The molecular origin of the electrostatic gating of single-molecule field-effect biosensors investigated by molecular dynamics simulations
Bibliographic record
Abstract
Field-effect biosensors (bioFETs) offer a novel way to measure the kinetics of biomolecular events such as protein function and DNA hybridization at the single-molecule level on a wide range of time scales. These devices generate an electrical current whose fluctuations are correlated to the kinetics of the biomolecule under study. BioFETs are indeed highly sensitive to changes in the electrostatic potential (ESP) generated by the biomolecule. Here, using all-atom solvent explicit molecular dynamics simulations, we further investigate the molecular origin of the variation of this ESP for two prototypical cases of proteins or nucleic acids attached to a carbon nanotube bioFET: the function of the lysozyme protein and the hybridization of a 10-nt DNA sequence, as previously done experimentally. Our results show that the ESP changes significantly on the surface of the carbon nanotube as the state of these two biomolecules changes. More precisely, the ESP distributions calculated for these molecular states explain well the magnitude of the conductance fluctuations measured experimentally. The dependence of the ESP with salt concentration is found to agree with the reduced conductance fluctuations observed experimentally for the lysozyme, but to differ for the case of DNA, suggesting that other mechanisms might be at play in this case. Furthermore, we show that the carbon nanotube does not impact significantly the structural stability of the lysozyme, corroborating that the kinetic rates measured using bioFETs are similar to those measured by other techniques. For DNA, we find that the structural ensemble of the single-stranded DNA is significantly impacted by the presence of the nanotube, which, combined with the ESP analysis, suggests a stronger DNA-device interplay. Overall, our simulations strengthen the comprehension of the inner working of field-effect biosensors used for single-molecule kinetics measurements on proteins and nucleic acids.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".