Structural and energetic profiling of SARS-CoV-2 receptor binding domain antibody recognition and the impact of circulating variants
Fig 5
Computational mapping of SARS-CoV-2 RBD hotspot residues.
Computational alanine scanning of RBD residues in antibody-RBD interfaces was performed using Rosetta [26], to generate binding energy change (ΔΔG) values for alanine substitutions at each RBD position based on modeling of residue substitutions and scoring using an energy-based function. ΔΔG values are in Rosetta Energy Units (REU) which are comparable to energies in kcal/mol. Alanine residues in the native complex were mutated to glycine for ΔΔG calculations, and glycine RBD residues were omitted from the analysis. In order to highlight substantial predicted binding energy changes, only ΔΔGs with absolute values > 0.5 REU are represented. RBD residues are ordered by hierarchical clustering based on ΔΔG profile similarities, with corresponding dendrogram shown at top. Antibodies (rows) are ordered and clustered as in Fig 2, based on the RBD contact profile similarities. For reference, ΔΔGs for ACE2 binding based on the ACE2-RBD complex structure (PDB code 6LZG) are shown in the top bar. RBD residues that are mutated in SARS-CoV-2 variants of concern (K417, L452, E484, N501) are labeled at bottom and highlighted with gray boxes in heatmap.