In silico designing breast cancer peptide vaccine for binding to MHC class I and II: A molecular docking study

(2016) In silico designing breast cancer peptide vaccine for binding to MHC class I and II: A molecular docking study. Computational Biology and Chemistry. pp. 110-116. ISSN 1476-9271

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Abstract

Antigenic, peptides or cancer peptide vaccines can be directly delivered to cancer patients to produce immunologic responses against cancer cells. Specifically, designed peptides can associate with Major Histocompatibility Complex (MHC) class I or II molecules on the cell surface of antigen presenting cells activating anti-tumor effector mechanisms by triggering helper T cell (Th) or cytotoxic T cells (CTL). In general, high binding to MHCs approximately correlates with in vivo immunogenicity. Consequently, a molecular docking technique was run on a library of novel discontinuous peptides predicted by PEPOP from Human epidermal growth factor receptor 2 (HER2 ECD) subdomain III. This technique is expected to improve the prediction accuracy in order to identify the best MHC class I and H binder peptides. Molecular docking analysis through GOLD identified the peptide 1412 as the best MHC binder peptide to both MHC class I and II molecules used in the study. The GOLD results predicted HLA-DR4, HLA-DP2 and TCR as the most often targeted receptors by the peptide 1412. These findings, based on bioinformatics analyses, can be exploited in further experimental analyses in vaccine design and cancer therapy to find possible proper approaches providing beneficial effects. (C) 2016 Elsevier Ltd. All rights reserved.

Item Type: Article
Keywords: her2 receptor docking mhc bioinformatics peptide vaccine t-cell epitopes major histocompatibility complex genetic algorithm bioinformatics immunization combination prediction receptor domain gold
Page Range: pp. 110-116
Journal or Publication Title: Computational Biology and Chemistry
Journal Index: ISI
Volume: 65
Identification Number: https://doi.org/10.1016/j.compbiolchem.2016.10.007
ISSN: 1476-9271
Depositing User: مهندس مهدی شریفی
URI: http://eprints.mui.ac.ir/id/eprint/2266

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