Nanomechanical measurements of the sequence-dependent folding landscapes of single nucleic acid hairpins
Bibliographic record
Abstract
Nucleic acid hairpins provide a powerful model system for probing the formation of secondary structure. We report a systematic study of the kinetics and thermodynamics of the folding transition for individual DNA hairpins of varying stem length, loop length, and stem GC content. Folding was induced mechanically in a high-resolution optical trap using a unique force clamp arrangement with fast response times. We measured 20 different hairpin sequences with quasi-random stem sequences that were 6-30 bp long, polythymidine loops that were 3-30 nt long, and stem GC content that ranged from 0% to 100%. For all hairpins studied, folding and unfolding were characterized by a single transition. From the force dependence of these rates, we determined the position and height of the energy barrier, finding that the transition state for duplex formation involves the formation of 1-2 bp next to the loop. By measuring unfolding energies spanning one order of magnitude, transition rates covering six orders of magnitude, and hairpin opening distances with subnanometer precision, our results define the essential features of the energy landscape for folding. We find quantitative agreement over the entire range of measurements with a hybrid landscape model that combines thermodynamic nearest-neighbor free energies and nanomechanical DNA stretching energies.
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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 | 0.000 |
| 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".