bioRxivImmunologyPreprint
Convergent IGHV3-53/3-66 antibodies elicited by Omicron BA.1 infection broadly neutralize emerging SARS-CoV-2 variants
Naveenchandra SuryadevaraiD, Seth J. ZostiD, John M. PowersiD, Bernadeta DadonaiteiD, Pavlo GilchukiD, Elad BinshteiniD, Suzanne M. ScheafferiD, Sarah R. LeistiD, Luke MyersiD, Silvia Ravera, Lily E. AdamsiD, Laura S. HandaliD, Shruthi Kannan, Edgar DavidsoniD, Benjamin Doranz, Andrew Trivette, Masako Abney, Doan C. NguyeniD, Frances Eun-Hyung LeeiD, Robert H. CarnahaniD, Jesse D. BloomiD, Ralph Steven BariciD, Michael S. DiamondiD, James E. CroweiD
Abstract
Natural SARS-CoV-2 infections or vaccinations induce neutralizing antibodies (nAbs) offer protection from severe disease. The shared use of IGHV3-53/3-66 genes makes this class of monoclonal antibodies (mAbs) a public clonotype and is well established, but the evolution and structural basis of how these public antibodies maintain broad binding and acquire potent neutralizing activity is not completely understood. To understand how these features are facilitated by the IGHV3-53/3-66 germline segments and enhanced by somatic mutations, we investigated the biology of a panel of 242 human mAbs isolated from an individual infected with SARS-CoV-2 BA.1 strain and recovered. Interestingly, a mAb designated COV2-3731 encoded by IGHV3-53/IGKV1-33 retained potent neutralizing activity against SARS-CoV-2 variants BA.2.86, JN.1, KP.2, BA.3.2, and, to some extent, KP.3. Studies using deep mutational scanning with a BA.2 lentiviral library and determination of the structural complex of the BA.2 S protein and COV2-3731 Fab fragments using cryo-EM revealed key contact residues. Further, germline revertant analysis of the COV2-3731 mAb provided additional insights into how this COV2-3731 and other IGHV3-53/3-66-encoded public antibodies evolve to gain breadth against antigenically distinct SARS-CoV-2 variants such as BA.2.86, JN.1, KP.2, and BA.3.2.
- organism: human
- assay: proteomics
- research focus: cell type annotation
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
- Citations
- 0
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