General Events

Back to Listing
Dr. Wei

Theoretical & Computational Biophysics Group Seminar: Guo-Wei Wei

Event Type
Seminar/Symposium
Sponsor
Theoretical & Computational Biophysics Group
Location
Beckman Institute Room 3269 (3rd Floor Tower Room)
Virtual
wifi event
Date
Nov 28, 2022   3:00 pm  
Speaker
Guo-Wei Wei, MSU Foundation Professor, Department of Mathematics, Michigan State University, East Lansing, Michigan. 
Contact
Lesley Butler
E-Mail
lesleym@illinois.edu
Views
10
Originating Calendar
Beckman and Campus Calendars

Title: "Mechanisms of SARS-CoV-2 Evolution and Transmission."

Speaker: Guo-Wei Wei, MSU Foundation Professor, Department of Mathematics, Michigan State University, East Lansing, Michigan. 

Discovering the mechanisms of SARS-CoV-2 evolution and transmission is one of the greatest challenges of our time. By integrating artificial intelligence (AI), viral genomes isolated from patients, tens of thousands of mutational data, biophysics, bioinformatics, and algebraic topology, the SARS-CoV-2 evolution was revealed to be governed by infectivity-based natural selection in early 2020 (J. of Mole. Biol. 2020, 432, 5212-5226). Two key mutation sites, L452 and N501 on the viral spike protein receptor-binding domain (RBD), were predicted in the summer of 2020, long before they occur in prevailing variants Alpha, Beta, Gamma, Delta, Kappa, Theta, Lambda, Mu, and Omicron. Our recent studies identified a new mechanism of natural selection: antibody resistance (J. Phys. Chem.. Lett. 2021, 12, 49, 11850 11857). AI-based forecasting of Omicrons infectivity, vaccine breakthrough, and antibody resistance was later nearly perfectly confirmed by experiments (J. Chem. Inf. Model. 2022, 62, 2, 412-422). The replacement of dominant BA.1 by BA.2 in later March was foretold in early February (J. Phys. Chem. Lett. 2022, 13, 17, 3840-3849). On May 1, 2022, we projected Omicron BA.4 and BA.5 to become the new dominating COVID-19 variants (arXiv:2205.00532). This prediction became reality in late June. Our models accurately forecast mutational impacts on the efficacy of monoclonal antibodies (mAbs).

link for robots only