Brewpitopes: a pipeline to refine B-cell epitope predictions during public health emergencies

Abstract

The application of B-cell epitope identification to develop therapeutic antibodies and vaccine candidates is well established. However, the validation of epitopes is time-consuming and resource-intensive. To alleviate this, in recent years, multiple computational predictors have been developed in the immunoinformatics community. Brewpitopes is a pipeline that curates bioinformatic B-cell epitope predictions obtained by integrating different state-of-the-art tools. We used additional computational predictors to account for subcellular location, glycosylation status, and surface accessibility of the predicted epitopes. The implementation of these sets of rational filters optimizes in vivo antibody recognition properties of the candidate epitopes. To validate Brewpitopes, we performed a proteome-wide analysis of SARS-CoV-2 with a particular focus on S protein and its variants of concern. In the S protein, we obtained a fivefold enrichment in terms of predicted neutralization versus the epitopes identified by individual tools. We analyzed epitope landscape changes caused by mutations in the S protein of new viral variants that were linked to observed immune escape evidence in specific strains. In addition, we identified a set of epitopes with neutralizing potential in four SARS-CoV-2 proteins (R1AB, R1A, AP3A, and ORF9C). These epitopes and antigenic proteins are conserved targets for viral neutralization studies. In summary, Brewpitopes is a powerful pipeline that refines B-cell epitope bioinformatic predictions during public health emergencies in a high-throughput capacity to facilitate the optimization of experimental validation of therapeutic antibodies and candidate vaccines.