MétaCan
Menu
Back to cohort
Record W3214683560 · doi:10.1039/d1sc04793k

Asymmetric patterning drives the folding of a tripodal DNA nanotweezer

2021· article· en· W3214683560 on OpenAlexafffund
Daniel Saliba, Tuan Trinh, Christophe Lachance‐Brais, Alexander L. Prinzen, Felix J. Rizzuto, Donatien de Rochambeau, Hanadi F. Sleiman

Bibliographic record

VenueChemical Science · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University
FundersKillam TrustsNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationCanada Council for the Arts
KeywordsDNA origamiDNAFolding (DSP implementation)TweezersStreptavidinBiophysicsMagnetic tweezersA-DNAChemistryNanotechnologyComputational biologyBiologyMaterials scienceBiochemistryEngineering

Abstract

fetched live from OpenAlex

DNA tweezers have emerged as powerful devices for a wide range of biochemical and sensing applications; however, most DNA tweezers consist of single units activated by DNA recognition, limiting their range of motion and ability to respond to complex stimuli. Herein, we present an extended, tripodal DNA nanotweezer with a small molecule junction. Simultaneous, asymmetric elongation of our molecular core is achieved using polymerase chain reaction (PCR) to produce length- and sequence-specific DNA arms with repeating DNA regions. When rigidified, our DNA tweezer can be addressed with streptavidin-binding ligands. Full control over the number, separation, and location of these ligands enables site-specific streptavidin recognition; all three arms of the DNA nanotweezer wrap around multiple streptavidin units simultaneously. Our approach combines the simplicity of DNA tile arrays with the size regime normally provided by DNA origami, offering an integrated platform for the use of branched DNA scaffolds as structural building blocks, protein sensors, and dynamic, stimuli-responsive materials.

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)0.001
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 score0.010
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.270
Teacher spread0.261 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueChemical ScienceSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207