Assemblage of Signaling DNA Enzymes with Intriguing Metal-Ion Specificities and pH Dependences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We report a group of new DNA enzymes that possess a synchronized RNA-cleavage/fluorescence-signaling ability and exhibit wide-ranging metal-ion and pH dependences. These DNA catalysts were derived from a random-sequence DNA pool in a two-stage process: (1) establishment of a catalytic DNA population through repetitive rounds of in vitro selection at pH 4.0, and (2) sequence-diversification and catalytic-activity optimization through five parallel paths of in vitro evolution conducted at pH 3.0, 4.0, 5.0, 6.0, and 7.0, respectively. The deoxyribozymes were evolved to cleave the phosphodiester bond of a single ribonucleotide embedded in DNA and flanked immediately by two deoxyribonucleotides modified with a fluorophore and a quencher, respectively--a setting that synchronizes catalysis with fluorescence signaling. The most dominant catalyst from each pool was examined for metal-ion specificity, catalytic efficiency, pH dependence, and fluorescence-signaling capability. Individual catalysts have different metal-ion requirements and can generate as much as a 12-fold fluorescence enhancement upon RNA cleavage. Most of the DNA enzymes have a pH optimum coinciding with the selection pH and exhibit a rate constant approximating 1 min(-)(1) under optimal reaction conditions. The demonstration of DNA enzymes that are functional under extremely high acidity (such as pH 3 and 4) indicates that DNA has the ability to perform efficient catalysis even under harsh reaction conditions. The isolation of many new signaling DNA enzymes with broad pH optima and metal-ion specificities should facilitate the development of diverse deoxyribozyme-based biosensors.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it