@phdthesis{CalzadiazRamirez2020,
  author    = {Calzadiaz Ramirez, Liliana},
  title     = {Engineering highly efficient NADP-dependent formate dehydrogenases using a NADPH biosensor Escherichia coli strain},
  school      = {Universit{\"a}t Potsdam},
  pages     = {114},
  year      = {2020},
  abstract  = {NADPH is an essential cofactor that drives biosynthetic reactions in all living organisms. It is a reducing agent and thus electron donor of anabolic reactions that produce major cellular components as well as many products in biotechnology. Indeed, the engineering of metabolic pathways for the production of many products is often limited by the availability of NADPH. One common strategy to address this issue is to swap cofactor specificity from NADH to NADPH of enzymes. However, this process is time consuming and challenging because multiple parameters need to be engineered in parallel. Therefore, the first aim of this project is to establish an efficient metabolic biosensor to select enzymes that can reduce NADP+. An NADPH auxotroph strain was constructed by deleting major reactions involved in NADPH biosynthesis in E. coli's central carbon metabolism with the exception of 6-phosphogluconate dehydrogenase. To validate this strain, two enzymes were tested in the presence of several carbon sources: a dihydrolipoamide dehydrogenase variant of E. coli harboring seven mutations and a formate dehydrogenase (FDH) from Mycobacterium vaccae N10 harboring four mutations were found to support NADPH biosynthesis and growth. The strain was subjected to adaptive laboratory evolution with the goal of testing its robustness under different carbon sources. Our evolution experiment resulted in the random mutagenesis of the malic enzyme (maeA), enabling it to produce NADPH. The additional deletion of maeA rendered a more robust second-generation biosensor strain for NADP+ reduction. We devised a structure-guided directed evolution approach to change cofactor specificity in Pseudomonas sp. 101 FDH. To this end, a library of >106 variants was tested using in vivo selection. Compared to the best engineered enzymes reported, our best variant carrying five mutations shows 5-fold higher catalytic efficiency and 13-fold higher specificity towards NADP+, as well as 2-fold higher affinity towards formate. In conclusion, we demonstrate the potential of in vivo selection and evolution-guided approaches to develop better NADPH biosensors and to engineer cofactor specificity by the simultaneous improvement of multiple parameters (kinetic efficiency with NADP+, specificity towards NADP+, and affinity towards formate), which is a major challenge in protein engineering due to the existence of tradeoffs and epistasis.},
  language  = {en}
}