Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization
Name
pnas.2122954119.pdf
Description
Published version
Size
2.96 MB
Format
Adobe PDF
Checksum (MD5)
91a700e1be2cd12ef8d786331dc1ff67
Author(s)
Shan, Sisi
Luo, Shitong
Yang, Ziqing
Hong, Junxian
Su, Yufeng
Ding, Fan
Fu, Lili
Li, Chenyu
Chen, Peng
Ma, Jianzhu
Date Issued
2022
Journal
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
Abstract
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.
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.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1073/PNAS.2122954119
