Next Article in Journal
Low Seroprevalence of Measles-Specific IgG in Children of Three Ethnic Groups from Mexico: Influence of Age, Sex, Malnutrition and Family Size
Next Article in Special Issue
Immunogenicity and Antiviral Response of Therapeutic Hepatitis B Vaccination in a Mouse Model of HBeAg-Negative, Persistent HBV Infection
Previous Article in Journal
COVID-19 Vaccination Willingness among Chinese Adults under the Free Vaccination Policy
Previous Article in Special Issue
Fendrix® Vaccine Effectiveness in Healthcare Workers Who Are Non-Responsive to Engerix B® Vaccination
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Vaccines
Volume 9
Issue 3
10.3390/vaccines9030293
vaccines-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Immuno-Informatics Analysis of Pakistan-Based HCV Subtype-3a for Chimeric Polypeptide Vaccine Design

by
Sajjad Ahmad
1,†,
Farah Shahid
2,†,
Muhammad Tahir ul Qamar
3,*,
Habib ur Rehman
4,
Sumra Wajid Abbasi
5,
Wasim Sajjad
5,
Saba Ismail
5,
Faris Alrumaihi
6,
Khaled S. Allemailem
6,
Ahmad Almatroudi
6 and
Hafiz Fahad Ullah Saeed
7
1
Department of Health and Biological Sciences, Abasyn University, Peshawar 25000, Pakistan
2
Department of Bioinformatics and Biotechnology, Government College University, Faisalabad 38000, Pakistan
3
College of Life Science and Technology, Guangxi University, Nanning 530004, China
4
Department of Medical, DHQ Hospital, Faisalabad Medical University, Faisalabad 38000, Pakistan
5
NUMS Department of Biological Sciences, National University of Medical Sciences, Rawalpindi 46000, Pakistan
6
Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
7
King Edward Medical University, Lahore 54000, Pakistan
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this study.
Vaccines 2021, 9(3), 293; https://doi.org/10.3390/vaccines9030293
Submission received: 3 March 2021 / Revised: 17 March 2021 / Accepted: 18 March 2021 / Published: 21 March 2021
(This article belongs to the Special Issue Vaccines against Hepatitis Viruses)
Browse Figures
Versions Notes

Abstract

:
Hepatitis C virus (HCV) causes chronic and acute hepatitis infections. As there is extreme variability in the HCV genome, no approved HCV vaccine has been available so far. An effective polypeptide vaccine based on the functionally conserved epitopes will be greatly helpful in curing disease. For this purpose, an immuno-informatics study is performed based on the published HCV subtype-3a from Pakistan. First, the virus genome was translated to a polyprotein followed by a subsequent prediction of T-cell epitopes. Non-allergenic, IFN-γ producer, and antigenic epitopes were shortlisted, including 5 HTL epitopes and 4 CTL, which were linked to the final vaccine by GPGPG and AAY linkers, respectively. Beta defensin was included as an adjuvant through the EAAAK linker to improve the immunogenicity of the polypeptide. To ensure its safety and immunogenicity profile, antigenicity, allergenicity, and various physiochemical attributes of the polypeptide were evaluated. Molecular docking was conducted between TLR4 and vaccine to evaluate the binding affinity and molecular interactions. For stability assessment and binding of the vaccine-TLR4 docked complex, molecular dynamics (MD) simulation and MMGBSA binding free-energy analyses were conducted. Finally, the candidate vaccine was cloned in silico to ensure its effectiveness. The current vaccine requires future experimental confirmation to validate its effectiveness. The vaccine construct produced might be useful in providing immune protection against HCV-related infections.

1. Introduction

Hepatitis C virus (HCV) is a serious public health concern and a major cause of chronic liver disease [1]. HCV is responsible for hepatitis C, human lymphoma, and liver cancer (hepatocellular carcinoma) [2]. HCV is a member of the family Flaviviridae (genus Hepacivirus). It is a single-stranded, enveloped, small, and positive-sense RNA virus (55–65 nm in size) [3,4]. The virus causes both acute and chronic infections and affects more than 180 million people each year [5]. Asymptomatic acute HCV is often associated with life-threatening conditions and 15–45% of patients clear up the virus spontaneously within six months. Chronic HCV infections are associated with liver cirrhosis in 15–30% of the patients [6]. This disease is very common and found worldwide with a prevalence of 2.3% and 15% in WHO Eastern Mediterranean and European Regions, respectively. HCV prevalence in other WHO regions ranges between 0.5% to 1.0% (https://www.who.int/news-room/fact-sheets/detail/hepatitis-c; accessed on 10 March 2021). The infection rate is high (>3.5%) in Middle East, North Africa, and Central and East Asia. While in Adeana, Caribbean, Oceania, South and Southeast Asia, Australasia, sub-Saharan Africa, and Eastern and Western Europe, the rate is intermediate (1.5% to 3.5%). In regions, like North America, Asia-Pacifica and Tropical Latin America, the rate is <1.5% [7]. A high variation of HCV disease has been noticed and can be elevated in a certain population [8]. This variability is highly dependent on the usage of drugs, used syringes, other equipment, and surgical instruments. Additionally, there are multiple genotypes/subtypes of HCV with a variable distribution by regions that can also contribute to the disease burden [9].
Genome sequence analysis showed eight major genotypes of HCV (1–8) [10,11]. Genotype characterization is not only limited to the type of therapy needed for clearance of the virus but also suggests the level and risk of disease progression [12]. The prevalence of HCV genotype 1 is very high and contributes almost 49% of the disease followed by genotype 3, 4, and 2 which is 17.9%, 16.8%, and 11.0%, respectively while for the remaining 5% hepatitis, genotypes 5 and 6 are responsible [13]. Among all genotypes, HCV type 3 is poorly understood in the pegylated-interferon (PEGIFN) era with the lowest sustained virologic (SVR) response [14]. In the time period between 2001 and 2011, the standard therapy for chronic HCV infection was a blend of PEGIFN and ribavirin (RBV) and is refereed as PEGIFN era [15]. HCV type 3 genotype is considered to be one of the most complex types to treat in low and middle-income countries with a higher risk of disease progression and death. [16,17]. Infection caused by genotype 3 has also been linked to a severe and higher incidence of hepatocellular carcinoma (HCC) [18]. Accelerated liver fibrosis due to genotype 3 has been studied according to a meta-analysis and thus needs a more effective treatment to combat the genotype 3 virulence in developing countries [19]. Pakistan being an underdeveloped country, no good health surveillance system [20], low income and poverty has the second largest HCV burden. One out of every 20 Pakistanis is infected with HCV, and subtype-3a is the most common [21]. It is mostly transmitted by community (ear nose piercing, barbering), health workers (used syringes, blood transfusion) and high drug users through injection [22].
The current therapeutic approach for the treatment of HCV includes co-administration of antiviral drugs, more frequently PEGIFN, and simeprevir or sofosbuvir with ribavirin [23]. However, these treatments are linked to several drawbacks, such as high costs, drug resistance, or lack of protection against recurrence [24]. A variety of vaccine candidates are also in the development phase, with some having progressed to human clinical trials, including those based on synthetic peptides, recombinant proteins (core protein subunits and envelope proteins), viral vectors (virosome) and DNA (plasmid) [25,26,27,28,29,30]. Peptide and DNA-based vaccine candidates are currently being tested in murine models [31,32,33,34,35]. However, no HCV approved vaccine available to date. Hence, an effective and safe vaccine is, therefore, urgently needed to curb the global HCV disease burden [36].
Because of discrepancies in HCV genomic sequences and distribution by geographical region, a specific HCV vaccine produced for the Pakistani population would be ineffective. Since HCV subtype-3a infects a large proportion of the Pakistani population, the number of patients enrolled in public and private hospitals is steadily growing [37]. Hence, a vaccine against HCV, specifically HCV subtype-3a, is currently required to cover the majority of Pakistan’s population. Immunoinformatics approaches are a good alternative to predict and design highly immunogenic HCV multi-epitope vaccine candidates [38]. Immunoinformatics is an interface between computer science and experimental immunology that is used to examine essential immunological knowledge hidden in the immune system. A considerable number of studies have now been conducted to understand an immunoinformatics-based anti-HCV vaccine design [39]. Immunogenic epitopes that can generate successful responses from B and CD4+, CD8+ T cells should be included in an appropriate HCV polypeptide vaccine [40]. This study aims to investigate and design a polypeptide immunogenic vaccine candidate targeting Pakistani HCV subtype-3a using available computational immunology and immune-informatics tools and servers. The findings may be useful for experimentalists in designing either strain-specific or a broad-spectrum vaccine against HCV.

2. Materials and Methods

All steps performed in this study for identification of antigenic peptides and formulation of chimeric polypeptide vaccine are presented in Figure 1.

2.1. Sequence Retrieval

The complete genome and polyprotein sequences of the Pakistani origin HCV subtype-3a were downloaded from the NCBI-GenBank database (Accession number: GU294484/PK) [41]. In-house Perl scripts were utilized for initial quality checking. The GU294484/PK genome sequence was chosen because of its validity, completeness, and quality. Next, the polyprotein sequence was subjected to helper T-lymphocyte (HTL) and cytotoxic T-lymphocyte (CTL) epitopes prediction.

2.2. CTL Epitopes Prediction

To obtain immunogenic CTL epitopes, HCV subtype-3a polyprotein was analyzed in NetCTL 1.2 server [42]. The viral protein was submitted in the FASTA format at the default threshold (0.75) for predicting CTL epitopes. From the predicted epitopes, all epitopes having a combined score of >0.75 were picked. The predicted epitopes were then submitted to the IEDB MHC-I binders predictor using default parameters [43]. Antigenicity, allergenicity, and IFN-γ checks were further performed on the predicted CTL epitopes to prioritize antigenic, non-allergenic, and IFN-gamma positive epitopes. For antigenicity, allergenicity, IFN-γ epitopes, VaxiJen [44], AllerTOP v. 2.0 [45] and IFNepitope servers [46] were used, respectively.

2.3. HTL Epitopes Prediction

The HTL epitopes for the HCV protein sequence were forecasted using the MHC-II epitope prediction module of the IEDB database with default parameters [43]. Generated epitopes were ranked on the basis of percentile rank. A lower percentile rank score signifies that HTL receptors have a high binding affinity. Like CTL epitopes, HTL epitopes were filtered based on antigenicity, allergenicity, and IFN-gamma inducing potential as described above.

2.4. Chimeric Polypeptide Vaccine Construction and Structure Prediction

To enhance the antigenicity of the individual epitope, a multi-epitope polypeptide was constructed. A linker, EAAAK, was used to link the Beta-defensin [47] (Q5U7J2_HUMAN) as an adjuvant to the end of the N-terminal of a construct, while AAY and GPGPG linkers were employed for joining epitopes. Polypeptide vaccine 3D structure was obtained by employing an online I-tasser server [48]. I-tasser is based on a pure ab-initio method to generate a 3D model without any homology template. The vaccine structure was refined through Galaxy Refine [49]. Ramachandran plot analysis was used to validate that refined structure.

2.5. Codon Adaptation and In-Silico Cloning

An increased rate of foreign gene expression within the host can be achieved through codon adaptation of the construct sequence, especially when the host’s codon usage is different from that of the source organism of the foreign gene. A codon that fails to adapt can result in lower rate of expression in the host. For this reason, the Java Codon Adaptation Tool (JCAT) [50] was employed to adapt the vaccine peptide construct’s codon usage to the Escherichia coli K12 strain. The adapted sequence was then cloned into vector pET28a (+) via the Snap Gene tool (https://www.snapgene.com/snapgene-viewer/; accessed on 15 February 2021).

2.6. B-Cell Epitopes Prediction

A complete sequence of the vaccine was subjected to B-cell epitopes prediction using Bepipred with default parameters [51].

2.7. Antigenicity and Allergenicity Prediction

To determine the ability of the vaccine to bind to T-cell and B-cell receptors resulting in stimulating immune responses and memory cell formation, ANTIGENpro was used [52]. The allergenicity of the vaccine was evaluated via AllerTOP v. 2.0 [45].

2.8. Physiochemical Properties Assessment

ProtParam web server [53] was further used to analyze physicochemical properties of the peptide including estimated half-life, theoretical pI, grand average of hydropathicity index (GRAVY), molecular weight (kDa), aliphatic index, and so on.

2.9. Molecular Docking and Dynamics Simulations

Molecular docking of the vaccine model was performed with TLR4 (PDB ID: 4G8A) using ClusPro 2.0 [54]. A simulation run of 100-ns was carried for the docked complex using AMBER18 [55]. System topologies were recorded using ff14SB [56]. The system was neutralized when 3 Na+ ions had been added and it was put in a TIP3P water box with a padding distance of 12 Å. Later, preprocessing was performed that can be divided into seven steps: first hydrogen atoms of the system were minimized for 500 cycles, then the water box was minimized for 1000 cycles. Non-heavy atoms were minimized for 300 cycles and alpha-carbon atoms were minimized for 1000 cycles with the restraint of 100 kcal/mol-Å2 and 5 kcal/mol-Å2, respectively. Moving ahead, the system was heated for 20 picoseconds (ps) at 300 K. To maintain temperature, Langevin dynamics [57] was used with a gamma value of 1. For constrain on hydrogen bonds, the SHAKE algorithm [58] was used, while for heating, a constant-temperature, constant volume (NVT) ensemble was employed. The system was then subjected to 100-ps with a time step of 2 femtoseconds (fs) in the pre-equilibration phase, while for pressure equilibration; the isothermal-isobaric (NPT) ensemble was used with 5 kcal/mol-Å2 restraint on alpha-carbon atoms. After this, with a restraint of 1 kcal/mol-Å2after every 10-ps, the pressure phase was extended for additional 50-ps. Lastly, the system was equilibrated for 1-ns. The total production run was 100-ns using the Berendsen algorithm with an NVT ensemble. For non-bonded interactions, an 8.0 cut-off was used, and the SHAKE algorithm was used for hydrogen atoms. Simulation trajectories were assessed using CCPTRAJ [59] of AMBER.

2.10. Estimation of MM/GB-PBSA Binding Energy

Binding free energy was computed for the simulated trajectories using MM/GB-PBSA methods [60,61]. From the trajectories, 100 snapshots were taken at regular intervals and the MM/GB-PBSA free energy difference was computed. The equation below was used for each snapshot to estimate the binding free energy, and the final energy was taken as the average score of all snapshots.
ΔGbinding = Gcomplex − (Gprotein + Gvaccine)

2.11. Immune Simulation

The C-IMMSIM server characterizes the immunogenicity and immune response of the designed vaccine [62]. This server is an agent-based model that predicts peptide interactions with the immune system using position-specific scoring matrices (PSSM). Two doses were given, with a four-week interval between them, and the simulation was run for 1000 steps.

3. Results

3.1. Sequence Retrieval

Genome sequence of Pakistani isolate of HCV subtype-3a (9474 bp) encodes a polyprotein (3027 amino acids) which was found to be non-allergenic and antigenic, with an antigenic score of 0.22 (ANTIGENPro) and 0.456 (VaxiJen). Further, CTL and HTL epitopes were predicted from this polyprotein.

3.2. CTL Epitopes Prediction

The CD8+ are the cytotoxic T-lymphocytes with a major role in T cell response against different pathogens like viral, bacterial, or protozoan infections [63]. In the case of any foreign particle, the antigen is presented by MHC-I with specific receptors on antigen-presenting cells (APCs). As a result of the immune response to the virus activation of different effector cells occur to kill or eliminate the antigen by different approaches. CTL epitopes were predicted through the NETCTL server. The immunogenicity of the polyprotein epitopes was analyzed according to the IEDB and NETCTL guidelines and only epitopes that showed a higher score and a much higher capacity to elicit the immune response were opted. Table 1 shows selected epitopes for the HCV polyprotein. In total, 54 epitopes were predicted; only 4 epitopes were filtered based on the good NETCTL and IEDB immunogenicity score. These epitopes are also antigenic, non-allergenic and IFN-γ inducing peptides.

3.3. HTL Epitopes Prediction

Besides cellular immune responses, a humoral immune response is also considered one of the key immune responses to different foreign particles or antigens [64]. Helper cells are considered to be one of the main contributors to the development of different prophylactics and immune-therapeutics [64]. Epitopes with a lower IC50 value will be ranked as highly immunogenic with more specificity and sensitivity to elicit a stronger immune response. In short, 5 different HTL epitopes with the lowest IC50 and a greater potential to initiate a good immune response were selected for vaccine design. Those epitopes which have intermediate or no affinity of binding to HTL alleles were not considered for downward analysis. Additionally, all these epitopes were found to have a greater potential for IFN-γ production which is a key contributor to viral immune response. The capacity for IFN-γ was obtained from its positive score using IFN epitope server output. The epitopes were also antigenic and non-allergenic. The HTL epitopes are presented in Table 2.

3.4. Chimeric Polypeptide Vaccine Construction

A multi-epitope polypeptide vaccine was assembled by fusing all screened CTL and HTL epitopes (Figure 2A). Beta-defensin was used as an adjuvant molecule to improve the immunological response of the vaccine. The adjuvant was added to the N terminal site of the multi-epitope polypeptide using an EAAAK linker, allowing for the appropriate spacing of the functional domains as well as efficient expression and recognition by the host immune system. This was done for a reason as epitopes are weakly immunogenic therefore linking CTL and HTL epitopes via AAY and GPGPG linkers to attain maximum immunogenicity and expression of the epitopes results in the efficacy of the vaccine molecule. The final polypeptide consists of 195 amino acids.

3.5. Tertiary Structure Prediction, Refinement, and Validation

I-tasser server was employed to predict the vaccine candidate 3D model (Figure 2B). A total of 195 amino acid residues as a single domain with 1.5% disorder were modeled. From a secondary structure point of the view, the vaccine consists of 55% of helices, 39% coils, and only 4% of the beta-sheet. GalaxyRefine server for the protein refinement leads to enhance the number of residues in the favored regions. From an initial 87%, the number of residues in the Rama-favored region increased to 93% after refinement. The refined model was evaluated by Ramachandran plot and 2% residues in the outlier region, 5% residue in the allowed region, and 93% residue in the Rama-favored region were observed (Figure 2C).

3.6. In-Silico Cloning and Codon Adaptation

The designed vaccine candidate was then cloned in-silico into a pET28a (+) vector for E. coli K-12 system expression. Prior to this, reverse translation of the vaccine amino acid sequence was done to optimize the sequence according to the usage of E. coli. This will ensure the good expression of the vaccine for experimental studies [65]. Codon optimization was done using the JCat server that gave a CAI (codon adaptive index) value of 0.9 and a GC content of 59.21% which are considered ideal in molecular biology for efficient protein expression. These values suggest vaccine proficient expression as the values are ideal as per the need in the E. coli system. The clone vaccine is presented via indigo color in the pET28a (+) vector given in Figure 3.

3.7. B-Cell Epitopes Mapping

B-cell epitopes play a significant role in stimulating humoral immune responses [66]. Epitopes responding to B-cell receptors (BCRs) are very crucial in vaccine design for antibody production. For accuracy and reliable predictability of B-cell epitopes, the IEDB server was used. The selection criteria were based on default cut-off score of 0.50. A total of 6 B-cell epitopes of variable length were predicted from vaccine candidate (Table 3). The presence of high score B-cell epitopes in the vaccine makes it a suitable candidate for development as it will enhance the humoral immune responses as well as cellular immunity.

3.8. Vaccine Antigenicity and Allergenicity

To evoke a significant humoral immune response, a successful vaccine candidate must be highly immunogenic. ANTIGENpro server was used to determine the antigenicity potential of the design vaccine construct, which was found to be 0.9, thereby representing the good potential of antigenicity. Moreover, allergenicity is one of the key factors in evaluating vaccine safety in humans, so it is important to predict its potential of causing allergic responses. AllerTOP, an online server, was used to analyze the vaccine allergenicity and predict the vaccine as non-allergic.

3.9. Vaccine Physicochemical Properties

ProtParam server was used to characterize different parameters including the molecular weight of the vaccine candidate. It was found that the vaccine had a 69.2 kDa molecular weight. The PI value of the vaccine molecule was 7.1 which confer that the vaccine will carry no charge at the mention pH value. The estimated half-life investigated in vitro of the vaccine construct in host cells was 31 h, while it is estimated to be >20 and >10 h in yeast and E. coli, in vivo. The extinction coefficient was calculated at 280 nm of 119,530 M−1cm−1 in water. The score for instability index was 39 that represents a stable and solid nature of vaccine construct. There is a direct relation between the higher value of the aliphatic index and thermal stability. The grand average and aliphatic index of hydrophobicity was 0.344 and thereby represent hydrophilic (because of negative value GRAVY) and thermostable nature.

3.10. Molecular Docking

Molecular docking of TLR4 receptor with the vaccine candidate model was studied by ClusPro 2.0. Among the 30 models that were generated only the model with the lowest energy score and where the vaccine was properly engaged by the receptor was selected. Results revealed that the selected model number 1 accomplishes the desired criteria and considered the best-docked complex as shown in Figure 4. It was found that the dock complex (found to be −1187 kcal/mol) with the lowest energy score is in inverse relation with binding affinity. In our model we found a docked complex showing high binding affinity as the energy score was much lower. The vaccine molecule is engaged in the binding groove of chain A and chain C. From chain A, residues; Asn35, Glu286, Ser312, Arg355, Glu366, Asp379, Asp405, and Lys477 and from chain C, residues; Arg96, and Ser98 were determined to play a prime role in the binding of the vaccine within Å distance.

3.11. Molecular Dynamics Simulation

The dynamics of the vaccine-TLR4 complex was explained through 100 ns of molecular dynamics simulation. As shown in Figure 5A, the initial 20 ns accounts for the adaption phase where the complex root-mean-square deviation (RMSD) was determined in a continuous surge. This high RMSD pattern may explain the sudden exposure of the complex to a dynamic environment allowing the complex to attain a proper conformation. Once a stable, conformation was achieved the system remained stable throughout the length of simulation time. To question the initial RMSD deviations, we further performed flexibility of residues of both TLR receptor and designed chimeric vaccine (Figure 5B). The majority of the residue level alterations can be noticed in the vaccine and upon trajectories, investigation loop regions were identified responsible for deviations which in turn cause higher RMSD of the system. Further, the system was subjected to the radius of gyration analysis and found that the system is not very compact therefore adding to higher gyration deviations (Figure 5C). This is in line with the first two analyses which depicted system deviation and in the same manner corresponding to the vaccine molecule loop regions flexibility. Anyhow, the intermolecular binding between the TRL4 and vaccine is strong and complex binding remained stable throughout the simulated time. The vaccine flexibility was unveiled by measuring the beta factor structural attribute (Figure 5D). This analysis also reported the same finding as demonstrated by the root mean square fluctuation (RMSF).

3.12. Estimation of MM/GB-PBSA Binding Energy

MM/GB-PBSA binding free energy estimation is an acceptable approach in determining the real efficacy of a ligand molecule to its receptor and highlighted key energies dominating complex formation. As can be pointed in Table 4, the vaccine binding to TLR4 is mainly dominated by van der Waals energy (ΔEvdW) as well equal significant contribution is reported from electrostatic forces (ΔEele). This signifies that both energies govern the stable binding of the vaccine to the receptor. On the other hand, polar solvation energy (Epolar.solv) is contributing less whereas non-polar solvation energy (Enon-polar.solv) has a positive impact during the binding process. Because of van der Waals and high electrostatic energy, the net gas phase energy is stable (−319.64 kcal/mol). The overall binding energy (ΔGbind(MM/GBSA)) of the complex is −282.2 kcal/mol.

3.13. Host Immune System Dynamics to Vaccine

The C-IMMSIM server was utilized to analyze the designed vaccine’s immunogenic profile. All the tertiary, secondary, and primary immune responses were significant contributors to the vaccine immunity. In particular, the combination of IgG + IgM antibodies were noticed in high titer, followed by IgM and IgG1 (Figure 6A). In addition to this, different B cell isotypes were formed in the response to vaccine administration resulting in memory cell formation. Additionally, the vaccine candidate induces high levels of IL-2 and IFN-γ (Figure 6B).

4. Discussion

HCV is a significant global public health issue as it can lead to hepatocellular carcinoma (HCC) and chronic liver disease [67]. Vaccination has proved to be the most efficient prophylactic method for maintaining public health and controlling infection spread. However, the manufacturing or efficient development of attenuated or live vaccines is costly and may take years to complete [68]. The inclusion of excessive antigenic load in the attenuated vaccine does, however, make the situation more difficult by causing allergic reactions and contribute little to the protective immune response. Multi-epitope vaccines remove adverse components that can lead to pathological immune response or adverse effects in comparison to the conventional vaccine [69]. Currently, several strategies for developing and designing competent and effective next-generation multi-epitope vaccines based on immunoinformatics approaches are readily available [70]. Previously, immunoinformatics-based approaches have been employed to propose efficient multi epitope vaccine candidates for Lassa virus [71], Zika Virus [46], Dengue virus [72], influenza virus [73], Middle East Respiratory Syndrome-Coronavirus (MERS-CoV) virus [74,75], Respiratory Syncytial Virus [76], Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) [77,78,79], chikungunya virus (CHIKV) [80] and many others. Because of the enormously reported merits and wide acceptability we adopted immunoinformatics-based approaches to design an effective vaccine candidate against Pakistani-based HCV subtype-3a that could efficiently enhance immune response after vaccination.
Genome and polyprotein sequence of Pakistani isolate of HCV subtype-3a was retrieved from GenBank. Antigenicity and allergenicity of polyprotein was evaluated and it was reported to be non-allergenic and antigenic. This provides an excellent start to map antigenic epitopes. Next, CTL and HTL epitopes were predicted from polyprotein sequence using various servers and databases. HTL activates both humoral and cellular immune responses, while CTL prevents the virus from spreading by secreting antiviral cytokines and killing virally infected cells. The identification of HTL and CTL epitopes is important for the designing of multi-epitope vaccines [81]. The vaccine candidate was designed by combining the CTL, and HTL epitopes with AAY, and GPGPG linkers respectively. Linkers are necessary elements of vaccines to improve folding, stabilization, and expression [82]. β-defensin was also attached as an adjuvant to the first CTL epitope via the EAAAK linker. Owing to the good antimicrobial and immunomodulatory properties, β-defensin acts as a very good adjuvant [83] and it has been used in many previous reported studies [84,85]. Multiepitope-based subunit vaccines need adjuvant coupling to enhance their immunogenicity [86]. In a vaccine formulation, adjuvants are additives that influence stability, some immune responses, durability, and growth of antigens and protect against infection [87]. Adjuvants are also advantageous to use because they allow the selective modulation of both humoral and cellular immune responses. The EAAAK linker is employed to join the adjuvant and epitope to enable efficient separation of the bifunctional fusion protein domains [88]. The designed vaccine with CTL and HTL epitopes along with adjuvant could have the potential to evoke neutralizing antibodies but also can inhibit the entry into the host cell, thus resulting in cell cycle arrest to inhibit the further spread.
The 3D structural modeling provides sufficient knowledge about the spatial arrangement of the main components of the protein. It also provides help in the study of protein functions and interactions with other proteins and ligands [89]. The desired properties of the vaccine construct were greatly improved after refining. The Ramachandran plot analysis represents 93% of residues in a favored region with 2% of residues in the disallowed region, showing the satisfactory quality of the model. Characterizing the physiochemical properties is vital while designing any potential vaccine. It was found that the vaccine had a 69.2 kDa weight which makes the vaccine easy to purify in the experimental evaluation. The PI value of the vaccine molecule is 7.1 which confer that the vaccine will carry no charge at the mention pH value. Instability index depicted that the protein has a stable nature upon expression, therefore augmenting the usage capacity further. The designed polypeptide is considered to be immunogenic, thermophilic, and highly hydrophobic in nature with reduced cysteine residues. All these physiochemical properties make this protein a suitable candidate as a HCV subtype-3 vaccine.
A stable connection is required between immune receptors (e.g., TLR-4) and candidate vaccine so that it can efficiently be transported within the host body [90]. HCV infection induces directly the expression of TLR4 and thus activates B cells, which can lead to innate immune responses of the host [91]. Molecular Docking analysis and MD Simulation not only confirmed the strong interactions between TLR4 and the vaccine construct but also demonstrated that a much lower amount of energy was needed for this stable binding in MMGBSA analysis. Minor fluctuations were observed during MD simulations. Hence, these results strongly suggest that the vaccine can proficiently bind to the immune receptors. Immunoreactivity testing through serological assessment is a fundamental step to authenticate a vaccine construct [92]. The translation of the foreign genes varies within the host system and the explanation behind this variation is the mRNA codon inconsistency; which requires codon optimization to ensure a higher degree of expression in the host cell [93]. GC content (59.21%) and CAI value (0.9) of optimized codons showed promising outcomes for higher protein expression in E. coli expression system, which are extensively used for the recombinant protein synthesis [94,95]. Next, it was analyzed that whether vaccine can stimulate the host immune system or not, after binding with the human immune receptor and transported into the body. The vaccine should hypothetically trigger cellular and humoral immune reactions. During immune simulation validation, our vaccine showed the highest production of IFN-γ, with significant IL-10 and IL-2 activities. Antibodies offer extracellular protection against HCV. There have also been noticed excess active immunoglobulins (i.e., IgG, IgM, and their isotypes which can be involved in the isotype switching). Besides, a variety of immune responses that can be thought of as a subunit vaccine contain multiple T-cell and B-cell epitopes are recommended by Simpson index.
In the current research, a vaccine candidate capable of producing immunological responses against HCV was designed. We assume that the designed vaccine will produce cell-mediated and humoral immune responses successfully. There were higher and stable binding patterns and interactions between vaccine and receptor. Additionally, successful immune reactions were noted in real life in immune simulation. Therefore, a vaccine carefully designed using such a technique may become an essential asset to combat viral infections. Immunoinformatics/Computing approaches were based on experimental methods to produce raw data for research purposes. The accuracy of immunoinformatics predictions will be limited by the consistency and efficiency of computer algorithms. Hence, in-vitro and in-vivo experiments are necessary to ensure the true potential of the designed vaccine to combat HCV.

5. Conclusions

HCV infection is a serious health issue concerning mortality and morbidity worldwide. No vaccine is available for the disease yet. In the current study, a chimeric polypeptide vaccine was designed based on the polyprotein sequence of a Pakistan-based HCV subtype-3a. Immunoinformatics and molecular docking approaches were used to design a latent and safe vaccine that can elicit cellular, humoral, and innate immune reactions. However, there are some limitations of the study that need to be investigated through experimental studies. For experimental studies, the real potency in protecting the host from infection is required to be performed in animal models. The reported vaccine candidate might be effective against HCV infection and therefore must be evaluated for prophylactic and therapeutic purposes.

Author Contributions

Conceptualization, S.A., M.T.u.Q., S.W.A. and A.A.; methodology, S.A., F.S. and S.W.A.; validation, M.T.u.Q., H.u.R., F.S., W.S., S.I., F.A., K.S.A. and H.F.U.S.; formal analysis, S.A., M.T.u.Q. and S.W.A.; data curation, S.A., F.S. and S.I.; writing—original draft preparation, S.A., F.S., M.T.u.Q., S.W.A. and S.I.; writing—review and editing, H.u.R., W.S., F.A., K.S.A., A.A. and H.F.U.S.; supervision, S.W.A., M.T.u.Q. and A.A.; project administration, S.A. and A.A.; funding acquisition, F.A., K.S.A. and A.A. All authors have read and agreed to the published version of the manuscript.

Funding

The APC was funded by Qassim University, Saudi Arabia.

Data Availability Statement

The data presented in this study are available within the article.

Acknowledgments

Authors would like to acknowledge Guangxi University, China for providing facilities for this research.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Williams, R. Global Challenges in Liver Disease. Hepatology 2006, 44, 521–526. [Google Scholar] [CrossRef]
  2. Ashfaq, U.A.; Jalil, A.; Tahir ul Qamar, M. Antiviral Phytochemicals Identification from Azadirachta Indica Leaves against HCV NS3 Protease: An in Silico Approach. Nat. Prod. Res. 2016, 30, 1866–1869. [Google Scholar] [CrossRef]
  3. Rogo, L.D.; Akogwu, S.; Umar, U.Z.; Aliyu, A.M.; Aminu, B.M. The Genetic and Molecular Studies of Hepatitis C Virus: A Review. Bayero J. Pure Appl. Sci. 2011, 4, 72–74. [Google Scholar] [CrossRef]
  4. Csete, J.; Elliott, R.; Fischer, B. “Viral Time Bomb”: Health and Human Rights Challenges in Addressing Hepatitis C in Canada; Canadian HIV/AIDS Legal Network, Centre for Addictions Research of BC and Centre for Applied Research in Mental Health and Addiction: Victoria, BC, Canada, 2008; ISBN 1926789083. [Google Scholar]
  5. Razavi, H. Global Epidemiology of Viral Hepatitis. Gastroenterol. Clin. 2020, 49, 179–189. [Google Scholar] [CrossRef] [PubMed]
  6. Chen, S.L.; Morgan, T.R. The Natural History of Hepatitis C Virus (HCV) Infection. Int. J. Med. Sci. 2006, 3, 47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Houghton, M. The Long and Winding Road Leading to the Identification of the Hepatitis C Virus. J. Hepatol. 2009, 51, 939–948. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Memon, M.I.; Memon, M.A. Hepatitis C: An Epidemiological Review. J. Viral Hepat. 2002, 9, 84–100. [Google Scholar] [CrossRef]
  9. Thrift, A.P.; El-Serag, H.B.; Kanwal, F. Global Epidemiology and Burden of HCV Infection and HCV-related Disease. Nat. Rev. Gastroenterol. Hepatol. 2017, 14, 122. [Google Scholar] [CrossRef] [PubMed]
  10. Messina, J.P.; Humphreys, I.; Flaxman, A.; Brown, A.; Cooke, G.S.; Pybus, O.G.; Barnes, E. Global Distribution and Prevalence of Hepatitis C Virus Genotypes. Hepatology 2015, 61, 77–87. [Google Scholar] [CrossRef] [Green Version]
  11. Rajhi, M.; Haddad-Boubaker, S.; Chouikha, A.; Bourquain, D.; Michel, J.; Hammami, W.; Sadraoui, A.; Touzi, H.; Ghedira, K.; Triki, H. Identification of Two Novel Hepatitis C Virus Subtype 2 from Tunisia (2v and 2w). PLoS ONE 2021, 16, e0248249. [Google Scholar] [CrossRef]
  12. Lin, C.-L.; Kao, J.-H. The Clinical Implications of Hepatitis B Virus Genotype: Recent Advances. J. Gastroenterol. Hepatol. 2011, 26, 123–130. [Google Scholar] [CrossRef] [PubMed]
  13. Petruzziello, A.; Marigliano, S.; Loquercio, G.; Cozzolino, A.; Cacciapuoti, C. Global Epidemiology of Hepatitis C Virus Infection: An Up-date of the Distribution and Circulation of Hepatitis C Virus Genotypes. World J. Gastroenterol. 2016, 22, 7824. [Google Scholar] [CrossRef] [PubMed]
  14. Davis, G.L.; Wong, J.B.; McHutchison, J.G.; Manns, M.P.; Harvey, J.; Albrecht, J. Early Virologic Response to Treatment with Peginterferon Alfa-2b Plus Ribavirin in Patients with Chronic Hepatitis C. Hepatology 2003, 38, 645–652. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Yau, A.H.L.; Yoshida, E.M. Hepatitis C Drugs: The End of the Pegylated Interferon Era and the Emergence of All-oral, Interferon-free Antiviral Regimens: A Concise Review. Can. J. Gastroenterol. Hepatol. 2014, 28, 445–451. [Google Scholar] [CrossRef] [Green Version]
  16. Ford, N.; Kirby, C.; Singh, K.; Mills, E.J.; Cooke, G.; Kamarulzaman, A.; duCros, P. Chronic Hepatitis C Treatment Outcomes in Low-and Middle-income Countries: A Systematic Review and Meta-analysis. Bull. World Health Organ. 2012, 90, 540–550. [Google Scholar] [CrossRef] [Green Version]
  17. Jayasekera, C.R.; Barry, M.; Roberts, L.R.; Nguyen, M.H. Treating Hepatitis C in Lower-income Countries. N. Engl. J. Med. 2014, 370, 1869–1871. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Mittal, S.; El-Serag, H.B. Epidemiology of HCC: Consider the Population. J. Clin. Gastroenterol. 2013, 47, S2. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Probst, A.; Dang, T.; Bochud, M.; Egger, M.; Negro, F.; Bochud, P.-Y. Role of Hepatitis C Virus Genotype 3 in Liver Fibrosis Progression–A Systematic Review and Meta-analysis. J. Viral Hepat. 2011, 18, 745–759. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Waheed, Y.; Shafi, T.; Safi, S.Z.; Qadri, I. Hepatitis C Virus in Pakistan: A Systematic Review of Prevalence, Genotypes and Risk Factors. World J. Gastroenterol. WJG 2009, 15, 5647. [Google Scholar] [CrossRef] [PubMed]
  21. Al Kanaani, Z.; Mahmud, S.; Kouyoumjian, S.P.; Abu-Raddad, L.J. The Epidemiology of Hepatitis C Virus in Pakistan: Systematic Review and Meta-analyses. R. Soc. Open Sci. 2018. [Google Scholar] [CrossRef] [Green Version]
  22. Ali, S.A.; Donahue, R.M.J.; Qureshi, H.; Vermund, S.H. Hepatitis B and Hepatitis C in Pakistan: Prevalence and Risk Factors. Int. J. Infect. Dis. 2009, 13, 9–19. [Google Scholar] [CrossRef] [Green Version]
  23. Wei, Y.; Li, J.; Qing, J.; Huang, M.; Wu, M.; Gao, F.; Li, D.; Hong, Z.; Kong, L.; Huang, W.; et al. Discovery of Novel Hepatitis C Virus NS5B Polymerase Inhibitors by Combining Random Forest, Multiple e-Pharmacophore Modeling and Docking. PLoS ONE 2016. [Google Scholar] [CrossRef]
  24. Sarrazin, C.; Zeuzem, S. Resistance to Direct Antiviral Agents in Patients with Hepatitis C Virus Infection. Gastroenterology 2010, 138, 447–462. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Roohvand, F.; Kossari, N. Advances in Hepatitis C Virus Vaccines, Part One: Advances in Basic Knowledge for Hepatitis C Virus Vaccine Design. Expert Opin. Ther. Pat. 2011, 21, 1811–1830. [Google Scholar] [CrossRef] [PubMed]
  26. Center, R.J.; Boo, I.; Phu, L.; McGregor, J.; Poumbourios, P.; Drummer, H.E. Enhancing the Antigenicity and Immunogenicity of Monomeric Forms of Hepatitis C Virus E2 for Use as a Preventive Vaccine: EDITORS’PICK: Multimerization of HCV E2 Enhances Immunogenicity. J. Biol. Chem. 2020, 295, 7179–7192. [Google Scholar] [CrossRef] [Green Version]
  27. Akache, B.; Deschatelets, L.; Harrison, B.A.; Dudani, R.; Stark, F.C.; Jia, Y.; Landi, A.; Law, J.L.M.; Logan, M.; Hockman, D. Effect of Different Adjuvants on the Longevity and Strength of Humoral and Cellular Immune Responses to the HCV Envelope Glycoproteins. Vaccines 2019, 7, 204. [Google Scholar] [CrossRef] [Green Version]
  28. Beaumont, E.; Patient, R.; Hourioux, C.; Dimier-Poisson, I.; Roingeard, P. Chimeric Hepatitis B Virus/Hepatitis C Virus Envelope Proteins Elicit Broadly Neutralizing Antibodies and Constitute a Potential Bivalent Prophylactic Vaccine. Hepatology 2013, 57, 1303–1313. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Frey, S.E.; Houghton, M.; Coates, S.; Abrignani, S.; Chien, D.; Rosa, D.; Pileri, P.; Ray, R.; Di Bisceglie, A.M.; Rinella, P. Safety and Immunogenicity of HCV E1E2 Vaccine Adjuvanted with MF59 Administered to Healthy Adults. Vaccine 2010, 28, 6367–6373. [Google Scholar] [CrossRef] [Green Version]
  30. Forns, X.; Payette, P.J.; Ma, X.; Satterfield, W.; Eder, G.; Mushahwar, I.K.; Govindarajan, S.; Davis, H.L.; Emerson, S.U.; Purcell, R.H. Vaccination of Chimpanzees with Plasmid DNA Encoding the Hepatitis C Virus (HCV) Envelope E2 Protein Modified the Infection after Challenge with Homologous Monoclonal HCV. Hepatology 2000, 32, 618–625. [Google Scholar] [CrossRef]
  31. Wijesundara, D.K.; Gummow, J.; Li, Y.; Yu, W.; Quah, B.J.; Ranasinghe, C.; Torresi, J.; Gowans, E.J.; Grubor-Bauk, B. Induction of Genotype Cross-reactive, Hepatitis C Virus-specific, Cell-mediated Immunity in DNA-vaccinated Mice. J. Virol. 2018, 92. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Masavuli, M.G.; Wijesundara, D.K.; Underwood, A.; Christiansen, D.; Earnest-Silveira, L.; Bull, R.; Torresi, J.; Gowans, E.J.; Grubor-Bauk, B. A Hepatitis C Virus DNA Vaccine Encoding a Secreted, Oligomerized Form of Envelope Proteins is Highly Immunogenic and Elicits Neutralizing Antibodies in Vaccinated Mice. Front. Immunol. 2019, 10, 1145. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Filskov, J.; Andersen, P.; Agger, E.M.; Bukh, J. HCV p7 as a Novel Vaccine-target Inducing Multifunctional CD4+ and CD8+ T-cells Targeting Liver Cells Expressing the Viral Antigen. Sci. Rep. 2019, 9, 1–13. [Google Scholar] [CrossRef] [Green Version]
  34. Dawood, R.M.; Moustafa, R.I.; Abdelhafez, T.H.; El-Shenawy, R.; El-Abd, Y.; El Din, N.G.B.; Dubuisson, J.; El Awady, M.K. A Multiepitope Peptide Vaccine against HCV Stimulates Neutralizing Humoral and Persistent Cellular Responses in Mice. BMC Infect. Dis. 2019, 19, 1–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Marín, M.Q.; Pérez, P.; Ljungberg, K.; Sorzano, C.Ó.S.; Gómez, C.E.; Liljeström, P.; Esteban, M.; García-Arriaza, J. Potent Anti-hepatitis C Virus (HCV) T Cell Immune Responses Induced in Mice Vaccinated with DNA-launched RNA Replicons and Modified Vaccinia Virus Ankara-HCV. J. Virol. 2019, 93. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Serviddio, G. Practical Management of Chronic Viral Hepatitis; BoD–Books on Demand: Norderstedt, Germany, 2013; ISBN 9535111094. [Google Scholar]
  37. Shehzadi, A.; ur Rehman, S.; Idrees, M. Promiscuous Prediction and Conservancy Analysis of CTL Binding Epitopes of HCV 3a Viral Proteome from Punjab Pakistan: An in silico Approach. Virol. J. 2011, 8, 1–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Memarnejadian, A.; Roohvand, F.; Arashkia, A.; Rafati, S.; Shokrgozar, M.A. 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, 842–850. [Google Scholar] [CrossRef]
  39. Ikram, A.; Zaheer, T.; Awan, F.M.; Obaid, A.; Naz, A.; Hanif, R.; Paracha, R.Z.; Ali, A.; Naveed, A.K.; Janjua, H.A. Exploring NS3/4A, NS5A and NS5B Proteins to Design Conserved Subunit Multi-epitope Vaccine against HCV Utilizing Immunoinformatics Approaches. Sci. Rep. 2018, 8, 1–14. [Google Scholar] [CrossRef] [Green Version]
  40. Sabet, L.P.; Taheri, T.; Memarnejadian, A.; Azad, T.M.; Asgari, F.; Rahimnia, R.; Alavian, S.M.; Rafati, S.; Rad, K.S. Immunogenicity of Multi-epitope DNA and Peptide Vaccine Candidates Based on Core, E2, NS3 and NS5B HCV Epitopes in BALB/c Mice. Hepat. Mon. 2014, 14. [Google Scholar] [CrossRef] [Green Version]
  41. Benson, D.A.; Karsch-Mizrachi, I.; Lipman, D.J.; Ostell, J.; Sayers, E.W. GenBank. Nucleic Acids Res. 2010, 39, D32–D37. [Google Scholar] [CrossRef] [PubMed]
  42. Larsen, M.V.; Lundegaard, C.; Lamberth, K.; Buus, S.; Lund, O.; Nielsen, M. Large-scale Validation of Methods for Cytotoxic T-lymphocyte Epitope Prediction. BMC Bioinform. 2007, 8, 424. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Vita, R.; Mahajan, S.; Overton, J.A.; Dhanda, S.K.; Martini, S.; Cantrell, J.R.; Wheeler, D.K.; Sette, A.; Peters, B. The Immune Epitope Database (IEDB): 2018 Update. Nucleic Acids Res. 2018, 47, D339–D343. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Doytchinova, I.A.; Flower, D.R. VaxiJen: A Server for Prediction of Protective Antigens, Tumour Antigens and Subunit Vaccines. BMC Bioinform. 2007, 8, 4. [Google Scholar] [CrossRef] [Green Version]
  45. Dimitrov, I.; Flower, D.R.; Doytchinova, I. AllerTOP-a Server for in silico Prediction of Allergens. BMC Bioinform. 2013, 14, S4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Shahid, F.; Ashfaq, U.A.; Javaid, A.; Khalid, H. Immunoinformatics Guided Rational Design of a Next Generation Multi Epitope Based Peptide (MEBP) Vaccine by Exploring Zika Virus Proteome. Infect. Genet. Evol. 2020, 80, 104199. [Google Scholar] [CrossRef] [PubMed]
  47. Sugiarto, H.; Yu, P.-L. Avian Antimicrobial Peptides: The Defense Role of $β$-defensins. Biochem. Biophys. Res. Commun. 2004, 323, 721–727. [Google Scholar] [CrossRef] [PubMed]
  48. Zhang, Y. I-TASSER Server for Protein 3D Structure Prediction. BMC Bioinform. 2008, 9, 40. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Heo, L.; Park, H.; Seok, C. GalaxyRefine: Protein Structure Refinement Driven by Side-chain Repacking. Nucleic Acids Res. 2013, 41, W384–W388. [Google Scholar] [CrossRef] [Green Version]
  50. Grote, A.; Hiller, K.; Scheer, M.; Münch, R.; Nörtemann, B.; Hempel, D.C.; Jahn, D. JCat: A Novel Tool to Adapt Codon Usage of a Target Gene to Its Potential Expression Host. Nucleic Acids Res. 2005, 33, W526–W531. [Google Scholar] [CrossRef]
  51. Jespersen, M.C.; Peters, B.; Nielsen, M.; Marcatili, P. BepiPred-2.0: Improving Sequence-based B-cell Epitope Prediction Using Conformational Epitopes. Nucleic Acids Res. 2017, 45, W24–W29. [Google Scholar] [CrossRef] [Green Version]
  52. Cheng, J.; Randall, A.Z.; Sweredoski, M.J.; Baldi, P. SCRATCH: A Protein Structure and Structural Feature Prediction Server. Nucleic Acids Res. 2005, 33, W72–W76. [Google Scholar] [CrossRef] [Green Version]
  53. ProtParam, E. ExPASy-ProtParam Tool. 2017. Available online: https://web.expasy.org/protparam/ (accessed on 3 March 2021).
  54. Kozakov, D.; Hall, D.R.; Xia, B.; Porter, K.A.; Padhorny, D.; Yueh, C.; Beglov, D.; Vajda, S. The ClusPro Web Server for Protein-Protein Docking. Nat. Protoc. 2017, 12, 255. [Google Scholar] [CrossRef]
  55. Case, D.; Ben-Shalom, I.; Brozell, S.; Cerutti, D.; Cheatham III, T.; Cruzeiro, V.; Darden, T.; Duke, R.; Ghoreishi, D.; Gilson, M.; et al. Amber 18 Reference Manual; University of California: San Francisco, CA, USA, 2018. [Google Scholar]
  56. Dickson, C.J.; Rosso, L.; Betz, R.M.; Walker, R.C.; Gould, I.R. GAFFlipid: A General Amber Force Field for the Accurate Molecular Dynamics Simulation of Phospholipid. Soft Matter 2012, 8, 9617–9627. [Google Scholar] [CrossRef]
  57. Feller, S.E.; Zhang, Y.; Pastor, R.W.; Brooks, B.R. Constant Pressure Molecular Dynamics Simulation: The Langevin Piston Method. J. Chem. Phys. 1995, 103, 4613–4621. [Google Scholar] [CrossRef]
  58. Kräutler, V.; Van Gunsteren, W.F.; Hünenberger, P.H. A Fast SHAKE Algorithm to Solve Distance Constraint Equations for Small Molecules in Molecular Dynamics Simulations. J. Comput. Chem. 2001, 22, 501–508. [Google Scholar] [CrossRef]
  59. Roe, D.R.; Cheatham III, T.E. PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data. J. Chem. Theory Comput. 2013, 9, 3084–3095. [Google Scholar] [CrossRef]
  60. Miller, B.R.; McGee, T.D.; Swails, J.M.; Homeyer, N.; Gohlke, H.; Roitberg, A.E. MMPBSA.py: An Efficient Program for End-state Free Energy Calculations. J. Chem. Theory Comput. 2012, 8, 3314–3321. [Google Scholar] [CrossRef]
  61. Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA Methods to Estimate Ligand-binding Affinities. Expert Opin. Drug Discov. 2015, 10, 449–461. [Google Scholar] [CrossRef] [PubMed]
  62. Rapin, N.; Lund, O.; Castiglione, F. C-ImmSim 10.1 Server. PLoS Pathog. 2012, 8. [Google Scholar] [CrossRef]
  63. Schmidt, M.E.; Varga, S.M. The CD8 T Cell Response to Respiratory Virus Infections. Front. Immunol. 2018, 9, 678. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Luckheeram, R.V.; Zhou, R.; Verma, A.D.; Xia, B. CD4+ T Cells: Differentiation and Functions. Clin. Dev. Immunol. 2012, 2012. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Angov, E. Codon Usage: Nature’s Roadmap to Expression and Folding of Proteins. Biotechnol. J. 2011, 6, 650–659. [Google Scholar] [CrossRef] [Green Version]
  66. Sanchez-Trincado, J.L.; Gomez-Perosanz, M.; Reche, P.A. Fundamentals and Methods for T-and B-cell Epitope Prediction. J. Immunol. Res. 2017, 2017. [Google Scholar] [CrossRef] [Green Version]
  67. Manns, M.P.; Buti, M.; Gane, E.; Pawlotsky, J.M.; Razavi, H.; Terrault, N.; Younossi, Z. Hepatitis C Virus Infection. Nat. Rev. Dis. Prim. 2017. [Google Scholar] [CrossRef] [PubMed]
  68. Li, W.; Joshi, M.D.; Singhania, S.; Ramsey, K.H.; Murthy, A.K. Peptide Vaccine: Progress and Challenges. Vaccines 2014, 2, 515–536. [Google Scholar] [CrossRef] [Green Version]
  69. Zhang, L. Multi-epitope Vaccines: A Promising Strategy against Tumors and Viral Infections. Cell. Mol. Immunol. 2018, 15, 182. [Google Scholar] [CrossRef] [Green Version]
  70. María, R.R.; Arturo, C.J.; Alicia, J.; Paulina, M.G.; Gerardo, A. The Impact of Bioinformatics on Vaccine Design and Development; InTech: Rijeka, Croatia, 2017. [Google Scholar]
  71. Sayed, S.B.; Nain, Z.; Khan, M.S.A.; Abdulla, F.; Tasmin, R.; Adhikari, U.K. Exploring Lassa Virus Proteome to Design a Multi-epitope Vaccine through Immunoinformatics and Immune Simulation Analyses. Int. J. Pept. Res. Ther. 2020, 26, 2089–2107. [Google Scholar] [CrossRef] [PubMed]
  72. Ali, M.; Pandey, R.K.; Khatoon, N.; Narula, A.; Mishra, A.; Prajapati, V.K. Exploring Dengue Genome to Construct a Multi-epitope Based Subunit Vaccine by Utilizing Immunoinformatics Approach to Battle against Dengue Infection. Sci. Rep. 2017, 7, 1–13. [Google Scholar] [CrossRef]
  73. Goodman, A.G.; Heinen, P.P.; Guerra, S.; Vijayan, A.; Sorzano, C.O.S.; Gomez, C.E.; Esteban, M. A Human Multi-epitope Recombinant Vaccinia Virus as a Universal T Cell Vaccine Candidate against Influenza Virus. PLoS ONE 2011, 6, e25938. [Google Scholar] [CrossRef] [Green Version]
  74. Ashfaq, U.A.; Saleem, S.; Masoud, M.S.; Ahmad, M.; Nahid, N.; Bhatti, R.; Almatroudi, A.; Khurshid, M. Rational Design of Multi Epitope-based Subunit Vaccine by Exploring MERS-COV Proteome: Reverse Vaccinology and Molecular Docking Approach. PLoS ONE 2021, 16, e0245072. [Google Scholar] [CrossRef] [PubMed]
  75. Tahir Ul Qamar, M.; Saleem, S.; Ashfaq, U.A.; Bari, A.; Anwar, F.; Alqahtani, S. Epitope-based Peptide Vaccine Design and Target Site Depiction Against Middle East Respiratory Syndrome Coronavirus: An Immune-informatics Study. J. Transl. Med. 2019. [Google Scholar] [CrossRef] [PubMed]
  76. Qamar, M.T.U.; Shokat, Z.; Muneer, I.; Ashfaq, U.A.; Javed, H.; Anwar, F.; Bari, A.; Zahid, B.; Saari, N. Multiepitope-based Subunit Vaccine Design and Evaluation against Respiratory Syncytial Virus Using Reverse Vaccinology Approach. Vaccines 2020, 8, 288. [Google Scholar] [CrossRef]
  77. Enayatkhani, M.; Hasaniazad, M.; Faezi, S.; Gouklani, H.; Davoodian, P.; Ahmadi, N.; Einakian, M.A.; Karmostaji, A.; Ahmadi, K. Reverse Vaccinology Approach to Design a Novel Multi-epitope Vaccine Candidate Against COVID-19: An in silico Study. J. Biomol. Struct. Dyn. 2020, 1–16. [Google Scholar] [CrossRef] [Green Version]
  78. Tahir ul Qamar, M.; Shahid, F.; Aslam, S.; Ashfaq, U.A.; Aslam, S.; Fatima, I.; Fareed, M.M.; Zohaib, A.; Chen, L.-L. Reverse Vaccinology Assisted Designing of Multiepitope-based Subunit Vaccine Against SARS-CoV-2. Infect. Dis. Poverty 2020, 9, 1–14. [Google Scholar]
  79. Tahir ul Qamar, M.; Rehman, A.; Tusleem, K.; Ashfaq, U.A.; Qasim, M.; Zhu, X.; Fatima, I.; Shahid, F.; Chen, L.L. Designing of a Next Generation Multiepitope Based Vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico Approaches. PLoS ONE 2020. [Google Scholar] [CrossRef] [PubMed]
  80. Tahir Ul Qamar, M.; Bari, A.; Adeel, M.M.; Maryam, A.; Ashfaq, U.A.; Du, X.; Muneer, I.; Ahmad, H.I.; Wang, J. Peptide Vaccine against Chikungunya Virus: Immuno-informatics Combined with Molecular Docking Approach. J. Transl. Med. 2018, 16. [Google Scholar] [CrossRef]
  81. Van Regenmortel, M.H. V Mapping Epitope Structure and Activity: From One-dimensional Prediction to Four-dimensional Description of Antigenic Specificity. Methods 1996, 9, 465–472. [Google Scholar] [CrossRef] [PubMed]
  82. Shamriz, S.; Ofoghi, H.; Moazami, N. Effect of Linker Length and Residues on the Structure and Stability of a Fusion Protein with Malaria Vaccine Application. Comput. Biol. Med. 2016, 76, 24–29. [Google Scholar] [CrossRef] [PubMed]
  83. García-Valtanen, P.; Martinez-Lopez, A.; Ortega-Villaizan, M.; Perez, L.; Coll, J.M.; Estepa, A. In Addition to Its Antiviral and Immunomodulatory Properties, the Zebrafish β-defensin 2 (zfBD2) is a Potent Viral DNA Vaccine Molecular Adjuvant. Antiviral Res. 2014, 101, 136–147. [Google Scholar] [CrossRef] [PubMed]
  84. Mittal, A.; Sasidharan, S.; Raj, S.; Balaji, S.N.; Saudagar, P. Exploring the Zika Genome to Design a Potential Multiepitope Vaccine Using an Immunoinformatics Approach. Int. J. Pept. Res. Ther. 2020, 1–10. [Google Scholar] [CrossRef]
  85. Ojha, R.; Gupta, N.; Naik, B.; Singh, S.; Verma, V.K.; Prusty, D.; Prajapati, V.K. High Throughput and Comprehensive Approach to Develop Multiepitope Vaccine against Minacious COVID-19. Eur. J. Pharm. Sci. 2020, 151, 105375. [Google Scholar] [CrossRef]
  86. Meza, B.; Ascencio, F.; Sierra-Beltrán, A.P.; Torres, J.; Angulo, C. A Novel Design of a Multi-antigenic, Multistage and Multi-epitope Vaccine against Helicobacter Pylori: An in silico Approach. Infect. Genet. Evol. 2017, 49, 309–317. [Google Scholar] [CrossRef]
  87. Lee, S.; Nguyen, M.T. Recent Advances of Vaccine Adjuvants for Infectious Diseases. Immune Netw. 2015, 15, 51. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. Arai, R.; Ueda, H.; Kitayama, A.; Kamiya, N.; Nagamune, T. Design of the Linkers Which Effectively Separate Domains of a Bifunctional Fusion Protein. Protein Eng. 2001, 14, 529–532. [Google Scholar] [CrossRef]
  89. Durdagi, S.; Tahir ul Qamar, M.; Salmas, R.E.; Tariq, Q.; Anwar, F.; Ashfaq, U.A. Investigating the Molecular Mechanism of Staphylococcal DNA Gyrase Inhibitors: A Combined Ligand-based and Structure-based Resources Pipeline. J. Mol. Graph. Model. 2018, 85, 122–129. [Google Scholar] [CrossRef]
  90. Johnson, L.S.; Eddy, S.R.; Portugaly, E. Hidden Markov Model Speed Heuristic and Iterative HMM Search Procedure. BMC Bioinform. 2010, 11, 1–8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  91. Machida, K.; Cheng, K.T.H.; Sung, V.M.-H.; Levine, A.M.; Foung, S.; Lai, M.M.C. Hepatitis C Virus Induces Toll-like Receptor 4 Expression, Leading to Enhanced Production of Beta Interferon and Interleukin-6. J. Virol. 2006, 80, 866–874. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  92. Gori, A.; Longhi, R.; Peri, C.; Colombo, G. Peptides for Immunological Purposes: Design, Strategies and Applications. Amino Acids 2013, 45, 257–268. [Google Scholar] [CrossRef]
  93. Pandey, R.K.; Ojha, R.; Aathmanathan, V.S.; Krishnan, M.; Prajapati, V.K. Immunoinformatics Approaches to Design a Novel Multi-epitope Subunit Vaccine against HIV Infection. Vaccine 2018, 36, 2262–2272. [Google Scholar] [CrossRef]
  94. Chen, R. Bacterial Expression Systems for Recombinant Protein Production: E. coli and Beyond. Biotechnol. Adv. 2012, 30, 1102–1107. [Google Scholar] [CrossRef]
  95. Rosano, G.L.; Ceccarelli, E.A. Recombinant Protein Expression in Escherichia coli: Advances and Challenges. Front. Microbiol. 2014, 5, 172. [Google Scholar] [CrossRef] [Green Version]
Vaccines 09 00293 g001 550
Figure 1. Graphical representation of the methodology employed for identification of potential antigenic epitopes in hepatitis C virus (HCV) subtype-3a.
Figure 1. Graphical representation of the methodology employed for identification of potential antigenic epitopes in hepatitis C virus (HCV) subtype-3a.
Vaccines 09 00293 g001
Vaccines 09 00293 g002 550
Figure 2. (A) Amino acid sequence of the designed polypeptide vaccine. The red sequence are linkers for fusing the epitopes. (B) Predicted 3D structure of the vaccine and (C) Ramachandran plot analysis of the vaccine.
Figure 2. (A) Amino acid sequence of the designed polypeptide vaccine. The red sequence are linkers for fusing the epitopes. (B) Predicted 3D structure of the vaccine and (C) Ramachandran plot analysis of the vaccine.
Vaccines 09 00293 g002
Vaccines 09 00293 g003 550
Figure 3. In-silico cloning of the vaccine (shown by indigo color) into pET28a (+) vector (shown by grey color).
Figure 3. In-silico cloning of the vaccine (shown by indigo color) into pET28a (+) vector (shown by grey color).
Vaccines 09 00293 g003
Vaccines 09 00293 g004 550
Figure 4. Docked conformation of the vaccine candidate to TLR4 molecule.
Figure 4. Docked conformation of the vaccine candidate to TLR4 molecule.
Vaccines 09 00293 g004
Vaccines 09 00293 g005 550
Figure 5. Different structural analysis based of molecular dynamics simulation trajectories. (A) RMSD, (B) RMSF, (C) Radius of gyration and (D) Beta factor.
Figure 5. Different structural analysis based of molecular dynamics simulation trajectories. (A) RMSD, (B) RMSF, (C) Radius of gyration and (D) Beta factor.
Vaccines 09 00293 g005
Vaccines 09 00293 g006 550
Figure 6. In silico immune simulation of the host in response to the vaccine antigen administration. (A) Antibodies titer to the vaccine, (B) interferon, cytokines and interleukins response to the vaccine.
Figure 6. In silico immune simulation of the host in response to the vaccine antigen administration. (A) Antibodies titer to the vaccine, (B) interferon, cytokines and interleukins response to the vaccine.
Vaccines 09 00293 g006
Table
Table 1. Predicted cytotoxic T-lymphocyte (CTL) epitopes for HCV subtype 3.
Table 1. Predicted cytotoxic T-lymphocyte (CTL) epitopes for HCV subtype 3.
Sr. No.NETCTL ScoreIEDB Immunogenicity Score
1FSGVDAVTY 2.0440.1684
2LMTTVLLAY 2.1580.0949
3VTTGANLTY 3.0780.092
4LTLSLRWIY 2.6620.1325
Table
Table 2. Predicted helper T-lymphocyte (HTL) epitopes for HCV subtype 3.
Table 2. Predicted helper T-lymphocyte (HTL) epitopes for HCV subtype 3.
Sr. No.IEBD (HTL Peptide)Percentile Rank
1LRAWRHRARAVRAML0.17
2ITSLVVPPPPWPVLL0.29
3TSLVVPPPPWPVLLG0.29
4IITSLVVPPPPWPVL0.36
5SLVVPPPPWPVLLGR0.37
Table
Table 3. Predicted B-cell epitopes in the vaccine construct.
Table 3. Predicted B-cell epitopes in the vaccine construct.
Sr. No.StartEndPeptideLength
12450LPKEEQIGKCSTRGRKCCRRKKFSGVD27
3102105VPPP4
4115119VVPPP5
5132138VPPPPWP7
6143151VVPPPPWPV9
Table
Table 4. Binding free energies of the vaccine candidate to TLR4.
Table 4. Binding free energies of the vaccine candidate to TLR4.
Energy ComponentBinding Free Energy (kcal/mol)
ΔEvdw−234.48
ΔEele−85.16
Epolar.solv59.14
Enon-polar.solv−21.7
ΔGgas (GBSA)−319.64
ΔGsol (GBSA)37.44
ΔGbind (GBSA)−282.2
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ahmad, S.; Shahid, F.; Tahir ul Qamar, M.; Rehman, H.u.; Abbasi, S.W.; Sajjad, W.; Ismail, S.; Alrumaihi, F.; Allemailem, K.S.; Almatroudi, A.; et al. Immuno-Informatics Analysis of Pakistan-Based HCV Subtype-3a for Chimeric Polypeptide Vaccine Design. Vaccines 2021, 9, 293. https://doi.org/10.3390/vaccines9030293

AMA Style

Ahmad S, Shahid F, Tahir ul Qamar M, Rehman Hu, Abbasi SW, Sajjad W, Ismail S, Alrumaihi F, Allemailem KS, Almatroudi A, et al. Immuno-Informatics Analysis of Pakistan-Based HCV Subtype-3a for Chimeric Polypeptide Vaccine Design. Vaccines. 2021; 9(3):293. https://doi.org/10.3390/vaccines9030293

Chicago/Turabian Style

Ahmad, Sajjad, Farah Shahid, Muhammad Tahir ul Qamar, Habib ur Rehman, Sumra Wajid Abbasi, Wasim Sajjad, Saba Ismail, Faris Alrumaihi, Khaled S. Allemailem, Ahmad Almatroudi, and et al. 2021. "Immuno-Informatics Analysis of Pakistan-Based HCV Subtype-3a for Chimeric Polypeptide Vaccine Design" Vaccines 9, no. 3: 293. https://doi.org/10.3390/vaccines9030293

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop