Evaluating SARS-CoV-2 Antibody Resilience via Prediction and Design of Escape Viral Variants

Abstract

The evolutionary trajectory of SARS-CoV-2 is shaped by competing pressures for ACE2 binding, viability, and escape from neutralizing antibodies targeting its receptor-binding domain (RBD). Here, we present EscapeMap, a modular framework that enables prediction and design of variants escaping antibodies. EscapeMap integrates deep mutational scanning data for ACE2 and 31 monoclonal antibodies with a generative sequence model trained on pre-pandemic Coronaviridae. To experimentally probe escape potential, we designed RBD variants under pressure from four clinically relevant antibodies (SA55, S2E12, S309, VIR-7229). Among these designs, bearing up to 21 mutations from wildtype, 50% expressed as stable proteins. Binding assays confirm that S309 and VIR-7229 retain recognition across diverse mutation combinations. EscapeMap accurately forecasts which antibodies are vulnerable to escape by our designed sequences. Finally, by identifying correlated escape routes, we predict and experimentally verify, antibody combinations less prone to simultaneous escape, offering a quantitative basis for guiding therapeutic strategies.