Rational Design of a New Class of Versatile Enzyme-Based Biosensors
Bibliographic record
Abstract
The majority of enzyme-based sensors rely on electrochemical approaches for continuous monitoring. For example, commercially available glucometers are electrochemical-based sensors. However, these sensors are not suitable for contactless monitoring as electron signals require direct conduction from the enzyme reaction site to the signal analyzing unit. Fluorescent dyes, on the other hand, emit photons that can penetrate certain barriers, making them ideal candidates for contactless monitoring. In this study, we investigated the design and functionality of a new class of biosensors based on the conjugation of enzymes with pH-sensitive fluorophores, creating a novel single-molecule biosensor capable of versatile, contactless detection of different disease- and treatment-related biomarkers. We conjugated various enzymes (glucose oxidase, phenylalanine ammonia-lyase, and β-lactamase) with pH-sensitive fluorophores (FITC and pH-sensitive Cy7 derivatives) and tuned linkers' properties to modulate the distance between the enzyme and fluorophore, as well as the hydrophilicity of the linker. The experimental data demonstrate that fluorophore-conjugated enzymes exhibit substrate-dependent fluorescence responses under physiologically relevant buffered conditions, enabling the quantitative analysis of substrate concentrations through fluorescent signal detection. This innovative sensor design not only provides critical insights into enzyme-based fluorescent detection mechanisms but also represents a promising candidate for the development of next-generation contactless biosensing platforms.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".