Abstract
Alcohol-forming fatty acyl reductases (FARs) catalyze the reduction of thioesters to alcohols and are key enzymes for microbial production of fatty alcohols. Many metabolic engineering strategies utilize FARs to produce fatty alcohols from intracellular acyl-CoA and acyl-ACP pools; however, enzyme activity, especially on acyl-ACPs, remains a significant bottleneck to high-flux production. Here, we engineer FARs with enhanced activity on acyl-ACP substrates by implementing a machine learning (ML)-driven approach to iteratively search the protein fitness landscape. Over the course of ten design-test-learn rounds, we engineer enzymes that produce over twofold more fatty alcohols than the starting natural sequences. We characterize the top sequence and show that it has an enhanced catalytic rate on palmitoyl-ACP. Finally, we analyze the sequence-function data to identify features, like the net charge near the substrate-binding site, that correlate with in vivo activity. This work demonstrates the power of ML to navigate the fitness landscape of traditionally difficult-to-engineer proteins.
Highlights
Alcohol-forming fatty acyl reductases (FARs) catalyze the reduction of thioesters to alcohols and are key enzymes for microbial production of fatty alcohols
We show that the algorithm converges on highly active acyl-ACP reductases that produce 4.9-fold more fatty alcohols than MA-ACR
We focused our protein engineering efforts on MA-ACR from Marinobacter aquaeloei because it displays high in vivo activity on acyl-CoA substrates[7,8,9], and it was suspected to accept acyl-ACP substrates
Summary
Alcohol-forming fatty acyl reductases (FARs) catalyze the reduction of thioesters to alcohols and are key enzymes for microbial production of fatty alcohols. These enzymes can be incorporated to feed off of the reverse beta oxidation pathway to yield high levels of alcohols[8] Another common metabolic engineering strategy involves terminating the host organism’s fatty acid elongation cycle with a thioesterase to produce a fatty acid that can be converted to an acyl-CoA by an ATP-dependent ligase, and converted to an alcohol by a FAR7,9,12. We perform a statistical analysis of the landscape and identify key sequence elements that contribute to enzyme activity Many of these elements are located near the enzyme’s putative substrate entry channel and may be involved with modulating the preference between acyl-CoA and acyl-ACP substrates. These results open future directions to engineer enzymes for efficient microbial production of fatty alcohols
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