A structure-informed evolutionary model for predicting viral immune escape and evolution

Persistent emergence of viral variants capable of evading host immunity constitutes a significant threat to public health. This antigenic evolution frequently outpaces the development of vaccines and therapeutics, highlighting the necessity of predictive models surveilling the immune escape potential of emerging variants. However, existing models suffer from two key limitations: they inadequately incorporate protein structural information and neglect the importance of distinguishing large-impact mutations from neutral ones given multiple mutations. To address these gaps, we presented KEScape, a deep learning model designed to predict viral immune escape and evolution. KEScape integrates evol