Factors Controlling the Redox Potential of ZnCe6 in an Engineered Bacterioferritin Photochemical ‘Reaction Centre’
Bibliographic record
Abstract
Photosystem II (PSII) of photosynthesis has the unique ability to photochemically oxidize water. Recently an engineered bacterioferritin photochemical 'reaction centre' (BFR-RC) using a zinc chlorin pigment (ZnCe6) in place of its native heme has been shown to photo-oxidize bound manganese ions through a tyrosine residue, thus mimicking two of the key reactions on the electron donor side of PSII. To understand the mechanism of tyrosine oxidation in BFR-RCs, and explore the possibility of water oxidation in such a system we have built an atomic-level model of the BFR-RC using ONIOM methodology. We studied the influence of axial ligands and carboxyl groups on the oxidation potential of ZnCe6 using DFT theory, and finally calculated the shift of the redox potential of ZnCe6 in the BFR-RC protein using the multi-conformational molecular mechanics-Poisson-Boltzmann approach. According to our calculations, the redox potential for the first oxidation of ZnCe6 in the BRF-RC protein is only 0.57 V, too low to oxidize tyrosine. We suggest that the observed tyrosine oxidation in BRF-RC could be driven by the ZnCe6 di-cation. In order to increase the efficiency of tyrosine oxidation, and ultimately oxidize water, the first potential of ZnCe6 would have to attain a value in excess of 0.8 V. We discuss the possibilities for modifying the BFR-RC to achieve this goal.
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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 | 0.000 |
| 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".