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Record W2561814596 · doi:10.1093/nar/gkw1294

Salt-mediated two-site ligand binding by the cocaine-binding aptamer

2016· article· en· W2561814596 on OpenAlexaff
Miguel A. D. Neves, Sladjana Slavkovic, Zachary R. Churcher, Philip E. Johnson

Bibliographic record

VenueNucleic Acids Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsYork University
Fundersnot available
KeywordsAptamerIsothermal titration calorimetryLigand (biochemistry)Binding siteBiophysicsBiologyMolecular bindingBiomoleculeAffinitiesNuclear magnetic resonance spectroscopyBinding affinitiesBiochemistryChemistryStereochemistryMoleculeGeneticsReceptor

Abstract

fetched live from OpenAlex

Multisite ligand binding by proteins is commonly utilized in the regulation of biological systems and exploited in a range of biochemical technologies. Aptamers, although widely utilized in many rationally designed biochemical systems, are rarely capable of multisite ligand binding. The cocaine-binding aptamer is often used for studying and developing sensor and aptamer-based technologies. Here, we use isothermal titration calorimetry (ITC) and NMR spectroscopy to demonstrate that the cocaine-binding aptamer switches from one-site to two-site ligand binding, dependent on NaCl concentration. The high-affinity site functions at all buffer conditions studied, the low-affinity site only at low NaCl concentrations. ITC experiments show the two ligand-binding sites operate independently of one another with different affinities and enthalpies. NMR spectroscopy shows the second binding site is located in stem 2 near the three-way junction. This ability to control ligand binding at the second site by adjusting the concentration of NaCl is rare among aptamers and may prove a useful in biotechnology applications. This work also demonstrates that in vitro selected biomolecules can have functions as complex as those found in nature.

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)0.001
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 score0.071
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.025
GPT teacher head0.342
Teacher spread0.317 · 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

Citations53
Published2016
Admission routes1
Has abstractyes

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