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At present, the most common cause of cancer-related death in women is breast cancer. In a large proportion of breast cancers, there is the overexpression of human epidermal growth factor receptor 2 (HER2). This receptor is a 185 KDa growth factor glycoprotein, also known as the first tumor-associated antigen for different types of breast cancers. Moreover, HER2 is an appropriate cell-surface specific antigen for passive immunotherapy, which relies on the repeated application of monoclonal antibodies that are transferred to the patient. However, vaccination is preferable because it would stimulate a patient's own immune system to actively respond to a disease. In the current study, several bioinformatics tools were used for designing synthetic peptide vaccines. PEPOP was used to predict peptides from HER2 ECD subdomain III in the form of discontinuous-continuous B-cell epitopes. Then, T-cell epitope prediction web servers MHCPred, SYFPEITHI, HLA peptide motif search, Propred, and SVMHC were used to identify class-I and II MHC peptides. In this way, PEPOP selected 12 discontinuous peptides from the 3D structure of the HER2 ECD subdomain III. Furthermore, T-cell epitope prediction analyses identified four peptides containing the segments 77 (384-391) and 99 (495-503) for both B and T-cell epitopes. This work is the only study to our knowledge focusing on design of in silico potential novel cancer peptide vaccines of the HER2 ECD subdomain III that contain epitopes for both B and T-cells. These findings based on bioinformatics analyses may be used in vaccine design and cancer therapy; saving time and minimizing the number of tests needed to select the best possible epitopes.

In silico Design of Discontinuous Peptides Representative of B and T-cell Epitopes from HER2-ECD as Potential Novel Cancer Peptide Vaccines