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Record W3114379815 · doi:10.1002/elps.202000167

BirA*‐protein A fusion protein based BioEnhancer amplifies western blot immunosignal

2020· article· en· W3114379815 on OpenAlexaff
Zhen Wang, Ziyang Li, Tian Su, Xiao Han, Zhanwu Hou, Yupeng Zheng, Jiachen Liu, Jun Xu, Jeffy Yang, Huadong Liu

Bibliographic record

VenueElectrophoresis · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiotin and Related Studies
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsWestern blotBiotinFusion proteinDNA ligaseBlotTarget proteinMolecular biologyDot blotBiochemistryChemistryBiologyEnzymeDNARecombinant DNA

Abstract

fetched live from OpenAlex

Western blot (protein immunoblot) is a widely used analytical technique in molecular biology. Utilizing the specific recognizing primary antibody, proteins immobilized on various matrix are investigated by subsequent visualization steps, for example, by the horse radish peroxidase conjugated secondary antibody incubation. Methods to improve the sensitivity in protein identification or quantification are appreciated by biochemists. Herein, we report a new strategy to amplify Western blot signals by constructing a probe with proximal labeling and IgG targeting abilities. The R118G mutation attenuated the biotin-AMP binding affinity of the bacterial biotin ligase BirA*, offering a proximity-dependent labeling ability, which could be used as a signal amplifier. We built a BirA*-protein A fusion protein (BioEnhancer) that specifically binds to IgG and adds biotin tags to its proximal amine groups, enhancing the immunosignal of target proteins. In our experiments, the BioEnhancer system amplified the immunosignal by tenfold compared to the standard western blot. Additionally, our strategy could couple with other signal enhancement methods to further increase the western blot sensitivity.

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 score0.030
Threshold uncertainty score0.828

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 head0.218
Teacher spread0.208 · 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
Published2020
Admission routes1
Has abstractyes

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