Customizing the Enantioselectivity of a Cyclohexanone Monooxygenase by a Strategy Combining “Size‐Probes” with <i>in silico</i> Study
Bibliographic record
Abstract
Abstract Enzymatic Baeyer‐Villiger oxidation provides a promising green route utilizing molecular oxygen as the oxidant to produce chiral lactones. Wild‐type (WT) CHMOAcineto leads to enantioselectivity up to 99 % ee (S) in the synthesis of substituted ϵ‐caprolactones. To reverse the inherent enantiopreference of CHMOAcineto toward an array of cyclohexanones with various chain length, we herein reshaped the binding pocket with a minimal number of mutations by a rational design strategy combining “size‐probes” with in silico study, which drastically reduces the screening effort. By probing the binding pocket of variants with different‐sized 4‐substituted cyclohexanones substrates, single, double and triple mutants were identified as the best mutants providing highly reversed enantioselectivity for these probing molecules, respectively. The successful demonstration of the strategy combining “size‐probes” with in silico study in the protein engineering of CHMOAcineto may provide a valuable guidance for facile engineering other BVMOs with customized enantiopreference for the same classified substrates with their substituents on the chiral or prochiral central atom.
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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 | 0.000 | 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) | 0.001 | 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".