The ability to predict immunogenic regions in selected proteins by in-silico methods has broad implications, such as allowing a quick selection of potential reagents to be used as diagnostics, vaccines, immunotherapeutics, or research tools in several branches of biological and biotechnological research. However, the prediction of antibody target sites in proteins using computational methodologies has proven to be a highly challenging task, which is likely due to the somewhat elusive nature of B-cell epitopes. This paper proposes a web-based platform for scoring potential immunological reagents based on the structures or 3D models of the proteins of interest. The method scores a protein’s peptides set, which is derived from a sliding window, based on the average solvent exposure, with a filter on the average local model quality for each peptide. The platform was validated on a custom-assembled database of 1336 experimentally determined epitopes from 106 proteins for which a reliable 3D model could be obtained through standard modeling techniques. Despite showing poor sensitivity, this method can achieve a specificity of 0.70 and a positive predictive value of 0.29 by combining these two simple parameters. These values are slightly higher than those obtained with other established sequence-based or structure-based methods that have been evaluated using the same epitopes dataset. This method is implemented in a web server called B-Pred, which is accessible at http://immuno.bio.uniroma2.it/bpred. The server contains a number of original features that allow users to perform personalized reagent searches by manipulating the sliding window’s width and sliding step, changing the exposure and model quality thresholds, and running sequential queries with different parameters. The B-Pred server should assist experimentalists in the rational selection of epitope antigens for a wide range of applications.
Keywords: B-cell epitopes, immunoinformatics, bioinformatics, web server, epitope prediction