Baker, DavidBennett, Nathaniel2023-08-142023-08-142023-08-142023Bennett_washington_0250E_25492.pdfhttp://hdl.handle.net/1773/50187Thesis (Ph.D.)--University of Washington, 2023The ability to design protein-binding proteins is broadly useful. In this dissertation I will show our work to develop a deep learning-based pipeline for protein binder design. I will show how we configured AlphaFold2 to classify in silico designs which are likely to bind from those which are not likely to bind. I will then demonstrate how we can use the ProteinMPNN model, in combination with classical Rosetta protocols, to perform efficient sequence design on binder backbones. Finally, I will show how we trained a denoising diffusion model to generate protein backbones and how this can be used to massively accelerate the binder design pipeline. This deep learning-based pipeline is faster, easier to use, and has much higher experimental success rates than the previous Rosetta-based pipeline.application/pdfen-USCC BYBiochemistryMolecular engineeringThesis