Mixture of Experts for Predicting Antibody-Antigen Binding Affinity from Antigen Sequence

Abstract

Antibodies provide a key mode of defense employed by the immune system to fight disease, so eliciting potent antibodies is one of the main goals in vaccine development. Antibodies are also being conceived as powerful therapeutic agents, so engineering potent antibodies is one of the main goals in biologic drug development. The power of antibodies lies in their affinity and specificity in recognizing their cognate antigens. Unfortunately, experimental techniques to determine antibody-antigen binding affinities are difficult to scale up to large sets of new antibodies and antigens. Though computational methods are suitable for large-scale prediction, current methods lack sufficient accuracy. Here, we address the problem of predicting the binding affinity of an antibody against an antigen variant based on the amino acid sequence of that antigen variant. We develop a mixture of experts approach that learns models for individual antibodies against some antigen variants, and then combines information across the antibodies in order to make accurate predictions for a wide range of new variants. In evaluation on a dataset consisting of 52 antibodies and 608 strains of HIV, the predictive accuracy of our approach is demonstrated to be significantly better than that of an existing approach. Our method provides a fast and accurate way to predict antibody-antigen binding affinity, which has the potential to expedite the study of antibody-antigen interactions for vaccine design and therapy.