Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization
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Show full item recordAbstract
<jats:title>Significance</jats:title>
<jats:p>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.</jats:p>
Date issued
2022Department
Massachusetts Institute of Technology. Department of Mathematics; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence LaboratoryJournal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
Proceedings of the National Academy of Sciences
Citation
Shan, Sisi, Luo, Shitong, Yang, Ziqing, Hong, Junxian, Su, Yufeng et al. 2022. "Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization." Proceedings of the National Academy of Sciences of the United States of America, 119 (11).
Version: Final published version