| Since enzymes were first discovered as nature's catalysts, they have become an integral part of agrichem, materials, food, cleaning, health, and recently energy industries. While there are millions of naturally occurring enzymes capable of catalyzing thousands of chemical transformations, this is not sufficient to meet our current needs as we frequently desire to catalyze reactions not found in naturally occurring systems. Traditional evolutionary techniques for transforming nature's tools to meet specific application requirements often fail or fall into a local minimum due to current method limitations used to search the vast space possible protein sequences. The use of computational tools to explore novel enzyme sequences by modeling and evaluating their corresponding structure holds the promise to quickly re-engineer proteins for a desired function, as these methods can sample significantly larger spaces than possible with evolutionary techniques. For my thesis, I have focused on the application of recently developed computational tools for three enzyme-engineering projects. For each project I illustrate how the combination of computational and evolutionary enzyme engineering approaches can be used to develop novel catalysts. The specific aim of the first project is the de novo design of an enzyme to catalyze a bimolecular reaction that has not been observed in biological systems. The second project focuses on enhancing the production properties and thermostability of an enzyme being developed as a protein therapeutic, as well as redesigning the reaction specificity to elucidate the in vivo mechanism of action. The third project aims to engineer a novel biosynthetic pathway for the conversion of electricity and carbon dioxide into central metabolic intermediates. |
