Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production
| Autor: | Sarah A Fahlberg, Philip A. Romero, Brian F. Pfleger, Jonathan C. Greenhalgh |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Fitness landscape
Science General Physics and Astronomy Fatty alcohol Reductase Machine learning computer.software_genre General Biochemistry Genetics and Molecular Biology Article Metabolic engineering Applied microbiology Machine Learning chemistry.chemical_compound In vivo Catalytic rate Synthetic biology chemistry.chemical_classification Multidisciplinary biology business.industry Rational design General Chemistry Protein engineering Aldehyde Oxidoreductases Enzyme assay Enzyme chemistry Biochemistry Metabolic Engineering biology.protein lipids (amino acids peptides and proteins) Artificial intelligence Fatty Alcohols business computer Intracellular |
| Zdroj: | Nature Communications, Vol 12, Iss 1, Pp 1-10 (2021) Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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