Project Summary for The Development and Experimental Verification of Computational Methods to Design Therapeutic Proteins Therapeutic proteins are an important tool in modern medicine, and their use in treating serious illnesses such as cancer and autoimmune diseases continues to grow annually. Antibodies are one of the most important classes of therapeutic proteins. They occur naturally in the immune system, where they bind strongly and specifically to foreign molecules, acting as flags to the rest of the immune system by indicating the presence of materials that should be eliminated from the body. The use of antibodies by medical professionals allows them to guide patients’ immune responses to improve their health outcomes. Although antibodies offer tremendous benefits, they are not without their limitations. They are large, delicate proteins that are relatively expensive to produce, difficult to formulate at high concentrations, and sensitive to the conditions at which they are stored. Additionally, the experimental methods that are currently used to develop new antibodies are time consuming and while they can control the molecule the antibodies bind (i.e. antigens), it is extremely difficult to target specific regions (i.e. epitopes) of those molecules. Finally, there are many experimental and clinical applications where antibodies are currently used despite not being the most appropriate protein for the purpose because there are not convenient alternatives available. Advances in computational protein design over the last decade are poised to revolutionize the development of antibodies and other therapeutic proteins. Recently, the Pantazes Lab at Auburn University has created software capable of designing antibodies or any of 50+ other binding proteins in as little as a few minutes on a personal computer to bind any target epitope of any desired antigen. Preliminary experimental results of this method appear very promising. Over the next five years, the lab plans on building on this foundation to create a therapeutic protein development workflow with unprecedented flexibility. Proposed research includes: 1) Improving the computational design and selection criteria to enhance experimental viability, thereby providing end users confidence that what they design will function as predicted; 2) Expanding the design capabilities to include specific interactions, permitting the design of pH-sensitive binding proteins and enzymes; 3) Extending the design principles from binding proteins to peptides, enabling the design of any amino acid based binding moiety; and 4) Designing a synthetic binding protein with all of the benefits of antibodies and none of the drawbacks. Each project will involve both computational development as well as experimental validation. Altogether, this research will allow for the rapid design of an optimized binding protein for therapeutic applications. Whether it is developing personalized cancer treatments, fighting an an...