Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking

Abstract

The interaction between antibodies and antigens is one of the most important immune system mechanisms for clearing infectious organisms from the host. Antibodies bind to antigens at sites referred to as B-cell epitopes. Identification of the exact location of B-cell epitopes is essential in several biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping of B-cell epitopes has been moderate. Several issues regarding the evaluation data sets may however have led to the performance values being underestimated: Rarely, all potential epitopes have been mapped on an antigen, and antibodies are generally raised against the antigen in a given biological context not against the antigen monomer. Improper dealing with these aspects leads to many artificial false positive predictions and hence to incorrect low performance values. To demonstrate the impact of proper benchmark definitions, we here present an updated version of the DiscoTope method incorporating a novel spatial neighborhood definition and half-sphere exposure as surface measure. Compared to other state-of-the-art prediction methods, Discotope-2.0 displayed improved performance both in cross-validation and in independent evaluations. Using DiscoTope-2.0, we assessed the impact on performance when using proper benchmark definitions. For 13 proteins in the training data set where sufficient biological information was available to make a proper benchmark redefinition, the average AUC performance was improved from 0.791 to 0.824. Similarly, the average AUC performance on an independent evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version of DiscoTope is available at www.cbs.dtu.dk/services/DiscoTope-2.0.

Expression and Antigenic Evaluation of Helicobacter pylori UreB Fragment

“…In the present study, we applied these techniques as major methods of identification (7,18). In general, methods used to identify antigenic epitopes should be structurally flexible on the protein surface, exposed to solvent, and contain charged amino acids and epitope hydrophilic amino acids (3,(18)(19)(20).…”

A Bioinformatic Glimpse of Human-Origin Zika Virus Polyprotein

“…These 20 amino acids are within the nonstructural protein NS1 domain of the polyprotein; these 20 amino acids occupy numerical positions 311-330 of the mature NS1. This 20-amino acid sequence was computationally identified [15] by structural, conformational and epitopic mapping of Zika virus polyproteins by means of the combined use of Ellipro [16], Epitopia [17] and Discotope [18]. The protein structure and geometric properties are used by Ellipro to computationally predict immunogenic regions of the protein [16].…”

“…Protein structure and amino acid sequence are used by Epitopia [17] to computationally predict B-cell antigenicity of the protein. The occurrence of discontinuous B-cell epitopes is computationally predicted by Discotope [18] on the basis of threedimensional structure and surface accessibility of the protein. Thus, none of these epitope-prediction methods directly depend upon the propensity of a set of amino acids at a given position in a set of sequences to mutate, as does the Shannon information entropy reported here.…”

Databases and Algorithms in Allergen Informatics

“…Number of methods that utilize 3D structure of antigens for discontinuous epitope prediction have also been developed. These methods use different approaches for prediction such as solvent accessibility of surface residues [123,124], solvent accessibility with propensity scores [125], and propensity scores with packing density of amino acids [126]. An account of major tools/servers that are involved in conformational epitope prediction is provided in Studies have shown that the analysis of antigen-antibody complex structures is very useful for the characterization of conformational epitopes [134].…”