• Users Online: 604
  • Print this page
  • Email this page


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2020  |  Volume : 4  |  Issue : 4  |  Page : 355-364

Development of polyepitopic immunogenic contrast against hepatitis C virus 1a-6a genotype by in silico approach


1 Student Research Committee, Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences; Cellular & Molecular Biology Research Center Shahid Beheshti University of Medical Sciences, Tehran, Iran
2 Systems Biomedicine, Pasteur Institute of Iran, Tehran, Iran
3 Cellular & Molecular Biology Research Center Shahid Beheshti University of Medical Sciences; Department of Medical Biotechnology, School of Advanced Technologies in Medicine Shahid Beheshti University of Medical Sciences, Tehran, Iran
4 Infectious Diseases and Tropical Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
5 Cellular & Molecular Biology Research Center Shahid Beheshti University of Medical Sciences, Tehran, Iran
6 Isfahan Gastroenterology and Hepatology Research Center (IGHRC), Isfahan University of Medical Sciences, Isfahan, Iran

Date of Submission22-Oct-2020
Date of Acceptance05-Nov-2020
Date of Web Publication30-Dec-2020

Correspondence Address:
Dr. Fatemeh Yarian
Shahid Beheshti University of Medical Sciences, Tehran
Iran
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/bbrj.bbrj_186_20

Rights and Permissions
  Abstract 


Background: Hepatitis C is a viral disease associated with chronic hepatitis and hepatocellular carcinoma. Hepatitis C virus (HCV) plays a critical role in the pathogenesis of this disease. Nonstructural proteins including NS3, NS4A, and NS5A are important in viral replication and translation. Since recent therapies are not appropriate for anti-HCV activity in humans, the main objective of this study is the use of immunoinformatic approaches for designing a novel multiepitope peptide with antigenic properties and examining it as a vaccine against (1a-6a) genotypes of the virus. These types of studies can be helpful for the development of new vaccine strategies against hepatitis C disease. Methods: The conserved position of nonstructural proteins (NS3/NS4a and NS5A) of HCV genotypes was used for vaccine design. Linear and conformational epitopes of B cell, MHC-I, MHC-II binding epitopes, and interferon-gamma inducing epitopes were determined in the construction of the vaccine. Molecular dynamics (MD) simulation and protein docking multiepitope peptides with toll-like receptor (TLR) 3 and TLR8 were analyzed. Results: MD simulation revealed a stable structure of candidate vaccines. Hence, docking results showed multiepitope peptides interaction with TLR3 and TLR8 and epitopes related to NS3 protein have the most interaction. These analyses suggest that designed vaccines can induce humoral and cellular immune responses against HCV. Conclusions: These analyses suggest that designed vaccines can induce humoral and cellular immune responses against HCV. However, experimental tests are required to evaluate the safety and immunogenicity profile of designed multiepitope vaccines.

Keywords: Epitope, hepatitis C virus, immunoinformatics, protein–protein docking, vaccine


How to cite this article:
Dehghan Z, Lari A, Yarian F, Ahangarzadeh S, Sharifnia Z, Shahzamani K, Shahidi S. Development of polyepitopic immunogenic contrast against hepatitis C virus 1a-6a genotype by in silico approach. Biomed Biotechnol Res J 2020;4:355-64

How to cite this URL:
Dehghan Z, Lari A, Yarian F, Ahangarzadeh S, Sharifnia Z, Shahzamani K, Shahidi S. Development of polyepitopic immunogenic contrast against hepatitis C virus 1a-6a genotype by in silico approach. Biomed Biotechnol Res J [serial online] 2020 [cited 2021 Jan 15];4:355-64. Available from: https://www.bmbtrj.org/text.asp?2020/4/4/355/305640




  Introduction Top


Hepatitis C virus (HCV) causes approximately 70% non-A, non-B hepatitis around the world.[1] This virus causes chronic hepatitis and hepatocellular carcinoma.[2] The 1a and 3a genotypes are the most frequent HCV genotypes in Iran.[3] Vaccines are currently available for hepatitis A and hepatitis B, but a vaccine for hepatitis C is still under investigation.[4] Since recent therapies are not appropriate for HCV infection treatment and clearance, designing a vaccine with high immunogenicity and necessary immune responses can be helpful for protection against infection and prevention of this disease. To design an effective vaccine against HCV, information about the HCV structure and replication components is needed. HCV has an RNA genome that encodes a polyprotein with 3000 amino acids. This polyprotein is cleaved by viral protease and creates structural (E1, E2, and C) and nonstructural (NS2, NS3, NS4A, NS4B, NS5A, and NS5B) proteins.[1] Nonstructural proteins have important roles in the process of replication and maturation of HCV.[5] In the acute phase of HCV infection, specific T cell responses will be produced against nonstructural proteins[6] and also, several studies show that important regions of nonstructural proteins provide an affective T cell immunity response.[7],[8]

NS3 protein has serine protease and helicase activity. This helicase protein is essential for virus replication and translation. This protein also inhibits adenosine triphosphate-dependent protein kinases phosphorylation.[9] NS3 targets TRIF and MAVS proteins of the host immune system and functions as signaling adapters for toll-like receptor (TLR) 3 and mitochondrial antiviral signaling protein, respectively, and affects the host immune system.[10] Several studies have shown that T-cell immune response against NS3 is often associated with the resolution of the virus.[11],[12] The NS4 protein is the NS3 cofactor for 4B/5A cutting.[13] The transportation's role from ER to Golgi is played by the NS4A/B protein.[14] Moreover, this protein induces apoptosis through the mitochondrial pathway by reduction of mitochondrial membrane potential, releasing cytochrome c and activating caspase-3, so this protein has a main role in cell hepatic death.[15] NS5A protein modulates cell cycle and immunoregulatory genes[2] and is critical in viral persistence and pathogenesis. This nonstructural protein of the virus interacts with a large number of hosts and viral proteins during replication and assembly.[16],[17] NS5A has also an anti-apoptotic activity in mammalian cells. Signal transducers and activators of transcription (STATs) are cytoplasmic transcription factors that are activated in response to cytokines and growth factors. It plays an important role in the proliferation and prevention of apoptosis in the cell. JAK protein activates STAT by phosphorylation. NS5A can induce STAT phosphorylation by JAK and activate this factor.[2] In this study for designing an effective antigen-based vaccine, concerning the role of NS3/NS4A and NS5A non-structural proteins in the life cycle of HCV, these proteins were selected.

The CD8+ cytotoxic T lymphocytes and CD4+ T-helper cells are necessary for the control of HCV infection and are considered as the main effector cells in eliminating HCV-infected cells.[18] HCV-specific CD4+ T cells are involved in the initiation and maintenance of adaptive immunity. In addition, HCV-specific CD8+ T cells play a critical role in cytokine production, activation of antigen-presenting cells, B cell maturation, and regulatory functions.[19] There is a strong association between HLA-DRB1*0101, DRB1*1101, DQB1*0301 alleles, and viral infections. HLA-A*03, B*27, DRB1*0101, DRB1*0401, and DRB1*1501 alleles play a significant role in the acute phase of HCV removal.[20] HLA-DQB1*0301 alleles involved in the removal of viral infection are shown in colored-skin people. colored-skin people suppress viral infection by HLA-DRB1*0101 and HLA-DRB1*0101-DQB1 * 0501 alleles.[21] IgM and IgG (G1, G2, G3, and G4) antibodies are activated in response to HCV infection. IgG antibody produced in response to NS3, NS4, and NS5 proteins.[22],[23]

Most vaccines can reduce viral infections through antibody responses and neutralize viral particles to minimize the proliferation of viruses.[24],[25] One of the major problems in the development of an effective vaccine against HCV is the high genetic variability of HCV.[26] To overcome this problem, the computational vaccinology approaches are a successful option to design a multiepitope vaccine with high conservity against HCV.[27] In this study, the computational vaccinology approaches have been selected for designing new multiepitope peptide vaccines against HCV. Our vaccines contain several B-cell, T-cell, and interferon-gamma inducing epitopes of NS3/NS4A and NS5A proteins for (a) different genotypes of HCV.


  Methods Top


The overall procedures used for vaccine design were categorized into six main parts: (1) conserved motifs selection in NS3 and NS4A and NS5A protein (1a-6a) genotypes of HCV, (2) evaluation of physical, chemical, and antigenicity properties of fusion peptide, (3) prediction of B-cell and MHC binding epitopes and interferon-gamma (IFNγ) inducing epitope, (4) selection of overlapped motifs detected by MHCI, MHCII, B cell, and IFNγ and linking them by GGGS linker and designing of other multiepitope vaccines, (5) three-dimensional (3D) structure prediction of constructed vaccines, and (6) molecular dynamics (MD) simulation and molecular docking analysis of constructed vaccines with immune receptors TLR3 and TLR8. An overview of the methods used for vaccine design is shown in [Figure 1].
Figure 1: A summary of the methodology adopted for in silico design of the peptide vaccine in the study

Click here to view


Data collection

In this study, 1a, 2a, 3a, 4a, 5a, and 6a genotypes of HCV were selected as a model for antigenic epitope prediction. At least three clones of the NS3, NS4A, and NS5A protein sequences from the same patient were collected from the UniProt protein database (http://www.uniprot.org).[28] To design fusion protein, the sequences are submitted to the ClustalW server (http://www. genome. jp/tools/clustalw).[29] First, the sequences of collected clones from each genotype were aligned to obtain the consensus sequence. Finally, the obtained consensus sequences from all genotypes were aligned to conserve the sequence among all genotypes. Comparative analysis with human proteins was performed for selecting nonhuman homologs motifs by BLASTp.[30] Physical and chemical properties of designed vaccines, including the number of amino acids, molecular weight, aliphatic index, molecular weight, isoelectric point, and grand average of hydropathicity (GRAVY) were analyzed by ProtParam (http://web.expasy.org/protparam) of the ExPASy Bioinformatics Resource Portal.[31] Secondary and tertiary structures were predicted by (I-TASSER https://zhanglab.ccmb.med.umich.edu/I-TASSER/).[32]

Antigenicity identification

Vaxijen v2.0 is used for prediction antigenicity properties. This server predicts antigenicity based on the target organism such as bacterial, viral, and tumor antigens and determines antigenic probability based on the physicochemical properties.[33]

Prediction of B-cell epitopes

To predict B cell linear epitopes, the primary sequence of the peptide was submitted to BCpred (http://ailab.ist.psu.edu/bcpred/). This server predicts B cell linear epitopes by subsequences kernel and a support vector machine (SVM) with accuracy and specificity of 74.57 and 75%, respectively.[34] ABCpred (http://crdd.osdd.net/raghava//abcpred/) uses an artificial neural network which is based on applying fixed-length patterns with an accuracy of 65.93%, and a threshold of 0.51.[35] Conformational and discontinuous B cell epitopes were predicted by the ElliPro server (www.tools.immuneepitope.org/tools/ElliPro).[36]

Prediction of T-cell epitopes

Prediction of epitopes that interact with MHC and T-cells was determined by different servers including (a) EpiJen (http://www. ddg-pharmfac. net/epijen/EpiJen/EpiJen. htm) that predicts MHC binding epitopes based on quantitative matrices, derived by the additive method, and applied successively to select epitopes,[37] (b) MHCpred (http://www.ddg-harmfac.net/mhcpred/MHCPred/) with accuracy about 90% that predicts MHC binding motifs,[38] (c) IEDB (https://www.iedb.org/) that contains MHC binding data, antibody, and T cell epitopes for humans, nonhuman primates, rodents, and other animal species,[39] (d) Rankpep (http://imed.med.ucm.es/Tools/rankpep.html) uses the position-specific scoring matrices (PSSMs) for prediction of MHC-II binding epitopes.[40]

Prediction of interferon-gamma epitopes

In this study, IFN-γ inducing epitopes were predicted using the IFN epitope server (http://crdd.osdd.net/raghava/ifnepitope). This server generates all possible overlapping peptides form of antigen, based on their SVM score. The module identifies the best antigenic regions or IFN epitope in a query of antigen sequence that can induce IFN-γ.[41]

Another construction of the multiepitope vaccine

All of the selected epitopes were analyzed for overlap peptide epitopes that can bind to MHC-I, MHC-II, B-cell, and IFN-γ. Therefore, a GGGS flexible linker was added in each overlap peptide epitope sequence. The antigenicity property of the new multiepitope vaccine was calculated by Vaxijen v2.0. Finally, multiepitope vaccine and fusion protein structures were predicted by I-TASSER.

Molecular dynamics simulation and molecular docking analysis of the fusion protein and multiepitope vaccine candidate with toll-like receptor 3 and toll-like receptor 8 immune receptors

After modeling of the fusion protein and multiepitope vaccine, to stabilize the structure of the vaccine constructs, GROMACS 4.5.3 package, an MD simulation program, was used. MD simulation at 20 ns with 50,000 steps was run for equilibrated constructs. Root mean square deviation (RMSD) and radius of gyration (Rg) were obtained to predict dynamic behavior and structural changes of the studied two predicted protein vaccines and the results were generated in the form of graphs.

Molecular docking was performed between immune receptors (TLR-3 [PDB ID = 2A0Z] and TLR-8 [PDB ID = 3W3G]) and designed vaccines. For this protein–protein docking, the ClusPro server (https://cluspro.bu.edu/l) was applied.[42],[43] This server is widely used for protein–protein docking. To analyze the schematic illustration of the interactions between docked proteins, the PDBsum online database (http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/) was utilized. PDBsum is a database that analyses protein secondary structure and protein-ligand and protein-DNA interactions.[44]


  Results Top


In conversation analysis, regions with conversity >90% in NS3, NS4A, and NS5A proteins are selected for designing the fusion protein. The fusion protein comprised of 261 amino acid residues with 27,697.44 Da molecular weight. Based on ProtParam algorithms, the GRAVY, isoelectric pH, and its aliphatic index are - 0.034, 5.01, and 87.47, respectively. The two-dimensional structure of this peptide composes 26.1% helix, 13.4% sheet, 51% coil, 6.1% turn, and 3.4% 3–10 helix. The amino acid sequence of the fusion protein is shown NS3 (bold), NS4A (italic)., and. NS5A (line) (APITAYAQQ TRGLDLYLVTRHADVIPARR RGDLHAPTGSGK STKVPAAYAAQGYKVLVLNPSVAATITYS TYGKFLADGGCSGGAYDIIICD ECILGIGTVLDQA ETAGVRLIKGGRHLIFCHSKKKCDELALGLN AVAYYRGLDVSVIPTTDALMTG FTGDFDSVIDCNP QDAVSRSQR RGRTGRGRTPGLPVCQDHLEF WEGVFTGLTSTWVLVGGVLAALAAYCLDEME ECVGSQLPCEPE PDSSMPPLEGEPGDPDL).

Antigenicity assessment

VaxiJen prediction for the query sequence as an antigenic sequence with an overall prediction for the protective antigen was 0.5143.

Antigenic properties

B-cell epitopes

The B-cell linear epitopes were predicted with ABCpred, BCpred, and ElliPro servers. [Table 1] shows some epitope sequences extracted by these three servers. For each epitope, sequence, start position, and score were indicated. Motifs with a score of more than 0.7 were listed.
Table 1: B-cell linear motifs that were predicted for fusion protein presented in this study

Click here to view


Discontinuous B-cell epitopes

B-cell conformational and discontinuous epitopes of the fusion protein were predicted by the ElliPro server. This server identifies and rates the designed protein space epitopes based on 3D structure and reviews homology, so the tertiary structure of conformational epitopes with a score >0.5 was determined [Table 2] and [Figure 2].
Table 2: Position of B-cell conformational epitopes predicted by the ElliPro server

Click here to view
Figure 2: The three.dimensional structure of conformational B-cell epitopes predicted by the ElliPro server. The epitopes are spherical and colored

Click here to view


T-cell epitopes

T-cell-specific and MHC-I-related epitopes were predicted by EpiJen (with a score >7), MHCpred (with a logIC50 >7), and IEDB (with a Percentile_rank lower than 4) programs [Table 3]. Furthermore, MHC-II binding epitopes were predicted by Rankpep (with a score >7), MHCpred (with a logIC50 >7), and IEDB servers (with a Percentile rank <4) [Table 3].
Table 3: Prediction of MHC-I and MHC-II motifs of the fusion protein presented in this study

Click here to view


Interferon-gamma inducing epitopes

IFN-γ inducing epitopes were predicted by the IFN epitope server. [Table 4] shows epitopes sequences that extracted by this server, the score of them is >1.
Table 4: Prediction of interferon-gamma inducing epitopes for the fusion protein

Click here to view


Design of the new multiepitope vaccine

Overlap peptide motifs of the fusion protein detected by MHC-I, MHC-II, B cell, and IFN-γ linked through the GGGS linker. Secondary structure of the new protein composed of 18.2% helix, 9.4% sheet, 14.1% turn, and 58.2% coli. VaxiJen showed a query sequence as an antigen. The overall prediction for this protective antigen was 0.5973. Amino acid sequences of the multiepitope vaccine candidate with the GGGS linker is in the form (HAPTGSGKSTKVPAAGGGSGRHLIFCHSKK KCDGGGSLMTGFTGDFDGGG SITAYAQQTRGGGGS YYRGLDVSVIGGGGSTRHADVIPAR GGGSYLV TRHADVGGGSGLNAVAYYRGGGSDAVSRSQRR GGGSYSTYGKFLAGGGSTGSGKSTKVPA AGGGSLVLNPSVAA).

Molecular dynamics simulation and molecular docking of the fusion protein and multiepitope vaccine candidate

The secondary and the tertiary-dimensional structures of the fusion protein and multiepitope vaccine candidates were predicted by I-TASSER [Figure 3]a and [Figure 3]b. The energy minimization and MD simulation process of both models were made using the steepest descent algorithm of the GROMACS package. RMSD and Rg analysis are presented in Figure 3c and d. After 3 ns, the structure of the protein reached a certain distance from the reference structure and then was kept at that distance more or less, until it reached to the plateau on 54 ns and maintained its value.
Figure 3: (a) Tertiary structures of the fusion protein and (b) multiepitope vaccine candidate predicted by the ITASSER server. (c) Root mean square deviation evolution and (d) radius of gyration. The fusion protein (blue) and multiepitope vaccine (orange)

Click here to view


Four molecular dockings using ClusPro 2.0 were performed between the fusion protein and the multiepitope vaccine candidate with immune receptors TLR3 and TLR8 to investigate the binding mechanism and interaction modes of these two receptors. For each molecular docking result, among the 20 models built by this server, the best model with the most clusters and the lowest energy was selected [Figure 4]a,[Figure 4]b,[Figure 4]c,[Figure 4]d and [Figure 5]a,[Figure 5]b,[Figure 5]c,[Figure 5]d,[Figure 5]e,[Figure 5]f. After docking, interactions of epitopes with the desired receptors were investigated [Table 5].
Figure 4: Docking of vaccines with TLR-3. (a) An illustration of interacting residues between docked fusion protein vaccine (chain B) and TLR3 (chain A). (b) The fusion protein vaccine construct-TLR3 docked complex: Figure obtained after molecular docking between fusion protein vaccine and TLR-3, the red color shows the fusion protein vaccine construct while blue color is representing TLR-3. (c) An illustration of interacting residues between docked multiepitope vaccine (chain B) and TLR3 (chain A). (d) Multiepitope vaccine construct-TLR3 docked complex: Figure obtained after molecular docking between multiepitope vaccine and TLR-3, the red color shows multiepitope vaccine construct, while blue color is representing TLR-3. Interacting residues are shown in both docks with green color. TLR: Toll-like receptor

Click here to view
Figure 5: Docking of vaccines with TLR-8. (a and b) An illustration of interacting residues between docked fusion protein vaccine (chain C) and chain A and B related to TLR8, respectively. (c) The fusion protein vaccine construct-TLR8 docked complex: Figure obtained after molecular docking between fusion protein vaccine and TLR-8, the red color shows the fusion protein vaccine construct, while blue color is representing TLR-8. (d and e) An illustration of interacting residues between docked multiepitope vaccine (chain C) and chain A and B related to TLR8, respectively. (f) Multiepitope vaccine construct-TLR8 docked complex: Figure obtained after molecular docking between multiepitope vaccine and TLR-8, the red color shows the multiepitope vaccine construct, while blue color is representing TLR-8. Interacting residues are shown in both docks with green color. TLR: Toll-like receptor

Click here to view
Table 5: Interactions of the fusion protein and the multiepitope vaccine candidate epitopes with the desired receptors after docking

Click here to view



  Discussion Top


In the current research, two multiepitope vaccines were designed. The base of these vaccines is related to conserved B-cell, T-cell, and IFN-γ epitopes of NS3/NS4A and NS5A. The vaccines can induce a huge level of the cellular and humoral immunity response against HCV infection. Hence, they can be studied as new vaccines for protection against HCV. Today, designing of an effective vaccine against HCV infection is an interesting field. Nosrati and et al. design a multiepitope vaccine against E1 and E2 proteins of HCV. The results showed that E1 protein had more epitopes than E2 protein.[45] Zhu et al. designed a novel adeno-associated virus (AAV) expression vector including the full-length NS3 or NS3/4 protein of HCV genotype 1b. This candidate could well stimulate the immune system but cannot be used for another genotypes.[46] Ikram et al. explored NS3/4A, NS5A, and NS5B protein for designing a multiepitope protein as a vaccine for HCV. The results of this study indicated that the designed polypeptide had potentially antigenic properties.[8]

Vaccine new programs against HCV can induce both humoral and cellular immune responses. However, due to the high diversity of the virus (existence of quasispecies), it can escape from the immune system.[47],[48] Therefore, in designing an effective vaccine, selection of proteins and peptides with high antigenic properties is a better option to stimulate the immune response, since effective stimulation of the immune system will prevent and control this disease. An antigenic peptide should be contained epitopes for the B-cell receptor and also included MHC associated motifs that were detected by T lymphocyte cells receptor.[42] Immunoinformatics is an in silico new approach for the analysis and modeling of immunological data. One of the major applications of immunoinformatics is the design of epitope-based vaccines.[49],[50] The multiepitope peptide vaccines are one of the vaccines against infections. These vaccines include B-and T-cell epitopes that induce an immune response.[51] Prevention and inhibition of HCV are the most important aims of the multiepitope T-cell vaccines.

The results of our research revealed that designed peptide vaccines provided effective cellular and humoral immunity responses to (a) serotype of HCV. B lymphocytes produce specific secretion antibodies and have an important role in the immune memory and clearance of the pathogens. In the fusion protein, the linear epitope with a high score of 0.99 belongs to PPLEGEPGDPDL, and GGCSGGAYDIII was predicted by BCpred. These are located in NS5A and NS3 proteins respectively. In ElliPro, A1, _:P2, _:I3, _:T4, _:A5, _:Y6, _:A7, _:Q8, _:Q9, _:T10, _:R11, _:G12, _:L13, _:D14, _:L15, _:Y16 have high score (0.95) in discontinuous B-cell epitopes and located in NS3 protein.

The CD4+ T cell has an important role in innate and adaptive immune responses and CD8+ T cell involves in the destruction of virally infected cells,[52] both CD4+ and CD8+ T cell-related epitopes are important in vaccine efficacy. In this study, MHCpred, EpiJen, Rankpep, and IEDB servers were used to determine MHC-I and MHC-II associated peptide epitopes. According to the results of the prediction by used servers [Table 3], for the HLA-A0*301 allele (GIGTVLDQA (logIC50 [M] = 7.74) and TRHADVIPAR [Percentile_rank = 0.45]) epitopes and the HLA-B*27 allele (RRGRTGRGR [Percentile_rank = 0.43]) epitope that belong to the NS3 protein have potential to be processed and presented by MHC I molecules. HLA-DRB0*101 and HLA-DRB0*401 Alleles, (GYKVLVLNP (logIC50 [M] = 8.89)) epitope, the HLA-DRB1*1101 allele (VSRSQRRGR [score = 10.807]) epitope and the HLA-DRB1*04:01, HLA-DRB1*1501 and HLA-DRB0*101 alleles (AVAYYRGLDVSVIPT [percentile rank = 0.4]) epitope that be able to present with MHC II molecules, also belonged to the NS3 protein.

We performed docking of TLR-3 and TLR-8 with designed proteins for analyzing the immune response of TLR-3 and TLR-8 against predicted vaccines. TLR3 reduces viral load in HCV-positive patients,[53] and TLR8 plays an important role in inducing antiviral immune responses against HCV.[54] These results showed that the epitopes related to NS3 protein have the most interaction with TLR-3 and TLR-8.

In the designed multiepitope vaccine, overlap peptide epitopes of B cell, T cell, and IFN-γ epitopes have been selected to design the vaccine. The epitopes of overlapped B cell, T cell, and IFN-γ can promote both B and T cell responses and also induce produced pro-inflammatory cytokines with antiviral activity by IFN-γ.[55] The multiepitope vaccine has higher antigenicity properties than the fusion protein vaccine and also makes strong binding to TLR-3 and TLR-8 receptors.


  Conclusions Top


We determined B-cell, T-cell, and IFN-γ epitopes of NS3/NS4A and NS5A from (a) genotype HCV and design immunogenic multiepitope vaccines as epitope-based vaccines. These vaccines can be used for protecting against HCV infection. However, in vitro and in vivo immunological tests are needed to validate the efficiency of designed vaccines.

Acknowledgment

The authors would like to thank the Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Financial support and sponsorship

This study is related to project NO.1398/10190 from the Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran. We also appreciate the “Student Research Committee” and “Research and Technology Chancellor” in Shahid Beheshti University of Medical Sciences for their financial support of this study.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Chang JC, Seidel C, Ofenloch B, Jue DL, Fields HA, Khudyakov YE. Antigenic heterogeneity of the hepatitis C virus NS4 protein as modeled with synthetic peptides. Virology 1999;257:177-90.  Back to cited text no. 1
    
2.
Sarcar B, Ghosh AK, Steele R, Ray R, Ray RB. Hepatitis C virus NS5A mediated STAT3 activation requires co-operation of Jak1 kinase. Virology 2004;322:51-60.  Back to cited text no. 2
    
3.
Jahanbakhsh Sefidi F, Keyvani H, Monavari SH, Alavian SM, Fakhim S, Bokharaei-Salim F. Distribution of hepatitis C virus genotypes in Iranian chronic infected patients. Hepat Mon 2013;13:e7991.  Back to cited text no. 3
    
4.
Shahzamani K, Sabzali S.Therapeutic vaccines of hepatitis C virus with emphasis on DNA vaccine. Govaresh 2019;24:12-21.  Back to cited text no. 4
    
5.
Moradpour D, Penin F, Rice CM. Replication of hepatitis C virus. Nat Rev Microbiol 2007;5:453-63.  Back to cited text no. 5
    
6.
Blackard JT, Shata MT, Shire NJ, Sherman KE. Acute hepatitis C virus infection: A chronic problem. Hepatology 2008;47:321-31.  Back to cited text no. 6
    
7.
Wilson D.L.w.C.g, Yahoo N, Shahzamani K, Malboobi MA, Jabbari H, Sharifi H, et al, Mutations in the E2 and NS5A regions in patients infected with hepatitis C virus genotype 1a and their correlation with response to treatment J Med Virol 2011;83:1332-7.  Back to cited text no. 7
    
8.
Ikram A, Zaheer T, Awan FM, Obaid A, Naz A, Hanif R, et al. Exploring NS3/4A, NS5A and NS5B proteins to design conserved subunit multi-epitope vaccine against HCV utilizing immunoinformatics approaches. Sci Rep 2018;8:16107.  Back to cited text no. 8
    
9.
Krekulová L, Rehák V, Riley LW. Structure and functions of hepatitis C virus proteins: 15 years after. Folia Microbiol (Praha) 2006;51:665-80.  Back to cited text no. 9
    
10.
Morikawa K, Lange C, Gouttenoire J, Meylan E, Brass V, Penin F, et al. Nonstructural protein 3-4A: The Swiss army knife of hepatitis C virus. J. Viral Hepat 2011;18:305-15.  Back to cited text no. 10
    
11.
Pouriayevali MH, Bamdad T, Aghasadeghi MR, Sadat SM, Sabahi F. Construction and immunogenicity analysis of Hepatitis C Virus (HCV) Truncated Non-Structural Protein 3 (NS3) plasmid vaccine. Jundishapur J Microbiol 2016;9:e33909.  Back to cited text no. 11
    
12.
Krishnadas DK, Ahn JS, Han J, Kumar R, Agrawal B. Immunomodulation by hepatitis C virus-derived proteins: Targeting human dendritic cells by multiple mechanisms. Int Immunol 2010;22:491-502.  Back to cited text no. 12
    
13.
Tanji Y, Hijikata M, Satoh S, Kaneko T, Shimotohno K. Hepatitis C virus-encoded nonstructural protein NS4A has versatile functions in viral protein processing. J Virol 1995;69:1575-81.  Back to cited text no. 13
    
14.
Konan KV, Giddings TH, Ikeda M, Li K, Lemon SM, Kirkegaard K. Nonstructural protein precursor NS4A/B from hepatitis C virus alters function and ultrastructure of host secretory apparatus. J Virol 2003;77:7843-55.  Back to cited text no. 14
    
15.
Nomura-Takigawa Y, Nagano-Fujii M, Deng L, Kitazawa S, Ishido S, Sada K, et al. Non-structural protein 4A of Hepatitis C virus accumulates on mitochondria and renders the cells prone to undergoing mitochondria-mediated apoptosis. J Gen Virol 2006;87:1935-45.  Back to cited text no. 15
    
16.
Macdonald A, Harris M. Hepatitis C virus NS5A: Tales of a promiscuous protein. J Gen Virol 2004;85:2485-502.  Back to cited text no. 16
    
17.
Foster TL, Belyaeva T, Stonehouse NJ, Pearson AR, Harris M. All three domains of the hepatitis C virus nonstructural NS5A protein contribute to RNA binding. J Virol 2010;84:9267-77.  Back to cited text no. 17
    
18.
Gremion C, Cerny A. Hepatitis C virus and the immune system: A concise review. Rev Med Virol 2005;15:235-68.  Back to cited text no. 18
    
19.
Semmo N, Klenerman P. CD4+ T cell responses in hepatitis C virus infection. World J Gastroenterol 2007;13:4831-8.  Back to cited text no. 19
    
20.
McKiernan SM, Hagan R, Curry M, McDonald GS, Kelly A, Nolan N, et al. Distinct MHC class I and II alleles are associated with hepatitis C viral clearance, originating from a single source. Hepatology 2004;40:108-14.  Back to cited text no. 20
    
21.
Thio CL, Thomas DL, Goedert JJ, Vlahov D, Nelson KE, Hilgartner MW, et al. Racial differences in HLA class II associations with hepatitis C virus outcomes. J Infect Dis 2001;184:16-21.  Back to cited text no. 21
    
22.
Chen M, Sällberg M, Sönnerborg A, Weiland O, Mattsson L, Jin L, et al.Limited humoral immunity in hepatitis C virus infection. Gastroenterology 1999;116:135-43.  Back to cited text no. 22
    
23.
Quiroga JA, Campillo ML, Catillo I, Bartolomé J, Porres JC, Carreño V. IgM antibody to hepatitis C virus in acute and chronic hepatitis C. Hepatology 1991;14:38-43.  Back to cited text no. 23
    
24.
Elliott LN, Lloyd AR, Ziegler JB, Ffrench RA. Protective immunity against hepatitis C virus infection. ImmunolCell Biol 2006;84:239-49.  Back to cited text no. 24
    
25.
Jabbari H, Bayatian A, Sharifi AH, Zaer-Rezaee H, Fakharzadeh E, Asadi R, et al. Safety and efficacy of locally manufactured pegylated interferon in hepatitis C patients. Arch Iran Med 2010;13:306-12.  Back to cited text no. 25
    
26.
Serviddio G. Practical Management of Chronic Viral Hepatitis. BoD-Books on Demand; 2013.  Back to cited text no. 26
    
27.
Memarnejadian A, Roohvand F, Arashkia A, Rafati S, Shokrgozar MA. Polytope DNA vaccine development against hepatitis C virus: A streamlined approach from in silico design to in vitro and primary in vivo analyses in BALB/c mice. Protein Pept Lett 2009;16:842-50.  Back to cited text no. 27
    
28.
Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, et al. UniProt: The universal protein knowledgebase. Nucleic Acids Res 2004;32:D115-9.  Back to cited text no. 28
    
29.
Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 1994;22:4673-80.  Back to cited text no. 29
    
30.
Lavigne R, Seto D, Mahadevan P, Ackermann HW, Kropinski AM. Unifying classical and molecular taxonomic classification: Analysis of the Podoviridae using BLASTP-based tools. Microbiol Res 2008;159:406-14.  Back to cited text no. 30
    
31.
Gasteiger E, Hoogland C, Gattiker A, Wilkins MR, Appel RD and Bairoch A. Protein identification and analysis tools on the ExPASy server, The proteomics protocols handbook, Springer, 2005. pp. 571-607.  Back to cited text no. 31
    
32.
Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 2008;9:40.  Back to cited text no. 32
    
33.
Doytchinova IA, Flower DR. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007;8:4.  Back to cited text no. 33
    
34.
EL-Manzalawy Y, Dobbs D, Honavar V. Predicting linear B-cell epitopes using string kernels. J Mol Recognit 2008;21:243-55.  Back to cited text no. 34
    
35.
Saha S, Raghava G. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 2006;65:40-8.  Back to cited text no. 35
    
36.
Ponomarenko J, Bui HH, Li W, Fusseder N, Bourne PE, Sette A, et al. ElliPro: A new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics 2008;9:514.  Back to cited text no. 36
    
37.
Doytchinova IA, Guan P, Flower DR. EpiJen: A server for multistep T cell epitope prediction. BMC Bioinformatics 2006;7:131.  Back to cited text no. 37
    
38.
Guan P, Doytchinova IA, Zygouri C, Flower DR. MHCPred: A server for quantitative prediction of peptide-MHC binding. Nucleic Acids Res 2003;31:3621-4.  Back to cited text no. 38
    
39.
Vita R, Overton JA, Greenbaum JA, Ponomarenko J, Clark JD, Cantrell JR, et al. The immune epitope database (IEDB) 3.0. Nucleic Acids Res 2015;43:D405-12.  Back to cited text no. 39
    
40.
Reche PA, Glutting JP, Reinherz EL. Prediction of MHC class I binding peptides using profile motifs. Hum Immunol 2002;63:701-9.  Back to cited text no. 40
    
41.
Dhanda SK, Vir P, Raghava GP. Designing of interferon-gamma inducing MHC class-II binders. Biol Direct 2013;8:30.  Back to cited text no. 41
    
42.
Vajda S, Yueh C, Beglov D, Bohnuud T, Mottarella SE, Xia B, et al. New additions to the C lus P ro server motivated by CAPRI. Proteins 2017;85:435-44.  Back to cited text no. 42
    
43.
Kozakov D, Beglov D, Bohnuud T, Mottarella SE, Xia B, Hall DR, et al. How good is automated protein docking? PROTEINS 2013;81:2159-66.  Back to cited text no. 43
    
44.
Laskowski RA. PDBsum: Summaries and analyses of PDB structures. Nucleic Acids Res 2001;29:221-2.  Back to cited text no. 44
    
45.
Nosrati M, Mohabatkar H, Behbahani M. A novel multi-epitope vaccine for cross protection against Hepatitis C Virus (HCV): An immunoinformatics approach. Res Mol Med 2017;5:17-26.  Back to cited text no. 45
    
46.
Zhu F, Chen T, Zhang Y, Sun H, Cao H, Lu J, et al. A novel adeno-associated vzirus-Based genetic vaccine encoding the hepatitis C virus NS3/4 protein exhibits immunogenic properties in mice superior to those of an NS3-protein-based vaccine. PloS one 2015;10:e0142349.  Back to cited text no. 46
    
47.
Yahoo NS, Shahzamani K, Malboobi MA, Jabbari H, Sharifi H, Mousavi-Fard SH, et al. Mutations in the E2 and NS5A regions in patients infected with hepatitis C virus genotype 1a and their correlation with response to treatment. J Med Virol 2011;83:1332-7.  Back to cited text no. 47
    
48.
Heidari Z, Shahzamani K, Ghanbari R, Tahamsebifar A. Association of SNPs in interferon receptor genes in chronic hepatitis C with response to combined therapy of interferon and ribavirin. Acta Med Iran 2014;52:740-7.  Back to cited text no. 48
    
49.
Flower, D.R. and Timmis, J., 2007. In silico immunology. Springer.  Back to cited text no. 49
    
50.
Flower DR. Immunoinformatics and the in Silico Prediction of Immunogenicity, Immunoinformatics, Springer, 2007. p. 1-15.  Back to cited text no. 50
    
51.
Doytchinova IA, Flower DR. Quantitative approaches to computational vaccinology. Immunol Cell Biol 2002;80:270-9.  Back to cited text no. 51
    
52.
Swain SL, McKinstry KK, Strutt TM. Expanding roles for CD4+ T cells in immunity to viruses. Nat Rev Immunol 2012;12:136-48.  Back to cited text no. 52
    
53.
Wang N, Liang Y, Devaraj S, Wang J, Lemon SM, Li K. Toll-like receptor 3 mediates establishment of an antiviral state against hepatitis C virus in hepatoma cells. J Virol 2009;83:9824-34.  Back to cited text no. 53
    
54.
Wang CH, Eng HL, Lin KH, Chang CH, Hsieh CA, Lin YL, et al. TLR7 and TLR8 gene variations and susceptibility to hepatitis C virus infection. PLoS One 2011;6:e26235.  Back to cited text no. 54
    
55.
Wei X, Jia ZS, Lian JQ, Zhang Y, Li J, Ma L, et al. Inhibition of hepatitis C virus infection by interferon-γ through downregulating claudin-1. JICR 2009;29:171-8.  Back to cited text no. 55
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Methods
Results
Discussion
Conclusions
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed112    
    Printed0    
    Emailed0    
    PDF Downloaded17    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]