A multiplexed, systems-based approach for prediction of antibody neutralization breadth for soluble human receptors

For endemically circulating viruses, quantifying neutralization capacity of antibodies in a rapid and high-throughput manner is paramount given the ever-evolving targets of neutralization. Moreover, identifying features of an antibody response correlating with neutralization at the multivariate level can inform vaccine boosting strategies and next-generation monoclonal antibody therapies. In this study, we developed a systems-predicted neutralizing antibody platform that is capable of quantifying neutralization capacity at the multiplex level. We validated the platform using SARS-CoV-2 ancestral and highly diverged Omicron sublineage spikes simultaneously. Combining these results with a broader systems serology approach identified humoral signatures that predict broadly neutralizing responses that are divergent between vaccine or hybrid immunity. In both cases, though, predicted neutralization responses were enmeshed in antibody networks, showing that target-specific and broadly neutralizing antibody responses are multi-isotype- and subclass-influenced. We propose that neutralization against a diverse array of viral receptor-binding proteins can be quantified using a systems-based platform in a sample- and time-sparing approach, which can be further employed to predict protection against newly emerged viruses or variants.