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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. 

Abstract Microbial lipid metabolism is an attractive route for producing oleochemicals. The predominant strategy centers on heterologous thioesterases to synthesize desired chain-length fatty acids. To convert acids to oleochemicals (e.g., fatty alcohols, ketones), the narrowed fatty acid pool needs to be reactivated as coenzyme A thioesters at cost of one ATP per reactivation - an expense that could be saved if the acyl-chain was directly transferred from ACP- to CoA-thioester. Here, we demonstrate such an alternative acyl-transferase strategy by heterologous expression of PhaG, an enzyme first identified inPseudomonads, that transfers 3-hydroxy acyl-chains between acyl-carrier protein and coenzyme A thioester forms for creating polyhydroxyalkanoate monomers. We use it to create a pool of acyl-CoA’s that can be redirected to oleochemical products. Through bioprospecting, mutagenesis, and metabolic engineering, we develop three strains ofEscherichia colicapable of producing over 1 g/L of medium-chain free fatty acids, fatty alcohols, and methyl ketones. 

Bacteria have historically been a rich source of natural products ( e.g. polyketides and non-ribosomal peptides) that possess medically-relevant activities. Despite extensive discovery programs in both industry and academia, a plethora of biosynthetic pathways remain uncharacterized and the corresponding molecular products untested for potential bioactivities. This knowledge gap comes in part from the fact that many putative natural product producers have not been cultured in conventional laboratory settings in which the corresponding products are produced at detectable levels. Next-generation sequencing technologies are further increasing the knowledge gap by obtaining metagenomic sequence information from complex communities where production of the desired compound cannot be isolated in the laboratory. For these reasons, many groups are turning to synthetic biology to produce putative natural products in heterologous hosts. This strategy depends on the ability to heterologously express putative biosynthetic gene clusters and produce relevant quantities of the corresponding products. Actinobacteria remain the most abundant source of natural products and the most promising heterologous hosts for natural product discovery and production. However, researchers are discovering more natural products from other groups of bacteria, such as myxobacteria and cyanobacteria. Therefore, phylogenetically similar heterologous hosts have become promising candidates for synthesizing these novel molecules. The downside of working with these microbes is the lack of well-characterized genetic tools for optimizing expression of gene clusters and product titers. This review examines heterologous expression of natural product gene clusters in terms of the motivations for this research, the traits desired in an ideal host, tools available to the field, and a survey of recent progress.