Rapid microfluidic mixing allows intelligent biocatalyst engineering

Microfluidic system extracts kinetics and thermodynamics of complex enzymatic reactions in high-throughput.


Enzymes are now widely used in industrial applications as replacements for standard catalysts. That said, many processes require tailor-made versions of these biocatalysts that ensure specificity towards a given substrate and yield a unique product. Unfortunately, enzyme engineering is non-trivial because it requires a mechanistic understanding of the different catalytic steps at the molecular level. Extracting such information is difficult using conventional methods, since an enormous number of discrete experiments are required to screen enough reaction conditions.


David Hess and colleagues from the deMello group and Masaryk University have developed a droplet-based microfluidic platform that overcomes the aforementioned limitations, by leveraging chaotic advection, coupled to acceleration and deceleration of droplets in serpentine channels. Their system is able to reduce assay volumes by six orders of magnitude when compared to conventional methods and increase throughput to over 9000 discrete reactions per minute. Individual droplets are detected at different points along the flow path, using stroboscopic epifluorescence imaging, which ensures that the time resolution of the measurement itself can be decoupled from the integration times associated with signal acquisition.


The performance of the platform was initially assessed using three model enzymes; b-galactosidase, horseradish peroxidase and microperoxidase. Subsequently, a complex study on the transient kinetics and thermodynamics of LinBwt, a haloalkane dehalogenase, and two of its engineered variants, LinB32 and LinB86, was successfully performed against a fluorogenic substrate. Experimental data combined with molecular simulations provided new mechanistic insights on the substrate binding steps, including the transport of water in and out of the binding cavity.


Written by Thomas Moragues

Read the full paper here





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