Model Iron–Oxo Species and the Oxidation of\nImidazole: Insights into the Mechanism of OvoA and EgtB?
Bibliographic record
Abstract
A density functional theory cluster and first-principles\nquantum\nand statistical mechanics approach have been used to investigate the\nability of iron–oxygen intermediates to oxidize a histidine\ncosubstrate, which may then allow for the possible formation of 2-\nand 5-histidylcysteine sulfoxide, respectively. Namely, the ability\nof ferric superoxo (Fe<sup>III</sup>O<sub>2</sub><sup>•–</sup>), Fe<sup>IV</sup>O, and ferrous peroxysulfur (Fe<sup>III</sup>OOS) complexes to oxidize the imidazole of histidine via an electron\ntransfer (ET) or a proton-coupled electron transfer (PCET) was considered.\nWhile the high-valent mononuclear Fe<sup>IV</sup>O species\nis generally considered the ultimate biooxidant, the free energies\nfor its reduction (via ET or PCET) suggest that it is unable to directly\noxidize histidine’s imidazole. Instead, only the ferrous peroxysulfur\ncomplexes are sufficiently powerful enough oxidants to generate a\nhistidyl-derived radical via a PCET process. Furthermore, while this\nprocess preferably forms a HisN<sub>δ</sub>(−H)<sup>•</sup> radical, several such oxidants are also suggested to be capable\nof generating the higher-energy HisC<sub>δ</sub>(−H)<sup>•</sup> and HisC<sub>ε</sub>(−H)<sup>•</sup> radicals. Importantly, the present results suggest that formation\nof the sulfoxide-containing products (seen in both OvoA and EgtB)\nis a consequence of the reduction of a powerful Fe<sup>III</sup>OOS\noxidant via a PCET.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.017 | 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 teacher head, 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".