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Record W2301903195 · doi:10.1038/ncomms10902

Nitrogenase FeMoco investigated by spatially resolved anomalous dispersion refinement

2016· article· en· W2301903195 on OpenAlexfundno aff
Thomas Spatzal, Julia Schlesier, Eva-Maria Burger, Daniel Sippel, Limei Zhang, Susana L. A. Andrade, Douglas C. Rees, Oliver Einsle

Bibliographic record

VenueNature Communications · 2016
Typearticle
Languageen
FieldEnergy
TopicMetalloenzymes and iron-sulfur proteins
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaBeckman Institute, California Institute of TechnologyNational Institutes of HealthHoward Hughes Medical InstituteCalifornia Institute of TechnologyGordon and Betty Moore FoundationDeutsche Forschungsgemeinschaft
KeywordsNitrogenaseAnomalous scatteringCluster (spacecraft)MolybdenumNitrogen fixationOxidation stateChemistryChemical physicsReactivity (psychology)Density functional theoryRedoxMetalCrystallographyNitrogenComputational chemistryScatteringInorganic chemistryPhysics

Abstract

fetched live from OpenAlex

The [Mo:7Fe:9S:C] iron-molybdenum cofactor (FeMoco) of nitrogenase is the largest known metal cluster and catalyses the 6-electron reduction of dinitrogen to ammonium in biological nitrogen fixation. Only recently its atomic structure was clarified, while its reactivity and electronic structure remain under debate. Here we show that for its resting S=3/2 state the common iron oxidation state assignments must be reconsidered. By a spatially resolved refinement of the anomalous scattering contributions of the 7 Fe atoms of FeMoco, we conclude that three irons (Fe1/3/7) are more reduced than the other four (Fe2/4/5/6). Our data are in agreement with the recently revised oxidation state assignment for the molybdenum ion, providing the first spatially resolved picture of the resting-state electron distribution within FeMoco. This might provide the long-sought experimental basis for a generally accepted theoretical description of the cluster that is in line with available spectroscopic and functional data.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.645

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 science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.255
Teacher spread0.237 · 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 designNot applicable
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

Citations162
Published2016
Admission routes1
Has abstractyes

Explore more

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