Visualizing structure-mediated interactions in supercoiled DNA molecules
Bibliographic record
Abstract
ABSTRACT We directly visualize the topology-mediated interactions between an unwinding site on a supercoiled DNA plasmid and a specific probe molecule designed to bind to this site, as a function of DNA supercoiling and temperature. The visualization relies on containing the DNA molecules within an enclosed array of glass nanopits using the Convex Lens-induced Confinement (CLiC) imaging method. This method traps molecules within the focal plane while excluding signal from out-of-focus probes. Simultaneously, the molecules can freely diffuse within the nanopits, allowing for accurate measurements of exchange rates, unlike other methods which could introduce an artifactual bias in measurements of binding kinetics. We demonstrate that the plasmid’s structure influences the binding of the fluorescent probes to the unwinding site through the presence, or lack, of other secondary structures. With this method, we observe an increase in the binding rate of the fluorescent probe to the unwinding site with increasing temperature and negative supercoiling. This increase in binding is consistent with the results of our numerical simulations of the probability of site-unwinding. The temperature dependence of the binding rate has allowed us to distinguish the effects of competing higher order DNA structures, such as Z-DNA, in modulating local site-unwinding, and therefore binding.
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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 | 0.000 |
| 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".