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• W4303986656 endingPage "1842" @default.
• W4303986656 startingPage "1829" @default.
• W4303986656 abstract "Immunotherapies are a promising treatment option especially for the management of TNBC owing to its higher levels of tumour-associated antigens together with higher mutational load. Of note, the administration of preventive vaccines in the early stage of the cancer holds promise for effective disease management. Therefore, the present study aimed to develop a novel multi-epitope peptide-based vaccination against TNBC employing SOX9, which has recently been recognized as a key regulator of TNBC metastasis. The immunodominant regions from the SOX9 protein were computed and assessed based on their ability to elicit both T and B lymphocyte mediated responses. The resultant epitopes were fused using appropriate linkers (EAAAK, KK, AAY and GPGPG) and adjuvant (50S ribosomal protein L7/L12) to enhance the vaccine's immunogenicity. The physicochemical properties and population coverage were also anticipated for the constructed vaccine. Adding together, docking and dynamics simulation studies were performed on the modelled vaccine against TLR-4 to provide insight into the stability. Finally, the designed vaccine was cloned into the pET28 (+) vector and immunological simulation studies were carried out. These results demonstrate that our designed vaccine had the potency to trigger humoral and cellular immune responses. Based on these collective evidences, the final proposed vaccine could be an interesting therapeutics for the management of TNBC in the near future. Schematic representation of an efficient vaccine design framework by combining the range of immunoinformatics strategies." @default.
• W4303986656 created "2022-10-10" @default.
• W4303986656 creator A5011102112 @default.
• W4303986656 creator A5087314141 @default.
• W4303986656 date "2022-10-10" @default.
• W4303986656 modified "2023-10-05" @default.
• W4303986656 title "Designing a novel SOX9 based multi-epitope vaccine to combat metastatic triple-negative breast cancer using immunoinformatics approach" @default.
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• W4303986656 doi "https://doi.org/10.1007/s11030-022-10539-w" @default.
• W4303986656 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36214961" @default.
• W4303986656 hasPublicationYear "2022" @default.
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