Discovery of tumor antigenic epitopes is important for cancer vaccine development. Such epitopes can be designed and modified to become more antigenic and immunogenic in order to overcome immunosuppression towards the native tumor antigen. In silico-guided modification of epitope sequences allows predictive discrimination of those that may be potentially immunogenic. Therefore, only candidates predicted with high antigenicity will be selected, constructed, and tested in the lab. Here, we described the employment of in silico tools using a multiparametric approach to assess both potential T-cell epitopes (MHC class I/II binding) and B-cell epitopes (hydrophilicity, surface accessibility, antigenicity, and linear epitope). A scoring and ranking system based on these parameters was developed to shortlist potential mimotope candidates for further development as peptide cancer vaccines.
Keywords: B-cell epitope; IEDB; In silico prediction; Mimotope; T-cell epitope; Tumor antigen; Vaccine design.