Abstract
SignificanceSARS-CoV-2 continues to evolve through emerging variants, more frequently observed with higher transmissibility. Despite the wide application of vaccines and antibodies, the selection pressure on the Spike protein may lead to further evolution of variants that include mutations that can evade immune response. To catch up with the virus's evolution, we introduced a deep learning approach to redesign the complementarity-determining regions (CDRs) to target multiple virus variants and obtained an antibody that broadly neutralizes SARS-CoV-2 variants.
Keywords:
SARS-CoV-2 variants; broadly neutralizing antibodies; computational biology; deep learning; geometric neural networks.
Publication types
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Research Support, Non-U.S. Gov't
MeSH terms
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Antibodies, Neutralizing / immunology
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Antibodies, Viral / immunology
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Broadly Neutralizing Antibodies / immunology*
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Broadly Neutralizing Antibodies / pharmacology
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COVID-19 / immunology*
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COVID-19 Vaccines / immunology
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Complementarity Determining Regions
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Deep Learning
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Epitopes / immunology
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Humans
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Immunotherapy / methods
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Neutralization Tests / methods
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Protein Domains
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SARS-CoV-2 / immunology*
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SARS-CoV-2 / pathogenicity
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Spike Glycoprotein, Coronavirus / genetics
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Spike Glycoprotein, Coronavirus / immunology
Substances
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Antibodies, Neutralizing
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Antibodies, Viral
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Broadly Neutralizing Antibodies
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COVID-19 Vaccines
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Complementarity Determining Regions
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Epitopes
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Spike Glycoprotein, Coronavirus
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spike protein, SARS-CoV-2