AbstractThe combination of computational design and directed evolution could offer a general strategy to create enzymes with new functions. To date, this approach has delivered de novo enzymes for a handful of model reactions, selected based on previous achievements with catalytic antibodies. In this thesis we show that new catalytic mechanisms can be engineered into proteins to accelerate valuable chemical transformations for which no natural enzymes or catalytic antibodies are known. Evolutionary optimisation of a primitive designed âenone-binding proteinâ (BH32) afforded a highly efficient and enantioselective enzyme (BH32.10) that catalysed the Morita-Baylis-Hillman (MBH) reaction. BH32.10 is suitable for preparative scale transformations, accepts a broad range of aldehyde and enone coupling partners, and is able to promote highly selective mono-functionalisations of dialdehydes. Crystallographic, biochemical and computational studies reveal that evolution has led to a sophisticated catalytic mechanism comprising a His23 nucleophile paired with a fortuitously positioned Arg124. This catalytic arginine serves as a genetically encoded surrogate of privileged bidentate hydrogen bonding catalysts (e.g. thioureas), which promote a wide range of reactions in organic synthesis. In a separate study, we have exploited an expanded genetic code to develop de novo hydrolases in the BH32 scaffold that employ Me-His as a non-canonical catalytic nucleophile. This study showcases how the integration of new functional components into enzyme design and evolution workflows can open up new modes of reactivity in protein active sites. Combined, the research presented in this thesis describes new approaches to generate enzymes with catalytic mechanisms not seen in Nature, which will guide the development of de novo enzymes for a broader range of chemical transformations in the coming years.
|Date of Award||31 Dec 2021|
|Supervisor||Sabine Flitsch (Supervisor) & Anthony Green (Supervisor)|
- Enzyme Engineering
- Directed Evolution
- Genetic Code Expansion