Prediction of linear B cell epitopes is of interest for the production of antigen-specific antibodies and the design of peptide-based vaccines. Here, we present BCEPS, a web server for predicting linear B cell epitopes tailored to select epitopes that are immunogenic and capable of inducing cross-reactive antibodies with native antigens. BCEPS implements various machine learning models trained on a dataset including 555 linearized conformational B cell epitopes that were mined from antibody–antigen protein structures. The best performing model, based on a support vector machine, reached an accuracy of 75.38% ± 5.02. In an independent dataset consisting of B cell epitopes retrieved from the Immune Epitope Database (IEDB), this model achieved an accuracy of 67.05%. In BCEPS, predicted epitopes can be ranked according to properties such as flexibility, accessibility and hydrophilicity, and with regard to immunogenicity, as judged by their predicted presentation by MHC II molecules. BCEPS also detects if predicted epitopes are located in ectodomains of membrane proteins and if they possess N-glycosylation sites hindering antibody recognition. Finally, we exemplified the use of BCEPS in the SARS-CoV-2 Spike protein, showing that it can identify B cell epitopes targeted by neutralizing antibodies.
【저자키워드】 SARS-CoV-2, machine learning, B cells, prediction, Epitopes, 【초록키워드】 immunogenicity, Neutralizing antibodies, spike, antibody, Spike protein, Protein, Epitopes, Accessibility, Accuracy, membrane protein, SARS-CoV-2 spike protein, antigens, Membrane proteins, dataset, protein structures, support vector machine, epitope, specific antibodies, cross-reactive antibodies, flexibility, structures, B cell epitopes, B cell epitope, cross-reactive antibody, best, web server, linear B, Support, IEDB, immunogenic, ectodomain, antigen-specific antibody, hydrophilicity, ectodomains, Immune Epitope Database, MHC II molecules, peptide-based vaccines, N-glycosylation site, independent, predicted, identify, detect, linear, conformational, reached, machine learning model, retrieved, the SARS-CoV-2, 【제목키워드】 Epitopes, server, enhanced, Web,