Mechanistic Study of the Stereoselective Hydroxylation of [2‐<sup>2</sup>H<sub>1</sub>,3‐<sup>2</sup>H<sub>1</sub>]Butanes Catalyzed by Cytochrome P450 BM3 Variants
Bibliographic record
Abstract
Abstract Engineered bacterial cytochrome P450s are noted for their ability in the oxidation of inert small alkanes. Cytochrome P450 BM3 L188P A328F (BM3 PF) and A74E L188P A328F (BM3 EPF) variants are able to efficiently oxidize n‐butane to 2‐butanol. Esterification of the 2‐butanol derived from this reaction mediated by the aforementioned two mutants gives diastereomeric excesses (de) of −56±1 and −52±1 %, respectively, with the preference for the oxidation occurring at the C−HS bond. When tailored (2R,3R)‐ and (2S,3S)‐[2‐2H1,3‐2H1]butane probes are employed as substrates for both variants, the obtained de values from (2R,3R)‐[2‐2H1,3‐2H1]butane are −93 and −92 % for BM3 PF and EPF, respectively; whereas the obtained de values from (2S,3S)‐[2‐2H1,3‐2H1]butane are 52 and 56 % in the BM3 PF and EPF systems, respectively. The kinetic isotope effects (KIEs) for the oxidation of (2R,3R)‐[2‐2H1,3‐2H1]butane are 7.3 and 7.8 in BM3 PF and EPF, respectively; whereas KIEs for (2S,3S)‐[2‐2H1,3‐2H1]butanes are 18 and 25 in BM3 PF and EPF, respectively. The discrepancy in KIEs obtained from the two substrates supports the two‐state reactivity (TSR) that is proposed for alkane oxidation in cytochrome P450 systems. Moreover, for the first time, experimental evidence for tunneling in the oxidation mediated by P450 is given through the oxidation of the C−HR bond in (2S,3S)‐[2‐2H1,3‐2H1]butane.
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 | 0.000 | 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".