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.
【저자키워드】 deep learning, Computational biology, SARS-CoV-2 variants, broadly neutralizing antibodies, geometric neural networks., 【초록키워드】 Evolution, antibodies, SARS-CoV-2, Vaccine, immune response, Mutation, deep learning, Neutralizing antibodies, antibody, Computational biology, variant, virus, variants, Spike protein, Protein, Transmissibility, SARS-CoV-2 variants, selection pressure, Neutralizing, lead, approach, complementarity-determining regions, neutralize, CDRs, complementarity-determining region, include, introduced, evade, the Spike, catch, geometric, virus variant, 【제목키워드】 neutralization, SARS-CoV-2 variant, deep,