Benchmarking B-cell epitope prediction for the design of peptide-based vaccines: problems and prospects.
To better support the design of peptide-based vaccines, refinement of methods to predict B-cell epitopes necessitates meaningful benchmarking against empirical data on the cross-reactivity of polyclonal antipeptide antibodies with proteins, such that the positive data reflect functionally relevant cross-reactivity (which is consistent with antibody-mediated change in protein function) and the negative data reflect genuine absence of cross-reactivity (rather than apparent absence of cross-reactivity due to artifactual masking of B-cell epitopes in immunoassays). These data are heterogeneous in view of multiple factors that complicate B-cell epitope prediction, notably physicochemical factors that define key structural differences between immunizing peptides and their cognate proteins (e.g., unmatched electrical charges along the peptide-protein sequence alignments). If the data are partitioned with respect to these factors, iterative parallel benchmarking against the resulting subsets of data provides a basis for systematically identifying and addressing the limitations of methods for B-cell epitope prediction as applied to vaccine design.
Caoili
Salvador Eugenio C
SE
Department of Biochemistry and Molecular Biology, College of Medicine, University of the Philippines Manila, 547 Pedro Gil Street, Ermita, Manila 1000, Philippines. badong@post.upm.edu.ph
D000818:Animals; D015195:Drug Design; D018985:Epitopes, B-Lymphocyte / Q000276:immunology*; D006801:Humans; D018448:Models, Immunological; D015233:Models, Statistical; D022223:Vaccines, Subunit / Q000276:immunology*
D018985:Epitopes, B-Lymphocyte; D022223:Vaccines, Subunit