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Record W4406150917 · doi:10.1021/acs.jchemed.4c01023

Exploring Molecular Binding: A Fluorescence Anisotropy Lab for Undergraduates

2025· article· en· W4406150917 on OpenAlexafffund
Maxine Forder, Maira Rivera, Jasmine Phénix, Tara Shomali, Lisa Marie Munter, Maureen McKeague

Bibliographic record

VenueJournal of Chemical Education · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationGénome Québec
KeywordsAptamerFluorescence anisotropyNucleic acidFluorescenceMacromoleculeChemistryBiophysicsBiophysical chemistryNanotechnologyBiochemistryBiologyMaterials sciencePhysicsGenetics

Abstract

fetched live from OpenAlex

Measuring interactions between macromolecules is essential for elucidating their dynamics in solution and is critical for the design and study of potential therapeutics. Fluorescence anisotropy has been a powerful and widely used tool for studying binding interactions. When a fluorescent partner is excited with polarized light while bound to its cognate ligand, the emitted light is partially polarized. This process is dependent on the concentration of bound molecules, permitting the determination of the binding affinity. Here, we outline a highly modular undergraduate-level laboratory in which students use fluorescence anisotropy to measure binding interactions between a synthetic nucleic acid aptamer and its protein target. Students learn the theory of functional nucleic acids and the principles of fluorescence while exploring the relevance of aptamer sequence/structure activity relationships via mutations to aptamers. With this approach, students can deepen their knowledge about macromolecular interactions and are able to develop valuable analytical and biophysical laboratory skills. This experiment is highly adaptable to suit a range of funding and instructor availability, making it accessible and tailorable to most laboratory settings.

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 score0.285

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 head0.026
GPT teacher head0.319
Teacher spread0.293 · 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
Published2025
Admission routes2
Has abstractyes

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