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.
Bibliographical noteFunding Information:
We thank Zhe Sang for the analysis of antibody binding. J.T. acknowledges helpful discussion with Andrea Di Gioacchino and Simona Cocco. Funding: This work was supported by NIH grants R35GM137905 (Y.S.), R01AI163011 (Y.S. and D.S.), R01HL137709 (K.C.), ISF 1466/18 and Israeli Ministry of Science and Technology (D.S.), the Edmond J. Safra Center for Bioinformatics at Tel Aviv University, the Human Frontier Science Program (cross-disciplinary postdoctoral fellowship LT001058/2019-C ) (J.T.), and Len Blavatnik and the Blavatnik Family Foundation (H.J.W.).
- CP: Immunology
- CP: Microbiology
- Omicron variant of concern
- computational structural biology
- deep learning
- spike protein