CLICK-17, a DNA enzyme that harnesses ultra-low concentrations of either Cu+ or Cu2+ to catalyze the azide-alkyne ‘click’ reaction in water
Bibliographic record
Abstract
To enable the optimal, biocompatible and non-destructive application of the highly useful copper (Cu+)-mediated alkyne-azide 'click' cycloaddition in water, we have isolated and characterized a 79-nucleotide DNA enzyme or DNAzyme, 'CLICK-17', that harnesses as low as sub-micromolar Cu+; or, surprisingly, Cu2+ (without added reductants such as ascorbate) to catalyze conjugation between a variety of alkyne and azide substrates, including small molecules, proteins and nucleic acids. CLICK-17's Cu+ catalysis is orders of magnitude faster than that of either Cu+ alone or of Cu+ complexed to PERMUT-17, a sequence-permuted DNA isomer of CLICK-17. With the less toxic Cu2+, CLICK-17 attains rates comparable to Cu+, under conditions where both Cu2+ alone and Cu2+ complexed with a classic accelerating ligand, THPTA, are wholly inactive. Cyclic voltammetry shows that CLICK-17, unlike PERMUT-17, powerfully perturbs the Cu(II)/Cu(I) redox potential. CLICK-17 thus provides a unique, DNA-derived ligand environment for catalytic copper within its active site. As a bona fide Cu2+-driven enzyme, with potential for being evolved to accept only designated substrates, CLICK-17 and future variants promise the fast, safe, and substrate-specific catalysis of 'click' bioconjugations, potentially on the surfaces of living cells.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".