Genome Mining Leads to the Identification of a Stable and Promiscuous Baeyer‐Villiger Monooxygenase from a Thermophilic Microorganism
Bibliographic record
Abstract
Baeyer-Villiger monooxygenases (BVMOs) are NAD(P)H-dependent flavoproteins that convert ketones to esters and lactones. While these enzymes offer an appealing alternative to traditional Baeyer-Villiger oxidations, these proteins tend to be either too unstable or exhibit too narrow of a substrate scope for implementation as industrial biocatalysts. Here, sequence similarity networks were used to search for novel BVMOs that are both stable and promiscuous. Our genome mining led to the identification of an enzyme from Chloroflexota bacterium (strain G233) dubbed ssnBVMO that exhibits i) the highest melting temperature of any naturally sourced BVMO (62.5 °C), ii) a remarkable kinetic stability across a wide range of conditions, similar to those of PAMO and PockeMO, iii) optimal catalysis at 50 °C, and iv) a broad substrate scope that includes linear aliphatic, aromatic, and sterically bulky ketones. Subsequent quantitative assays using propiophenone demonstrated >95 % conversion. Several fusions were also constructed that linked ssnBVMO to a thermostable phosphite dehydrogenase. These fusions can recycle NADPH and catalyze oxidations with sub-stoichiometric quantities of this expensive cofactor. Characterization of these fusions permitted identification of PTDH-L1-ssnBVMO as the most promising protein that could have utility as a seed sequence for enzyme engineering campaigns aiming to develop biocatalysts for Baeyer-Villiger oxidations.
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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 | 0.000 | 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 | 0.000 | 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".