Variant escape site mapping and characterization of SARS-CoV-2 nucleocapsid rapid antigen test antibodies

Rapid antigen tests (RATs) were a critical tool for diagnosing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during the height of the pandemic, but their performance depends on antibodies that remain robust to viral evolution. Since most commercial RATs target the viral nucleocapsid protein, mutations in this domain can compromise binding and test performance. To systematically define this risk, we used deep mutational scanning (DMS) to map escape mutations for 82 antibodies across 43 commercially available tests, generating the most comprehensive escape database of SARS-CoV-2 viral mutations to date. Most antibodies target conformational epitopes, with 47 (57%) mapping to the RNA-binding domain and 23 (28%) to the dimerization domain. Principal component analysis and clustering revealed escape phenotypes, including shared features outside of predicted epitopes. As the pandemic proceeded, we noted a change in industry preference from IgG to IgM-type antibodies, which was associated with changes to epitope sub-class prevalence. Analysis of paired capture-detection antibodies revealed a preference for targeting the same structural domain (20/33 or 61%), indicating a slight preference for test antibodies to hit orthogonal yet proximal sites. Together, the results establish a comprehensive resource for understanding how SARS-CoV-2 nucleocapsid protein mutations affect antibody binding and the phenotypes that underlie RAT antibody selection. This resource and approach can help prioritize antibodies for follow-up performance testing and support the design of more mutation-resilient diagnostics.IMPORTANCEIt is critical during a pandemic for diagnostic tests to be accurate and for the false negative rate to be kept low. The rapidly changing nature of SARS-CoV-2 exposed a weakness with using rapid antigen tests to diagnose infection, as mutations to the viral nucleocapsid protein can cause the virus to evade detection. In this work, we systematically tested the robustness of 82 antibodies from 43 separate commercially available tests by deliberately mutating the nucleocapsid protein and investigating which changes caused weakened binding to the test antibodies. In addition to determining which specific tests were at risk from certain mutations, we collated our datasets into a meta-analysis, allowing us to identify some best practices for designing robust tests. We, therefore, expect that this resource will support the design of more mutation-resilient diagnostics, whether for future variants of SARS-CoV-2 or in applying these principles to other respiratory viruses, such as influenza virus.