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Record W2089438702 · doi:10.1042/bj20112200

Fe-haem bound to <i>Escherichia coli</i> bacterioferritin accelerates iron core formation by an electron transfer mechanism

2012· article· en· W2089438702 on OpenAlexafffund
Steve G. Wong, Raz Abdulqadir, Nick E. Le Brun, Geoffrey R. Moore, A. Grant Mauk

Bibliographic record

VenueBiochemical Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of British Columbia
FundersBiotechnology and Biological Sciences Research CouncilCanadian Institutes of Health Research
KeywordsCeruloplasminChemistryCofactorEscherichia coliHemeElectron transferMetalloproteinElectron transport chainBiochemistryCrystallographyPhotochemistryEnzyme

Abstract

fetched live from OpenAlex

BFR (bacterioferritin) is an iron storage and detoxification protein that differs from other ferritins by its ability to bind haem cofactors. Haem bound to BFR is believed to be involved in iron release and was previously thought not to play a role in iron core formation. Investigation of the effect of bound haem on formation of the iron core has been enabled in the present work by development of a method for reconstitution of BFR from Escherichia coli with exogenously added haem at elevated temperature in the presence of a relatively high concentration of sodium chloride. Kinetic analysis of iron oxidation by E. coli BFR preparations containing various amounts of haem revealed that haem bound to BFR decreases the rate of iron oxidation at the dinuclear iron ferroxidase sites but increases the rate of iron core formation. Similar kinetic analysis of BFR reconstituted with cobalt-haem revealed that this haem derivative has no influence on the rate of iron core formation. These observations argue that haem bound to E. coli BFR accelerates iron core formation by an electron-transfer-based mechanism.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.005
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.273
Teacher spread0.254 · 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

Citations28
Published2012
Admission routes2
Has abstractyes

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