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Record W4416808313 · doi:10.1101/2025.11.27.690961

Unconventional DNA architecture in a dopamine–bound aptamer complex

2025· preprint· W4416808313 on OpenAlexafffund
Emily Hoi Pui Chao, Eric Largy, Yunus A. Kaiyum, Minh‐Dat Nguyen, Philippe Dauphin‐Ducharme, Philip E. Johnson, Cameron D. Mackereth

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Nucleic Acid Chemistry
Canadian institutionsUniversité de SherbrookeYork University
FundersNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche ScientifiqueInstitut National de la Santé et de la Recherche Médicale
KeywordsAptamerDNAOligonucleotideFolding (DSP implementation)Duplex (building)Binding siteBase sequenceLigand (biochemistry)Nucleic acid

Abstract

fetched live from OpenAlex

Abstract Aptamers are oligonucleotides that have been selected to bind a particular target. Despite the growing popularity of functional DNA aptamers, there remains limited knowledge of their binding mechanisms as few have been characterized at the atomic level. Here we use NMR spectroscopy to obtain structural details of RKEC1, a shortened version of a DNA aptamer previously reported to bind dopamine. We find that RKEC1 forms a compact structure upon ligand binding that lacks any Watson-Crick duplex regions or G-quadruplex core, in stark contrast to nearly all predicted and observed DNA aptamer folds. The atomic details explain dopamine specificity amongst structurally similar compounds, and the determined DNA fold was used to guide biosensor design. The aptamer structure further suggests that DNA folding can access an extensive conformational landscape reminiscent of RNA, thus expanding the diversity traditionally captured by predictive algorithms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2025
Admission routes2
Has abstractyes

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