Split Locations and Secondary Structures of a DNAzyme Critical to Binding-Assembled Multicomponent Nucleic Acid Enzymes for Protein Detection
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide RNA-cleaving DNAzymes and their multicomponent nucleic acid enzymes (MNAzymes) have been successfully used to detect nucleic acids and proteins. The appropriate split of the catalytic cores of DNAzymes is critical to the formation of MNAzymes with high catalytic activities. However, for protein detection, no systematic investigation has been made on the effects of the split locations and secondary structures of MNAzymes on the catalytic activities of the cleavage reaction. We systematically studied how split locations and secondary structures affect the activity of the MNAzymes that catalyze multiple cleavage steps. We engineered the MNAzymes on the basis of the RNA-cleaving DNAzyme 10-23 as a model system. We designed 28 pairs of MNAzymes, representing 14 different split locations and two secondary structures: the three-arm and the four-arm structures. By comparing the multiple turnover numbers ( k obs.m ) of the 28 MNAzymes, we showed that the split location between the seventh cytosine and the eighth thymine of the catalytic core region and the four-arm structure resulted in optimum catalytic activity. Binding-induced DNA assembly of the optimized MNAzymes enabled sensitive detection of two model protein targets, demonstrating promising potential of the binding-assembled MNAzymes for protein analysis. The strategy of binding-assembled MNAzymes and systematic studies measuring multiple turnover numbers ( k obs.m ) provide a new approach to studying other partial (split) DNAzymes and engineering better MNAzymes for the detection of specific proteins.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".