Enzymes are the ultimate ground on which life stands, enabling all biosynthetic and degradation processes in nature. Unsurprisingly, scientists and engineers have focused much attention on leveraging enzymes in industrial applications and trying to engineer new biocatalysts with improved properties.
The genomic revolution has transformed the number of protein sequences available for study, but only a tiny fraction of such genes have been experimentally characterized. To address this issue, researchers at Masaryk University, Greifswald University and ETH Zurich have recently developed a highly efficient enzyme mining strategy that combines microfluidics with modern global data analysis tools.
In their Chem Catalysis paper, the researchers report a pipeline integrating advanced sequence and structural bioinformatics with microfluidic enzymology. In brief, advanced bioinformatic methods are used to prioritize a select list of candidates for ‘focused’ experimental characterization. Next, two different microfluidic platforms are used to extract thermostability data and steady-state kinetics/reaction thermodynamics parameters. Using such an approach, they were able to experimentally characterize large numbers of enzyme variants, and more importantly, showed that some of these enzymes surpass previously known variants in terms of their “catalytic performance”. Put simply, using their pipeline, the authors were able to obtain unique mechanistic insights regarding enzyme action and discover high-performance variants that have industrial utility. It is expected that the creation of an automated droplet-based microfluidic device, coupled with advanced bioinformatic algorithms, will allow the development of a new protein engineering tools and engender the data-driven prediction of enzyme function.
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