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

Author

Listed:
  • Jonathan C. Greenhalgh

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

  • Sarah A. Fahlberg

    (University of Wisconsin-Madison)

  • Brian F. Pfleger

    (University of Wisconsin-Madison)

  • Philip A. Romero

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

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.

Suggested Citation

  • Jonathan C. Greenhalgh & Sarah A. Fahlberg & Brian F. Pfleger & Philip A. Romero, 2021. "Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25831-w
    DOI: 10.1038/s41467-021-25831-w
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    Cited by:

    1. Simon d’Oelsnitz & Daniel J. Diaz & Wantae Kim & Daniel J. Acosta & Tyler L. Dangerfield & Mason W. Schechter & Matthew B. Minus & James R. Howard & Hannah Do & James M. Loy & Hal S. Alper & Y. Jessie, 2024. "Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Kerr Ding & Michael Chin & Yunlong Zhao & Wei Huang & Binh Khanh Mai & Huanan Wang & Peng Liu & Yang Yang & Yunan Luo, 2024. "Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. M. Tanvir Rahman & M. Kristian Koski & Joanna Panecka-Hofman & Werner Schmitz & Alexander J. Kastaniotis & Rebecca C. Wade & Rik K. Wierenga & J. Kalervo Hiltunen & Kaija J. Autio, 2023. "An engineered variant of MECR reductase reveals indispensability of long-chain acyl-ACPs for mitochondrial respiration," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    4. Chase R. Freschlin & Sarah A. Fahlberg & Pete Heinzelman & Philip A. Romero, 2024. "Neural network extrapolation to distant regions of the protein fitness landscape," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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