Electrostatic-Mediated Binding of DNA to Lysozymes: Evaluation of Aptamer-Based Assays for Highly Positively Charged Targets
Bibliographic record
Abstract
Lysozymes are a highly popular protein target for the development of aptamer-based biosensors. Because a lysozyme is a polycation and DNA is a polyanion, it is essential to separate the contribution of nonspecific electrostatic interactions from specific aptamer binding. In this study, various factors affecting the binding of DNA and lysozymes, including the DNA sequence, DNA length, pH, and salt concentration, were explored using fluorescence polarization. We concluded that direct fluorescence polarization and fluorescence intensity changes are unlikely to be directly applicable for aptamer-based biosensors to detect lysozymes because all of the tested DNA sequences showed binding. These fundamental studies confirm the dominant role of electrostatic binding. We further evaluated three other methods, including label-free fluorescent detection using a DNA staining dye, label-free colorimetric detection using gold nanoparticles, and a fluorescent sensor based on the strand displacement reaction. In each case, we focused on a random DNA sequence that is not expected to bind to the lysozyme as an aptamer. Of all the methods, only the strand displacement strategy can be potentially used to evaluate aptamer binding, as the other methods all responded to nonaptamer sequences. This study provides valuable insights for assaying aptamer binding to cationic proteins that can exhibit a nonspecific attraction to DNA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".