Controlled Oil/Water Partitioning of Hydrophobic Substrates Extending the Bioanalytical Applications of Droplet-Based Microfluidics.

Functional annotation of novel proteins lags behind the number of sequences discovered by the next-generation sequencing. The throughput of conventional testing methods is far too low compared to sequencing; thus, experimental alternatives are needed. Microfluidics offer high throughput and reduced sample consumption as a tool to keep up with a sequence-based exploration of protein diversity. The most promising droplet-based systems have a significant limitation: leakage of hydrophobic compounds from water compartments to the carrier prevents their use with hydrophilic reagents. Here, we present a novel approach of substrate delivery into microfluidic droplets and apply it to high-throughput functional characterization of enzymes that convert hydrophobic substrates. Substrate delivery is based on the partitioning of hydrophobic chemicals between the oil and water phases. We applied a controlled distribution of 27 hydrophobic haloalkanes from oil to reaction water droplets to perform substrate specificity screening of eight model enzymes from the haloalkane dehalogenase family. This droplet-on-demand microfluidic system reduces the reaction volume 65 000-times and increases the analysis speed almost 100-fold compared to the classical test tube assay. Additionally, the microfluidic setup enables a convenient analysis of dependences of activity on the temperature in a range of 5 to 90 °C for a set of mesophilic and hyperstable enzyme variants. A high correlation between the microfluidic and test tube data supports the approach robustness. The precision is coupled to a considerable throughput of >20 000 reactions per day and will be especially useful for extending the scope of microfluidic applications for high-throughput analysis of reactions including compounds with limited water solubility.ERC Advanced Investigator grant no. 69566

Advanced database mining integrating sequence and structure bioinformatics with microfluidics challenges enzyme engineering

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Advanced Database Mining of Efficient Biocatalysts by Sequence and Structure Bioinformatics and Microfluidics

Next-generation sequencing doubles genomic databases every 2.5 years. The accumulation of sequence data provides a unique opportunity to identify interesting biocatalysts directly in the databases without tedious and time-consuming engineering. Herein, we present a pipeline integrating sequence and structural bioinformatics with microfluidic enzymology for bioprospecting of efficient and robust haloalkane dehalogenases. The bioinformatic part identified 2,905 putative dehalogenases and prioritized a “small-but-smart” set of 45 genes, yielding 40 active enzymes, 24 of which were biochemically characterized by microfluidic enzymology techniques. Combining microfluidics with modern global data analysis provided precious mechanistic insights related to the high catalytic efficiency of selected enzymes. Overall, we have doubled the dehalogenation “toolbox” characterized over three decades, yielding biocatalysts that surpass the efficiency of currently available wild-type and engineered enzymes. This pipeline is generally applicable to other enzyme families and can accelerate the identification of efficient biocatalysts for industrial use