Multi-objective prioritisation of candidate epitopes for diagnostic test development

BackgroundThe development of peptide-based diagnostic tests requires the identification of epitopes that are at the same time highly immunogenic and, ideally, unique to the pathogen of interest, to minimise the chances of cross-reactivity. Existing computational pipelines for the prediction of linear B-cell epitopes tend to focus exclusively on the first objective, leaving considerations of cross-reactivity to later stages of test development. ResultsWe present a multi-objective approach to the prioritisation of candidate epitopes for experimental validation, in the context of diagnostic test development. The dual objectives of uniqueness (measured as dissimilarity from known epitope sequences from other pathogens) and predicted immunogenicity (measured as the probability score returned by the prediction model) are considered simultaneously. Validation was performed using data from three distinct pathogens (namely the nematode Onchocerca volvulus, the Epstein-Barr Virus and the Hepatitis C Virus), with predictions derived using an organism-specific prediction approach. The multi-objective rankings returned sets of non-dominated solutions as potential targets for the development of diagnostic tests with lower probability of false positives due to cross-reactivity. ConclusionsThe application of the proposed approach to three test pathogens led to the identification of 20 new potential epitopes, with both high probability and a high degree of exclusivity to the target organisms. The results indicate the potential of the proposed approach to provide enhanced filtering and ranking of potential candidates, highlighting potential cross-reactivities and including this information into the test development process right from the target identification and prioritisation step.