Insights into Sequence–Activity Relationships amongst Baeyer–Villiger Monooxygenases as Revealed by the Intragenomic Complement of Enzymes from <i>Rhodococcus jostii</i> RHA1
Bibliographic record
Abstract
Abstract The Rhodococcus jostii RHA1 genome encodes a number of enzymes that can be exploited as biocatalysts. Study of the substrate spectrum and enantioselectivity of Baeyer–Villiger monooxygenases from R. jostii allowed the identification of short amino acid sequences specific to groups displaying certain catalytic characteristics. The gel illustrates the substrate acceptance spectra and selectivities of the different proteins. magnified image Microbial genome sequences are providing a wealth of information on new enzymes that have considerable potential as biocatalysts. The recently sequenced genome of Rhodococcus jostii RHA1, for example, has revealed an impressive array of catabolic enzymes, including many putative Baeyer–Villiger monooxygenases (BVMOs). We have cloned 23 target BVMO sequences from the genome of R. jostii RHA1 and heterologously expressed 13 of these as soluble proteins to unearth new substrate specificities and selectivities. Whole‐cell biocatalysts expressing the genes were screened against seven different test substrates. Each of these catalysts displayed activity toward at least three ketones. We observed a remarkable diversity of both regio‐ and enantioselectivity among the BVMOs from R. jostii RHA1 for the transformation of two chiral substrates, with some enzymes displaying high enantioselectivity for the isomers of 2‐methylcyclopentanone. With the notable exception of the product of gene ro03437, named MO14, the biocatalysts' sequences correlated well with their respective activities and selectivities. This correlation allowed the identification of sequence motifs specific to subgroups of the BVMOs from R. jostii and other organisms. Overall, the data improve predictive models of BVMO activity from sequence and suggest new avenues to pursue in engineering these enzymes.
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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".