Integrative genomic mining for enzyme function to enable engineering of a non-natural biosynthetic pathway

Wai Shun Mak, Stephen Tran, Ryan Marcheschi, Steve Bertolani, James Thompson, David Baker, James C. Liao () and Justin B. Siegel ()
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Wai Shun Mak: University of California Davis, One Shields Avenue, Davis, California 95616, USA
Stephen Tran: University of California, Los Angeles
Ryan Marcheschi: University of California Los Angeles
Steve Bertolani: University of California Davis, One Shields Avenue, Davis, California 95616, USA
James Thompson: University of Washington
David Baker: University of Washington
James C. Liao: University of California Los Angeles
Justin B. Siegel: University of California Davis, One Shields Avenue, Davis, California 95616, USA

Nature Communications, 2015, vol. 6, issue 1, 1-10

Abstract: Abstract The ability to biosynthetically produce chemicals beyond what is commonly found in Nature requires the discovery of novel enzyme function. Here we utilize two approaches to discover enzymes that enable specific production of longer-chain (C5–C8) alcohols from sugar. The first approach combines bioinformatics and molecular modelling to mine sequence databases, resulting in a diverse panel of enzymes capable of catalysing the targeted reaction. The median catalytic efficiency of the computationally selected enzymes is 75-fold greater than a panel of naively selected homologues. This integrative genomic mining approach establishes a unique avenue for enzyme function discovery in the rapidly expanding sequence databases. The second approach uses computational enzyme design to reprogramme specificity. Both approaches result in enzymes with >100-fold increase in specificity for the targeted reaction. When enzymes from either approach are integrated in vivo, longer-chain alcohol production increases over 10-fold and represents >95% of the total alcohol products.

Date: 2015
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DOI: 10.1038/ncomms10005

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