Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization-Reference-Cited by-同舟云学术

Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization

Published:2022-03 Issue:11 Volume:119 Page:
ISSN:0027-8424
Container-title:Proceedings of the National Academy of Sciences
language:en
Short-container-title:Proc. Natl. Acad. Sci. U.S.A.

Author:

Shan Sisi¹,Luo Shitong²,Yang Ziqing¹,Hong Junxian¹,Su Yufeng³,Ding Fan²,Fu Lili¹,Li Chenyu¹,Chen Peng¹,Ma Jianzhu²,Shi Xuanling¹,Zhang Qi¹,Berger Bonnie⁴⁵,Zhang Linqi¹,Peng Jian²³⁶

Affiliation:

1. NexVac Research Center, Comprehensive AIDS Research Center, Center for Infectious Disease Research, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China

2. HeliXon Limited, Beijing 100084, China

3. Department of Computer Science, University of Illinois at Urbana–Champaign, Champaign, IL 61822

4. Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139

5. Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139

6. Institute for Industry AI Research, Tsinghua University, Beijing 100084, China

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.

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

Link

https://pnas.org/doi/pdf/10.1073/pnas.2122954119

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