Optimization of operation conditions for improved cytochrome <scp>P450BM3</scp> enzymatic reaction yield
Bibliographic record
Abstract
Abstract The P450 cytochrome monooxygenase CYP102A1 from Bacillus megaterium , better known as P450BM3, is a heme‐thiolate enzyme that catalyzes the hydroxylation of numerous substrates. Many of the resulting products are of commercial interest to the pharmaceutical and fine chemical industries. Unlike most other P450 cytochromes, P450BM3 is both soluble and fused to its natural redox partner, which supplies the necessary electrons from NADPH to drive its reaction forward. However, the industrial use of this enzyme is limited by its poor stability and its expensive cofactor. In this work, we explore the effects of buffer formulation and temperature on the stability of wildtype and P450BM3 mutant R966D/W1046S as well as on the stability of nicotinamide cofactors NADPH, NADH, and the biomimetic cofactor N‐benzyl‐1,4‐dihydronicotinamide. We demonstrate that cofactor stability is more important to increase product yield than that of the enzyme. We also demonstrate that low temperatures enhance oxido‐reduction reactions coupling, thus resulting in an increase in the molar ratio of p‐ nitrophenolate produced from 10‐ p NCA per oxidized cofactor. Overall, the optimized reaction conditions lead to a 2 to 2.6‐fold increase in total product output when wildtype P450BM3 or R966D/W1046S mutant is used with either of these three aforementioned cofactors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".