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Record W3090427828 · doi:10.1093/femsle/fnaa162

Temperature-sensitive recombinant subtilisin protease variants that efficiently degrade molecular biology enzymes

2020· article· en· W3090427828 on OpenAlexafffund
Vanessa C. Thompson, Bailey E. McGuire, Mia S. Frier, Max S G Legg, Tyler W Dyer, Geoff Gudavicius, Sheila Potter, Francis E. Nano

Bibliographic record

VenueFEMS Microbiology Letters · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Production and Characterization
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSubtilisinRecombinant DNAEnzymeProteaseBiochemistryBiologyAlkaline proteaseChemistryComputational biologyGene

Abstract

fetched live from OpenAlex

We used error-prone PCR to generate mutations in a subtilisin protease-encoding gene, and screened for recombinants that expressed temperature-sensitive (TS) variants. From the dozens of mutations that we detected in the recombinant genes we found that those mutations that affected aspartate-75 had the most profound effect on temperature stability. We thus focused our analysis on two variants of subtilisin C, the more heat-sensitive variant 24 (V24), with amino acid changes D75G, L234M and Q274P; and variant 25 (V25), with a single amino acid change, D75A. For V24 a two log-fold reduction in activity occurs in under 10 min at 50°C. For V25, a two log-fold reduction occurs at 60°C, a temperature that reduces the activity of the wild type enzyme by about 30%. The V24 variant fully inactivates enzymes commonly used in molecular biology research and in molecular diagnostics, and is stabilized against autolysis with propylene glycol concentrations of 10% or greater. The subtilisin variants are produced by a strain of Bacillus subtilis that lacks expression of its native secreted proteases, and the variants can be isolated from the supernatants using nickel affinity chromatography.

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 categoriesMeta-epidemiology (narrow)
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 score0.016
Threshold uncertainty score1.000

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 head0.010
GPT teacher head0.223
Teacher spread0.213 · 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.

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

Citations1
Published2020
Admission routes2
Has abstractyes

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