Angelica Ochoa (Mentors: Dr. Lu Shan & Dr. Melissa Damschroder, Antibody Discovery and Protein Engineering, MedImmune, LLC)
December 18th, 2015, 2:00pm, Room 1300, Harris Building
Antibody engineering technologies have been used to develop a spectrum of antibodies with altered properties such as changes in effector functions, antigen binding, protease-resistance, and bispecificity. Currently, experimental approaches involve multiple rounds of screening to identify and optimize antibodies which requires considerable time, effort, and expense. Bioinformatics and computational tools are key to addressing these needs but require further optimization to bridge the gap between in silico and experimental design. Here, we describe two bioinformatics-aided strategies for engineering antibodies with optimal antigen binding. The first strategy requires a resolved crystal structure of the antibody:antigen complex to be used as a template for predicting binding affinity. Modeling and biophysical characterization were done in parallel to elucidate and refine the rational design process. This strategy was then tested by engineering antibodies with predicted antigen binding and measuring their binding affinity in vitro. The second strategy does not require a resolved crystal structure of the antibody:antigen complex. Instead, high-throughput experimental methods (ELISA, binding kinetics) are used to screen for antibodies with optimal antigen binding. Sub-groups of antibodies were then categorized based on their binding affinity to identify shared characteristics that can guide rational design and modeling in silico. Although further refinement is necessary, we were able to successfully apply these bioinformatics-aided strategies to rationally design antibodies with optimal antigen binding and identify groups of residues that influence affinity.