| Summary: | Epitope mapping studies aim to identify the binding sites of antibody-antigen interactions to enhance the development of vaccines, diagnostics and immunotherapeutic compounds. However, mapping is a laborious process employing time- and resource-consuming 'wet bench' techniques or epitope prediction software that are still in their infancy. For polymorphic antigens, another challenge is characterizing cross-reactivity between epitopes, teasing out distinctions between broadly cross-reactive responses, limited cross-reactions among variants and the truly type-specific responses. A refined understanding of cross-reactive antibody binding could guide the selection of the most informative subsets of variants for diagnostics and multivalent subunit vaccines. We explored the antibody binding reactivity of sera from human patients and Peromyscus leucopus rodents infected with Borrelia burgdorferi to the polymorphic outer surface protein C (OspC), an attractive candidate antigen for vaccine and improved diagnostics for Lyme disease. We constructed a protein microarray displaying 23 natural variants of OspC and quantified the degree of cross-reactive antibody binding between all pairs of variants, using Pearson correlation calculated on the reactivity values using three independent transforms of the raw data: (1) logarithmic, (2) rank, and (3) binary indicators. We observed that the global amino acid sequence identity between OspC pairs was a poor predictor of cross-reactive antibody binding. Then we asked if specific regions of the protein would better explain the observed cross-reactive binding and performed in silico screening of the linear sequence and 3-dimensional structure of OspC. This analysis pointed to residues 179 through 188 the fifth C-terminal helix of the structure as a major determinant of type-specific cross-reactive antibody binding. We developed bioinformatics methods to systematically analyze the relationship between local sequence/structure variation and cross-reactive antibody binding patterns among variants of a polymorphic antigen, and this method can be applied to other polymorphic antigens for which immune response data is available for multiple variants.
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