Thermodynamic Basis for Engineering High-Affinity, High-Specificity Binding-Induced DNA Clamp Nanoswitches
Bibliographic record
Abstract
Naturally occurring chemoreceptors almost invariably employ structure-switching mechanisms, an observation that has inspired the use of biomolecular switches in a wide range of artificial technologies in the areas of diagnostics, imaging, and synthetic biology. In one mechanism for generating such behavior, clamp-based switching, binding occurs via the clamplike embrace of two recognition elements onto a single target molecule. In addition to coupling recognition with a large conformational change, this mechanism offers a second advantage: it improves both affinity and specificity simultaneously. To explore the physics of such switches we have dissected here the thermodynamics of a clamp-switch that recognizes a target DNA sequence through both Watson-Crick base pairing and triplex-forming Hoogsteen interactions. When compared to the equivalent linear DNA probe (which relies solely on Watson-Crick interactions), the extra Hoogsteen interactions in the DNA clamp-switch increase the probe's affinity for its target by ∼0.29 ± 0.02 kcal/mol/base. The Hoogsteen interactions of the clamp-switch likewise provide an additional specificity check that increases the discrimination efficiency toward a single-base mismatch by 1.2 ± 0.2 kcal/mol. This, in turn, leads to a 10-fold improvement in the width of the "specificity window" of this probe relative to that of the equivalent linear probe. Given these attributes, clamp-switches should be of utility not only for sensing applications but also, in the specific field of DNA nanotechnology, for applications calling for a better control over the building of nanostructures and nanomachines.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".