Antibody-antigen interaction – at antigenic local environments called B-cell epitopes – is a prominent mechanism for neutralization of infection. Effective mimicry, and display, of B-cell epitopes is key to vaccine design. Here, a physical approach is evaluated for the discovery of epitopes which evolve slowly over closely related pathogens (conserved epitopes). The approach is 1) protein flexibility-based and 2) demonstrated with clinically relevant enveloped viruses, simulated via molecular dynamics. The approach is validated against 1) seven structurally characterized enveloped virus epitopes which evolved the least (out of thirty-eight enveloped virus-antibody structures) and 2) eight preexisting epitope and peptide discovery algorithms. Rationale for a new benchmarking scheme is presented. A data-driven epitope clustering algorithm is introduced. The prediction of eleven Zika virus epitopes (for future exploration on recombinant vaccine technologies) is demonstrated. For the first time, protein flexibility is shown to outperform solvent accessible surface area as an epitope discovery metric.

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1 results in Jason S. Grosch in Authors: Jason S. Grosch

B-Cell Epitope Discovery: The First Protein Flexibility-Based Algorithm – Zika Virus Conserved Epitope Demonstration

1 results in Authors: Jason S. Grosch