2026-09-20T10:32:07Zhttps://digital.lib.washington.edu/server/oai/request

oai:digital.lib.washington.edu:1773/551202026-02-06T11:00:57Zcom_1773_4888col_1773_44474

Baker, David Gershon, Jacob 2026-02-05T19:30:05Z 2026-02-05T19:30:05Z 2026-02-05 2025 Gershon_washington_0250E_29052.pdf https://hdl.handle.net/1773/55120 Thesis (Ph.D.)--University of Washington, 2025 Recent advances in de novo protein design have made it increasingly feasibleto create proteins with novel functions, driven by rapid progress in both com- putational modeling and high-throughput experimentation. Modern tools can explore vast sequence-structure spaces and evaluate biomolecular interactions, while experimental assays can now screen billions of variants in parallel. Yet, a key limitation remains: our current predictive models still struggle to capture the complex physical and dynamical factors that underlie enzyme function. My the- sis addresses this gap by developing an integrated experimental–computational framework for enzyme design that couples large-scale protein library construc- tion with data-driven model development. I design and test extensive libraries of enzyme variants to both optimize catalytic activity and generate training data for next-generation predictors of protein function. Ultimately, this approach ad- vances our ability to connect sequence, structure, and function. application/pdf en_US none ai4science protein protein design protein engineering protein modeling protein optimization Biochemistry Computer science Molecular engineering Protein Design at Library Scale Thesis Open Access open.access