Building Closed-Loop Frameworks for AI-Guided Protein Design

The design of proteins with tailored properties remains a central challenge in protein engineering, with profound implications for therapeutics, sustainable manufacturing, and environmental remediation. Recent advances in artificial intelligence have dramatically improved our ability to design novel proteins, yet the precision required for many applications remains elusive. This thesis details the development and implementation of closed-loop frameworks that integrate AI-guided protein design with quantitative experimental data to iteratively improve design outcomes. First, I present Protein CREATE (Computational Redesign via an Experiment-Augmented Training Engine), a high-throughput platform that combines phage display with molecular counting techniques to generate quantitative binding data at scale. This platform enables rapid evaluation of thousands (and is in the process of being scaled to millions) of designed protein variants against multiple targets simultaneously. In subsequent chapters, I explore two separate strands of protein design as they reach for each other to close the loop. One thread focuses on collecting data on binders I engineered to the interleukin 7 receptor alpha (IL7RA) and Insulin receptor while the other investigates the value data, even when limited, adds to improve the design process of enzymes to solve a pressing environmental remediation problem: cleaning up per and polyfluoroalkyl substances (PFAS). While all of the targets discussed so far have benefited from developments in artificial intelligence, I explore one target where the benefits are limited, the human sweet taste receptor. Here, I leverage alternative computational methods coupled to experimental testing to chart a course for design. Finally, I discuss the technologies we are integrating within the Protein CREATE framework to enable rapid in vitro and in vivo testing. Throughout my PhD, I have been bringing the two threads of computational design and experimental characterization closer together for not only theoretically interesting, but also practically relevant, engineering cases. The methodologies developed here represent a significant advancement in our ability to design proteins with precisely tailored properties for diverse applications.