Mechanistic Analysis of NADPH Auto-Oxidation in a Baeyer-Villiger Monooxygenase
Bibliographic record
Abstract
Introduction Baeyer-Villiger monooxygenases (BVMOs) are a class of enzymes that catalyze the oxidation of ketones to esters and lactones. Since biocatalytic processes enable ester and lactone production under milder, more sustainable conditions than traditional oxidation, BVMOs offer a promising route to more sustainable ester and lactone production. Among these enzymes, ssnBVMO has emerged as a particularly promising biocatalyst due to its stability and ability to oxidize a wide variety of ketones. BVMOs require NADPH to catalyze oxidations, yet a major hurdle to their industrial use is their tendency to consume NADPH through "auto-oxidation." However, the mechanism of BVMO-catalyzed NADPH auto-oxidation remains unclear. This study employs steady state kinetic experiments with ssnBVMO and several mutants thereof to elucidate the catalytic mechanism by which this protein auto-oxidizes NADPH. By determining exactly how ssnBVMO catalyzes this undesirable process, this work aims to permit the development of next-generation BVMOs that are not plagued by the auto-oxidation of NADPH. Approach To elucidate the auto-oxidation mechanism of ssnBVMO, site-directed mutagenesis targeted amino acids predicted to bind or activate NADPH for auto-oxidation. Mutant variants were overexpressed in Escherichia coli, purified via immobilized metal affinity chromatography, and subjected to kinetic assays with NADPH to assess auto-oxidation activity relative to the wild-type enzyme. Implications Preliminary findings suggest that specific active-site residues play outsized roles in the auto-oxidation of NADPH. These results provide insight into the enzyme’s catalytic mechanism, revealing potential avenues for rational engineering to improve its catalytic efficiency by limiting the amount of NADPH auto-oxidation in ssnBVMO, and the wider family of BVMOs. This study’s implications also extend to the development of other enzymes into biocatalysts for green chemistry applications, including pharmaceutical synthesis and fine chemical production. Ultimately, this research contributes to expanding the toolkit of biocatalysts available for environmentally friendly chemical transformations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".