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Visualizing the gas channel of a monofunctional carbon monoxide dehydrogenase

2022· article· en· W4212857681 on OpenAlexaff
Alison Biester, Sébastien Dementin, Catherine L. Drennan

Bibliographic record

VenueJournal of Inorganic Biochemistry · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsCanadian Institute for Advanced Research
FundersArgonne National LaboratoryBasic Energy SciencesNational Center for Research ResourcesNational Institute of General Medical SciencesCentre National de la Recherche ScientifiqueNational Institutes of HealthOffice of ScienceHoward Hughes Medical InstituteAgence Nationale de la RechercheU.S. Department of Energy
KeywordsCarbon monoxide dehydrogenaseChemistryDesulfovibrio vulgarisCarbon monoxideBiochemistryBacteria

Abstract

fetched live from OpenAlex

Carbon monoxide dehydrogenase (CODH) plays an important role in the processing of the one‑carbon gases carbon monoxide and carbon dioxide. In CODH enzymes, these gases are channeled to and from the Ni-Fe-S active sites using hydrophobic cavities. In this work, we investigate these gas channels in a monofunctional CODH from Desulfovibrio vulgaris, which is unusual among CODHs for its oxygen-tolerance. By pressurizing D. vulgaris CODH protein crystals with xenon and solving the structure to 2.10 Å resolution, we identify 12 xenon sites per CODH monomer, thereby elucidating hydrophobic gas channels. We find that D. vulgaris CODH has one gas channel that has not been experimentally validated previously in a CODH, and a second channel that is shared with Moorella thermoacetica carbon monoxide dehydrogenase/acetyl-CoA synthase (CODH/ACS). This experimental visualization of D. vulgaris CODH gas channels lays groundwork for further exploration of factors contributing to oxygen-tolerance in this CODH, as well as study of channels in other CODHs. We dedicate this publication to the memory of Dick Holm, whose early studies of the Ni-Fe-S clusters of CODH inspired us all.

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 score0.004
Threshold uncertainty score0.430

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations26
Published2022
Admission routes1
Has abstractyes

Explore more

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