bioRxivMicrobiology
An ultrapotent RBD-targeted biparatopic nanobody neutralizes broad SARS-CoV-2 variants
X. Chi, X. ZhangiD, S. Pan, Y. Yu, T. Lin, H. Duan, X. Liu, W. Chen, X. Yang, Q. Ding, J. Wang, W. Yang
Abstract
The wide transmission and host adaptation of SARS-CoV-2 have led to the rapid accumulation of mutations, posing significant challenges to the effectiveness of vaccines and therapeutic antibodies. Although several neutralizing antibodies were authorized for emergency clinical use, convalescent patients derived natural antibodies are vulnerable to SARS-CoV-2 Spike mutation. Here, we describe the screen of a panel of SARS-CoV-2 receptor-binding domain (RBD) targeted nanobodies (Nbs) from a synthetic library and the design of a biparatopic Nb, named Nb1-Nb2, with tight affinity and super wide neutralization breadth against multiple SARS-CoV-2 variants of concern. Deep-mutational scanning experiments identify the potential binding epitopes of the Nbs on the RBD and demonstrate that biparatopic Nb1-Nb2 has a strong escape resistant feature against more than 60 tested RBD amino acid substitutions. Using pseudovirion-based and trans-complementation SARS-CoV-2 tools, we determine that the Nb1-Nb2 broadly neutralizes multiple SARS-CoV-2 variants, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), Lambda (C.37), Kappa (B.1.617.1) and Mu (B.1.621). Furthermore, a heavy chain antibody is constructed by fusing the human IgG1 Fc to Nb1-Nb2 (designated as Nb1-Nb2-Fc) to improve its neutralization potency, yield, stability and potential half-life extension. For the new Omicron variant (B.1.1.529) that harbors unprecedented multiple RBD mutations, Nb1-Nb2-Fc keeps a firm affinity (KD < 1.0x10-12 M) and strong neutralizing activity (IC50 = 0.0017 nM). Together, we developed a tetravalent biparatopic human heavy chain antibody with ultrapotent and broad-spectrum SARS-CoV-2 neutralization activity which highlights the potential clinical applications.
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: experimental assay
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