Predicting the antigenic evolution of SARS-COV-2 with deep learning

Wenkai Han, Ningning Chen, Xinzhou Xu, Adil Sahil, Juexiao Zhou, Zhongxiao Li, Huawen Zhong, Elva Gao, Ruochi Zhang, Yu Wang, Shiwei Sun (), Peter Pak-Hang Cheung () and Xin Gao ()
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Wenkai Han: King Abdullah University of Science and Technology (KAUST)
Ningning Chen: King Abdullah University of Science and Technology (KAUST)
Xinzhou Xu: Chinese University of Hong Kong
Adil Sahil: King Abdullah University of Science and Technology (KAUST)
Juexiao Zhou: King Abdullah University of Science and Technology (KAUST)
Zhongxiao Li: King Abdullah University of Science and Technology (KAUST)
Huawen Zhong: King Abdullah University of Science and Technology (KAUST)
Elva Gao: The KAUST School, King Abdullah University of Science and Technology (KAUST)
Ruochi Zhang: Syneron Technology
Yu Wang: Syneron Technology
Shiwei Sun: Chinese Academy of Sciences
Peter Pak-Hang Cheung: Chinese University of Hong Kong
Xin Gao: King Abdullah University of Science and Technology (KAUST)

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract The relentless evolution of SARS-CoV-2 poses a significant threat to public health, as it adapts to immune pressure from vaccines and natural infections. Gaining insights into potential antigenic changes is critical but challenging due to the vast sequence space. Here, we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithms to predict the viral fitness landscape and explore antigenic evolution via in silico directed evolution. By analyzing existing SARS-CoV-2 variants, MLAEP accurately infers variant order along antigenic evolutionary trajectories, correlating with corresponding sampling time. Our approach identified novel mutations in immunocompromised COVID-19 patients and emerging variants like XBB1.5. Additionally, MLAEP predictions were validated through in vitro neutralizing antibody binding assays, demonstrating that the predicted variants exhibited enhanced immune evasion. By profiling existing variants and predicting potential antigenic changes, MLAEP aids in vaccine development and enhances preparedness against future SARS-CoV-2 variants.

Date: 2023
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DOI: 10.1038/s41467-023-39199-6

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