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

Abstract

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 productivity and fatty acid diversity of E. coli strains expressing variant acyl-ACP TEs. A single round of directed in vitro evolution, coupled with a high-throughput colorimetric screen, identified 26 novel acyl-ACP TE variants, which convey up to 10-fold increase in fatty acid productivity, and altered fatty acid profiles when expressed in a bacterial host strain. These in vitro generated variant acyl-ACP TEs, in combination with 31 natural variants isolated from diverse phylogenetic origins were analyzed with a random forest classifier machine learning tool, generating a quantitative model that identified 22 amino acid residues, which define important structural features that determine the substrate specificity of acyl-ACP TE.