Native Characterization of Noncanonical Nucleic Acid Thermodynamics via Programmable Dynamic DNA Chemistry
Bibliographic record
Abstract
Folding thermodynamics, quantitatively described using parameters such as Δ G fold °, Δ H fold °, and Δ S fold °, is essential for characterizing the stability and functionality of noncanonical nucleic acid structures but remains difficult to measure at the molecular level. Leveraging the programmability of dynamic deoxyribonucleic acid (DNA) chemistry, we introduce a DNA-based molecular tool capable of performing a free energy shift assay (FESA) that directly characterizes the thermodynamics of noncanonical DNA structures in their native environments. FESA operates by the rational design of a reference DNA probe that is energetically equivalent to a target noncanonical nucleic acid structure in a series of toehold-exchange reactions, yet is structurally incapable of folding. As a result, a free energy shift (ΔΔ G rxn ° ) is observed when plotting the reaction yield against the free energy of each toehold-exchange. We mathematically demonstrated that Δ G fold °, Δ H fold °, and Δ S fold ° of the analyte can be calculated based on ΔΔ G rxn ° . After validating FESA using six DNA hairpins by comparing the measured Δ G fold °, Δ H fold °, and Δ S fold ° values against predictions made by NUPACK software, we adapted FESA to characterize noncanonical nucleic acid structures, encompassing DNA triplexes, G-quadruplexes, and aptamers. This adaptation enabled the successful characterization of the folding thermodynamics for these complex structures under various experimental conditions. The successful development of FESA marks a paradigm shift and a technical advancement in characterizing the thermodynamics of noncanonical DNA structures through molecular tools. It also opens new avenues for probing fundamental chemical and biophysical questions through the lens of molecular engineering and dynamic DNA chemistry.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".