Journal
CHEM
Volume 7, Issue 4, Pages 1066-1079Publisher
CELL PRESS
DOI: 10.1016/j.chempr.2021.02.011
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Funding
- Swiss National Science Foundation [205321/176011/1]
- ETH Research Grant [ETH-02 14-1]
- CETOCOEN EXCELLENCE Teaming 2 project - Horizon 2020 Program [857560]
- Czech Ministry of Education, Youth and Sports [02.1.01/0.0/0.0/18_046/0015975]
- Operational Program Research, Development and Education Project, MSCA fellow @ MUNI [CZ.02.2.69/0.0/0.0/17_050/0008496]
- RECETOX research infrastructure of the Czech Ministry of Education, Youth and Sports [LM2018121]
- Swiss National Science Foundation (SNF) [205321_176011] Funding Source: Swiss National Science Foundation (SNF)
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The development of a droplet-based microfluidic platform has enabled high-throughput acquisition of transient kinetic data for enzymes under various substrate concentrations and temperatures. This platform significantly reduces assay volumes and increases throughput when compared to conventional methods. Through studying model enzymes and engineered variants, new insights into enzyme design and applications have been gained, complemented by molecular dynamics simulations.
The ability to engineer enzymes for industrial and biomedical applications is primarily limited by a paucity of mechanistic understanding. To gain insight into the mechanisms of enzyme catalysis, one must screen enormous numbers of discrete reaction conditions, which is a laborious task using conventional technologies. To address such limitations, we develop a droplet-based microfluidic platform for high-throughput acquisition of transient kinetic data over a range of substrate concentrations and temperatures. When compared with conventional methods, our platform reduces assay volumes by six orders of magnitude and increases throughput to 9,000 reactions/min. To demonstrate their utility, we measure the transient kinetics of three model enzymes, namely, beta-galactosidase, horseradish peroxidase, and microperoxidase. Additionally, we conduct a complex kinetic and thermodynamic study of engineered variants of haloalkane dehalogenases. Datasets are globally analyzed and complemented by molecular dynamics simulations, providing new insights into the molecular basis of substrate specificity and the role of hydration-related entropy.
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