BepiPred-2.0 is a web server for predicting B-cell epitopes from antigen sequences. It uses a random forest algorithm trained on epitopes derived from antibody-antibody protein structures. The method outperforms other tools in both epitope data from solved 3D structures and a large collection of linear epitopes from the leDB database. The tool provides user-friendly and informative results for both computer-savvy and non-expert users. BepiPred-2.0 is based on epitope data from crystal structures, which is considered higher quality and leads to improved predictive power. The training dataset consists of 649 antigen-antibody crystal structures, with epitopes defined as residues within 4 Å of any antibody residue's heavy atom. The method uses 46 variables, including residue type, hydrophobicity, polarity, and secondary structure. The predictions are validated using AUC, AUC10%, PPR, and TPR. BepiPred-2.0 outperforms BepiPred-1.0, LBtope, and NetSurfP in both structural and linear epitope validation datasets. The web server allows users to submit protein sequences in FASTA format and provides results in a user-friendly interface. The results are visualized with color-coded epitopes and can be downloaded in JSON or CSV format. The server also includes advanced visualization options for structural predictions. The results show that BepiPred-2.0 significantly improves the accuracy of B-cell epitope prediction, especially for high-scoring residues. The study highlights the importance of using structural epitope data for training and evaluation, leading to better performance compared to tools trained on linear peptides. The case study on lysozyme demonstrates that BepiPred-2.0 achieves higher accuracy when considering multiple epitope regions. The study concludes that BepiPred-2.0 is a valuable tool for the bioinformatics and immunology community.BepiPred-2.0 is a web server for predicting B-cell epitopes from antigen sequences. It uses a random forest algorithm trained on epitopes derived from antibody-antibody protein structures. The method outperforms other tools in both epitope data from solved 3D structures and a large collection of linear epitopes from the leDB database. The tool provides user-friendly and informative results for both computer-savvy and non-expert users. BepiPred-2.0 is based on epitope data from crystal structures, which is considered higher quality and leads to improved predictive power. The training dataset consists of 649 antigen-antibody crystal structures, with epitopes defined as residues within 4 Å of any antibody residue's heavy atom. The method uses 46 variables, including residue type, hydrophobicity, polarity, and secondary structure. The predictions are validated using AUC, AUC10%, PPR, and TPR. BepiPred-2.0 outperforms BepiPred-1.0, LBtope, and NetSurfP in both structural and linear epitope validation datasets. The web server allows users to submit protein sequences in FASTA format and provides results in a user-friendly interface. The results are visualized with color-coded epitopes and can be downloaded in JSON or CSV format. The server also includes advanced visualization options for structural predictions. The results show that BepiPred-2.0 significantly improves the accuracy of B-cell epitope prediction, especially for high-scoring residues. The study highlights the importance of using structural epitope data for training and evaluation, leading to better performance compared to tools trained on linear peptides. The case study on lysozyme demonstrates that BepiPred-2.0 achieves higher accuracy when considering multiple epitope regions. The study concludes that BepiPred-2.0 is a valuable tool for the bioinformatics and immunology community.