Baeyer−Villiger Monooxygenases: More Than Just Green Chemistry
Bibliographic record
Abstract
What is life without oxygen is a rhetorical question. On the other hand, unraveling the intricacies and understanding the various mechanisms underpinning the biological processes beg many answers. The activation of hydrocarbon C-H bonds by oxygenases exemplifies an important biological process and prerequisite to the eventual transformation of these raw materials into value-added chemicals or bioproducts. Oxygenases come in two forms: those that introduce one atom of molecular oxygen into an organic substrate, called monooxygenases (also referred to as mixed function oxygenases) and those that insert both oxygen atoms into a substrate, namely, dioxygenases. For a historical account on the discovery of oxygenases see a review by Hayaishi.(1) In monooxygenase-catalyzed reactions, the other oxygen atom undergoes reduction to water. Hence, in a biotransformation or biocatalysis setting, having water as a byproduct cannot be greener. This review focuses on the monooxygenase-catalyzed Baeyer-Villiger oxidation of linear or cyclic ketones as a green chemistry tool to address environmental sustainability, a system to study its molecular diversity and catalytic mechanism, industrial-scale bioprocess development, and a challenging model for protein engineering to evolve new biotechnological applications.Biocatalysis at large is poised to play an ever increasingly important role in meeting the needs of industrial and environmental sustainability as manufacturers and industries are striving to improve efficiency and implement cleaner processes.(2) In the context of the three pillars of sustainable development, the use of biocatalysts, as opposed to strictly harsh chemical methods, is meeting the needs of environmental care and social responsibility, although rapid economic progress requires serious financial investment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".