An automated data-driven pipeline for improving heterologous enzyme expression.

Enzymes are the ultimate entities responsible for chemical transformations in natural and engineered biosynthetic pathways. However, many natural enzymes suffer from suboptimal functional expression due to poor intrinsic protein stability. Further, stability enhancing mutations often come at the cost of impaired function. Here we demonstrate an automated protein engineering strategy for stabilizing enzymes while retaining catalytic function using deep mutational scanning coupled to classification-based screening and combinatorial mutagenesis. We validated this strategy by improving the functional expression of a Type III polyketide synthase from the Atropa belladonna biosynthetic pathway for tropane alkaloids. The best variant had a total of 8 mutations with over 25-fold improved activity over wild-type in E. coli cell lysates, an improved melting temperature of 11.5 ± 0.6C, and only minimal reduction in catalytic efficiency. We show that our classification-based filtering maintains acceptable sensitivity with homology modeling structures up to 4 Å RMS. Our results highlight an automated protein engineering tool for improving the stability and solubility of difficult to express enzymes, which has impact for biotechnological applications.