Multicopper Laccase Mimicking Nanozymes with Nucleotides as Ligands
Bibliographic record
Abstract
Using nanomaterials to achieve functional enzyme mimics (nanozymes) is attractive for both applied and fundamental research. Laccases are multicopper oxidases highly important for biotechnology and environmental remediation. In this work, we report an exceptionally simple yet functional laccase mimic based on guanosine monophosphate (GMP) coordinated copper. It forms an amorphous metal–organic framework (MOF) material. The ratio of copper and GMP is 3:4 as determined by isothermal titration calorimetry. It has excellent laccase-like activity and converts a diverse range of phenol containing substrates such as hydroquinone, naphthol, catechol and epinephrine. Comparative work shows that the activity is originated from guanosine coordination instead of phosphate binding in GMP. Cu 2+ is required and cannot be substituted by other metal ions. At the same mass concentration, the Cu/GMP nanozyme has a higher V max and similar K m compared to the protein laccase. To achieve the same catalytic efficiency, the cost of the Gu/GMP is ∼2400-fold lower than that of laccase. The Cu/GMP is much more stable at extreme pH, high salt, high temperature and for long-term storage. This is one of the first laccase-mimicking nanozymes, which will find important applications in analytical chemistry, environmental protection, and biotechnology.
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 | 0.000 |
| 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 | 0.001 | 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 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".