Shendure, JaySuiter, Chase Cameron2026-02-052026-02-052026-02-052025Suiter_washington_0250E_29120.pdfhttps://hdl.handle.net/1773/55273Thesis (Ph.D.)--University of Washington, 2025This thesis focuses on the development of multiplex experimental methods for profiling and programming intracellular protein degradation. In addition, it explores the synergistic application of deep-learning models for understanding and engineering protein function at scale.Chapter 1 provides an overview of the mechanisms governing proteome homeostasis and outlines the technological gap that has historically limited our ability to study these systems. I review how the convergence of multiplex cellular assays and deep learning models offers a new paradigm for dissecting endogenous regulatory networks and engineering novel protein functions. Chapter 2 presents COMET (COmbinatorial Mapping of E3 Targets), a pooled assay for mapping E3 ubiquitin ligases to their substrates. By coupling combinatorial libraries of dual-fluorescent reporters with E3-targeting CRISPR guides, we enabled the many-by-many measurement of E3-dependent changes in protein abundance within a single experiment. I apply COMET to identify substrates of SCF complex E3 ubiquitin ligases as well as map the E3s mediating degradation of short-lived transcription factors, revealing that proteolytic regulation is often characterized by complex, many-to-many connectivity rather than simple one-to-one relationships. Finally, I demonstrate the use of deep-learning-based structural prediction models for the in silico validation of COMET-nominated E3–substrate pairs, pointing toward a future where computational nomination guides experimental validation. Chapter 3 shifts from mapping endogenous degradation to programming it. Here, we demonstrate a multiplex framework for the discovery of functional de novo designed “proximity handles.” We designed a library of binders targeting various effector proteins and characterized them using LABEL-seq, a multiplex RNA-barcoded protein abundance assay. This approach identified hundreds of designs capable of mediating the stabilization or degradation of a target protein. We further validated a subset of designs in orthogonal assays, demonstrated handle-mediated degradation of the endogenous oncoprotein MCL1, and applied these handles to remodel mitochondrial organization. This study establishes a generalizable pipeline linking computational protein design to high-throughput cell-based readouts. To conclude, Chapter 4 discusses future directions at the interface of multiplex technology and deep learning, specifically focusing on the potential for closed-loop design-build-test-learn cycles and the expansion of these methods to enzyme engineering.application/pdfen-USCC BYMolecular biologyMolecular and cellular biologyThesis