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Record W2023710052 · doi:10.1139/o06-038

Kinetic properties of chimeric class I aldehyde dehydrogenases for retinal isomers

2006· article· en· W2023710052 on OpenAlexafffundvenue
Hélène Brodeur, Samuel Chagnon, Maxime Parisotto, Sylvie Mader, Pangala V. Bhat

Bibliographic record

VenueBiochemistry and Cell Biology · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsUniversité de MontréalHôtel-Dieu de MontréalCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health Research
KeywordsAldehyde dehydrogenaseRetinalChimera (genetics)EnzymeRetinaldehydeBiochemistryAldehydeChemistryIn vivoBiologyStereochemistryGeneGenetics

Abstract

fetched live from OpenAlex

Retinal dehydrogenase type 1 (RALDH1) catalyzes the oxidation of all-trans and 9-cis retinal to the respective retinoic acids (RAs), whereas another member of the aldehyde dehydrogenase (ALDH) family, the phenobarbital-induced aldehyde dehydrogenase (PB-ALDH), is very poorly active. We have previously generated chimeras between these 2 enzymes that displayed selectivity for retinal isomers in crude bacterial extracts. Here we have characterized the kinetic properties of the corresponding purified recombinant proteins. The all-trans selective chimera RALDH-131 converted all-trans retinal to all-trans RA with 2.9-fold lower efficiency than the wild-type RALDH1 and had only residual activity with 9-cis retinal. The converse chimera PB-131 was specific for 9-cis retinal, with no residual activity for all-trans retinal. MgCl2 inhibited the activities of RALDH1 and PB-131, but not of RALDH-131, suggesting that amino acids 132-510 in RALDH are necessary for inhibition by MgCl2. These data demonstrate that the chimeric enzymes act as retinal isomer-selective ALDHs, and suggest that these enzymes may be useful to study the roles of cis RA isomers in embryogenesis and differentiation in vivo.

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 score0.026
Threshold uncertainty score0.745

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 head0.207
Teacher spread0.200 · 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

Citations2
Published2006
Admission routes3
Has abstractyes

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