Search

Motivation B-cell epitopes (BCEs) play a pivotal role in the development of peptide vaccines, immuno-diagnostic reagents and antibody production, and thus in infectious disease prevention and diagnostics in general. Experimental methods used to determine BCEs are costly and time-consuming. Therefore, it is essential to develop computational methods for the rapid identification of BCEs. Although several computational methods have been developed for this task, generalizability is still a major concern, where cross-testing of the classifiers trained and tested on different datasets has revealed accuracies of 51–53%. Results We describe a new method called EpitopeVec, which uses a combination of residue properties, modified antigenicity scales, and protein language model-based representations (protein vectors) as features of peptides for linear BCE predictions. Extensive benchmarking of EpitopeVec and other state-of-the-art methods for linear BCE prediction on several large and small datasets, as well as cross-testing, demonstrated an improvement in the performance of EpitopeVec over other methods in terms of accuracy and area under the curve. As the predictive performance depended on the species origin of the respective antigens (viral, bacterial and eukaryotic), we also trained our method on a large viral dataset to create a dedicated linear viral BCE predictor with improved cross-testing performance. Availability and implementation The software is available at https://github.com/hzi-bifo/epitope-prediction. Supplementary information Supplementary data are available at Bioinformatics online.

ObjectiveNeutralising antibodies are key effectors of infection-induced and vaccine-induced immunity. Quantification of antibodies’ breadth and potency is critical for understanding the mechanisms of protection and for prioritisation of vaccines. Here, we used a unique collection of human specimens and HCV strains to develop HCV reference viruses for quantification of neutralising antibodies, and to investigate viral functional diversity.DesignWe profiled neutralisation potency of polyclonal immunoglobulins from 104 patients infected with HCV genotype (GT) 1–6 across 13 HCV strains representing five viral GTs. Using metric multidimensional scaling, we plotted HCV neutralisation onto neutralisation maps. We employed K-means clustering to guide virus clustering and selecting representative strains.ResultsViruses differed greatly in neutralisation sensitivity, with J6 (GT2a) being most resistant and SA13 (GT5a) being most sensitive. They mapped to six distinct neutralisation clusters, in part composed of viruses from different GTs. There was no correlation between viral neutralisation and genetic distance, indicating functional neutralisation clustering differs from sequence-based clustering. Calibrating reference viruses representing these clusters against purified antibodies from 496 patients infected by GT1 to GT6 viruses readily identified individuals with extraordinary potent and broadly neutralising antibodies. It revealed comparable antibody cross-neutralisation and diversity between specimens from diverse viral GTs, confirming well-balanced reporting of HCV cross-neutralisation across highly diverse human samples.ConclusionRepresentative isolates from six neutralisation clusters broadly reconstruct the functional HCV neutralisation space. They enable high resolution profiling of HCV neutralisation and they may reflect viral functional and antigenic properties important to consider in HCV vaccine design.