Exploring Molecular Binding: A Fluorescence Anisotropy Lab for Undergraduates
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".