Deep Mutational Learning Predicts ACE2 Binding and Antibody Escape to Combinatorial Mutations in the SARS-CoV-2 Receptor Binding DomainA machine learning-guided, protein engineering method enables the prediction of how SARS-CoV-2 RBD combinatorial mutations will impact therapeutic antibody escape and ACE2 affinity. This method facilitates the identification of multisite mutations that are major drivers of antibody escape and the evaluation of neutralizing antibody efficacy on heavily mutated viral variants.A machine learning-guided, protein engineering method enables the prediction of how SARS-CoV-2 RBD combinatorial mutations will impact therapeutic antibody escape and ACE2 affinity. This method facilitates the identification of multisite mutations that are major drivers of antibody escape and the evaluation of neutralizing antibody efficacy on heavily mutated viral variants.Joseph M. Taft, Cédric R. Weber, Beichen Gao, Roy A. Ehling, Jiami Han, Lester Frei, Sean W. Metcalfe, Max Overath, Alexander Yermanos, William Kelton, Sai T. Reddyhttps://www.cell.com/cell/fulltext/S0092-8674(22)01119-9?rss=yeshttp://www.cell.com/cell/inpress.rssCellCell RSS feed.Wireless News CampaignSeptember 1, 2022

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