AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies

Project Summary Due to massive vaccination, coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been partially under control. However, emerging contagious variants such as Delta are still fueling new waves of infections around the world. Vaccine-escape (or vaccine-breakthrough) variants pose renewed threats to our battle against COVID-19. Understanding viral mutagenesis and evolution is of preeminent importance. By integrating genomic analysis, artificial intelligence (AI), computational biophysics, advanced mathematics, and experimental data, the PIs have built a comprehensive program with the experimental level of accuracy and population-level of reliability for predicting SARS-CoV-2 variant infectivity and antibody disruption. It remains challenging to forecast future emerging vaccine-escape variants, to develop the next-generation of vaccines, and to design mutation- proof antibody therapeutics. These challenges are tackled in the proposed project. New mathematical tools and AI algorithms will be developed to further improve the current state-of- the-art in predicting mutation-induced viral infectivity changes, vaccine breakthroughs, and antibody disruptions. Vital mutations in future emerging variants will be forecasted based on molecular mechanisms, natural selection, and evolutionary effects. New mutation-proof antibody drugs will be designed and tested based on those antibodies that had gone through earlier clinical trials. The predictive models will be implemented into a user-friendly platform with online servers for researchers to design mutation-proof new vaccines and antibody therapies. The proposed methods will be applied to forecast emerging variants in the flu and improve the efficacy of seasonal flu vaccines.