BepiPred

BepiPred is a computational tool and web server for predicting linear B-cell epitopes in protein sequences. It is used in immunoinformatics to identify regions of proteins that are likely to be recognized by antibodies, supporting tasks such as vaccine design, diagnostic development, and epitope mapping. The original method, introduced in 2006, combines an amino acid propensity scale with a hidden Markov model to assign an epitope propensity score to each residue in a protein sequence. The output includes per-residue scores and predicted epitopes that exceed a user-defined threshold, allowing researchers to locate potential antigenic regions along the sequence.

A subsequent update, BepiPred-2.0, employs a supervised machine learning approach based on a random forest model trained on epitope data derived from antibody–antigen crystal structures. This version integrates sequence-derived features and inferred structural context to improve prediction accuracy for linear epitopes compared with the original method. It remains focused on linear epitopes and does not directly predict conformational or discontinuous epitopes arising from three-dimensional protein structure.

Applications and limitations: BepiPred is widely used to generate hypotheses about antigenic regions in proteins of interest. Its predictions are probabilistic and should be interpreted as guides rather than definitive answers. Because it predicts linear epitopes, conformational epitopes formed by noncontiguous residues may be missed, and experimental validation is typically required to confirm immunogenicity.

Availability and impact: The tool is available as an online server and has been cited in numerous studies within immunology and vaccine research as a convenient first-pass epitope predictor. Related resources include other B-cell epitope prediction tools that address different feature sets and epitope types.