An Exceptionally Selective DNA Cooperatively Binding Two Ca<sup>2+</sup> Ions
Bibliographic record
Abstract
Abstract Ca2+ is a highly important metal ion in biology and in the environment, and thus there is extensive work in developing sensors for Ca2+ detection. Although many Ca2+‐binding proteins are known, few nucleic acids can selectively bind Ca2+. DNA‐based biosensors are attractive for their high stability and excellent programmability. We report a RNA‐cleaving DNAzyme, EtNa, cooperatively binding two Ca2+ ions but to only one Mg2+. Four DNAzymes with known Ca2+‐dependent activity were compared, and the EtNa had the best selectivity for Ca2+. The EtNa is 90 times more active in Ca2+ than in Mg2+. Phosphorothioate (PS) modification showed that both non‐bridging oxygen atoms at the scissile phosphate contribute equally to Ca2+ binding. The pH–rate profile suggests two concurrent deprotonation reactions. EtNa was further engineered for Ca2+ sensing, and found to have a detection limit of 17 μm Ca2+ and excellent selectivity. The detection of Ca2+ in tap water was performed, and the result was comparable with that by ICP‐MS. This study offers new fundamental insights into Ca2+ binding by nucleic acids and improved metal selectivity by having multiple cooperative metal binding sites.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".