Mechanism of Ferrous Iron Binding and Oxidation by Ferritin from a Pennate Diatom
Bibliographic record
Abstract
A novel ferritin was recently found in Pseudo-nitzschia multiseries (PmFTN), a marine pennate diatom that plays a major role in global primary production and carbon sequestration into the deep ocean. Crystals of recombinant PmFTN were soaked in iron and zinc solutions, and the structures were solved to 1.65–2.2-Å resolution. Three distinct iron binding sites were identified as determined from anomalous dispersion data from aerobically grown ferrous soaked crystals. Sites A and B comprise the conserved ferroxidase active site, and site C forms a pathway leading toward the central cavity where iron storage occurs. In contrast, crystal structures derived from anaerobically grown and ferrous soaked crystals revealed only one ferrous iron in the active site occupying site A. In the presence of dioxygen, zinc is observed bound to all three sites. Iron oxidation experiments using stopped-flow absorbance spectroscopy revealed an extremely rapid phase corresponding to Fe(II) oxidation at the ferroxidase site, which is saturated after adding 48 ferrous iron to apo-PmFTN (two ferrous iron per subunit), and a much slower phase due to iron core formation. These results suggest an ordered stepwise binding of ferrous iron and dioxygen to the ferroxidase site in preparation for catalysis and a partial mobilization of iron from the site following oxidation. Background: Ferritin stores iron by ferroxidation to form a mineral core, enabling diatom blooms upon iron input. Results: Ferrous iron binds solely to ferroxidase site A anaerobically. Ferroxidation kinetics has two observed phases. Conclusion: Ferrous iron and dioxygen binding to the di-iron ferroxidase site is stepwise. Significance: Iron storage by ferritins requires a coordinated binding of iron and dioxygen.
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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) | 0.000 | 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".