Programming strand displacement circuits for dynamic protein assembly in cancer cells
Date
2019
Authors
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Publisher
University of Delaware
Abstract
Nature has evolved biological systems to exist as highly intricate and dynamic networks, which is exemplified in complex diseases like cancer. To tackle this multifaceted disease, there has been a strong push to develop “smart therapeutics” that can match these complexities. Despite new targeted approaches, there are still challenges in developing multi-input responsive therapeutics. This dissertation addresses these challenges by building towards a therapeutic computing device that utilizes programmable nucleic acid circuits to control protein-based therapeutic action. A flexible platform technology was established to harness toehold-mediated strand displacement for dynamic protein assembly. Key aspects for realizing this platform as a novel class of smart therapeutics are explored in this thesis. ☐ First, the foundation was laid by synthesizing protein-DNA conjugates to be tested within strand displacement circuits. We showed that DNA strand displacement can be used to dynamically control the spatial proximity and corresponding fluorescence resonance energy transfer (FRET) between two fluorescent proteins with multi-input, reversible, and amplification architectures. Next, the power and utility of this technology as a synthetic computing platform was demonstrated by driving the dynamic reconstitution of a split enzyme for targeted prodrug activation based on the sensing of cancer-specific miRNAs. ☐ Subsequently, we addressed the major bottleneck that lays in using sequence constrained biological inputs to run de novo circuits. A novel strategy called associative strand displacement was developed to elegantly interface miRNA inputs with synthetic components. This sequence decoupling allows any miRNA sequence to be targeted without compromising function or efficiency of circuits that have been optimized de novo. We applied the design principles of our strategy towards integrating Boolean logic and amplification architectures, as well as creating a four-input miRNA classifier. ☐ Lastly, to further prove the feasibility of our technology as a therapeutic, we implemented a genetically-encoded hybrid device inside live HeLa cells. Our strategy uniquely utilizes Cas6 endoribonucleases for their picomolar binding affinity and cleavage activity to drive self-assembly of protein and RNA device components. This Cas6-guided approach allowed protein assembly and disassembly to be controlled by RNA hybridization and strand displacement, respectively. These promising results are important stepping stones that support the future execution of more complex architectures with therapeutic outputs. ☐ Ultimately, our technology shows the powerful utility of combing nucleic acids and proteins into hybrid devices, especially when toehold-mediated strand displacement is used to generate computing power for dynamic behavior. Beyond disease therapeutics, this technology has widespread applicability and can be expanded to generate synthetic programmable protein switches for any biological system of interest.