Baker, DavidFavor, Andrew2026-04-202026-04-202026-04-202026Favor_washington_0250E_29335.pdfhttps://hdl.handle.net/1773/55432Thesis (Ph.D.)--University of Washington, 2026Nucleic acids fold into sequence-dependent three-dimensional structures and carry out diverse biological functions, much like proteins. However, while considerable advances have been made in the de novo design of protein structure and function, the same has not yet been achieved for RNA structures of similar intricacy. In this work, I describe the development of structure-generative diffusion models for generalized de novo biopolymer design, and I demonstrate their use in creating novel RNA folds and nucleoprotein complexes. With these tools in hand, I investigate design principles governing pseudoknot topologies and show how precise tertiary interactions can stabilize conformationally variable structures. I validate the robustness of this design approach through experimental characterization, demonstrating that designed sequences reliably self-assemble into their intended three-dimensional structures, and that engineered nucleoprotein complexes can be realized with high accuracy. Together, this work extends the principles of structure-based de novo protein design to nucleic acids and hybrid biopolymer assemblies, providing a foundation for creating a wide range of new structures and advancing the broader goals of molecular engineering.application/pdfen-USCC BYde novo designdiffusionmachine learningproteinsRNABiochemistryArtificial intelligenceBioinformaticsMolecular engineeringThesis