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Record W4293817200 · doi:10.1049/enb2.12024

A curcumin direct protein biosensor for cell‐free prototyping

2022· article· en· W4293817200 on OpenAlexaff
Agata Kennedy, Guy D. Griffin, Paul S. Freemont, Karen M. Polizzi, Simon J. Moore

Bibliographic record

VenueEngineering Biology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsKensington Health
FundersEngineering and Physical Sciences Research Council
KeywordsBiosensorSynthetic biologyCurcuminFluorescenceSubstrate (aquarium)Protein engineeringGreen fluorescent proteinCell-free protein synthesisChemistryCombinatorial chemistryEscherichia coliBiochemistryCofactorCell-free systemDirected evolutionNanotechnologyEnzymeComputational biologyBiologyProtein biosynthesisMaterials scienceGene

Abstract

fetched live from OpenAlex

Abstract In synthetic biology, biosensors are routinely coupled with a gene expression system for detecting small molecules and physical signals. We reveal a fluorescent complex, based on the interaction of an Escherichia coli double bond reductase ( Ec CurA), as a detection unit with its substrate curcumin—we call this a direct protein (DiPro) biosensor. Using a cell‐free synthetic biology approach, we use the Ec CurA DiPro biosensor to fine tune 10 reaction parameters (cofactor, substrate, and enzyme levels) for cell‐free curcumin biosynthesis, assisted through acoustic liquid handling robotics. Overall, we increase Ec CurA‐curcumin DiPro fluorescence within cell‐free reactions by 78‐fold. This finding adds to the growing family of protein–ligand complexes that are naturally fluorescent and potentially exploitable for a range of applications, including medical imaging to engineering high‐value chemicals.

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 score0.003
Threshold uncertainty score0.011

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.006
GPT teacher head0.273
Teacher spread0.267 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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