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Modeling of ACE2 and antibodies bound to SARS-CoV-2 provides insights into infectivity and immune evasion
Joseph H. Lubin, Christopher Markosian, D. Balamurugan, Minh T. Ma, Chih-Hsiung Chen, Dongfang Liu, Renata Pasqualini, Wadih Arap, Stephen K. Burley, Sagar D. Khare
Joseph H. Lubin, Christopher Markosian, D. Balamurugan, Minh T. Ma, Chih-Hsiung Chen, Dongfang Liu, Renata Pasqualini, Wadih Arap, Stephen K. Burley, Sagar D. Khare
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Research Article COVID-19

Modeling of ACE2 and antibodies bound to SARS-CoV-2 provides insights into infectivity and immune evasion

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Abstract

Given the COVID-19 pandemic, there is interest in understanding ligand-receptor features and targeted antibody-binding attributes against emerging SARS-CoV-2 variants. Here, we developed a large-scale structure-based pipeline for analysis of protein-protein interactions regulating SARS-CoV-2 immune evasion. First, we generated computed structural models of the Spike protein of 3 SARS-CoV-2 variants (B.1.1.529, BA.2.12.1, and BA.5) bound either to a native receptor (ACE2) or to a large panel of targeted ligands (n = 282), which included neutralizing or therapeutic monoclonal antibodies. Moreover, by using the Barnes classification, we noted an overall loss of interfacial interactions (with gain of new interactions in certain cases) at the receptor-binding domain (RBD) mediated by substituted residues for neutralizing complexes in classes 1 and 2, whereas less destabilization was observed for classes 3 and 4. Finally, an experimental validation of predicted weakened therapeutic antibody binding was performed in a cell-based assay. Compared with the original Omicron variant (B.1.1.529), derivative variants featured progressive destabilization of antibody-RBD interfaces mediated by a larger set of substituted residues, thereby providing a molecular basis for immune evasion. This approach and findings provide a framework for rapidly and efficiently generating structural models for SARS-CoV-2 variants bound to ligands of mechanistic and therapeutic value.

Authors

Joseph H. Lubin, Christopher Markosian, D. Balamurugan, Minh T. Ma, Chih-Hsiung Chen, Dongfang Liu, Renata Pasqualini, Wadih Arap, Stephen K. Burley, Sagar D. Khare

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Figure 4

Consensus scoring of energetic effects on binding to ACE2 or polypeptide TEs by substituted residue of Spike RBD across B.1.1.529, BA.2.12.1, and BA.5.

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Consensus scoring of energetic effects on binding to ACE2 or polypeptide...
(A) Per-residue interface scores (in Rosetta energy units) at substituted residue sites for REGN10933 (PDB ID: 6XDG) modeled with B.1.1.529 as an illustrative example of consensus scoring. Colored bars represent the 4 different modeling methods. The gray box represents the 1.4-REU threshold for counting as stabilizing/destabilizing. In this case, the consensus string, with overall consensus score contributions shown in parenthesis, is K417N++++ (+1), S477N+ (+0.125), T478K– (–0.125), E484A++ (+0.25), and Q493R* (+0). The complex consensus score is +1.25. (B) Energy-based consensus scoring by residue substitution represented as heatmaps for RBD-bound ACE2 in addition to antibodies, nanobodies/synbodies, and other polypeptide TEs by Barnes class (C1, C2, C3, and C4) for B.1.1.529, BA.2.12.1, and BA.5. Consensus scores are totaled for each site across all models with ACE2 or a TE of a given class. Coloration scale is normalized to the model count for each class, with red indicating overall destabilization and blue indicating overall stabilization. Cells with darker shades indicate greater overall stabilization or destabilization. Substitutions highlighted in yellow indicate residue substitutions that are inconsistent across B.1.1.529, BA.2.12.1, and BA.5.

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