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Bioorthogonal Modification of the Major Sheath Protein of Bacteriophage M13: Extending the Versatility of Bionanomaterial Scaffolds

2016· article· en· W2519755791 on OpenAlexafffund
Taylor Urquhart, Elisabeth Daub, John F. Honek

Bibliographic record

VenueBioconjugate Chemistry · 2016
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversity of Waterloo
FundersWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsBioorthogonal chemistryChemistryBacteriophageAzideCycloadditionCombinatorial chemistryBacteriophage MS2Escherichia coliBiophysicsBiochemistryClick chemistryGeneOrganic chemistry

Abstract

fetched live from OpenAlex

Daltons and composed of approximately 2700 proteins, bacteriophage M13 has been employed as a molecular scaffold in bionanomaterials fabrication. In order to extend the versatility of M13 in this area, residue-specific unnatural amino acid incorporation was employed to successfully display azide functionalities on specific solvent-exposed positions of the pVIII major sheath protein of this bacteriophage. Employing a combination of engineered mutants of the gene coding for the pVIII protein, the methionine (Met) analog, l-azidohomoalanine (Aha), and a suitable Escherichia coli Met auxotroph for phage production, conditions were developed to produce M13 bacteriophage labeled with over 350 active azides (estimated by fluorescent dye labeling utilizing a strain-promoted azide-alkyne cycloaddition) and capable of azide-selective attachment to 5 nm gold nanoparticles as visualized by transmission electron microscopy. The capability of this system to undergo dual labeling utilizing both chemical acylation and bioorthogonal cycloaddition reactions was also verified. The above stratagem should prove particularly advantageous in the preparation of assemblies of larger and more complex molecular architectures based on the M13 building block.

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 score0.090
Threshold uncertainty score0.619

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.273
Teacher spread0.251 · 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

Citations19
Published2016
Admission routes2
Has abstractyes

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