In a groundbreaking development for addressing future viral pandemics, a multi-institutional team involving LLNL researchers has successfully combined an artificial intelligence-backed platform with supercomputing to redesign and restore the effectiveness of antibodies whose ability to fight viruses has been compromised by viral evolution. The work was published in the journal Nature. Graphic by Adam Connell/LLNL, image by Amanda Levasseur.
(Download Image)GUIDE team develops approach to redesign antibodies against viral pandemics
In a groundbreaking development for addressing future viral pandemics, a multi-institutional team involving LLNL researchers has successfully combined an AI-backed platform with supercomputing to redesign and restore the effectiveness of antibodies whose ability to fight viruses has been compromised by viral evolution. The team’s research is published in the journal Nature and showcases a novel antibody design platform comprising experimental data, structural biology, bioinformatic modeling and molecular simulations—driven by a machine-learning algorithm. With funding from the Department of Defense’s Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense’s (JPEO-CBRND’s) Generative Unconstrained Intelligent Drug Engineering (GUIDE) program, the interagency team used the platform to computationally optimize an existing SARS-CoV-2 antibody to restore its effectiveness to emerging SARS-CoV-2 Omicron subvariants, while ensuring continued efficacy against the then-dominant Delta variant. Their computational approach has the potential to significantly accelerate the drug-development process and improve pandemic preparedness. Read more at LLNL News.
