Beyond B-Cell Epitopes: Curating Positive Data on Antipeptide Paratope Binding to Support Development of Computational Tools for Vaccine Design and Other Translational Applications

B-cell epitope prediction was first developed to help design peptide-based vaccines for protective antibody-mediated immunity exemplified by neutralization of biological activity (e.g., pathogen infectivity). Requisite computational tools are benchmarked using experimentally obtained paratope-epitope binding data, which also serve as training data for machine-learning approaches to development of said tools. Such data are curated in the Immune Epitope Database (IEDB). However, IEDB curation guidelines define B-cell epitopes primarily on the basis of paratope-bound epitope structures, obscuring the crucial role of conformational disorder in the underlying immune recognition process. For the present work, pertinent IEDB B-cell assay records were retrieved and analyzed in relation to other data from both IEDB and external sources including the Protein Data Bank (PDB) and published literature, with special attention to data on conformational disorder among B-cell epitopes. This revealed examples of antipeptide antibodies that recognize conformationally disordered B-cell epitopes and thereby neutralize the biological activity of cognate targets (e.g., proteins and pathogens), with inconsistency noted in the definition of some epitopes. These results suggest an alternative approach to curating paratope-epitope binding data based on neutralization of biological activity by polyclonal antipeptide antibodies, with reference to immunogenic peptide sequences and their conformational disorder in the unbound state.