Structural and functional dissection of neutralisation differences among SARS-CoV-2 variants using antigenicity prediction and CR3022 binding analysis.

Background: Progressive waves of coronavirus disease 2019 (COVID-19) have been driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants carrying mutations in the spike glycoprotein, particularly in immunodominant regions such as the receptor-binding domain (RBD) and the N-terminal domain (NTD). These mutations can alter antigenic surfaces and are associated with changes in antibody recognition and vaccine-induced protection. Integrating experimental neutralisation data with computational analyses may contextualise variant-associated differences in antibody responses. Methods: Neutralisation responses were evaluated using a pseudovirus-based luciferase reporter assay including SARS-CoV-2 spike proteins from B.1 (Wuhan), B.1.617.2, AY.2 (Delta), and B.1.1.529 (Omicron). Plasma samples from three cohorts: naturally infected (I), vaccinated (V), and vaccinated-infected individuals (V+I), along with RBD-directed monoclonal antibody CR3022, were assessed to determine neutralizing titres (NT50). In parallel, spike sequences were analysed using epitope prediction, antigenicity profiling, and structural modelling. Docking simulations of CR3022 with variant RBDs were performed using HADDOCK, and binding parameters were estimated using PRODIGY. Results: Neutralisation responses varied across cohorts and viral variants, reflecting differences in immune exposure history. Plasma from V group individuals showed comparatively higher neutralisation titre, whereas B.1.617.2 and B.1.1.529 exhibited reduced susceptibility to neutralisation by infection-elicited antibodies. Computational analyses indicated variant-associated differences in predicted antigenicity and epitope landscapes within the RBD and NTD. Structural modelling and docking suggested that spike mutations may influence the CR3022-RBD interaction interface, with corresponding changes in predicted binding affinity across variants. These computational observations provide structural context for experimentally observed trends in reduced neutralisation but do not establish a direct mechanistic relationship. Conclusion: This study provides a combined experimental and computational characterization of SARS-CoV-2 variant-specific neutralisation across infection, vaccination and hybrid immunity-driven cohorts. The integration of pseudovirus neutralisation data with structural and in silico analyses offers a hypothesis-generating framework to contextualize observed differences in antibody responses and epitope recognition across variants.