AI Designed Mutation Resistant Broad Neutralization Antibodies

Against Multiple SARS-CoV-2 Strains

Webinar: AI Designed Mutation Resistant Broad Neutralization Antibodies Against Multiple SARS-CoV-2 Strains

Presented by Sino Biological

Speaker: Dr. Lurong Pan, Founder and CEO of AInnocence

 

Content

Artificial Intelligence has the advantage of analyzing big and constantly growing data such as the dynamic genome information of a constantly evolving virus. In this study, we have applied AI technologies in the computational design of broad neutralization antibodies against over 1300 different historical SARS-CoV-2 strains with very small computational cost. The AI-designed antibodies were tested in vitro and demonstrated high potency against multiple strains in pseudo-virus and real virus neutralization assays. These AI-designed antibodies also exhibited a very high cross-binding hit rate against different RBD mutants in ELISA assay. The results shade light in future therapeutic design for pandemic preparedness. The study also indicates that there are hidden patterns in viral evolution and these patterns can be learned by AI to design antibodies against current and future mutant strains within certain evolutionary window.

Key learnings:
• AI can design antibodies with in vitro efficacy and drastically reduce the time and cost of antibody engineering such as affinity maturation.
• AI can design cross-binding antibodies against a large number of different antigen population such as viral mutant strains.
• AI can learn hidden patterns of a viral evolution process and design antibodies against future virus beyond current strains.