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Record W2003941871 · doi:10.1271/bbb.68.2171

Biotransformation of Various Alkanes Using the<i>Escherichia coli</i>Expressing an Alkane Hydroxylase System from<i>Gordonia</i>sp. TF6

2004· article· en· W2003941871 on OpenAlexaff
Tadashi Fujii, Tatsuya NARIKAWA, Koji Takeda, Junichi Kato

Bibliographic record

VenueBioscience Biotechnology and Biochemistry · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial metabolism and enzyme function
Canadian institutionsNutrasource
FundersMinistry of Economy, Trade and Industry
KeywordsBiotransformationAlkaneEscherichia coliMonooxygenaseStereochemistryChemistryReductaseRubredoxinBacteriaBiochemistryOctaneMicroorganismOrganic chemistryEnzymeGeneBiologyHydrocarbonCytochrome P450Genetics

Abstract

fetched live from OpenAlex

Biotransformation using alkane-oxidizing bacteria or their alkane hydroxylase (AH) systems have been little studied at the molecular level. We have cloned and sequenced genes from Gordonia sp. TF6 encoding an AH system, alkB2 (alkane 1-monooxygenase), rubA3 (rubredoxin), rubA4 (rubredoxin), and rubB (rubredoxin reductase). When expressed in Escherichia coli, these genes allowed the construction of biotransformation systems for various alkanes. Normal alkanes with 5 to 13 carbons were good substrates for this biotransformation, and oxidized to their corresponding 1-alkanols. Surprisingly, cycloalkanes with 5 to 8 carbons were oxidized to their corresponding cycloalkanols as well. This is the first study to achieve biotransformation of alkanes using the E. coli expressing the minimum component genes of the AH system. Our biotransformation system has facilitated assays and analysis leading to improvement of AH systems, and has indicated a cycloalkane oxidation pathway in microorganisms for the first time.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.213
Teacher spread0.205 · 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 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

Citations41
Published2004
Admission routes1
Has abstractyes

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