Water Oxidation by a Cytochrome P450: Mechanism and Function of the Reaction
Bibliographic record
Abstract
P450(cam) (CYP101A1) is a bacterial monooxygenase that is known to catalyze the oxidation of camphor, the first committed step in camphor degradation, with simultaneous reduction of oxygen (O2). We report that P450(cam) catalysis is controlled by oxygen levels: at high O2 concentration, P450(cam) catalyzes the known oxidation reaction, whereas at low O2 concentration the enzyme catalyzes the reduction of camphor to borneol. We confirmed, using (17)O and (2)H NMR, that the hydrogen atom added to camphor comes from water, which is oxidized to hydrogen peroxide (H2O2). This is the first time a cytochrome P450 has been observed to catalyze oxidation of water to H2O2, a difficult reaction to catalyze due to its high barrier. The reduction of camphor and simultaneous oxidation of water are likely catalyzed by the iron-oxo intermediate of P450(cam) , and we present a plausible mechanism that accounts for the 1:1 borneol:H2O2 stoichiometry we observed. This reaction has an adaptive value to bacteria that express this camphor catabolism pathway, which requires O2, for two reasons: 1) the borneol and H2O2 mixture generated is toxic to other bacteria and 2) borneol down-regulates the expression of P450(cam) and its electron transfer partners. Since the reaction described here only occurs under low O2 conditions, the down-regulation only occurs when O2 is scarce.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".