Reduced B cell antigenicity of Omicron lowers host serologic response

Jérôme Tubiana, Yufei Xiang, Li Fan, Haim J. Wolfson, Kong Chen*, Dina Schneidman-Duhovny*, Yi Shi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The SARS-CoV-2 Omicron variant evades most neutralizing vaccine-induced antibodies and is associated with lower antibody titers upon breakthrough infections than previous variants. However, the mechanism remains unclear. Here, we find using a geometric deep-learning model that Omicron's extensively mutated receptor binding site (RBS) features reduced antigenicity compared with previous variants. Mice immunization experiments with different recombinant receptor binding domain (RBD) variants confirm that the serological response to Omicron is drastically attenuated and less potent. Analyses of serum cross-reactivity and competitive ELISA reveal a reduction in antibody response across both variable and conserved RBD epitopes. Computational modeling confirms that the RBS has a potential for further antigenicity reduction while retaining efficient receptor binding. Finally, we find a similar trend of antigenicity reduction over decades for hCoV229E, a common cold coronavirus. Thus, our study explains the reduced antibody titers associated with Omicron infection and reveals a possible trajectory of future viral evolution.

Original languageEnglish
Article number111512
JournalCell Reports
Issue number3
StatePublished - 18 Oct 2022


FundersFunder number
Edmond J. Safra Center for Bioinformatics
National Institutes of HealthR35GM137905, R01AI163011, ISF 1466/18
National Institutes of Health
National Heart, Lung, and Blood InstituteR01HL137709
National Heart, Lung, and Blood Institute
Blavatnik Family Foundation
Human Frontier Science Program
Tel Aviv University
Ministry of Science and Technology, Israel


    • CP: Immunology
    • CP: Microbiology
    • Omicron variant of concern
    • SARS-CoV-2
    • antigenicity
    • computational structural biology
    • deep learning
    • spike protein


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