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Record W2527736092 · doi:10.1002/btpr.2376

Recovery of functionally‐active protein from inclusion bodies using a thermal‐cycling method

2016· article· en· W2527736092 on OpenAlexafffund
Rahul Sadavarte, Carlos D. M. Filipe, Raja Ghosh

Bibliographic record

VenueBiotechnology Progress · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein purification and stability
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsInclusion bodiesTemperature cyclingEnzymeChemistryEscherichia coliHeterologousCyclingRecombinant DNABiochemistryUreaProtein foldingBiophysicsBiologyThermalGene

Abstract

fetched live from OpenAlex

Heterologous overexpression of genes in Escherichia coli has made it possible to obtain high titers of recombinant proteins. However, this can result in the formation of aggregated protein particles known as ‘inclusion bodies’. Protein sequestered as inclusion body is inactive and needs to be converted back to its functional form by refolding using appropriate techniques. In the current study inclusion bodies of the enzyme aminoglycoside nucleotidyl transferase (or ANT(2″)‐Ia) were first solubilized in urea and subsequently subjected to thermal cycling under controlled conditions as part of the refolding strategy. Thermal cycling led to disaggregation of the individual protein chains and simultaneously refolding the released protein molecules to their native state. The optimum condition was identified as 10–80°C thermal cycling at 3°C s −1 for 2 h. Enzyme activity measurements showed that thermal cycling under optimized conditions resulted in 257% activity recovery when compared with nonrefolded protein. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:133–139, 2017

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 score0.149
Threshold uncertainty score0.413

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 science0.0000.001
Research integrity0.0010.000
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 head0.017
GPT teacher head0.287
Teacher spread0.270 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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