Karina Kapusta, PhD

In Silico Study on the SARS-CoV-2 Mutants’ Immune Escape and Infectability: Compatibility Maps

Since its emergence in 2019, the SARS-CoV-2 viral strain has undergone thousands of mutations, some of which are silent while others have been associated with increased mortality, transmissibility, reinfections, immune evasion, and enhanced virus replication. The impact of these mutations sometimes is uncertain, and thus it requires further investigation. The virus enters host cells through the binding of its spike glycoprotein to the host ACE2 receptor. This same region is also targeted by human antibodies, which can trigger an immune response and clear the virus from the host. The N501Y mutation in the Receptor Binding Domain (RBD) of the spike protein has been shown to increase its affinity for the host protein, while the K417N/T mutation is thought to decrease it. Additionally, the L452R and T478K mutations found in the Delta variant have made it highly transmissible and more resistant to neutralizing antibodies. The Omicron variant has a record-breaking 15 mutations in its RBD. Although some experimental and theoretical studies investigating the effects of amino acid substitutions on various viral properties are available in the literature, a comprehensive quantitative analysis of each mutation’s impact on viral entry and immune evasion is still lacking. Currently, studies are only conducted on a limited number of variants and do not provide a complete picture of the effects of specific mutations. Computational modeling is a cost-effective and efficient approach that, if accurately performed, can provide valuable insights into the effects of mutations on viral infectivity and immune evasion. In this study, our goal is to perform both qualitative and quantitative analyses of how specific mutations can impact these viral properties. To achieve this, we will create homology models for a large number of viral RBDs and simulate their interactions with the host receptor ACE2 and the neutralizing human antibodies. By doing so, we can quantitatively evaluate and predict how selective mutations can alter the immune escape and viral entry processes. The results of our binding strength and stability evaluations for RBD-ACE2 and RBD-antibody complexes can be used to create compatibility maps, which will allow us to predict viral infectivity and immune escape. This approach will save time and resources while providing necessary information for understanding the effects of mutations on viral properties. Additionally, if our approach is successful, the compatibility maps we create could be used to predict the viral properties of newly emerged variants when only limited information (such as the genomic sequence) is available. This could be especially important in situations where timely identification and characterization of new variants are critical for effective public health interventions.