Biophysical characterization and design of a minimal version of the Hoechst RNA aptamer
Bibliographic record
Abstract
RNA aptamers are oligonucleotides, selected through Systematic Evolution of Ligands by EXponential Enrichment (SELEX), that can bind to specific target molecules with high affinity. One such molecule is the RNA aptamer that binds to a blue-fluorescent Hoechst dye that was modified with bulky t-Bu groups to prevent non-specific binding to DNA. This aptamer has potential for biosensor applications; however, limited information is available regarding its conformation, molecular interactions with the ligand, and binding mechanism. The study presented here aims to biophysically characterize the Hoechst RNA aptamer when complexed with the t-Bu Hoechst dye and to further optimize the RNA sequence by designing and synthesizing new sequence variants. Each variant aptamer-t-Bu Hoechst complex was evaluated through a combination of fluorescence emission, native polyacrylamide gel electrophoresis, fluorescence titration, and isothermal titration calorimetry experiments. The results were used to design a minimal version of the aptamer consisting of only 21 nucleotides. The performed study also describes a more efficient method for synthesizing the t-Bu Hoechst dye derivative. Understanding the biophysical properties of the t-Bu Hoechst dye-RNA complex lays the foundation for nuclear magnetic resonance spectroscopy studies and its potential development as a building block for an aptamer-based biosensor that can be used in medical, environmental or laboratory settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".