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Monitoring Enzymatic Proteolysis Using Either Enzyme- or Substrate-Bioconjugated Quantum Dots
S A Díaz1, J C Breger1, I L Medintz2
1Center for Bio/Molecular Science and Engineering, Code 6900, U.S. Naval Research Laboratory, Washington, District of Columbia, USA; American Society for Engineering Education, Washington, District of Columbia, USA.
Abstract:
Rational design of enzyme-nanoparticle hybrids is still in its infancy and the design is often inspired by potential access to many beneficial sensing properties such as increased stability, sensitivity, and even enhanced enzyme activities in specific cases. Deriving quantitative kinetic data from these constructs is not trivial, however, since the intrinsic design gives rise to unique properties that can influence the enzymatic assays that are central to the application of the hybrids. Here, we present two distinct assay methodologies for following the kinetic activity of composite enzyme-nanoparticle constructs. We utilize luminescent semiconductor nanocrystals or quantum dots (QDs) as the prototypical nanoparticulate platform for these sensing formats and target proteolytic enzyme activity as the main assay. The first assay is analogous to most current enzymatic assays and is designed to compare QD-enzyme constructs; this format is based on utilizing a fixed concentration of enzyme displayed on the QD and excess substrate in the solution, and the analysis utilizes data from initial velocities. The second assay is designed to analyze kinetics using a QD-substrate construct, in which the enzyme and QD interactions are short lived. Here, the nanoparticle-substrate concentration is held constant and exposed to increasing concentrations of the enzyme in solution. This later methodology is based on a fluorescent ratiometric signal that follows the entire progress curve of the enzyme reaction. A comparison of these two different assays of the series of enzyme-nanoparticle and substrate-nanoparticle constructs provides deeper insight into the enzyme kinetics of these hybrids, while still testing of individual variables within a given format, to allow for further optimization within each set.
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