Regio- and stereoselectivity in the CYP450<sub>BM3</sub>-catalyzed hydroxylation of complex terpenoids: a QM/MM study
Bibliographic record
Abstract
The site-selective C-H oxidation of terpenoids by P450 attracts great attention because of their wide range of biological activities. However, the binding and catalytic mechanism of P450 for the hydroxylation of complex terpenoid substrates remains elusive, which has limited the rational engineering of P450 as a biocatalyst for terpenoid biosynthesis. Here, we studied the origin of the selectivity and reactivity of P450BM3 in the hydroxylation of terpenoids by combining molecular dynamics simulations and QM/MM calculations, using artemisinin as a model compound. We found that the conformational change of the β1 sheet at the substrate entrance and the displacement of the β' helix were critical for reshaping the binding pocket to modulate substrate entrance and positioning the C-H to be activated toward the oxidative species of P450 for the subsequent hydrogen abstraction, the rate-determining step of hydroxylation. There is a distinct linear correlation between activation barriers and reaction coordinates, indicating that reaction coordinates can be used as a facile descriptor for predicting the reactivity of P450BM3. These findings would provide valuable guidance for predicting the selectivity and reactivity of P450BM3 for the selective hydroxylation of non-native terpenoid substrates so as to prioritize the rationally designed enzymes for terpenoid biosynthesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".