bioRxivImmunologyPreprint
Genetic and structural basis for recognition of SARS-CoV-2 spike protein by a two-antibody cocktail
J. Dong, S. Zost, A. Greaney, T. N. Starr, A. S. Dingens, E. C. Chen, R. Chen, B. Case, R. Sutton, P. Gilchuk, J. Rodriguez, E. Armstrong, C. Gainza, R. Nargi, E. Binshtein, X. Xie, X. Zhang, P.-Y. Shi, J. Logue, S. Weston, M. McGrath, M. Frieman, T. Brady, K. Tuffy, H. Bright, , J. E. Crowe
Abstract
The SARS-CoV-2 pandemic has led to an urgent need to understand the molecular basis for immune recognition of SARS-CoV-2 spike (S) glycoprotein antigenic sites. To define the genetic and structural basis for SARS-CoV-2 neutralization, we determined the structures of two human monoclonal antibodies COV2-2196 and COV2-21301, which form the basis of the investigational antibody cocktail AZD7442, in complex with the receptor binding domain (RBD) of SARS-CoV-2. COV2-2196 forms an "aromatic cage" at the heavy/light chain interface using germline-encoded residues in complementarity determining regions (CDRs) 2 and 3 of the heavy chain and CDRs 1 and 3 of the light chain. These structural features explain why highly similar antibodies (public clonotypes) have been isolated from multiple individuals1-4. The structure of COV2-2130 reveals that an unusually long LCDR1 and HCDR3 make interactions with the opposite face of the RBD from that of COV2-2196. Using deep mutational scanning and neutralization escape selection experiments, we comprehensively mapped the critical residues of both antibodies and identified positions of concern for possible viral escape. Nonetheless, both COV2-2196 and COV2-2130 showed strong neutralizing activity against SARS-CoV-2 strain with recent variations of concern including E484K, N501Y, and D614G substitutions. These studies reveal germline-encoded antibody features enabling recognition of the RBD and demonstrate the activity of a cocktail like AZD7442 in preventing escape from emerging variant viruses.
- organism: virus
- assay: protein structure
- research focus: host-pathogen & immune response
- approach: deep learning
- approach: statistical model
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