Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production

• Published in: Nature communications Vol. 12; no. 1; p. 5825
• Main Authors: Greenhalgh, Jonathan C., Fahlberg, Sarah A., Pfleger, Brian F., Romero, Philip A.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05-10-2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/326/2522; 631/553/552; 631/61/318; 82/80; Alcohol; Alcohols; Aldehyde Oxidoreductases - genetics; Aldehyde Oxidoreductases - metabolism; Binding sites; Biosynthesis; Engineers; Enzymatic activity; Enzyme activity; Enzymes; Fatty Alcohols - metabolism; Fitness; Humanities and Social Sciences; In vivo methods and tests; Learning algorithms; Machine Learning; Metabolic engineering; Metabolic Engineering - methods; Microorganisms; multidisciplinary; Protein engineering; Proteins; Reductases; Science; Science (multidisciplinary); Substrates; Thioesters
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-25831-w

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.
Fatty acyl reductases (FARs) are critical enzymes in the biosynthesis of fatty alcohols and have the ability to directly acces acyl-ACP substrates. Here, authors couple machine learning-based protein engineering framework with gene shuffling to optimize a FAR for the activity on acyl-ACP and improve fatty alcohol production.