The COVID-19 pandemic, caused by SARS-CoV-2, poses a significant global health threat. SARS-CoV-2's spike (S) protein, particularly its receptor-binding domain (RBD), is crucial for viral entry and is a primary target for neutralizing antibodies and vaccines. The high mutation rate of SARS-CoV-2 leads to the emergence of variants with altered antigenicity and immune escape capabilities. Variants of concern (VOCs), including Alpha, Beta, Gamma, and Delta, exhibit increased transmissibility and reduced antibody neutralization. The Omicron variant, with numerous spike protein mutations, is of particular concern due to its rapid spread and high immune evasion potential. Computational interface analysis offers a powerful tool for predicting the effects of mutations on protein-protein interactions, potentially enabling early identification of concerning variants. This study employs both computational and experimental methods to profile mutations in various SARS-CoV-2 variants.

The literature extensively documents the emergence of SARS-CoV-2 variants and their impact on vaccine efficacy and antibody neutralization. Studies have shown that mutations in the RBD, such as N501Y, K417, and E484, significantly reduce the effectiveness of antibodies elicited by infection or vaccination. The D614G mutation, an early example, demonstrated increased transmissibility. Computational methods, particularly those leveraging advancements in protein structure prediction (like AlphaFold2), offer promising avenues for predicting the effects of mutations on protein interactions. However, the application of these methods for real-time SARS-CoV-2 variant surveillance is still developing.

This study utilized a parallel approach combining computational interface analysis and in vitro experimental assays to profile SARS-CoV-2 variants. Computational analysis employed FlexddG, a Rosetta-based tool, to evaluate the impact of mutations on the RBD's interaction with 14 neutralizing antibodies and ACE2. The change in interfacial free energy (ΔΔG) was calculated for each mutation, with |ΔΔG| ≥ 1 kcal/mol considered significant. In vitro assays included biolayer interferometry (BLI) to determine binding kinetics (KD values and maximum binding signals) of spike proteins from various variants with the antibodies and ACE2. Pseudovirus neutralization assays were performed to evaluate the variants' resistance to neutralization by the antibodies. Further analyses involved structural exploration of mutational impacts on the RBD-antibody interface, and in vitro assembly trials to assess complex formation.

Computational interface analysis revealed that mutations at E484, K417, and N501 significantly reduced binding affinity of several antibodies to the RBD. Omicron exhibited a unique pattern, combining effects of E484 and N501 mutations, leading to decreased affinity for multiple antibodies. Structural analysis showed that these mutations disrupted key polar interactions between the RBD and antibodies. BLI results largely confirmed the computational predictions, showing decreased KD values and maximum binding signals for antibodies targeting sites with these mutations. The Omicron variant demonstrated substantially reduced binding to nearly all antibodies. Pseudovirus neutralization assays corroborated the findings, showing Omicron's strong resistance to neutralization by most antibodies tested. The study also highlighted that while some antibodies showed similar neutralization efficiency against different variants, the underlying molecular mechanisms for resistance varied greatly among the variants.

The findings demonstrate the power of combining computational and experimental approaches for rapid characterization of SARS-CoV-2 variants. Computational interface analysis proved effective in identifying mutations likely to impair antibody binding, which was confirmed experimentally. The study emphasizes the importance of considering both binding affinity and maximum binding capacity in assessing antibody effectiveness. Omicron's exceptional immune escape highlights the need for continuous surveillance and development of new therapeutic strategies, such as antibody cocktails targeting diverse epitopes. The observation that mutations can impact binding kinetics differently for various antibodies underscores the importance of detailed, variant-specific analyses.

This study provides a comprehensive profile of antigenicity alteration and immune escape in SARS-CoV-2 variants, particularly Omicron. The combination of computational and experimental methods proved highly effective in identifying critical mutations and their mechanisms of action. Omicron's significant resistance to current antibodies underscores the urgent need for continued variant surveillance and development of novel countermeasures. Future research should focus on predicting the effects of more complex mutations and exploring alternative therapeutic approaches.

The study focused on a panel of 14 antibodies and might not represent the full spectrum of antibody responses. The computational analysis relied on existing structural information, and limitations of the input structures could affect the accuracy of FlexddG predictions. The pseudovirus neutralization assay may not perfectly mimic in vivo infection dynamics. Further studies with larger antibody panels and more diverse variant strains are needed to strengthen the conclusions.