Linker Optimization in Breast Cancer Multiepitope Peptide Vaccine Design Based on Molecular Study

Abstract

Breast cancer is most common cancer diagnosed in women. The urgency of developing effective therapeutic approaches is needed, both passive and active immunotherapy using vaccines. Immunoinformatics approach for epi-tope prediction of cancer proteins is one of promising approach in peptide vaccine development. Linker optimization is important parameters in peptide vaccine construction which will affect the conformation, folding and vaccine stability. From our previous study, we generate multiepitope peptide-vaccine consist of seven epitopes: DPVALVAPF, SVAYRLGTL, SQINTLNTL, RFRELVSEF, VTSANIQEF, RPRFRELVS, and MYFEFPQPL. Here we made attempt to optimize the multiepitope structure linked by 5 linker such as AAY, EAAAK, GPGPG, GGGGS, KK using in silico approach. 3D modelling of the multi epitope sequence was conducted via GalaxyTBM. Validation of tertiary structure conducted using Ramachandran plot and quality factor of the structures is being analyzed using ERRAT. Solubility of the designed vaccine was assessed using the Protein Sol webserver. The multi-epitope vaccine physicochemical parameters (pI, hydropho-bicity, GRAVY, charges, and molecular weight) were conducted via Peptide Analyzing Tools from Thermofisher Scientific. From the protein validation results and physicochemical features, the best peptide model is model 1 which linked with EAAAK linker. Model 1 can be used as potential multi-epitope agents for breast cancer vaccines.