Polyketides are a large class of natural products with broad chemical and structural diversity. Many polyketides possess potent bioactivities including antibiotic, antifungal, and anti-cancer effects. (+)-Discodermolide is one such small molecule with potent anti-cancer activities; however, with no known biosynthetic pathway it has become an ongoing beacon of modern synthetic organic chemistry. Canonical type I polyketide synthases (PKSs) are responsible for the biosynthesis of many polyketides and have been the focus of significant engineering effort due to their unique assembly line organization. Yet, the full potential of PKSs has yet to be realized owing to practical challenges of working with large multimeric enzymes and our incomplete understanding of protein-protein and substrate-protein interactions. Here I present our work to integrate synthetic biology, machine learning, and automation into a retrobiosynthesis platform based on PKSs. We have deployed the platform in an easy-to-use web app that provides access to other engineers. Using the platform we have built and characterized a library of PKS parts which could be used in a biosynthetic pathway towards (+)-Discodermolide.
Chapter 1 provides an introduction into (+)-Discodermolide and outlines this dissertation. Chapter 2 discusses the historical background of engineering PKSs for the de novo biosynthesis of polyketide natural products. Chapter 3 details our development of an automated Design-Build-Test-Learn synthetic biology platform. Chapter 4 describes the application of the automated platform to construct and test a library of PKS parts. Chapter 5 focuses on our work to advance the understanding of PKS behavior through a combination of evolutionary and structural modeling. Finally, Chapter 6 illustrates a potential pathway towards (+)-Discodermolide utilizing our PKS platform.
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