Directed evolution of copper nitrite reductase to a chromogenic reductant
Bibliographic record
Abstract
Directed evolution methods were developed for Cu-containing nitrite reductase (NiR) from Alcaligenes faecalis S-6. The PCR cloning strategy allows for the efficient production of libraries of 100 000 clones by a modification of a megaprimer-based whole-plasmid synthesis reaction. The high-throughput screen includes colony lift onto a nylon membrane and subsequent lysis of NiR-expressing colonies in the presence of Cu(2+) ions for copper incorporation into intracellularly expressed NiR. Addition of a chromogenic substrate, 3, 3'-diaminobenzidine (DAB), results in deposition of red, insoluble color at the site of oxidation by functional NiR. Twenty-thousand random variants of NiR were screened for improved function with DAB as a reductant, and five variants were identified. These variants were shuffled and screened, yielding two double variants. An analog of the DAB substrate, o-dianisidine, which is oxidized to a water-soluble product was used for functional characterization. The double variant M150L/F312C was most proficient at o-dianisidine oxidation with dioxygen as the electron acceptor (5.5X wt), and the M150L single variant was most proficient at o-dianisidine oxidation with nitrite as the electron acceptor (8.5X wt). The library generation and screening method can be employed for evolving new reductase functions in NiR and for screening of efficient folding of engineered NiRs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".