Login Register Cart 0
Generic placeholder image

Current Proteomics

Editor-in-Chief

ISSN (Print): 1570-1646
ISSN (Online): 1875-6247

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

In Silico Designing a Novel Multi-epitope DNA Vaccine against Anti-apoptotic Proteins in Tumor Cells

Author(s): Shirin Mahmoodi* and Navid Nezafat

Volume 16, Issue 3, 2019

Page: [222 - 230] Pages: 9

DOI: 10.2174/1570164616666181127142214

Price: $65

Abstract

Background: Cancer therapy has been known as one of the most important challenges in the world. Various therapeutic methods such as cancer immunotherapy are used to eradicate tumor cells. Vaccines have an important role among different cancer immunotherapeutic approaches. In the field of vaccine production, bioinformatics approach is considered as a useful tool to design multi-epitope cancer vaccines, mainly for selecting immunodominant Cytotoxic T Lymphocytes (CTL) and Helper T Lymphocytes (HTL) epitopes.

Objective: Generally, to design efficient multi-epitope cancer vaccines, Tumor-Specific Antigens (TSA) are targeted. In the context of DNA-based cancer vaccines, they contain genes that code tumor antigens and are delivered to host by different methods.

Methods: In this study, the anti-apoptotic proteins (BCL2, BCL-X, survivin) that are over-expressed in different tumor cells were selected for CTL and HTL epitopes prediction through different servers such as RANKPEP, CTLpred, and BCPREDS.

Results: Three regions from BCL2 and one region from BCL-X were selected as CTL epitopes and two segments from survivin were defined as HTL epitopes. In addition, β-defensin was used as a proper adjuvant to enhance vaccine efficacy. The aforesaid segments were joined together by appropriate linkers, and some important properties of designed vaccine such as antigenicity, allergenicity and physicochemical characteristics were determined by various bioinformatics servers.

Conclusion: Based on the bioinformatics results, the physicochemical and immunological features showed that the designed vaccine construct can be used as an efficient cancer vaccine after its efficacy was confirmed by in vitro and in vivo immunological assays.

Keywords: Cancer immunotherapy, epitope, DNA vaccine, anti-apoptotic proteins, bioinformatics, tumor.

« Previous Next »
Graphical Abstract

[1]
Torre LA. Global cancer incidence and mortality rates and trends- an update. Cancer Epidemiol Biomarkers Prev 2016; 25(1): 16-27.
[2]
Finn O. Immuno-oncology: Understanding the function and dysfunction of the immune system in cancer. Ann Oncol 2012; 23: 6-9.
[3]
Guo C. Therapeutic cancer vaccines: past, present, and future. Adv Cancer Res 2013; 119: 421-75.
[4]
Dougan M, Dranoff G. Immune therapy for cancer. Annu Rev Immunol 2009; 27: 83-117.
[5]
Sette A, Fikes J. Epitope-based vaccines: an update on epitope identification, vaccine design and delivery. Curr Opin Immunol 2003; 15(4): 461-70.
[6]
Srivastava PK. Therapeutic cancer vaccines. Curr Opin Immunol 2006; 18(2): 201-5.
[7]
Yamada A. Next‐generation peptide vaccines for advanced cancer. Cancer Sci 2013; 104(1): 15-21.
[8]
Nezafat N. A novel multi-epitope peptide vaccine against canceran in silico approach. J Theor Biol 2014; 349: 121-34.
[9]
Negahdaripour M. A novel HPV prophylactic peptide vaccine, designed by immunoinformatics and structural vaccinology approaches. Infect Genet Evol 2017; 54: 402-6.
[10]
Mahmoodi S. Harnessing bioinformatics for designing a novel multiepitope peptide vaccine against breast cancer. Curr Pharm Biotechnol 2016; 17(12): 1100-14.
[11]
Fulda S. Modulation of apoptosis B natural products for cancer therapy. Planta Med 2010; 76(11): 1075-9.
[12]
Suhrbier A. Multi‐epitope DNA vaccines. Immunol Cell Biol 1997; 75(4): 402-8.
[13]
Thor Straten P, Andersen MH. The anti-apoptotic members of the BCl-2 family are attractive tumor-associated antigens. Oncotarget 2010; 1(4): 239.
[14]
Hassan M. Apoptosis and molecular targeting therapy in cancer. BioMed Res Int 2014; 2014: 150845.
[15]
Kelly, R.J. Impacting tumor cell-fate by targeting the inhibitor of apoptosis protein survivin. Mol. Cancer, 2011, 10, 1, 35.
[16]
Huang J. MicroRNA regulation and therapeutic targeting of survivin in cancer. Am J Cancer Res 2015; 5(1): 20-31.
[17]
Soleimanpour E, Babaei E. Survivin as a potential target for response to therapeutics cancer therapy. Asian Pac J Cancer Prev 2015; 16(15): 6187-91.
[18]
Hata AN, Engelman JA, Faber AC. The BCl2 family: Key mediators of the targeted anticancer. Cancer Discov 2015; 5(5): 475-87.
[19]
Mei HF. β-Defensin 2 as an adjuvant promotes anti-melanoma immune responses and inhibits the growth of implanted murine melanoma in vivo. PLoS One 2012; 7(2): 31328.
[20]
Suarez-Carmona M. Defensins: “Simple” antimicrobial peptides or broad-spectrum molecules. Cytokine Growth Factor Rev 2015; 26(3): 361-70.
[21]
Ferris LK. Human beta-defensin 3 induces maturation of human Langerhans cell-like dendritic cells: an antimicrobial peptide that functions as an endogenous adjuvant. J Invest Dermatol 2013; 133(2): 460-8.
[22]
Kumar Pandey R. Designing B‐and T‐cell multi‐epitope based subunit vaccine using immunoinformatics approach to control Zika virus infection. J Cell Biochem 2018; 119(9): 7631-42.
[23]
Rammensee, H.-G.; Bachmann, J.; Stevanovic, S. MHC ligands and peptide motifs. Mol. Biol. Intellig.Unit, Ed. Springer-Verlag Berlin Heidelberg, 2013, pp. VIII, 462.
[24]
Reche PA, Glutting JP, Reinherz EL. Prediction Prediction of MHC class I binding peptides using profile motifs. Hum Immunol 2002; 63(9): 701-9.
[25]
Singh H, Raghava G. ProPred1: prediction of promiscuous MHC class-I binding sites. Bioinformatics 2003; 19(8): 1009-14.
[26]
Zhang Q. Immune epitope database analysis resource (IEDB-AR). Nucleic Acids Res 2008; 36(2): 513-8.
[27]
Bhasin M, Raghava G. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 2004; 22(23): 3195-204.
[28]
Larsen JE, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome Res 2006; 2(1): 2.
[29]
EL‐Manzalawy, Y.; Dobbs, D.; Honavar, V. Predicting linear B‐cell epitopes using string kernels. Aging Soc 2008; 21(4): 243-55.
[30]
Magnan CN. High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics 2010; 26(23): 2936-43.
[31]
Doytchinova IA, Flower DR. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007; 8(1): 4.
[32]
Wang J, Zhang D, Li J. PREAL: prediction of allergenic protein by maximum Relevance Minimum Redundancy (mRMR) feature selection. BMC Syst Biol 2013; 7(5): 9.
[33]
Ivanciuc O, Schein CH, Braun W. SDAP: database and computational tools for allergenic proteins. Nucleic Acids Res 2003; 31(1): 359-62.
[34]
Xu X, Zhao P, Chen S-J. Vfold: A web server for RNA structure and folding thermodynamics prediction. PLoS One 2014; 9(9): 107504.
[35]
Gasteiger, E. Protein identification and analysis tools on the ExPASy server. In: John, M.W (Ed): The Proteomics Proto., Handbook, Humana Press. 2005, pp. 571-607.
[36]
Yu CS. Prediction of protein subcellular localization. Proteins 2006; 64(3): 643-51.
[37]
Chou K-C, Shen H-B. Predicting a top-down strategy to augment the power for plant protein subcellular localization. PLoS One 2010; 5(6): 11335.
[38]
Briesemeister S, Rahnenführer J, Kohlbacher O. Going from where to why interpretable prediction of protein subcellular localization. Bioinformatics 2010; 26(9): 1232-8.
[39]
Pierleoni A. BaCelLo: a balanced subcellular localization predictor. Bioinformatics 2006; 22(14): 408-16.
[40]
Almagro-Armenteros JJ, Sønderby CK, Sønderby SK, Nielsen H, Winther O. DeepLoc: Prediction of protein subcellular localization using deep learning. Bioinformatics 2017; 33(21): 3387-95.
[41]
Blom N. Prediction of post‐translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics 2004; 4(6): 1633-49.
[42]
Gupta R, Jung E, Brunak S. Predicition of N-glycosylation sites in human proteins. Center for Biological Sequence Analysis at Technical University of Denmark DTU 2004.
[43]
Monigatti F. The Sulfinator: predicting tyrosine sulfation sites in protein sequences. Bioinformatics 2002; 5(18): 769-70.
[44]
Ghaffari-Nazari H. Improving multi-epitope long peptide vaccine potency by using a strategy that enhances CD4+ T help in BALB/c mice. PLoS One 2015; 10(1): e0142563.
[45]
Khalili S. In silico analysis Wilms’ tumor protein to designing a novel multi-epitope DNA vaccine against cancer. J Theor Biol 2015; 379: 66-78.
[46]
Mellman I, Coukos G, Dranoff G. Cancer immunotherapy comes of age. Nature 2011; 480: 7378-480.
[47]
Yang B. DNA vaccine for cancer immunotherapy. Hum Vaccin Immunother 2014; 10(11): 3153-64.
[48]
Gilboa E. The promise of cancer vaccines. Nat Rev Cancer 2004; 4(5): 401.
[49]
Rice J, Ottensmeier CH, Stevenson FK. DNA vaccines: precision tools for activating effective immunity against cancer. Nat Rev Cancer 2008; 8(2): 108.
[50]
Cho H-I, Celis E. Design of immunogenic and effective multi-epitope DNA vaccines for melanoma. Cancer Immunol Immunother 2012; 61(3): 343-51.
[51]
Fioretti D, Iurescia S, Fazio VM, Rinaldi M. DNA vaccines: developing new strategies against cancer. J Biomed Biotechnol 2010; 1-16.
[52]
Garg H. Survivin: A unique target for tumor therapy. Cancer Cell Int 2016; 16(1): 49.
[53]
Yip K, Reed J. BCl2 family proteins and cancer. Oncogene 2008; 27(50): 6398.
[54]
Mobahat M, Narendran A, Riabowol K. Survivin as a preferential target for cancer therapy. Int J Mol Med Sci 2014; 15: 22494-516.
[55]
Oblak A, Jerala R. Toll-like receptor 4 activation in cancer progression and therapy. Clin Dev Immunol 2011; 2011: 609579.
[56]
Farhani I, Nezafat N, Mahmoodi S. Designing a novel multi-epitope peptide vaccine against pathogenic Shigella spp. Based immunoinformatics approaches. Int J Pept Res Ther 2018; 1-13.
[http://dx.doi.org/10.1007/s10989-018-9698-5]
[57]
Khong H, Overwijk WW. Adjuvants for peptide-based cancer vaccines. J Immunother Cancer 2016; 4(1): 56.
[58]
Takamatsu N. Production of enkephalin in tobacco protoplasts using tobacco mosaic virus RNA vector. FEBS Lett 1990; 269(1): 73-6.
[59]
Livingston B. A rational strategy to design multiepitope immunogens based on multiple Th lymphocyte epitopes. J Immunol 2002; 168(11): 5499-506.
[60]
Leitner WW. Immune responses induced by intramuscular or gene gun injection of protective deoxyribonucleic acid vaccines that express the circumsporozoite protein from plasmodium berghei malaria parasites. J Immunol 1997; 159(12): 6112-9.
[61]
Kensil CR, Patel U, Lennick M, Marciani D. Separation and characterization of saponins with adjuvant activity from Quillaja saponaria Molina cortex. J Immunol 1991; 146(2): 431-7.
[62]
Olsen JV, Blagoev B, Gnad F, et al. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 2006; 127(3): 635-48.
[63]
Dougan D, Micevski D, Truscot K. The N-end rule pathway: from recognition by N-recognins, to destruction by AAA+ proteases. Biochim Biophys Acta Bioenerg 2012; 1823(1): 83-91.

Rights & Permissions Print Cite
Article Metrics
51
1
© 2024 Bentham Science Publishers | Privacy Policy