A high sensitivity strategy to screen NAD(P)H-dependent oxidoreductase activity by coupled enzyme cascade
Bibliographic record
Abstract
Abstract Enzymes play a pivotal role in “green chemistry” as tools for biocatalysis. Oxidoreductase enzymes are especially useful for carrying out key electron transfer (redox) steps towards a wide range of chemical transformations (e.g., asymmetric hydrogenation, oxygenation, hydroxylation, epoxidation, or Baeyer-Villiger oxidation) that might not otherwise be available to chemists through conventional (nonbiological) synthetic approaches. The ability to screen oxidoreductase activity is important in identifying useful biocatalysts from nature, and also towards engineering novel ones through directed evolution. Many valuable redox enzymes are dependent upon NAD(P)H as an electron donating co-substrate (or conversely, upon NAD(P) + as an electron acceptor), and the common method to detect their activity is to monitor the change in absorbance at 340 nm as NAD(P)H is converted to NAD(P) + (or vice versa). The limited sensitivity of this method presents a challenge in detecting very low levels of oxidoreductase activity, and this can prove very difficult to begin engineering enzymes as improved biocatalysts when the rates of natural enzymes may be slow for a desired redox reaction. Herein, we report a fluorescence-based, enzyme cascade-coupled system that we have developed to detect oxidoreductase activity with orders of magnitude more sensitivity than conventional absorbance-based assays. While recycling NAD(P)H from NAD(P) + , the coupled enzyme cascade triggers cleavage of a fluorogenically labeled probe, releasing a strong fluorescent signal. This allows detection of very low levels of a specific oxidoreductase activity that we may wish to magnify by directed evolution using our assay in high-throughput screening.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".