Model Iron–Oxo Species and the Oxidation of Imidazole: Insights into the Mechanism of OvoA and EgtB?
Bibliographic record
Abstract
A density functional theory cluster and first-principles quantum and statistical mechanics approach have been used to investigate the ability of iron-oxygen intermediates to oxidize a histidine cosubstrate, which may then allow for the possible formation of 2- and 5-histidylcysteine sulfoxide, respectively. Namely, the ability of ferric superoxo (Fe(III)O(2)(•-)), Fe(IV)═O, and ferrous peroxysulfur (Fe(III)OOS) complexes to oxidize the imidazole of histidine via an electron transfer (ET) or a proton-coupled electron transfer (PCET) was considered. While the high-valent mononuclear Fe(IV)═O species is generally considered the ultimate biooxidant, the free energies for its reduction (via ET or PCET) suggest that it is unable to directly oxidize histidine's imidazole. Instead, only the ferrous peroxysulfur complexes are sufficiently powerful enough oxidants to generate a histidyl-derived radical via a PCET process. Furthermore, while this process preferably forms a HisN(δ)(-H)(•) radical, several such oxidants are also suggested to be capable of generating the higher-energy HisC(δ)(-H)(•) and HisC(ε)(-H)(•) radicals. Importantly, the present results suggest that formation of the sulfoxide-containing products (seen in both OvoA and EgtB) is a consequence of the reduction of a powerful Fe(III)OOS oxidant via a PCET.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".