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Record W2984728022 · doi:10.7554/elife.39451.031

Author response: Redox-dependent rearrangements of the NiFeS cluster of carbon monoxide dehydrogenase

2018· peer-review· en· W2984728022 on OpenAlexaff
Elizabeth C. Wittenborn, Mériem Merrouch, Chie Ueda, Laura Fradale, Christophe Léger, Vincent Fourmond, Maria‐Eirini Pandelia, Sébastien Dementin, Catherine L. Drennan

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsCanadian Institute for Advanced Research
Fundersnot available
KeywordsCarbon monoxide dehydrogenaseChemistryCluster (spacecraft)Carbon monoxideRedoxCarbon fixationStereochemistryBiochemistryCatalysisComputer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Life relies on countless chemical reactions, almost all of which need to be sped up by enzymes. About half of all enzymes carry metal ions that expand the range of the reactions that they can catalyze. In some enzymes these metal ions assemble with sulfur ions to form so-called metalloclusters. These structures can carry out many different types of reactions, including converting simple forms of elements like nitrogen and carbon into other forms that can be used to make more complicated biological molecules. One enzyme that contains metalloclusters is carbon monoxide dehydrogenase. Known as CODH for short, this enzyme uses a metallocluster called the “C-cluster” to interconvert two gases: the pollutant carbon monoxide and the greenhouse gas carbon dioxide. CODH enzymes are found inside certain bacteria, but they are also of interest for humans, who wish to use them to remove the harmful gases from the environment. But this is not as simple as it may at first seem: CODH enzymes usually become inactive when exposed to air because the metalloclusters fall apart in the presence of oxygen. One CODH enzyme from a widespread bacterium called Desulfovibrio vulgaris, however, is an attractive target for industrial use because it can tolerate oxygen better. Yet, it is still unclear why this enzyme does not get inactivated the way other CODHs do. Wittenborn et al. have now characterized the CODH enzyme from D. vulgaris in more depth via a technique called X-ray crystallography, which can reveal the location of individual atoms within a molecule. By a happy accident, the structures revealed that the C-cluster can adopt a dramatically different arrangement of metal and sulfur ions after being exposed to oxygen. This rearrangement is fully reversible; when oxygen is removed, the metal and sulfur ions move back to their normal positions. This ability to flip between different arrangements appears to protect the metallocluster from losing its metal ions when exposed to oxygen. By providing structural snapshots of how CODH responds to oxygen these results provide a more complete understanding of an enzyme that plays a key role in the global carbon cycle. This understanding could help scientists to develop bioremediation tools to remove carbon monoxide and carbon dioxide from the atmosphere and to engineer bacteria to capture carbon to make biofuels.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.149
Threshold uncertainty score0.500

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0080.005
Insufficient payload (model declined to judge)0.1490.048

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 head0.020
GPT teacher head0.271
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2018
Admission routes1
Has abstractyes

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