Quantum Dot-Based Concentric FRET Configuration for the Parallel Detection of Protease Activity and Concentration
Bibliographic record
Abstract
Protease expression, activity, and inhibition play crucial roles in a multitude of biological processes; however, these three aspects of their function are difficult for any one bioanalytical probe to measure. To help address this challenge, we report a multifunctional concentric Förster resonance energy transfer (FRET) configuration that combines two modes of biorecognition using aptamers and peptide substrates coassembled to a central semiconductor quantum dot (QD). The aptamer is sensitive to the concentration of protease and the peptide is sensitive to its hydrolytic activity. The role of the QD is to serve as a nanoscale scaffold and initial donor for energy transfer with both Cyanine 3 (Cy3) and Alexa Fluor 647 (A647) fluorescent dyes associated with the aptamer and peptide, respectively. Using thrombin as a model protease, we show that a ratiometric analysis of the emission from the QD, Cy3, and A647 permits discrimination between thrombin and thrombin-like activity, and distinguishes between active, reversibly inhibited, and irreversibly inhibited thrombin. Reliable quantitative results were obtained from a kinetic analysis of the changes in FRET. This concentric FRET format, which capitalizes on both the physical and optical properties of QDs, should be adaptable to other protease targets for which both peptide substrates and binding aptamers are known. It is thus expected to become valuable a tool for the real-time analysis of protease activity and regulation.
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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 | 0.000 |
| 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".