Water-in-oil droplets, made and handled in microfluidic devices, provide a new experimental format, in which ultrahigh throughput experiments can be conducted faster and with minimal reagent consumption. An increasing number of studies have emerged that applied this approach to directed evolution and metagenomic screening of enzyme catalysts. Here, we review the considerations necessary to implement robust workflows, based on choices of device design, detection modes, emulsion formulations and substrates, and scope out which enzyme classes have become amenable to droplet screening.
Keywords: Catalytic promiscuity; Directed evolution; Functional metagenomics; Hydrolase; In vitro compartmentalization; Machine learning; Microdroplets; Next generation sequencing; Oxidoreductase; Ultrahigh throughput screening.
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