Predictive profiling of SARS-CoV-2 variants by deep mutational learning ...

• Main Authors: Taft, Joseph M., Weber, Cédric, Gao, Beichen, Ehling, Roy A., Han, Jiami, Frei, Lester, Metcalfe, Sean, Yermanos, Alexander, Kelton, William, Reddy, Sai T.
• Format: Text
• Language: English
• Published: ETH Zurich 2021
• Subjects: DML
• Online Access: https://dx.doi.org/10.3929/ethz-b-000528562; http://hdl.handle.net/20.500.11850/528562

The continual evolution of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the emergence of variants that show resistance to vaccines and neutralizing antibodies (1–4) threaten to prolong the coronavirus disease 2019 (COVID-19) pandemic (5). Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine learning-guided protein engineering technology, which is used to interrogate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly ... : bioRxiv ...