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Record W3186925921 · doi:10.1021/acscatal.1c02017

An Overview of Cytochrome P450 Immobilization Strategies for Drug Metabolism Studies, Biosensing, and Biocatalytic Applications: Challenges and Opportunities

2021· article· en· W3186925921 on OpenAlexafffund
Donya Valikhani, Juan M. Bolívar, Joelle N. Pelletier

Bibliographic record

VenueACS Catalysis · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsUniversité de MontréalPROTEOCentre in Green Chemistry and Catalysis
FundersNatural Sciences and Engineering Research Council of CanadaUniversidad Complutense de MadridComunidad de Madrid
KeywordsCytochrome P450Context (archaeology)BiosensorBiochemical engineeringDrug metabolismChemistryBiochemistryComputational biologyEnzymeBiologyEngineering

Abstract

fetched live from OpenAlex

Cytochrome P450s (P450s or CYPs) are a large superfamily of ubiquitous heme proteins. Consistent with their natural roles in oxidative metabolism of a broad range of substrates including antibiotics and xenobiotics and in the biosynthesis of complex natural products, there are two main biotechnological applications: drug metabolism studies and biocatalytic transformations. Metabolic analyses are typically performed with the P450 enzyme immobilized on a biosensing platform. Biocatalytic reactions also increasingly make use of immobilization methods with the goal of improving operational stability of the P450s. In this Review, we briefly introduce the growing field of enzyme immobilization then focus on the advances, benefits, and challenges that apply specifically to P450s. Classical immobilization methods are presented, and recently developed materials and strategies that address the need for operational stability, the multicomponent structure of P450s, their requirement for a source of electrons, and the need to overcome limited O2 availability are discussed in the context of metabolic, biosensing, and biocatalytic applications.

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: none
Teacher disagreement score0.289
Threshold uncertainty score0.656

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 head0.088
GPT teacher head0.333
Teacher spread0.245 · 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

Citations44
Published2021
Admission routes2
Has abstractyes

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