Benchmarking B-Cell Epitope Prediction for the Design of Peptide-Based Vaccines: Problems and Prospects

Abstract

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.

OEP80, an essential protein paralogous to the chloroplast protein translocation channel Toc75, exists as a 70-kD protein in the Arabidopsis thaliana chloroplast outer envelope

“…Alternatively, it may be explained by electrical charges. Even if a peptide and a protein segment share the same sequence, they may contain different charges at their N-or C-terminal ends or different protonation states of side chains (Caoili 2010). Indeed, Liang et al (1996) showed that antisera against a peptide conjugated to a carrier protein at its N terminus often recognized the free carboxyl group of the peptide and hence could not react with their target protein when such a charge was not available.…”

Benchmarking B-Cell Epitope Prediction with Quantitative Dose-Response Data on Antipeptide Antibodies: Towards Novel Pharmaceutical Product Development

“…To facilitate the utilization of benchmark datasets comprising continuous dose-response data on antibody-mediated modulation of biological activity, such data typically can be normalized to yield quotients in the range of zero to unity that represent the magnitude of an observed antibody-mediated biological effect relative to its theoretical or empirically determined maximum magnitude [9, 10]. Each quotient may thus be obtained as $\begin{array}{c}q=\frac{B}{B_{\mathrm{0}}},\end{array}$ where B and B 0 are the observed and maximum magnitudes of the antibody-mediated biological effect, respectively.…”

An integrative framework for structure-based prediction of biological effects mediated by antipeptide antibodies

“…Such methods are typically developed on the basis of qualitative rather than quantitative training data, which is practically appealing given the preponderance of qualitative empirical results available through publicly accessible databases. However, these results are generated via artificial dichotomization of experimental outcomes, which is inherently problematic as regards benchmarking of predictive performance [4]. This issue would be circumvented by adopting the proposed framework, although more rigorous validation thereof demands further accumulation of quantitative benchmark data, particularly dose-response data on antipeptide antibodies [6], towards development of peptidebased constructs for biomedical applications.…”