Analyte-Driven Switching of DNA Charge Transport: <i>De Novo</i> Creation of Electronic Sensors for an Early Lung Cancer Biomarker
Bibliographic record
Abstract
A general approach is described for the de novo design and construction of aptamer-based electrochemical biosensors, for potentially any analyte of interest (ranging from small ligands to biological macromolecules). As a demonstration of the approach, we report the rapid development of a made-to-order electronic sensor for a newly reported early biomarker for lung cancer (CTAP III/NAP2). The steps include the in vitro selection and characterization of DNA aptamer sequences, design and biochemical testing of wholly DNA sensor constructs, and translation to a functional electrode-bound sensor format. The working principle of this distinct class of electronic biosensors is the enhancement of DNA-mediated charge transport in response to analyte binding. We first verify such analyte-responsive charge transport switching in solution, using biochemical methods; successful sensor variants were then immobilized on gold electrodes. We show that using these sensor-modified electrodes, CTAP III/NAP2 can be detected with both high specificity and sensitivity (K(d) ~1 nM) through a direct electrochemical reading. To investigate the underlying basis of analyte binding-induced conductivity switching, we carried out Förster Resonance Energy Transfer (FRET) experiments. The FRET data establish that analyte binding-induced conductivity switching in these sensors results from very subtle structural/conformational changes, rather than large scale, global folding events. The implications of this finding are discussed with respect to possible charge transport switching mechanisms in electrode-bound sensors. Overall, the approach we describe here represents a unique design principle for aptamer-based electrochemical sensors; its application should enable rapid, on-demand access to a class of portable biosensors that offer robust, inexpensive, and operationally simplified alternatives to conventional antibody-based immunoassays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".