Literature DB >> 27816827

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

Manijeh Mahdavi1, Violaine Moreau2.   

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 II 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. Copyright Â
© 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Bioinformatics; Docking; HER2 receptor; MHC; Peptide vaccine

Mesh:

Substances:

Year:  2016        PMID: 27816827     DOI: 10.1016/j.compbiolchem.2016.10.007

Source DB:  PubMed          Journal:  Comput Biol Chem        ISSN: 1476-9271            Impact factor:   2.877


  5 in total

1.  High-Throughput Identification of MHC Class I Binding Peptides Using an Ultradense Peptide Array.

Authors:  Amelia K Haj; Meghan E Breitbach; David A Baker; Mariel S Mohns; Gage K Moreno; Nancy A Wilson; Victor Lyamichev; Jigar Patel; Kim L Weisgrau; Dawn M Dudley; David H O'Connor
Journal:  J Immunol       Date:  2020-02-14       Impact factor: 5.422

2.  General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept.

Authors:  Dinler A Antunes; Didier Devaurs; Mark Moll; Gregory Lizée; Lydia E Kavraki
Journal:  Sci Rep       Date:  2018-03-12       Impact factor: 4.379

Review 3.  Epitope Prediction by Novel Immunoinformatics Approach: A State-of-the-art Review.

Authors:  Ehsan Raoufi; Maryam Hemmati; Samane Eftekhari; Kamal Khaksaran; Zahra Mahmodi; Mohammad M Farajollahi; Monireh Mohsenzadegan
Journal:  Int J Pept Res Ther       Date:  2019-08-20       Impact factor: 1.931

4.  Antigenic Peptide Prediction From E6 and E7 Oncoproteins of HPV Types 16 and 18 for Therapeutic Vaccine Design Using Immunoinformatics and MD Simulation Analysis.

Authors:  Basit Jabbar; Shazia Rafique; Outi M H Salo-Ahen; Amjad Ali; Mobeen Munir; Muhammad Idrees; Muhammad Usman Mirza; Michiel Vanmeert; Syed Zawar Shah; Iqra Jabbar; Muhammad Adeel Rana
Journal:  Front Immunol       Date:  2018-12-19       Impact factor: 7.561

Review 5.  Therapeutic peptides: current applications and future directions.

Authors:  Lei Wang; Nanxi Wang; Wenping Zhang; Xurui Cheng; Zhibin Yan; Gang Shao; Xi Wang; Rui Wang; Caiyun Fu
Journal:  Signal Transduct Target Ther       Date:  2022-02-14
  5 in total

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