Abstract
Antibody-based immunotherapies require the tedious identification and development of antibodies with specific properties. In particular, vaccine development for mutating pathogens is challenged by their fast evolution, the complexity of immunodominance, and the heterogeneous immune history of individuals. Mathematical models are critical for predicting successful vaccine conditions or designing potent antibodies. Existing models are limited by their abstract and poorly structural representations of antigen epitopes. Here, we propose a structural lattice-based model for antibody–antigen affinity. An efficient algorithm is given that predicts the best binding structure of an antibody’s amino acid sequence around an antigen with shortened computational time. It is suitable for large simulations of affinity maturation. This structural representation contains key physiological properties, such as affinity jumps and cross-reactivity, and successfully reflects the topology of antigen epitopes, such as pockets and shielded residues. We perform in silico immunizations via germinal center simulations and show that our model can explain complex phenomena like recognition of the same epitope from unrelated clones. We show that the use of cocktails of similar epitopes promotes the development of cross-reactive antibodies. This model opens a new avenue for optimizing multivalent vaccines with combined cocktails or sequential immunizations, and to reveal reasons for vaccine success or failure on a structural basis.
