Project Summary/Abstract It is hard to overstate the importance of monoclonal antibodies in the life sciences. Antibodies are critical tools in biomedical research and diagnostics (e.g. western blotting, immunoprecipitation, cytometry, biomarker discovery, and histology), are one of the most rapidly growing class of therapeutics, and are the basis for myriad new strategies in cancer therapy, such as checkpoint inhibitors that are revolutionizing treatment. Unfortunately, current methods for the generation of custom antibodies, including animal immunization and phage display, are slow, costly, inaccessible to most researchers, and often unsuccessful. We propose Autonomously EvolvinG Yeast-displayed antibodieS (AEGYS), a system for the continuous and rapid evolution of high-quality antibodies against custom antigens that requires only the simple culturing of yeast cells. We believe this can be achieved by combining cutting-edge generative machine learning algorithms for antibody library design with a new technology for in vivo continuous evolution and a yeast antigen-presenting cell that we will engineer. If successful, AEGYS should have a transformative impact across the whole of biomedicine by turning monoclonal antibody generation into a rapid, scalable, and accessible process where any lab with standard molecular biology capabilities can generate custom antibodies on demand simply by “immunizing” a test tube of yeast cells with an antigen. We anticipate that this democratization of antibody generation will also result in an explosion of crowdsourced antibody sequence data that will train our machine learning algorithms to design better antibody libraries for AEGYS, starting a virtuous cycle. We ourselves will use AEGYS to generate a panel of subtype- and conformation-specific nanobodies against biogenic amine receptors including those that respond to acetylcholine, adrenaline, dopamine, and other neurotransmitters, so that we can understand their role in neurobiology and addiction.!