Functional Annotation of an Enzyme Family by Integrated Strategy Combining Bioinformatics with Microanalytical and Microfluidic Technologies
ChemRxiv(2021)
摘要
Next-generation
sequencing technologies enable doubling of the genomic databases every 2.5
years. Collected sequences represent a rich source of novel biocatalysts. However,
the rate of accumulation of sequence data exceeds the rate of functional
studies, calling for acceleration and miniaturization of biochemical assays. Here,
we present an integrated platform employing bioinformatics, microanalytics, and
microfluidics and its application for exploration of unmapped sequence space,
using haloalkane dehalogenases as model enzymes. First, we employed bioinformatic analysis for identification
of 2,905 putative dehalogenases and rational selection of 45 representative
enzymes. Second, we expressed and experimentally characterized 24 enzymes showing
sufficient solubility for microanalytical and microfluidic testing. Miniaturization
increased the throughput to 20,000 reactions per day with 1000-fold lower
protein consumption compared to conventional assays. A single run of the platform
doubled dehalogenation toolbox of family members characterized over three
decades. Importantly, the dehalogenase activities of nearly one-third of these novel
biocatalysts far exceed that of most published HLDs. Two enzymes showed unusually
narrow substrate specificity, never before reported for this enzyme family. The
strategy is generally applicable to other enzyme families, paving the way
towards the acceleration of the process of identification of novel biocatalysts
for industrial applications but also for the collection of homogenous data for machine
learning. The automated in silico workflow has been
released as a user-friendly web-tool EnzymeMiner: https://loschmidt.chemi.muni.cz/enzymeminer/.
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