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

dc.contributor.authorShan, Sisi
dc.contributor.authorLuo, Shitong
dc.contributor.authorYang, Ziqing
dc.contributor.authorHong, Junxian
dc.contributor.authorSu, Yufeng
dc.contributor.authorDing, Fan
dc.contributor.authorFu, Lili
dc.contributor.authorLi, Chenyu
dc.contributor.authorChen, Peng
dc.contributor.authorMa, Jianzhu
dc.contributor.authorShi, Xuanling
dc.contributor.authorZhang, Qi
dc.contributor.authorBerger, Bonnie
dc.contributor.authorZhang, Linqi
dc.contributor.authorPeng, Jian
dc.date.accessioned2022-09-27T18:07:15Z
dc.date.available2022-09-27T18:07:15Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/1721.1/145590
dc.description.abstract<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>en_US
dc.language.isoen
dc.publisherProceedings of the National Academy of Sciencesen_US
dc.relation.isversionof10.1073/PNAS.2122954119en_US
dc.rightsArticle 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.en_US
dc.sourcePNASen_US
dc.titleDeep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralizationen_US
dc.typeArticleen_US
dc.identifier.citationShan, 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).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mathematicsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.relation.journalProceedings of the National Academy of Sciences of the United States of Americaen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-09-27T18:02:16Z
dspace.orderedauthorsShan, S; Luo, S; Yang, Z; Hong, J; Su, Y; Ding, F; Fu, L; Li, C; Chen, P; Ma, J; Shi, X; Zhang, Q; Berger, B; Zhang, L; Peng, Jen_US
dspace.date.submission2022-09-27T18:02:19Z
mit.journal.volume119en_US
mit.journal.issue11en_US
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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