Frontiers in Bioengineering and Biotechnology (Apr 2024)

Machine learning model of the catalytic efficiency and substrate specificity of acyl-ACP thioesterase variants generated from natural and in vitro directed evolution

  • Fuyuan Jing,
  • Fuyuan Jing,
  • Fuyuan Jing,
  • Keting Chen,
  • Keting Chen,
  • Marna D. Yandeau-Nelson,
  • Marna D. Yandeau-Nelson,
  • Marna D. Yandeau-Nelson,
  • Basil J. Nikolau,
  • Basil J. Nikolau,
  • Basil J. Nikolau

DOI
https://doi.org/10.3389/fbioe.2024.1379121
Journal volume & issue
Vol. 12

Abstract

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Modulating the catalytic activity of acyl-ACP thioesterase (TE) is an important biotechnological target for effectively increasing flux and diversifying products of the fatty acid biosynthesis pathway. In this study, a directed evolution approach was developed to improve the fatty acid titer and fatty acid diversity produced by E. coli strains expressing variant acyl-ACP TEs. A single round of in vitro directed evolution, coupled with a high-throughput colorimetric screen, identified 26 novel acyl-ACP TE variants that convey up to a 10-fold increase in fatty acid titer, and generate altered fatty acid profiles when expressed in a bacterial host strain. These in vitro-generated variant acyl-ACP TEs, in combination with 31 previously characterized natural variants isolated from diverse phylogenetic origins, were analyzed with a random forest classifier machine learning tool. The resulting quantitative model identified 22 amino acid residues, which define important structural features that determine the catalytic efficiency and substrate specificity of acyl-ACP TE.

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