Secondary-Structure Characterization of Two Proficient Kinase Deoxyribozymes
Bibliographic record
Abstract
Dk1 and Dk2 are two catalytically proficient, manganese-dependent, guanine-rich deoxyribozymes previously isolated for DNA phosphorylation. In this study, we carried out a series of experiments that aimed to understand the structural properties of Dk1 and Dk2 and compare the structural similarities or differences of these two distinct deoxyribozymes that carry out similar catalytic functions. First, we performed reselections from two partially randomized DNA libraries on the basis of the original Dk1 and Dk2 sequences to isolate catalytically active sequence variants and identify nucleotides that are invariable, well-conserved, or highly mutagenized. Sequence analysis of these variants assisted by secondary-structure predictions led to the identification of possible Watson-Crick base-pairing regions within each deoxyribozyme. Sequence truncation and base-pair partner exchange experiments were conducted to confirm, or rule out, the existence of the predicted secondary-structure elements. Finally, methylation interference experiments were applied to identify nucleotides that are potentially important for the tertiary structure folding of the deoxyribozymes. Our data suggest that Dk1 and Dk2, despite the differences in their primary sequences and NTP requirements, use an analogous stem-loop element to anchor a structural domain of substantial tertiary interactions to execute their catalytic functions.
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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) | 0.000 | 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) | 0.001 | 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".