Computational fingerprinting of immune-mediated pressure on SARS-CoV-2 viral evolution reveals preliminary evidence for immune-evasion

Abstract

Abstract The global emergence of SARS-CoV-2 variants and Omicron in particular, has highlighted the risk of novel variants that will evade adaptive immune responses following vaccination and natural infection. Determining which viral mutations escape T-cells and antibodies is crucial for designing therapeutics and vaccines and assessing the implications of newly emerging variants. We developed an automated immunoinformatic pipeline that considers both data on viral sequences and experimentally identified T-cell and antibody responses, to identify specific viral mutations that may be associated with immune escape. We used an unsupervised clustering method to cluster a set of 108 non-redundant anti-SARS-CoV-2 spike protein antibodies with solved 3D structures from the PDB database, and identified 9 unique antibody clusters. We computed the predicted changes in binding energies (ΔΔG) for each antibody and each specific mutation within its contact footprint. Using these ΔΔG scores, we computed an antibody escape score for each mutation. We also predicted the change in binding between predicted and experimentally verified T-cell epitopes and their presenting MHC molecules. We ranked frequent spike protein mutations based on their predicted effect on immune evasion, as well as individual SARS-CoV-2 variants, with a specific focus on VOCs. Our novel immune escape scores identified several key mutations in the Omicron variant, which were not present in previous VOCs, and may be used to identify key mutations that are likely associated with immune escape, which may appear in novel variants of concern. Supported by an NIH grant (75N93021C00016)