Bait and Cleave: Exosite-Binding Peptides on Quantum Dots Selectively Accelerate Protease Activity for Sensing with Enhanced Sensitivity
Bibliographic record
Abstract
The advantageous optical properties of quantum dots (QDs) motivate their use in a wide variety of applications related to imaging and bioanalysis, including the detection of proteases and their activity. Recent studies have shown that surface chemistry on QDs is able to modulate protease activity, but only nonspecifically. Here, we present a strategy to selectively accelerate the activity of a particular target protease by as much as two orders of magnitude. Exosite-binding "bait" peptides were derived from proteins that span a range of biological roles─substrate, receptor, and inhibitor─and were used to increase the affinity of the QD-peptide conjugates for either thrombin or factor Xa, resulting in increased rates of proteolysis for coconjugated substrates. Unlike effects from QD surface chemistry, the acceleration was specific to the target protease with negligible acceleration of other proteases. Benefits of this "bait and cleave" sensing approach included detection limits that improved by more than an order of magnitude, reenabled detection of target protease against an overwhelming background of nontarget proteolysis, and mitigation of the action of inhibitors. The cumulative results point to a generalizable strategy, where the mechanism of acceleration, considerations for the design of bait peptides and conjugates, and routes to expanding the scope of this approach are discussed. Overall, this research represents a major step forward in the rational design of nanoparticle-based enzyme sensors that enhance sensitivity and selectivity.
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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 | 0.000 | 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) | 0.001 | 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".