Genome mining leads to the identification of a stable and promiscuous Baeyer-Villiger monooxygenase from a thermophilic microorganism
Bibliographic record
Abstract
Abstract Baeyer-Villiger monooxygenases are NAD(P)H-dependent flavoproteins that catalyze oxygen insertion reactions which convert ketones to valuable 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 Baeyer-Villiger monooxygenases that are both stable and substrate promiscuous. Our genome mining efforts led to the identification of an enzyme from Chloroflexota bacterium (strain G233) dubbed ssn BVMO that exhibits i) the highest melting temperature recorded to date for a naturally sourced Baeyer-Villiger monooxygenase, ii) a remarkable kinetic stability across a wide range of conditions, and iii) a broad substrate scope that includes linear aliphatic, aromatic, and sterically bulky ketones. Kinetic characterization of this enzyme was undertaken to identify the optimal conditions for ssn BVMO catalysis, and a subsequent quantitative assay using propiophenone as a substrate afforded more than 95% conversion. To spur the implementation of this enzyme as an oxidative biocatalyst, several fusion proteins were constructed that linked ssn BVMO to a thermostable phosphite dehydrogenase. These self-sufficient enzymes can recycle NADPH and permit oxidations to be run with sub-stoichiometric quantities of this expensive cofactor. Extensive characterization of these fusion enzymes permitted identification of PTDH-L1- ssn BVMO as the most promising oxidative biocatalyst. Results described herein demonstrate that this new monooxygenase has significant potential as a useful industrial biocatalyst for Baeyer-Villiger oxidations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".