Unsupervised viral antibody escape prediction for future-proof vaccines

Effective pandemic preparedness relies on predicting immune-evasive viral mutations to ensure early detection of variants of concern and to design future-proofed vaccines and therapeutics. However, current experimental strategies for viral evolution prediction are not available early in a pandemic since  they require host polyclonal antibodies.  Furthermore, the existing paradigm for vaccine evaluation relies on retrospective evaluation against past variants, instead of proactive evaluation against future viral evolution. To address these limitations, we developed EVEscape, a model which integrated fitness predictions from evolutionary models, structure-based features that assess antibody binding potential, and biochemical distances between mutated and wild-type residues. EVEscape quantifies the viral escape potential of mutations at scale and has the advantage of being applicable before surveillance sequencing, experimental scans, or 3D structures of antibody complexes are available. Using only information available pre-pandemic, EVEscape is as accurate as high-throughput experimental scans at anticipating pandemic variation for SARS-CoV-2 and is generalizable to other viruses. Using EVEscape we forecast future SARS-CoV-2 evolution and present a novel and proactive approach for evaluating  and designing vaccines.