MétaCan
Menu
Back to cohort
Record W4377097045 · doi:10.1016/j.isci.2023.106902

Development of an in vitro method for activation of X-succinate synthases for fumarate hydroalkylation

2023· article· en· W4377097045 on OpenAlexafffund
Mary C. Andorfer, Devin T. King-Roberts, Christa Imrich, Balyn Brotheridge, Catherine L. Drennan

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Molecular Research
Canadian institutionsCanadian Institute for Advanced Research
FundersNational Institute of Environmental Health SciencesNational Institutes of HealthCanadian Institute for Advanced ResearchNational Institute of General Medical SciencesDepartment of Chemistry, University of YorkHoward Hughes Medical Institute
KeywordsIn vitroChemistryATP synthaseEnzymeBiochemistryComputational biologyCombinatorial chemistryCell biologyBiology

Abstract

fetched live from OpenAlex

Anaerobic microbial degradation of hydrocarbons is often initiated through addition of the hydrocarbon to fumarate by enzymes known as X-succinate synthases (XSSs). XSSs use a glycyl radical cofactor, which is installed by an activating enzyme (XSS-AE), to catalyze this carbon-carbon coupling reaction. The activation step, although crucial for catalysis, has not previously been possible in vitro because of insolubility of XSS-AEs. Here, we take a genome mining approach to find an XSS-AE, a 4-isopropylbenzylsuccinate synthase (IBSS)-AE (IbsAE) that can be solubly expressed in Escherichia coli . This soluble XSS-AE can activate both IBSS and the well-studied benzylsuccinate synthase (BSS) in vitro , allowing us to explore XSSs biochemically. To start, we examine the role of BSS subunits and find that the beta subunit accelerates the rate of hydrocarbon addition. Looking forward, the methodology and insight gathered here can be used more broadly to understand and engineer XSSs as synthetically useful biocatalysts.

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)0.000
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 score0.058
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.038
GPT teacher head0.368
Teacher spread0.330 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueiScienceSame topicBiochemical and Molecular ResearchFrench-language works237,207