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Record W2990744283 · doi:10.1002/bit.27229

The phage display of <i>Bacillus subtilis</i> Lipase A significantly enhances catalytic activity due to altered nanoscale distribution in colloidal solution

2019· article· en· W2990744283 on OpenAlexafffund
Sharifun Nahar, Esen Sokullu, Marc A. Gauthier

Bibliographic record

VenueBiotechnology and Bioengineering · 2019
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBacillus subtilisLipaseNanoscopic scaleColloidChemistryDistribution (mathematics)Phage displayEnzymeCatalysisBiochemistryBiologyNanotechnologyBacteriaMaterials scienceOrganic chemistryPeptideMathematicsGenetics

Abstract

fetched live from OpenAlex

Screening libraries of mutant proteins by phage display is now relatively common. However, one unknown factor is how the bacteriophage scaffold itself influences the properties of the displayed protein. This communication evaluates the effect of solution parameters on the catalytic activity of phage displayed Bacillus subtilis Lipase A (BSLA), compared to the free enzyme in solution. While the pH- and temperature-activity profiles of BSLA were not intrinsically affected by phage display, the nanoscale distribution of BSLA within the micellar assay buffer was. This lead to a pronounced increase of activity of phage-BSLA relative to the free enzyme, owing to the accumulation of phage-BSLA at the substrate-rich micelles. Considering this result obtained for BSLA, caution is warranted and similar effects should be considered when selecting other enzymes/proteins by phage display, as the activity of the displayed protein may differ from that of the free protein.

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 score0.033
Threshold uncertainty score0.315

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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

Citations5
Published2019
Admission routes2
Has abstractyes

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