In addition, as pandemic viruses can evolve rapidly (tens of thousands of new SARS-CoV-2 variants are sequenced each month), systematically testing all variants as they emerge is intractable, even without considering the effects of potential mutations on circulating strains. However, these experimental methods require antibodies or sera representative of the aggregate immune selection imposed on the virus, which become available only as large swaths of the population are infected or vaccinated, limiting the impact for early prediction of immune escape. Ideally, we would be able to anticipate viral immune evasion using experimental methods such as pseudovirus assays 1 and higher-throughput deep mutational scans 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 (DMSs) that measure the ability of viral variants to bind to relevant antibodies. Therefore, anticipating viral variants that avoid immune detection with sufficient lead time is key to developing optimal vaccines and therapeutics.

Antibody escape mutations affect viral reinfection rates and the duration of vaccine efficacy. Viral diseases involve a complex interplay between immune detection in the host and viral evasion, often leading to the evolution of viral antigenic proteins. We provide continually revised escape scores for all current strains of SARS-CoV-2 and predict probable further mutations to forecast emerging strains as a tool for continuing vaccine development ( ). We demonstrate that EVEscape, trained on sequences available before 2020, is as accurate as high-throughput experimental scans at anticipating pandemic variation for SARS-CoV-2 and is generalizable to other viruses including influenza, HIV and understudied viruses with pandemic potential such as Lassa and Nipah. EVEscape quantifies the viral escape potential of mutations at scale and has the advantage of being applicable before surveillance sequencing, experimental scans or three-dimensional structures of antibody complexes are available. To address this, we developed EVEscape, a generalizable modular framework that combines fitness predictions from a deep learning model of historical sequences with biophysical and structural information. However, current strategies for viral evolution prediction are not available early in a pandemic-experimental approaches require host polyclonal antibodies to test against 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, and existing computational methods draw heavily from current strain prevalence to make reliable predictions of variants of concern 17, 18, 19. Effective pandemic preparedness relies on anticipating viral mutations that are able to evade host immune responses to facilitate vaccine and therapeutic design.

Spike protein vaccines