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Record W2172506815 · doi:10.1074/jbc.m115.669713

A Diatom Ferritin Optimized for Iron Oxidation but Not Iron Storage

2015· article· en· W2172506815 on OpenAlexafffund
Stephanie Pfaffen, Justin M. Bradley, Raz Abdulqadir, Marlo Firme, Geoffrey R. Moore, Nick E. Le Brun, M.E.P. Murphy

Bibliographic record

VenueJournal of Biological Chemistry · 2015
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of British Columbia
FundersSLAC National Accelerator LaboratoryBiological and Environmental ResearchNational Center for Research ResourcesNational Institute of General Medical SciencesNational Research Council CanadaCanadian Light SourceNatural Sciences and Engineering Research Council of CanadaBiotechnology and Biological Sciences Research CouncilOffice of ScienceWestern Economic Diversification CanadaDirectorate for Biological SciencesNational Institutes of HealthUniversity of East AngliaCanadian Institutes of Health ResearchLeverhulme TrustUniversity of SaskatchewanU.S. Department of Energy
KeywordsFerritinDiatomChemistryBiologyBiochemistryBotany

Abstract

fetched live from OpenAlex

Ferritin from the marine pennate diatom Pseudo-nitzschia multiseries (PmFTN) plays a key role in sustaining growth in iron-limited ocean environments. The di-iron catalytic ferroxidase center of PmFTN (sites A and B) has a nearby third iron site (site C) in an arrangement typically observed in prokaryotic ferritins. Here we demonstrate that Glu-44, a site C ligand, and Glu-130, a residue that bridges iron bound at sites B and C, limit the rate of post-oxidation reorganization of iron coordination and the rate at which Fe(3+) exits the ferroxidase center for storage within the mineral core. The latter, in particular, severely limits the overall rate of iron mineralization. Thus, the diatom ferritin is optimized for initial Fe(2+) oxidation but not for mineralization, pointing to a role for this protein in buffering iron availability and facilitating iron-sparing rather than only long-term iron storage.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.295
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2015
Admission routes2
Has abstractyes

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