The Structural Basis for BVMO Substrate Profile and Stereospecificity
Bibliographic record
Abstract
The Baeyer-Villiger monooxygenases (BVMOs) are a group of bacterial enzymes that are able to catalyze the synthetically useful Baeyer-Villiger oxidation reaction. As such, these enzymes have attracted considerable attention as potential industrial biocatalysts. The interest in these enzymes has led to a desire to be able to rationally design them for tailored biocatalytic applications. While recent years have seen the publication of a number of crystal structures (1-3), we have been lacking a structure of a BVMO that has its native substrate or product bound in a conformation that will allow the determination of substrate specificity and stereospecificity. Without such a structure, progress towards tailored BVMOs has been hampered. We have been able to solve two crystal structures of cyclohexanone monooxygenase (CHMO) with its lactone product, ε-caprolactone, bound. These structures place the lactone in an ideal position for the determination of its substrate specificity and stereospecificity. These structures have provided us with a better understanding of the structural basis for substrate binding, paving the way for the rational design of tailored BVMOs. At the same time, we have pursued small-angle X-ray scattering (SAXS) and nuclear magnetic resonance (NMR) studies to better understand the dynamic nature of the enzyme. These studies have allowed us to explain the relationship between the various crystallized states of BVMOs and their complex, fourteen step enzyme mechanism.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".