Open AccessDeep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization
Author(s) -
Sicong Shan,
Shitong Luo,
Ziqing Yang,
Junxian Hong,
Yufeng Su,
Fan Ding,
Lili Fu,
Chenyu Li,
Peng Chen,
Jianzhu Ma,
Xuanling Shi,
Qi Zhang,
Bonnie Berger,
Linqi Zhang,
Jian Peng
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2122954119
Subject(s) - transmissibility (structural dynamics) , covid-19 , neutralization , virology , antibody , virus , biology , computational biology , directed molecular evolution , directed evolution , positive selection , immune escape , immune system , genetics , medicine , gene , infectious disease (medical specialty) , mutant , physics , disease , vibration isolation , quantum mechanics , pathology , outbreak , vibration
Significance SARS-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.