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Record W2028851272 · doi:10.1021/ac302047e

DNase-Mediated Single-Cycle Selection of Aptamers for Proteins Blotted on a Membrane

2012· article· en· W2028851272 on OpenAlexafffund
Yanming Liu, Chuan Wang, Feng Li, Shengwen Shen, D. Lorne Tyrrell, X. Chris Le, Xing‐Fang Li

Bibliographic record

VenueAnalytical Chemistry · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsChemistryAptamerSelection (genetic algorithm)Membrane proteinMembraneBiophysicsChromatographyBiochemistryComputational biologyMolecular biologyArtificial intelligence

Abstract

fetched live from OpenAlex

We describe a single-cycle DNA aptamer selection strategy that is able to obtain high affinity aptamers (K(d) of sub-nM) directly from a protein blotted on membrane. The key to the success of this strategy is the unique use of DNase I digestion to remove unwanted ssDNA from the membrane, leaving only the strongest bound aptamers. A crude Hepatitis B virus core protein (HBcAg) was separated using polyacrylamide gel electrophoresis (PAGE) and electro-blotted onto a polyvinylidene fluoride (PVDF) membrane. The membrane strip containing HBcAg and a second membrane strip containing human serum proteins were coincubated with a ssDNA library consisting of ∼10 copies each of 10(15) random sequences. Unbound and weakly bound sequences were efficiently removed from the membrane containing HBcAg using DNase I digestion and gradient wash with urea buffers. The remaining ssDNA bound to the target consisted of approximately 500 molecules, from which two aptamers with high affinity (K(d) ∼100 and 200 pM) were identified. This technique can be potentially used for selection of aptamers directly from multiple proteins that are separated by gel electrophoresis from a biological mixture.

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 score0.004
Threshold uncertainty score0.581

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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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

Citations39
Published2012
Admission routes2
Has abstractyes

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