments, with a goal to minimize its size and identify the nucleotides that are crucial to its catalytic function. A trans-acting deoxyribozyme that can process an external substrate was also successfully designed. Stretches of 30 and 17 nucleotides from the 5′ and 3′ ends of the trans catalyst, respectively, were found to be completely dispensable; in contrast, few nucleotides could be deleted internally without producing a detrimental effect. The reselection experiment led to the discovery of 7 and 5 absolutely conserved nucleotides located at the 5′ and 3′ ends of the minimized catalyst, respectively, separated by a 31-nt element in which 14 highly conserved nucleotides were scattered among 17 variable nucleotides. The shortened deoxyribozyme and the original catalyst showed a similar pH profile with the optimal activity at pH 3; however, the minimized deoxyribozyme still exhibited strong catalytic activity at pH 2.5, while the full-length catalyst was barely active at this pH. Finally, it was found that this deoxyribozyme generated two cleavage fragments, one with 2′,3′-cyclic phosphate and the other with 5′-OH.Key words: DNA, deoxyribozyme, RNA cleavage, in vitro selection, catalysis.
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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 | 0.000 |
| 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 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".