BepiPred20

BepiPred-2.0 is a computational method for predicting linear B-cell epitopes from protein amino acid sequences. It is an updated version of the original BepiPred method and aims to improve sequence-based epitope prediction by training on epitopes annotated from three-dimensional structures.

The method uses a supervised machine learning approach, employing a random forest classifier trained on a curated dataset of residues known to participate in B-cell epitopes or non-epitope residues, drawn from structural data. Per-residue scores are generated for an input protein sequence, reflecting the likelihood that a given residue is part of a linear epitope. A sliding window is applied to smooth scores and identify contiguous epitope regions. The output includes residue-level scores and predicted epitope regions, with recommended thresholds to define epitopes.

BepiPred-2.0 is typically accessed via a web server or downloadable software and is widely used to guide experimental epitope mapping and peptide design when structural information is limited. It focuses on linear (continuous) epitopes and does not directly predict conformational epitopes formed by noncontiguous residues in a folded protein.

In evaluation, BepiPred-2.0 generally provides improved specificity and competitive sensitivity relative to its predecessor, though it remains subject to the limitations common to sequence-based predictions, including false positives and reliance on high-quality input sequences. Predictions should be interpreted as prioritization guidance and require experimental validation.

BepiPred-2.0 is part of the broader landscape of B-cell epitope prediction methods and is frequently cited in immunology and vaccinology research. It is complemented by other tools that predict conformational epitopes and physicochemical properties of antigenic regions.