An Automated Data-Driven Pipeline for Improving Heterologous Enzyme Expression

Emily E. Wrenbeck, Matthew A. Bedewitz, Justin R. Klesmith, Syeda Noshin, Cornelius S. Barry, Timothy A. Whitehead

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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 multiple-filter 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 ± 0.6 °C, and only minimal reduction in catalytic efficiency. We show that the multiple-filter approach 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.

Original languageEnglish (US)
Pages (from-to)474-481
Number of pages8
JournalACS Synthetic Biology
Volume8
Issue number3
DOIs
StatePublished - Mar 15 2019

Keywords

  • deep mutational scanning
  • enzyme stability
  • heterologous pathway expression
  • high-throughput screening
  • polyketide synthase
  • tropane alkaloids

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

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