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Original Article

Immunoinformatics approach for design novel multi-epitope prophylactic and therapeutic vaccine based on capsid proteins L1 and L2 and oncoproteins E6 and E7 of human papillomavirus 16 and human papillomavirus 18 against cervical cancer

Nicholas Ryan1orcid, Sari Eka Pratiwi2orcid, Mardhia Mardhia3orcid, Ysrafil Ysrafil4orcid, Delima Fajar Liana3orcid, Mahyarudin Mahyarudin3orcid
Osong Public Health and Research Perspectives 2024;15(4):307-328.
Published online: July 23, 2024

1Medical Study Program, Faculty of Medicine, Universitas Tanjungpura, Pontianak, Indonesia

2Department of Biology and Pathobiology, Faculty of Medicine, Universitas Tanjungpura, Pontianak, Indonesia

3Department of Microbiology, Faculty of Medicine, Universitas Tanjungpura, Pontianak, Indonesia

4Department of Pharmacotherapy, Faculty of Medicine, Universitas Palangka Raya, Palangka Raya, Indonesia

Corresponding author: Ysrafil Ysrafil Department of Pharmacotherapy, Faculty of Medicine, Universitas Palangka Raya, Palangka Raya, Indonesia E-mail: ysrafil@med.upr.ac.id
• Received: January 11, 2024   • Revised: May 11, 2024   • Accepted: May 13, 2024

© 2024 Korea Disease Control and Prevention Agency.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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  • Objectives
    This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process involved using capsid epitopes L1 and L2, as well as oncoproteins E5, E6, and E7 from human papillomavirus (HPV) types 16 and 18.
  • Methods
    An experimental in silico analysis with an immunoinformatics approach was used to develop 2 multi-epitope vaccine constructs (A and B). Further analysis was then conducted to compare the constructs and select the one with the highest potential against cervical cancer.
  • Results
    This study produced 2 antigenic, non-allergenic, and nontoxic multi-epitope vaccine constructs (A and B), which exhibited the ideal physicochemical properties for a vaccine. Further analysis revealed that construct B effectively induced both cellular and humoral immune responses.
  • Conclusion
    The multi-epitope vaccine construct B for HPV 16 and 18, designed for both prophylactic and therapeutic purposes, met the development criteria for a cervical cancer vaccine. However, these findings need to be validated through in vitro and in vivo experiments.
  • Keywords: Antigenic epitopes; Human papillomavirus viruses; Immunoinformatics; Prevention; Therapeutics; Uterine cervical neoplasms
Cervical cancer is the leading cause of cancer-related deaths among women worldwide. According to the World Health Organization, this disease is now the fourth most common cancer in women globally, with approximately 660,000 new cases reported in 2022 and around 350,000 deaths [1–3]. The incidence and mortality rates of cervical cancer are highest in low- and middle-income countries [3]. Several factors influence the incidence of cervical cancer, including early sexual activity, multiple sexual partners, smoking, and low socioeconomic status [4]. Persistent human papillomavirus (HPV) infection is considered the most significant factor. The high-risk HPV groups associated with this condition include HPV 16, 18, 31, 52, and 58, with HPV 16 and 18 accounting for 84.5% of global cases [5].
HPV infection is typically prevented using commercially available prophylactic vaccines such as Gardasil-4, Cervarix, and Gardasil-9. Although these vaccines are promising, they do not possess therapeutic functions [6]. Additionally, many patients are diagnosed with cervical cancer at a late stage due to the scarcity of resources to implement effective vaccination and screening programs in developing countries. Therefore, it is urgently necessary to develop a multi-epitope vaccine that combines both prophylactic and therapeutic properties. Advances in genome sequencing have made it possible to predict potential B and T cell epitopes using immunoinformatics. This approach offers promising prospects as a more effective technique than traditional methods that depend on pathogen culture processes [7–10].
The HPV genome is categorized into early regions containing oncoproteins (E1, E2, E4, E5, E6, and E7), late regions comprising capsid proteins (L1 and L2), and the long control region. Capsid proteins are commonly targeted when designing prophylactic vaccines [11]. The expression of these proteins in the extracellular environment typically results in the production of antigenic virus-like proteins (VLPs), which stimulate the synthesis of antibodies that prevent the virus from penetrating cervical cells [7]. Several studies have targeted oncoproteins when designing therapeutic vaccines [12]. Presenting oncoproteins as antigens can activate the cellular immune response by stimulating type 1 helper T cells (HTLs) and cytotoxic T cells (CTLs), thereby initiating anti-tumorigenic processes [13]. Therefore, this study aimed to identify the optimal protein configuration for a multi-epitope vaccine designed to offer both prophylactic and therapeutic effects against cervical cancer. The construction process utilized capsid epitopes L1 and L2, along with oncoproteins E5, E6, and E7 from HPV types 16 and 18.
The immunoinformatics approach for the design and analysis of the current chimeric vaccine is presented in Figure 1.
Prediction of Antigenicity and Physicochemical Properties of HPV 16 and HPV 18 Proteins
The capsid proteins L1, L2, and oncoproteins E5, E6, and E7 of HPV 16 and HPV 18 in this study were obtained from the National Center for Biotechnology Information (NCBI) virus website (https://www.ncbi.nlm.nih.gov/). The antigenic properties of these compounds were then predicted using Vaxijen 2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) [14]. A protein was deemed antigenic if its prediction score exceeded a predefined threshold of 0.4. All proteins identified as antigenic underwent further analysis on the ProtParam website (https://web.expasy.org/protparam/). This analysis focused on identifying proteins with optimal physicochemical properties, specifically those with a molecular weight of 40 to 110 kDa, thermostability (aliphatic index between 66.5 and 84.3), stability (instability index below 40), and hydrophilicity (grand average of hydropathicity [GRAVY] index below 0). The objective was to increase the solubility and biological activity of the vaccine [15].
Prediction of CTL and HTL Epitopes
The IEDB website was used to predict CTL (http://tools.iedb.org/mhci/) and HTL epitopes (http://tools.iedb.org/mhcii/). CTL epitopes are designed to induce cellular immune responses through major histocompatibility complex (MHC) I receptors, whereas HTL epitopes aim to stimulate humoral immune responses via MHC-II receptors. The prediction of CTL and HTL epitopes was based on their predetermined lengths of 9 and 12 amino acids, respectively [16,17]. Additional analyses were conducted using the IFN-epitopes, IL4Pred, and IL10Pred websites to assess HTL epitopes that had percentile values of ≤2, focusing on their capacity to induce interferon (IFN)-γ, interleukin (IL)-4, and IL-10 production [18–20].
Prediction of B-Cell Epitopes
B-cell epitopes were predicted using the IEDB website (http://tools.iedb.org/bcell/), selecting a predetermined epitope length of 16 amino acids based on the Chou and Fasman beta turn prediction method [21]. Epitopes with a value of ≥0.5 were identified and further analyzed for their potential to induce 3 immunoglobulin subtypes (IgG, IgE, and IgA) using the IgPred website (http://crdd.osdd.net/raghava/igpred/) [22].
Prediction of CTL, HTL, and B-Cell Epitope Properties
The antigenicity, allergenicity, and toxicity of CTL, HTL, and B-cell epitopes were sequentially predicted. First, the antigenicity of epitopes was predicted using the VaxiJen v2.0 website (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) with a predetermined threshold (0.4) [14]. Second, allergenicity was predicted twice using the AllergenFP v.1.0 (http://ddg-pharmfac.net/AllergenFP/) and AllerTOP v.2.0 (https://www.ddg-pharmfac.net/) websites [23,24]. Finally, toxicity was predicted using the ToxinPred website (https://webs.iiitd.edu.in/raghava/toxinpred/protein.php). Throughout this process, only CTL, HTL, and B-cell epitopes that were antigenic, non-allergenic, and nontoxic were selected for vaccine construction [25].
Prediction of CTL and HTL Epitope Population Coverage
The population coverage of CTL and HTL epitopes was predicted using the IEDB website (http://tools.iedb.org/population/) based on the highest allele frequency in each geographic region. These regions included Australia, Europe, North Africa, Sub-Saharan Africa, North America, Central and South America, Oceania, Northeast Asia, Southeast Asia, South Asia, and West Asia [26]. This was performed to assess the overall prophylactic and therapeutic properties of vaccines in various regions of the world. The target alleles used for CTL epitopes were HLA-A*02:01, HLA-A*11:01, HLA-A*23:01, HLA-A*24:02, HLA-B*07:02, HLA-B*13:01, HLA-B*35:01, HLA-B*40:01, HLA-B*50:01, and HLA-B*51:01. The target alleles for the HTL epitope were HLA-DRB1*08:03, HLA-DRB1*07:01, HLA-DRB1*15:03, HLA-DRB1*12:09, HLA-DRB1*04:07, HLA-DRB1*12:02, HLA-DRB1*09:01, and HLA-DRB1*03:04 [27].
Construction of the Multi-Epitope Vaccine
The selected CTL, HTL, and B-cell epitopes were linked together to form a multi-epitope vaccine using specific linkers. AAY (Ala-Ala-Tyr) linkers were employed to connect CTL epitopes, GPGPG (Gly-Pro-Gly-Pro-Gly) linkers for binding HTL epitopes and their combination with CTL epitopes, and KK (Lys-Lys) linkers for B-cell epitopes. Additionally, the assembled epitopes were combined with an adjuvant (50S L7/L12 ribosomal protein) using an EAAAK linker and a 6× His-tag. Two multi-epitope vaccine constructs were developed, referred to as multi-epitope vaccine constructs A and B. Construct A included one CTL, HTL, and B-cell epitope each, derived from both HPV 16 and 18 proteins. In contrast, construct B contained all CTL, HTL, and B-cell epitopes from both HPV 16 and 18 proteins. These constructs were subsequently assessed for their antigenicity, allergenicity, toxicity, and physicochemical properties [9,10,28].
Prediction of Multi-Epitope Vaccine Affinity towards TAP Molecules
The affinity of the multi-epitope vaccine for transporter associated with antigen processing (TAP) molecules was predicted using the Tappred website (https://webs.iiitd.edu.in/raghava/tappred/) [29]. This analysis was conducted to assess the vaccine’s capability to bind to the TAP molecule, which is essential for the presentation of endogenous antigens by MHC-I.
Prediction of the Secondary and Tertiary Structure of the Multi-Epitope Vaccine
The secondary structure of the multi-epitope vaccine was predicted using the PSIPRED (http://bioinf.cs.ucl.ac.uk/psipred/) and SOPMA (https://npsa-pbil.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html) websites [30,31]. The multi-epitope vaccine tertiary structure was predicted using the Trosetta website (https://yanglab.qd.sdu.edu.cn/trRosetta/), and the results were visualized using the PyMOL application [32].
Refinement and Validation of the Multi-Epitope Vaccine’s Tertiary Structure
The multi-epitope vaccine’s tertiary structure was refined using the GalaxyRefine website (http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE), which produced 5 refined models [33]. Overall tertiary structure protein quality was validated for the best-refined model using the ProSA (https://prosa.services.came.sbg.ac.at/prosa.php), ERRAT (https://saves.mbi.ucla.edu/) and PROCHECK (https://saves.mbi.ucla.edu/) website [34,35]. Subsequently, tertiary structure protein flexibility was evaluated using the CABSFLEX website (http://biocomp.chem.uw.edu.pl/CABSflex2/submit) [36].
Disulfide Engineering of the Multi-Epitope Vaccine
Disulfide engineering aims to facilitate disulfide bond formation in vaccine structures to improve the folded vaccine structure by decreasing entropy and increasing free energy during denaturation. In this study, we used the Disulfide by Design 2.0 (DbD2) webserver (http://cptweb.cpt.wayne.edu/DbD2/) to design disulfide bonds in the vaccine construct. The use Χ3 value was −87° to +97° (set to 30), Cα–Cβ–Sγ angles (114.6°) were set to 10, and the energy value was <3.0 kcal/mol [37–39].
Analysis of Discontinuous B-Cell epitopes of 3-Dimensional Structure of Vaccines
Analyzing the discontinuous B-cell epitopes in the 3-dimensional (3D) structure of vaccines is crucial for understanding the antigen-antibody response. In this study, the epitopes were analyzed using the ElliPro webserver, which predicts and visualizes antibody epitopes in the 3D structure of vaccines using Thornton’s method. These were further visualized using the Jmol viewer. The default settings for the minimum score and maximum distance were set at 0.5 and 6 Å, respectively [40].
Prediction of Multi-Epitope Vaccine Affinity toward Toll-Like Receptor 2 and Toll-Like Receptor 4
The multi-epitope vaccine affinity toward toll-like receptor (TLR) 2 and TLR4, obtained from the RSCB website (https://www.rcsb.org/), was predicted using the ClusPro (https://cluspro.bu.edu/home.php) and HDOCK (http://hdock.phys.hust.edu.cn/) websites [41]. The X-ray crystallized structures of TLRs were prepared for docking by removing solvent molecules and other ligands, and by adding hydrogen atoms. The multi-epitope vaccine demonstrated high affinity for TLR2 and TLR4, indicated by negative energy and a confidence score of ≥0.7 on ClusPro and HDOCK, respectively [42,43].
Selection of Multi-Epitope Vaccine Constructs
The multi-epitope vaccine constructs with the highest potential were selected based on specific criteria. These criteria included favorable vaccine properties such as antigenicity, non-allergenicity, non-toxicity, and optimal physicochemical properties, as well as the quality of the refined tertiary structure and affinity towards TLR2 and TLR4. The chosen multi-epitope vaccine construct underwent further analyses, including molecular dynamics simulation, codon optimization, in silico cloning, prediction of post-translational modifications, and immune simulation.
Molecular Dynamics Simulation
Molecular dynamics of the multi-epitope vaccine TLR complex were simulated using the iMODS website (http://imods.chaconlab.org) based on eigenvalues showing the degree of deformability [44].
Codon Optimization and In Silico Cloning of the Multi-Epitope Vaccine
Codon optimization was conducted using the Integrated DNA Technologies (IDT) website (https://www.idtdna.com/pages/tools/codon-optimizationtool). The target organism was Pichia pastoris due to its ability to produce high-quality proteins more efficiently and cost-effectively than bacteria [45]. The optimization results indicated the complexity of the protein, with scores below 7 representing low-complexity proteins. Additionally, in silico cloning was performed using the SnapGene v4.2 software. During this process, the multi-epitope vaccine DNA sequence was inserted into the pUC19(+) cloning vector at 2 specific restriction enzyme sites: BamHI and KPN1.
Prediction of Post-Translational Modifications of the Multi-Epitope Vaccine
The post-translational modifications of the multi-epitope vaccine were predicted using the MusiteDeep website (https://www.musite.net/) [46]. The focus was on phosphorylation (p), glycosylation (gl), ubiquitination (ub), acetylation (ac), methylation (me), and hydroxylation (hy). These processes were specifically selected to ensure optimum protein folding, thereby improving the solubility, stability, and biological activity of the vaccine [47].
Immune Simulation
To characterize the effective immune response elicited by the multi-epitope vaccine construct, the C-ImmSim 10.1 website (https://kraken.iac.rm.cnr.it/C-IMMSIM/index.php?page=1) was used to monitor the immune response after 3 injections at a 30-day interval. The simulation was conducted using the default settings, with a simulation volume of 10 and 1,050 simulation steps [48]. This study primarily examined the development of B cell and T helper cell populations, the activity of CTLs, and cytokine production.
HPV 16 and 18 Antigenicity and Physicochemical Properties
The key physicochemical properties of the L1, L2, E5, E6, and E7 proteins of HPV 16 and 18 were depicted in Table 1. As shown in table, the proteins from HPV 16 and 18 suitable for vaccine development were the capsid proteins L1 and L2 and the oncoproteins E6 and E7. In contrast, the E5 oncoproteins of HPV 16 and 18 were found to be non-antigenic and were therefore excluded from the vaccine construction process (Table S1).
CTL and HTL Epitopes

CTL epitope prediction

CTL epitopes from HPV 16 and 18 proteins were important in vaccine design that should provide good antigenicity profiles (scores ≥0.4), nonallergenic, and nontoxic. The results of the predicted were presented below detail the HPV 16 and 18 CTL epitopes that fulfilled these selection criteria.

HPV 16

In total, 64, 46, 3, and 5 epitopes with percentile scores of ≤2 were selected from the L1, L2, E6, and E7 proteins of HPV 16, respectively, for further analysis. These predicted epitopes were then evaluated for their antigenicity scores and physicochemical properties. Upon analysis, 4 epitopes from L1, 4 from L2, 1 from E6, and 1 from E7 were identified as having antigenicity scores of ≥0.4 and were found to be non-allergenic and nontoxic (Table 1).

HPV 18

A total of 46, 48, 14, and 7 epitopes having percentile scores of ≤2 in L1, L2, E6, and E7 proteins of HPV 18, respectively, were selected for further analysis. These predicted epitopes were then evaluated for their antigenicity scores and physicochemical properties. Four, 5, 3, and 1 epitopes from the L1, L2, E6, and E7 proteins, respectively, were identified as having antigenicity scores of ≥0.4, and were found to be non-allergenic and nontoxic (Table 1).

HTL epitope prediction

In addition to the CTL epitope, the other most important part in multi-epitope vaccine design is the HTL epitope. They should also have a good antigenicity, allergenicity, and toxicity profile. Additionally, each chosen HTL epitope were suggested to had the capacity to induce IFN-γ, IL-4, and IL-10 production.

HPV 16

Three, 10, 3, and 3 epitopes from the L1, L2, E6, and E7 proteins of HPV 16, respectively, with percentile scores of ≤2 were selected for further analysis. These predicted epitopes were then evaluated for their antigenicity score, physicochemical properties, and capacity to induce production of IFN-γ, IL-4, and IL-10. Subsequent analysis revealed that 1, 2, 1, and 0 epitopes from the L1, L2, E6, and E7 proteins, respectively, had antigenicity scores of ≥0.4 and were found to be non-allergenic and nontoxic (Table 2).

HPV 18

Five, 6, 10, and 8 epitopes with percentile scores of ≤2 were selected from the L1, L2, E6, and E7 proteins of HPV 18, respectively, for further analysis. These predicted epitopes were then evaluated based on their antigenicity score, physicochemical properties, and capacity to induce production of IFN-γ, IL-4, and IL-10. Subsequent analysis revealed that 0, 2, 2, and 1 of the epitopes from the L1, L2, E6, and E7 proteins, respectively, had antigenicity scores of ≥0.4 and were found to be non-allergenic and nontoxic (Table 2).

B-cell epitopes

B-cell epitope prediction

B-cell epitopes from HPV 16 and 18 proteins were predicted using the IEDB website. The selection of B-cell epitopes was based on criteria including antigenicity, allergenicity, and toxicity. Epitopes suitable for multi-epitope vaccine design were identified as those with an antigen value of ≥0.4, non-allergenic, and nontoxic properties. Additionally, each chosen B-cell epitope was evaluated for its potential to stimulate the production of immunoglobulins (IgG, IgA, and IgE). The results presented here include the HPV 16 and 18 B-cell epitopes that met these selection criteria.

HPV 16

Fifteen epitopes with percentile scores of ≥0.5 were selected from each of the L1, L2, E6, and E7 proteins of HPV 16 for further analysis. These selected epitopes were evaluated based on their antigenicity score, physicochemical properties, and potential to induce the production of immunoglobulins (IgG, IgA, and IgE). Two epitopes from L1, 1 from L2, 2 from E6, and 2 from E7 had antigenicity scores of ≥0.4 and were both non-allergenic and nontoxic (Table 3).

HPV 18

Fifteen epitopes with percentile scores of ≥0.5 were selected from each of the L1, L2, E6, and E7 proteins of HPV 18 for further analysis. These predicted epitopes were evaluated for their antigenicity score, physicochemical properties, and potential to induce the production of immunoglobulins (IgG, IgA, and IgE). Two epitopes from L1, 1 from L2, and none from E6 or E7 had antigenicity scores of ≥0.4 and were determined to be non-allergenic and nontoxic (Table 3).
CTL and HTL Epitope Population Coverage
The population coverage of CTL and HTL epitopes was simultaneously predicted using the IEDB website. A total of 32 T-cell epitopes were identified, comprising 23 CTL epitopes and 9 HTL epitopes. The estimated worldwide population coverage was 82.63%. The T-cell epitope population coverage for various geographic regions is shown in Figure 2.
Construction of the Multi-Epitope Vaccine
The multi-epitope vaccine constructs consisted of adjuvants, linkers, CTL, HTL, B-cell epitopes, and His-tags. The chosen adjuvant was the 50S L7/L12 ribosomal protein (Locus RL7_MYCTU), which was attached to the N-terminal end of the vaccine and linked via an EAAAK linker. Following this, the CTL, HTL, and B-cell epitopes were combined to form the multi-epitope vaccine, utilizing AAY, GPGPG, and KK linkers, respectively.
In this study, we used 1 CTL, HTL, and B-cell epitope from each HPV 16 and 18 protein to create a multi-epitope vaccine construct (A). Another set of multi-epitope vaccine constructs (B) incorporated all CTL, HTL, and B-cell epitopes from all HPV 16 and 18 proteins (Figure 3). We evaluated the final results of these 2 multi-epitope vaccine constructs based on their antigenicity, allergenicity, and physicochemical properties using VaxiJen v.2.0, antigenPRO, AllerTOP v.2.0, and ProtParam (Table 4).
Prediction of Multi-Epitope Vaccine Affinity towards TAP Molecules
The affinity of multi-epitope vaccine constructs A and B for the TAP molecule was predicted using the Tappred website. In construct A, which consists of 444 amino acids, 30 amino acids (6.8%) exhibited high affinity and 145 amino acids (32.7%) showed intermediate affinity. In contrast, construct B, containing 749 amino acids, had 65 amino acids (8.7%) with high affinity and 243 amino acids (32.4%) with intermediate affinity.
Prediction of the Secondary and Tertiary Structure of the Multi-Epitope Vaccine
The secondary structures of multi-epitope vaccine constructs A and B were predicted using the PSIPRED and SOPMA websites. PSIPRED’s predictions indicated high-quality secondary structures for both constructs. Additionally, SOPMA’s analysis revealed that in multi-epitope vaccine construct A, which consists of 444 amino acids, the composition was as follows: 171 (38.5%) alpha helices, 97 (21.8%) extended strands, 32 (7.2%) beta-turns, and 144 (32.4%) random coils. In contrast, multi-epitope vaccine construct B, comprising 749 amino acids, contained 329 (43.9%) alpha helices, 140 (18.7%) extended strands, 52 (6.9%) beta-turns, and 228 (30.4%) random coils (Figure 4A).
The tertiary structures of constructs A and B were predicted using the Trosetta website. Out of the 5 models generated, the one with the highest template modeling (TM) value was selected for each construct. According to the predictions from Trosetta, model 1 was chosen for both constructs, with TM values of 0.529 and 0.127, respectively. The tertiary structure prediction results for the 2 multi-epitope vaccine constructs were then visualized using the PyMOL application (Figure 4B).
Refinement and Validation of the Multi-Epitope Vaccine’s Tertiary Structure
The tertiary structures of multi-epitope vaccine constructs A and B were refined using the GalaxyRefine website, which generated 5 refined models for each construct. The optimal model for each construct was chosen based on the Ramafavored criteria. According to the prediction results, refined model 5 for construct A and refined model 1 for construct B were selected (Table 5).
The ProSA, ERRAT, and PROCHECK were used to compare the overall quality of both refined models. Additionally, the ProSA results indicated that the Z-scores for constructs A and B were –0.625 and –2.63, respectively (Figure 5A, B). The ERRAT prediction results showed values of 82.370 and 97.903 for constructs A and B, respectively (Figure 5C, D). The Ramachandran plot, generated by PROCHECK, revealed that 88.0% and 97.7% of residues were present in the favored regions for constructs A and B, respectively (Figure 5E, F). Further analysis using the CABSflex website predicted a higher root mean square fluctuation (RMSF) value for the refined multi-epitope vaccine model of construct A (Figure 6).
Disulfide Engineering of the Multi-Epitope Vaccine
Disulfide engineering was conducted to improve the structural stability of the vaccine using the Disulfide by Design 2.0 webserver. The analysis identified 22 and 37 potential pairs of disulfide bonds in vaccine constructs A and B, respectively (Tables S2, S3). Among these, we identified one pair in vaccine A and 8 pairs in vaccine B with an energy lower than 3.0 kcal/mol and chi3 angles ranging from –87 to +97, with each residue of the pairs shown in Figure S1.
Discontinuous B-Cell Epitopes of the 3D Vaccine Structure
The analysis of discontinuous B-cell epitopes in the 3D structure of vaccines using the ElliPro server revealed that vaccine constructs A and B contained 7 and 10 epitopes, respectively (Figure S2). Each epitope scored above 0.5, with the highest scores being 0.886 in vaccine A and 0.988 in vaccine B. Additionally, the lengths of these epitopes ranged from 4 to 48 in vaccine A and from 3 to 100 in vaccine B (Tables 6, 7).
Prediction of the Multi-Epitope Vaccine’s Affinity toward TLR2 and TLR4
The affinity of both multi-epitope vaccine constructs for TLR2 and TLR4 was assessed using the ClusPro webserver. Docking results revealed that multi-epitope vaccine A with TLR2 and TLR4 exhibited the lowest energy values of –1,186.5 and –1,050.7, respectively. These figures suggest a high binding affinity of the vaccine to these receptors (Table 8).
The optimal complex from multi-epitope vaccine constructs A and B, obtained from ClusPro, was analyzed using the HDOCK website to determine the affinity based on the confidence score. A prediction result with a confidence score of ≥0.7 indicated high affinity. The complex with the highest affinity from both constructs was selected and visualized (Figure 7). Subsequent comparisons between the constructs revealed that multi-epitope vaccine construct B exhibited higher affinity towards TLR2 and TLR4 (Table 9).
Selection of Multi-Epitope Vaccine Constructs
The most promising vaccine candidate was selected between multi-epitope vaccine constructs A and B. This process involved comparing the 2 constructs based on several criteria, including antigenicity, allergenicity, toxicity, physicochemical properties, refined tertiary structure (ERRAT and PROCHECK), and affinity towards TLR2 and TLR4 (ClusPro and HDOCK). The comparison revealed that multi-epitope vaccine construct B exhibited higher potential as a vaccine candidate and was therefore chosen for further analysis (Table 10).
Molecular Dynamics Simulation
Molecular dynamics of the multi-epitope vaccine construct B interacting with TLR2 and TLR4 were simulated using iMODs. This tool utilizes normal mode analysis (NMA) to calculate interior coordinates by computing collective functional motions and generating a feasible transition path between 2 homologous structures on protein molecules. NMA is commonly used to assess the interactivity of vaccines or polypeptides with receptors or other target proteins due to its high speed and efficacy, similar to other molecular dynamics tools such as GROMACS and NAMD [9,44,49]. The simulations were conducted to determine the degree of deformability of the complex, as indicated by the eigenvalue (with a low eigenvalue indicating higher deformability). The iMODS results demonstrated that the complex formed between the multi-epitope vaccine construct B and TLR4 had higher deformability, as reflected by a low eigenvalue of 3.67×10-8, indicating easier deformation of the complex (Figure 8). The results in the figure also display favorable β-factor graphs, calculated by comparing NMA to the root mean square, which indicates the calculated uncertainty of each atom.
Codon Optimization and In Silico Cloning of Multi-Epitope Vaccine
The multi-epitope vaccine construct B was processed for codon optimization to maximize protein expression in P. pastoris using the IDT website. The optimized construct had a complexity score of <7, showing high expression and low complexity. In addition, the plasmid vector was prepared using the SnapGene application into pUC19(+), and the DNA sequence of the multi-epitope vaccine construct was inserted between 2 restriction enzyme locations (BamHI and KPN1) (Figure 9).
Prediction of Post-Translational Modifications of the Multi-Epitope Vaccine
Post-translational modification analysis was conducted to identify parts of the vaccine structure that required modifications, including phosphorylation (p), glycosylation (gl), ubiquitination (ub), acetylation (ac), methylation (me), and hydroxylation (hy). The process was carried out using the MusiteDeep website and the total numbers of post-translational modification processes that occurred in multi-epitope vaccine construct B were as follows: glycosylation (14), ubiquitination (13), phosphorylation (12), methylation (4), acetylation (4), and hydroxylation (2).
Immune Simulation
The results from the C-ImmSimm prediction indicated an effective immune response from the vaccine candidate, evidenced by high levels of IgM and increased expression of various immunoglobulins (IgG1+IgG2, IgM, and IgG+IgM). However, the T helper cell population and the state of CTLs decreased following the third injection. Cytokine and interleukin production was observed, alongside an increase in B- and T-cell populations (Figure 10).
Cervical cancer is a malignant tumor arising from abnormal cell changes in the transformation zone, which is located between the endocervix and ectocervix [50]. The severity of this condition can be classified based on cervical intraepithelial neoplasia. The degree of dysplasia includes information on the stage of differentiation, maturation, cell stratification, and nuclear abnormalities [51]. Persistent HPV infection is the primary cause, detectable in 99.7% of cases. Consequently, current guidelines recommend HPV vaccination to avoid HPV infection [52]. Current prophylactic vaccines can prevent more than 95% of HPV infections, underscoring their effectiveness in preventing cervical cancer. However, these vaccines are most effective when administered before infection [53]. In light of these findings, efforts are underway to develop therapeutic vaccines, with the goal of improving treatment outcomes, preventing recurrence, and providing an alternative for inoperable patients [7].
This study focused on the capsid proteins L1 and L2, as well as the oncoproteins E5, E6, and E7, due to their potential to elicit robust humoral and cellular immune responses against HPV infection and subsequent cancer development. The L1 protein, a major component, plays a crucial role in facilitating virus entry into cells through interactions with heparin sulfate and in virus assembly. It also has the ability to spontaneously form VLPs, which are highly immunogenic because they are recognized by B cells [54]. In contrast, the L2 protein is a minor component with a reduced capacity to form VLPs, indicating lower immunogenicity than the L1 protein. However, recent research has highlighted the potential of the L2 protein for vaccine development, owing to its ability to be expressed as a single antigen in bacterial systems. Studies using animal models have also demonstrated the L2 protein’s capability to induce antibody production targeting a broader range of HPV subtypes [55]. Thus, it could potentially strengthen the prophylactic effect of L1-based vaccine while still retaining high immunogenicity.
In this study, the E5, E6, and E7 oncoproteins were utilized to explore the therapeutic properties of a vaccine, leveraging their roles in activating HPV-specific cytotoxic CD8+ T lymphocyte and CD4+ T helper lymphocyte immune responses. E6 and E7, the primary virus-transforming proteins in high-risk HPV, are commonly employed in the development of therapeutic vaccines. The E6 protein promotes the degradation of p53, leading to unchecked cell proliferation and immortality. Concurrently, the E7 protein facilitates the degradation of the pRb protein and the subsequent release of E2F, triggering the transcriptional activation of S-phase genes such as cyclin A and cyclin E, which results in uncontrolled cell division [56]. Additionally, the co-expression of E6 and E7 creates an optimal tumor microenvironment (TME) conducive to cell proliferation, immortalization, and transformation in human epithelial cells. E5 was included due to its ability to evade immune responses by downregulating MHC-I and T helper cell signaling, thereby suppressing antigen presentation and the anti-tumorigenic immune response [57]. However, antigenicity predictions made using the Vaxijen 2.0 website indicated that the oncoproteins selected for developing the multi-epitope vaccine were E6 and E7 from HPV types 16 and 18.
Previous attempts to develop an HPV vaccine primarily utilized capsid proteins, oncoproteins, or a combination of both. The most frequently employed proteins in these efforts were the L1 capsid protein and the E6 and E7 oncoproteins [13,58]. Additionally, the L2 capsid protein became a popular candidate in vaccine development due to the cost-effective production of L2-based vaccines in Escherichia coli, which could address the affordability issues associated with HPV vaccines [59,60]. Research indicated that the combined use of E5, E6, and E7 oncoproteins along with L1 and L2 capsid proteins from highly oncogenic HPV variants had not been previously explored. Consequently, the antigens selected for CTL, HTL, and B-cell epitope prediction in this study were derived from the L1 and L2 capsid proteins and the E6 and E7 oncoproteins of HPV types 16 and 18, representing a novel approach. The predicted epitopes underwent evaluation for antigenicity, allergenicity, and toxicity. In this study, we compared the population coverage of T-cell epitopes, sourced from the IEDB website, with that of the latest prophylactic vaccine, Gardasil-9. The coverage of the selected T-cell epitopes was 82.63%, which was lower than that of the Gardasil-9 vaccine (89.72%) [61]. However, this comparison was not definitive, as the IEDB website only provided predictions for each CTL and HTL epitope individually, rather than for the complete vaccine construct. Additionally, the prediction results were significantly influenced by the allele used [26].
The structure of multi-epitope vaccines is crucial because it influences its physicochemical properties and secondary/tertiary structures. Therefore, the composition and arrangement of epitopes, adjuvants, and linkers within the vaccine are critical. The 50S L7/L12 ribosomal protein adjuvant, derived from Mycobacterium tuberculosis and known for its affinity for TLR4, along with a His-tag, were added at the N-terminal and C-terminal, respectively [13]. Adjuvants are incorporated to enhance the vaccine's immunogenicity and to promote a sustained immune response. The first linker, EAAAK, connects the adjuvant to the CTL epitope and also contributes to the vaccine’s stability [62]. The AAY linker is used to join CTL epitopes and helps reduce junctional immunogenicity. This arrangement facilitates effective antigen presentation and boosts the vaccine’s immunogenicity [63,64]. The GPGPG linker, which connects HTL epitopes, improves the vaccine’s solubility, biological activity, accessibility, and flexibility of adjacent epitopes [65,66].
The KK linker was added to connect B-cell epitopes based on its potential to improve vaccine immunogenicity [65]. This approach was distinct from other studies concentrating solely on T-cell epitopes associated with cellular immune responses. In some instances, such as the multi-epitope vaccine for SARS-CoV-2 developed by Ysrafil et al. [10], B-cell epitopes were included during the construction process. However, GPGPG linkers, rather than KK linkers, were used to connect the nontoxic B and B-cell derived T-cell epitopes. The GPGPG linker is rigid and maintains separation between the epitopes, facilitating their recognition by the host immune system for efficient processing [67]. This compound significantly improved the presentation of antigens to HTLs. Thus, the KK linker was considered ideal due to its ability to improve the presentation of B-cell epitopes to antibodies, while preventing the induction of antibodies to the same amino acid sequence that could result from the binding of 2 peptides [68].
The 2 multi-epitope vaccine constructs (A and B) developed in this study were designed based on their antigenicity, allergenicity, and physicochemical properties (Table 10). The prediction results confirmed that both constructs were antigenic and non-allergenic, suggesting they are capable of eliciting a strong immune response without triggering harmful allergic reactions. The ideal molecular weight for a protein was 40 to 110 kDa, as proteins within this range are easier to purify [69]. The instability index, was calculated to assess protein stability during chemical reactions, with a value of less than 40 signifying stability. The aliphatic index measures the proportion of aliphatic side chains in a protein, with a higher value indicating greater stability [15]. Thermostability was defined as an aliphatic index of 66.5 to 84.3, and a higher aliphatic index showed higher thermostability [70]. The GRAVY index, calculated as the sum of the hydropathy values of all amino acids divided by the number of residues in the sequence, helps determine the hydrophilicity of a protein. A negative GRAVY index indicates a hydrophilic protein [15,70,71]. All physicochemical property parameters used in this study were also selected by other research for evaluating vaccine candidates. These parameters are considered to significantly affect stability, transport, and immunogenicity. Furthermore, the results align with those from previously published in silico-designed vaccine constructs, which have been proven ideal for vaccine development [65,68,72].
In the MHC-I presentation pathway, the TAP molecule functions to translocate antigen degradation products from the cytoplasm into the lumen of the endoplasmic reticulum. This process facilitates the efficient binding of antigenic epitopes to MHC-I molecules [73,74]. Multi-epitope vaccine construct B demonstrated a stronger affinity for the TAP molecule, indicating a higher therapeutic potential compared to construct A. The secondary structures of both constructs showed no significant differences in the PSIPRED and SOPMA predictions. However, a TM score greater than 0.5 for construct A, as predicted by Trosetta, indicated a superior tertiary structure compared to construct B [32]. After determining the tertiary structures of both constructs, a refining process was undertaken to improve their quality and increase their resemblance to the native structure.
The refined model obtained from GalaxyRefine was further validated to assess improvements in the tertiary structure quality of both multi-epitope vaccine constructs. The interpretation of the ProSA prediction results relied on the z-score, with a negative score indicating high quality in the protein's tertiary structure model [35]. The ERRAT results were presented as the percentage of the protein with a calculated error value below the 95% rejection limit. Typically, a good tertiary structure quality yields values around 95% or higher, whereas an average protein quality scores about 91% [34]. The Ramachandran plot, generated from the PROCHECK results, categorized the total number of protein residues into regions labeled as most favored, additionally favored, generously favored, and disallowed. A high-quality tertiary structure had over 90% of its residues in the most favored regions [75]. The flexibility of both refined models was demonstrated by the CABSFLEX results, which were based on the average RMSF value of the refined model; a higher RMSF indicated greater flexibility [36]. According to the data presented in Figures 6 and 7, the overall tertiary structure quality of the refined model from multi-epitope vaccine construct B was superior, making it a more promising candidate for the development of a multi-epitope vaccine.
In this study, TLR2 and TLR4 were selected for molecular docking prediction due to their higher expression levels in cervical cancer compared to other cancers. TLRs are known to regulate the TME in the development of cervical cancer cells caused by HPV infection [76]. Moreover, TLRs play a crucial role in the proliferation and differentiation of Th1 and Th2 cells, which are essential for activating cellular and humoral immune responses, respectively [77]. The interpretation of the ClusPro results was based on the lowest binding energy, which indicated a spontaneous interaction between TLRs and the multi-epitope vaccine construct [41]. The confidence score from HDOCK was utilized to further specify and compare the binding affinities, with scores greater than 0.7 indicating high affinity [43]. According to the results from both predictions, the refined model of the multi-epitope vaccine construct B demonstrated higher affinity towards both TLR2 and TLR4, suggesting a more effective induction of cellular and humoral immune responses. Additionally, vaccine B contained a greater number of discontinuous B-cell epitopes than vaccine A, a critical factor in enhancing the antigen-to-antibody response upon administration. Based on these findings, we predict that our candidate vaccine has the potential to effectively induce a humoral immune response, as it contains 10 discontinuous B-cell epitopes [40].
The refined multi-epitope vaccine construct B was identified as the optimal candidate for vaccine development, based on the predictive findings presented in Table 10. The molecular dynamics results, generated by iMODS, indicated the deformability of the molecular docking, as determined from ClusPro data. These results were analyzed using the eigenvalue; a lower eigenvalue suggested greater deformability. Consequently, the iMODS results indicated that a higher amount of energy was necessary to alter the binding between TLR4 and the refined multi-epitope vaccine construct B [44]. Codon optimization was performed by IDT to improve the expression of the vaccine candidate in P. pastoris. The IDT results, with a score of less than 7, confirmed that the refined multi-epitope vaccine B met the development criteria and was well-suited for expression in the target organism, exhibiting low complexity. The pUC19(+) vector was chosen for in silico cloning due to its compact size (2.7 kb) and its capability to maintain a high copy number (500–700) along with multiple cloning sites [78].
The findings from MusiteDeep regarding post-translational modifications provided insights into the quantity and locations of modifications necessary to enhance the synthesis of the refined multi-epitope vaccine construct B. Figure 10 indicates that glycosylation was the most required modification during this process. This supports the choice of P. pastoris as the target organism, given its capacity for efficient glycosylation. This modification process is crucial as it improves protein folding, which in turn enhances the solubility, stability, and biological activity of the vaccine construct [79].
The immune simulation results depicted in Figure 10 illustrate the progression of various immunological parameters, including levels of immunoglobulins, B cells, CTLs, T helper cells, cytokines, and interleukins, following 3 injections. The data indicate a consistent increase in the concentrations of immunoglobulins (IgM, IgG1, and IgG2) after each injection (Figure 10A). Each subtype of immunoglobulin plays a crucial role in the immune response against viruses and tumors. Specifically, an increase in IgM concentration directly contributes to the cytotoxic effects on the early development of tumor cells by activating the complement system. Additionally, elevated levels of IgG1 and IgG2 promote antitumor activity through the induction of polymorphonuclear cells [80,81]. However, following the third vaccine injection, there was a notable decline in the concentrations of all immunoglobulin subtypes, except for IgM, which persisted for approximately 1 year (Figure 10B). These observations underscore the vaccine's potential to confer protective effects with broad cross-reactivity against the virus [82].
The immune simulation for the T cell population revealed an increased concentration of HTLs following the first 2 injections. However, the concentration of HTLs was lower after the third injection. Subsequently, the total T cell population progressively declined until stabilizing around day 150. Additionally, a higher ratio of memory HTLs further underscored the vaccine’s potential to strengthen the immune response against secondary infections. In comparison to HTLs, the CTL population exhibited a similar pattern of development during and after the third injection. A significant rise in active CTLs demonstrated the vaccine’s capacity to elicit and maintain an effective cellular immune response with each injection (Figure 10D). These findings were corroborated by a marked increase in IFN-γ, relative to other evaluated cytokines (IL-4 and IL-10), following the first injection. When comparing immune simulation results with those from the SARS-CoV-2 multi-epitope vaccine study by Safavi et al. [65], it was found that both induced comparable immune responses in terms of total populations of B cells, HTLs, and IFN-γ production. However, despite identical amounts of antigen present at each injection, this vaccine induced higher levels of IgM, IgG1, and IgG2 after the third injection [83].
The immune simulations revealed that the designed vaccine construct could effectively induce the appropriate immune response against HPV infection and the development of cervical cancer. However, the process of developing a cancer vaccine proved to be more complex, with most candidates being rejected during preclinical and early clinical development stages. Additionally, fewer than 1 in 15 candidates that progressed to Phase II received approval. This low approval rate was attributed to several factors: a lack of understanding of protection correlates, the use of inappropriate animal models for predicting human responses, the complex dynamics and responses of the human immune system to antigens, and the interactions and effects among various combined components. The complexity of vaccine development may explain the scarcity of in silico models in this field. Challenges such as the variability of pathogens and tumors, immunological responses, antigen selection, and memory of the response continue to pose significant hurdles. These challenges are compounded by genetic factors, age, disease status, and other variables, all of which contributed to inconsistent results. Therefore, given these complexities, employing a sophisticated immunoinformatics approach necessitated an experimental study to precisely evaluate the therapeutic efficacy of the designed vaccine [68,84].
In conclusion, the multi-epitope vaccine construct developed in this study has provided crucial insights for the future development of both prophylactic and therapeutic vaccines against HPV. Furthermore, this vaccine is ideal for further development, as it has been shown to effectively induce both cellular and humoral immune responses. Additionally, immunoinformatics approaches allow the development of a vaccine that incorporates selected adjuvants and antigens. This would result in a vaccine that is antigenic, non-allergenic, and nontoxic, with optimal physicochemical and structural properties.
• Cervical cancer ranks as the fourth most common cancer worldwide, with approximately 660,000 cases reported and 350,000 deaths in 2022.
• Persistent infections with high-risk human papillomavirus (HPV) types, primarily HPV 16 and HPV 18, are a major cause of cervical cancer globally.
• An HPV vaccination program utilizing available prophylactic vaccines is the most effective strategy for preventing HPV infection. However, this approach has proven ineffective for therapeutic purposes.
• Advancements in immunoinformatics technology have enabled various studies to utilize genomic information and computer analysis in developing multi-epitope prophylactic and therapeutic vaccines.
Supplementary data are available at https://doi.org/10.24171/j.phrp.2024.0013.
Table S1.
HPV 16 & 18 protein antigenicity and physicochemical properties.
j-phrp-2024-0013-Supplementary-Table-1.pdf
Table S2.
Disulfide engineering of the 3-dimensional structure of vaccine construct A.
j-phrp-2024-0013-Supplementary-Table-2.pdf
Table S3.
Disulfide engineering of the 3-dimensional structure of vaccine construct B.
j-phrp-2024-0013-Supplementary-Table-3.pdf
Figure S1.
Disulfide engineering of the vaccine. (A) Vaccine A that has 1 pair. (B) Vaccine B with 8 pairs
j-phrp-2024-0013-Supplementary-Figure-1.pdf
Figure S2.
Discontinuous B-cell epitopes of the 3-dimensional structure of vaccines. (A) Vaccine A with 7 epitopes. (B) Vaccine B with 10 epitopes
j-phrp-2024-0013-Supplementary-Figure-2.pdf

Ethics Approval

Not applicable.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Funding

None.

Availability of Data

The datasets are not publicly available but are available from the corresponding author upon reasonable request.

Authors’ Contributions

Conceptualization: NR, SEP, MMar; Data curation: all authors; Formal analysis: NR; Funding acquisition: NR; Investigation: NR; Methodology: NR, SEP, MMah, YY; Project administration: NR, SEP, MMar; Resources: NR; Software: NR; Supervision: NR, SEP, MMah, YY; Validation: NR, SEP, MMar, DFL, MMah; Visualization: NR; Writing–original draft: NR; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Figure 1.
Workflow of the immunoinformatics approach used in this study.
HPV, human papillomavirus; CTL, cytotoxic T cell; HTL, helper T cell; IFN, interferon; IL, interleukin.
Objectives: This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process
Figure 2.
Population coverage.
Objectives: This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process
Figure 3.
The structural arrangement of the multi-epitope vaccine constructs A (top) and B (bottom) using selected CTL, HTL and B-Cell epitopes using specific linkers, AAY (Ala-Ala-Tyr), GPGPG (Gly-Pro-Gly-Pro-Gly), KK (Lys-Lys) linkers, adjuvant and His-tag
HTL, helper T cell; CTL, cytotoxic T cell.
Objectives: This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process
Figure 4.
(A) The secondary structure configuration of the multi-epitope vaccine A (left) and B (right) (B) Visualization of multi-epitope vaccine A (left) and B (right).
HTL, helper T cell; CTL, cytotoxic T cell.
Objectives: This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process
Figure 5.
Validation of the tertiary refined model of the multi-epitope vaccine constructs A and B using the ProSA, ERRAT, and PROCHECK website. (A) Z-score of construct A, (B) Z-score of construct B (C) ERRAT result for construct A, (D) ERRAT result for construct B, (E) PROCHECK result for construct A, (F) PROCHECK result for construct B.
Objectives: This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process
Figure 6.
Flexibility prediction results for the multi-epitope vaccine construct (A) and (B).
Objectives: This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process
Figure 7.
Docked complex of the vaccine construct (shown in yellow) and toll-like receptor (TLR) (shown in brown). (A) Complex between the multi-epitope vaccine construct A and TLR2 (left) and TLR4 (right). (B) Complex between the multi-epitope vaccine construct B and TLR2 (left) and TLR4 (right).
Objectives: This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process
Figure 8.
Molecular dynamics of vaccine B with toll-like receptor (TLR) 4. (A) Deformability. (B) β-factor. (C) Eigenvalue. (D) Variance. (E) Residue index. (F) Atom index.
Objectives: This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process
Figure 9.
In silico cloning of the multi-epitope vaccine construct (shown in red) in pUC19 (+) vector.
Objectives: This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process
Figure 10.
Immune stimulation after 3-dose antigen injections of the multi-epitope vaccine construct. Production of immunoglobulin; detailed subclasses are showed as colored peaks (A). Evolution of B-cell populations (B). T helper cell population over time (C). T-cytotoxic cell populations (D). Production of cytokines and interleukins (E).
Objectives: This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process
Objectives: This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process
Table 1.
Cytotoxic T-cell epitopes of the L1, L2, E6, and E7 proteins of HPV 16 and 18
Table 1.
Virus Protein No. Allele Peptide Percentile score Antigen Allergen Toxic
HPV 16 L1 1 HLA-A*11:01 TTYKNTNFK 0.01 1.3626 Nonallergen Nontoxic
2 HLA-B*35:01 LPDPNKFGF 0.02 0.8886 Nonallergen Nontoxic
HLA-B*07:02 0.16
3 HLA-B*13:01 AQIFNKPYW 0.09 0.7527 Nonallergen Nontoxic
4 HLA-B*50:01 GEHWGKGSP 0.13 0.8590 Nonallergen Nontoxic
L2 1 HLA-B*51:01 IPFGGAYNI 0.01 0.7776 Nonallergen Nontoxic
2 HLA-A*02:01 YLHPSYYML 0.03 0.8733 Nonallergen Nontoxic
HLA-B*13:01 0.16 Nonallergen Nontoxic
3 HLA-A*11:01 RASATQLYK 0.06 0.6897 Nonallergen Nontoxic
E6 1 HLA-B*07:02 RPRKLPQLC 0.09 0.8151 Nonallergen Nontoxic
E7 1 HLA-B*50:01 HEYMLDLQP 0.06 0.5709 Nonallergen Nontoxic
HPV 18 L1 1 HLA-B*13:01 VQLPDPNKF 0.01 1.0850 Nonallergen Nontoxic
HLA-A*23:01 0.13
2 HLA-B*35:01 IPKVSAYQY 0.04 1.0702 Nonallergen Nontoxic
3 HLA-A*02:01 QLFNKPYWL 0.08 1.3159 Nonallergen Nontoxic
HLA-B*13:01 0.12
4 HLA-A*11:01 TVPPSLYIK 0.1 0.4472 Nonallergen Nontoxic
L2 1 HLA-B*35:01 SPIAPSPEY 0.01 0.6346 Nonallergen Nontoxic
HLA-B*07:02 0.17
2 HLA-B*50:01 IELQPLVSA 0.01 0.4261 Nonallergen Nontoxic
3 HLA-B*07:02 VPKVEGTTL 0.02 0.4217 Nonallergen Nontoxic
HLA-B*51:01 0.16
4 HLA-B*13:01 TLADKILQW 0.08 0.7527 Nonallergen Nontoxic
5 HLA-A*23:01 VYTGPDITL 0.09 0.6565 Nonallergen Nontoxic
HLA-A*24:02 0.06
E6 1 HLA-A*11:01 SVYGDTLEK 0.01 0.8812 Nonallergen Nontoxic
2 HLA-A*24:02 VYGDTLEKL 0.02 1.1863 Nonallergen Nontoxic
HLA-A*23:01 0.04 1.1863 Nonallergen Nontoxic
3 HLA-B*40:01 LEKLTNTGL 0.15 1.0422 Nonallergen Nontoxic
E7 1 HLA-A*02:01 TLQDIVLHL 0.01 0.4750 Nonallergen Nontoxic

HPV, human papillomavirus.

Table 2.
Helper T-cell epitopes of the HPV proteins 16 and 18
Table 2.
Virus Protein No. Allele Peptide Percentile score Antigen Allergen Toxic IFN-γ IL-4 IL-10
HPV 16 L1 1 HLA-DRB1*03:04 KVVSTDEYVART 1.80 0.8909 Nonallergen Nontoxic + + –
L2 1 HLA-DRB1*12:09 DIVALHRPALTS 0.10 0.5593 Nonallergen Nontoxic – + –
HLA-DRB1*12:02 0.20
2 HLA-DRB1*04:07 DTFIVSTNPNTV 0.20 0.9235 Nonallergen Nontoxic + + –
E6 1 HLA-DRB1*12:02 DLCIVYRDGNPY 2 0.5459 Nonallergen Nontoxic – + –
HPV 18 L2 1 HLA-DRB1*12:09 MDIIRLHRPALT 0.10 0.6840 Nonallergen Nontoxic + + –
2 HLA-DRB1*03:04 TTLTFDPRSDVP 0.19 0.5652 Nonallergen Nontoxic – + –
E6 1 HLA-DRB1*08:03 0.92 1.4681 Nonallergen Nontoxic – + +
HLA-DRB1*12:02 SRIRELRHYSDS 1.10
HLA-DRB1*12:09 1.10
2 HLA-DRB1*12:09 EKLRHLNEKRRF 0.50 0.9669 Nonallergen Nontoxic – – +
E7 1 HLA-DRB1*15:03 ARIELVVESSAD 0.68 0.8041 Nonallergen Nontoxic – + –

HPV, human papillomavirus; IFN, interferon; IL, interleukin.

Table 3.
B-cell epitopes of the HPV proteins 16 and 18
Table 3.
Virus Protein No. Peptide Score Antigen Allergen Toxic Immunoglobulin
HPV 16 L1 1 EEYDLQFIFQLCKITL 0.819 0.6976 Nonallergen Nontoxic IgA
2 IFQLCKITLTADVMTY 0.821 0.4326 Nonallergen Nontoxic IgA
L2 1 LSTIDPAEEIELQTIT 0.859 0.8839 Nonallergen Nontoxic IgA
E6 1 QLCTELQTTIHDIILE 0.819 0.5079 Nonallergen Nontoxic IgA
2 HDIILECVYCKQQLLR 0.862 0.6540 Nonallergen Nontoxic IgA
E7 1 LCVQSTHVDIRTLEDL 0.895 0.4476 Nonallergen Nontoxic IgG
2 HDIILECVYCKQQLLR 0.862 0.6540 Nonallergen Nontoxic IgG
HPV 18 L1 1 EEYDLQFIFQLCTITL 0.816 0.5530 Nonallergen Nontoxic IgG
2 RHVEEYDLQFIFQLCT 0.84 0.6073 Nonallergen Nontoxic IgA
L2 1 VRFSRLGQRATMFTRS 0.917 1.0957 Nonallergen Nontoxic IgG

HPV, human papillomavirus; Ig, immunoglobulin.

Table 4.
Evaluation of the antigenicity, allergenicity, and physicochemical properties of the vaccine
Table 4.
Construct Antigenicity
 Allergenicity Molecular weight Instability index Aliphatic index GRAVY index
VaxiJen v.2.0 Antigen PRO
Vaccine A (444 aa) 0.5785 (antigenic) 0.6880 (antigenic) Nonallergen 48,628.93 (40–110) 37.20 (stable) 89.30 (thermostable) –0.248 (hydrophilic)
Vaccine B (749 aa) 0.5504 (antigenic) 0.6565 (antigenic) Nonallergen 82,895.36 (40–110) 36.15 (stable) 88.56 (thermostable) –0.199 (hydrophilic)

GRAVY, grand average of hydropathicity.

Table 5.
Results of model refinement
Table 5.
Construct Model GDT-HA RMSD MolProbity Clash score Poor rotamers Rama favored
A Initial 1.0000 0.000 2.066 8.5 0.8 87.6
MODEL 1 0.9443 0.445 1.944 9.8 0.3 93.4
MODEL 2 0.9358 0.460 1.981 10.8 0.8 93.4
MODEL 3 0.9319 0.482 1.957 8.8 0.8 92.1
MODEL 4 0.9426 0.454 2.048 11.1 1.1 93.0
MODEL 5 0.9324 0.491 1.939 10.3 0.6 93.9
B Initial 1.0000 0.000 0.856 1.3 0.0 99.1
MODEL 1 0.9680 0.365 1.143 3.5 0.0 99.5
MODEL 2 0.9756 0.341 1.174 3.9 0.0 99.3
MODEL 3 0.9680 0.360 1.151 3.6 0.2 99.5
MODEL 4 0.9780 0.348 1.110 3.2 0.0 99.3
MODEL 5 0.9640 0.364 1.102 3.1 0.3 99.5
Table 6.
Discontinuous B-cell epitopes of vaccine construct A
Table 6.
No. Residues No. of residues Score
1 A:M1, A:A2, A:K3, A:L4, A:S5, A:T6, A:D7, A:E8, A:L9, A:L10, A:D11, A:A12, A:F13, A:K14, A:E15, A:M16, A:T17, A:L18, A:L19, A:E20, A:L21, A:S22, A:D23, A:F24, A:V25, A:K26, A:K27, A:F28, A:E29, A:E30, A:T31, A:F32, A:V34, A:T35, A:A36, A:A37, A:A38, A:P39, A:V40, A:A41, A:V42, A:A43, A:A44, A:A45, A:G46, A:A47, A:A48, A:P49 48 0.886
2 A:V65, A:I66, A:L67, A:E68, A:A69, A:A70, A:G71, A:D72, A:K73, A:K74, A:I75, A:G76, A:V77, A:I78, A:K79, A:V80, A:V81, A:R82, A:E83, A:I84, A:V85, A:S86, A:G87, A:L88, A:G89, A:L90, A:K91, A:E92, A:A93, A:K94, A:D95, A:L96, A:V97, A:D98, A:G99, A:A100, A:P101, A:K102, A:P103, A:L104, A:L105, A:E106, A:K107, A:V108, A:A109, A:A112, A:A113, A:D114, A:E115, A:A116, A:K117, A:A118, A:K119, A:L120, A:E121, A:A122, A:A123, A:G124, A:A125, A:T126, A:V127, A:T128 62 0.798
3 A:N140, A:T141, A:N142, A:F143, A:K144, A:A145, A:A146, A:I148, A:P149, A:F150, A:G151, A:Q166, A:M175, A:D177, A:L178, A:Q179, A:P180, A:A181, A:A182, A:Y183, A:V184, A:Q185, A:L186, A:P187, A:D188, A:P189, A:N190, A:K191, A:F192, A:S208, A:V209, A:Y210, A:G211, A:D212, A:T213, A:L214, A:E215, A:K216, A:A217, A:A218, A:Y219, A:T220, A:Q222, A:D223, A:I224, A:V225, A:L226, A:H227, A:L228, A:G229, A:P230, A:G231, A:P232, A:G233, A:K234, A:V235, A:V236, A:S237, A:T238, A:D239, A:Y241, A:V242, A:A243, A:T245, A:G246, A:P247, A:G248, A:P249, A:G250, A:I252, A:G276, A:N277, A:P278, A:Y279, A:G297, A:P298, A:G299, A:P300, A:G301, A:E302, A:K303, A:L304, A:R305, A:H306, A:L307, A:E309, A:K310, A:R311, A:F313, A:G314, A:P315, A:G316, A:P317, A:G318, A:A319, A:R320 96 0.621
4 A:P161, A:K163, A:L164, A:P165, A:L167, A:C168, A:G280, A:P281, A:G282, A:P283, A:G284, A:M285, A:D286, A:I287, A:I288 15 0.608
5 A:A193, A:A194, A:Y195, A:S196 4 0.582
6 A:I396, A:T398, A:L399, A:E400, A:D401, A:L402, A:K403 7 0.574
7 A:H393, A:V394, A:D395, A:R397 4 0.501
Table 7.
Discontinuous B-cell epitopes of vaccine construct B
Table 7.
No. Residues No. of residues Score
1 A:H745, A:H746, A:H747 3 0.988
2 A:K725, A:V726, A:R727, A:F728, A:S729, A:R730, A:L731, A:G732, A:Q733, A:R734, A:A735, A:T736, A:M737, A:F738, A:T739, A:R740, A:S741, A:K742 18 0.981
3 A:M1, A:A2, A:K3, A:L4, A:S5, A:T6, A:D7, A:E8, A:L9, A:L10, A:D11, A:A12, A:F13, A:K14, A:E15, A:M16, A:T17, A:L18, A:L19, A:E20, A:L21, A:S22, A:D23, A:F24, A:V25, A:K26, A:K27, A:F28, A:E29, A:E30, A:T31, A:F32 32 0.908
4 A:E690, A:E691, A:Y692, A:D693, A:L694, A:Q695, A:F696, A:I697, A:F698, A:Q699, A:L700, A:C701, A:T702, A:I703, A:T704, A:L705, A:K706, A:K707, A:R708, A:H709, A:V710, A:E711, A:E712, A:Y713, A:D714, A:L715, A:Q716, A:F717, A:I718, A:F719, A:Q720, A:L721, A:C722, A:T723, A:K724 35 0.849
5 A:L590, A:T591, A:A592, A:D593, A:V594, A:M595, A:T596, A:Y597, A:K598, A:K599, A:L600, A:S601, A:T602, A:I603, A:D604, A:P605, A:A606, A:E607, A:E608, A:I609, A:E610, A:L611, A:Q612, A:T613, A:I614, A:T615, A:K616, A:K617, A:Q618, A:L619, A:C620, A:T621, A:E622, A:L623, A:Q624, A:T625, A:T626, A:I627, A:H628, A:D629, A:I630, A:I631, A:L632, A:E633, A:K634, A:K635, A:H636, A:D637, A:I638, A:I639, A:L640, A:E641, A:C642, A:V643, A:Y644, A:C645, A:K646, A:Q647, A:Q648, A:L649, A:L650, A:R651, A:K652, A:K653, A:L654, A:C655, A:V656, A:Q657, A:S658, A:T659, A:H660, A:V661, A:D662, A:I663, A:R664, A:T665, A:L666, A:E667, A:D668, A:L669, A:K670, A:K671, A:H672, A:D673, A:I674, A:I675, A:L676, A:E677, A:C678, A:V679, A:Y680, A:C681, A:K682, A:Q683, A:Q684, A:L685, A:L686, A:R687, A:K688, A:K689 100 0.779
6 A:K144, A:A145, A:A146, A:Y147, A:L148, A:P149, A:D150, A:P151, A:N152, A:K153, A:F154, A:G155, A:F156, A:A157, A:A158, A:Y159, A:A160, A:Q161, A:I162, A:F163, A:N164, A:K165, A:P166, A:Y167, A:W168, A:A169, A:A170, A:Y171, A:G172, A:E173, A:H174, A:W175, A:G176, A:K177, A:G178, A:S179, A:P180, A:A181, A:A182, A:Y183, A:I184, A:P185, A:F186, A:G187, A:G188, A:A189, A:Y190, A:N191, A:I192, A:A193, A:A194, A:Y195, A:Y196, A:L197, A:H198, A:P199, A:S200, A:Y201, A:Y202, A:M203, A:L204, A:A205, A:A206, A:R208 64 0.691
7 A:L258, A:P259, A:D260, A:P261, A:N262, A:K263, A:F264, A:A265, A:A266, A:Y267, A:I268, A:P269, A:K270, A:V271, A:S272, A:A273, A:Y274, A:Q275, A:Y276, A:A277, A:A278, A:Y279, A:Q280, A:L281, A:F282, A:K284, A:P285, A:Y286, A:W287, A:L288, A:A289, A:A290, A:Y291, A:T292, A:V293, A:P294, A:P295, A:S296, A:L297, A:Y298, A:I299, A:K300 42 0.671
8 A:K107, A:E111, A:A112, A:A113, A:D114, A:E115, A:A116, A:K117, A:A118, A:K119, A:L120, A:E121, A:A122, A:A123, A:G124, A:A125, A:T126, A:V127, A:T128, A:V129, A:K130 21 0.65
9 A:V34, A:T35, A:A36, A:A37, A:A38, A:P39, A:V40, A:A41, A:V42, A:A43, A:A44, A:A45, A:G46, A:A47, A:A48, A:P49, A:A50, A:G51, A:A52, A:A53, A:V54, A:E55, A:A56, A:A57, A:E58, A:E59, A:Q60, A:S61, A:E62, A:F63, A:D64, A:V65, A:I66, A:L67, A:E68, A:A69, A:A70, A:G71, A:D72, A:K73, A:K74, A:I75, A:G76, A:V77 44 0.578
10 A:E131, A:A132, A:A133, A:A134, A:K135, A:T136 6 0.52
Table 8.
ClusPro prediction result for molecular docking between the multi-epitope vaccine construct and TLR2 and TLR4
Table 8.
Construct Receptor Lowest energy
Vaccine A TLR2 –1,186.5
TLR4 –1,050.7
Vaccine B TLR2 –1,352.6
TLR4 –1,540.5

TLR, toll-like receptor.

Table 9.
HDOCK prediction result for molecular docking between the multi-epitope vaccine construct and TLR2 and TLR4
Table 9.
Construct Receptor Docking score Confidence score
Vaccine A TLR2 –270.87 0.9181
TLR4 –284.82 0.9368
Vaccine B TLR2 –316.31 0.9653
TLR4 –259.23 0.8989

TLR, toll-like receptor.

Table 10.
Comparison between multi-epitope vaccine constructs A and B
Table 10.
Construct Antigen Allergen Toxic Physicochemical properties Refined tertiary structure Affinity toward TLR2 Affinity toward TLR4
Vaccine A Antigenic Nonallergen Nontoxic 1. Molecular weight: 48,628.93 Da 1. ERRAT: 82,370 HDOCK: 0.9554 HDOCK: 0.9417
2. Theoretical pI: 6.72 2. PROCHECK: 88.0%
3. Estimated half-life:
 ● Mammalian reticulocyte (in vitro): 30 h
 ● Yeast (in vivo): >20 h
 ● Escherichia coli (in vivo): >10 h
4. Instability index: 37.20
5. Aliphatic index: 89.30
6. GRAVY index: –0.248
Vaccine B Antigenic Nonallergen nontoxic 1. Molecular weight: 82,895.36 Da 1. ERRAT: 97,903a) HDOCK: 0.9677a) HDOCK: 0.9870a)
2. Theoretical pI: 8.02 2. PROCHECK: 97.7%a)
3. Estimated half-life:
 ● Mammalian reticulocyte (in vitro): 30 h
 ● Yeast (in vivo): >20 h
 ● Escherichia coli (in vivo): >10 h
4. Instability index: 36.15
5. Aliphatic index: 88.56
6. GRAVY index: –0.199

TLR, toll-like receptor; GRAVY, grand average of hydropathicity.

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Immunoinformatics approach for design novel multi-epitope prophylactic and therapeutic vaccine based on capsid proteins L1 and L2 and oncoproteins E6 and E7 of human papillomavirus 16 and human papillomavirus 18 against cervical cancer
Osong Public Health Res Perspect. 2024;15(4):307-328.   Published online July 23, 2024
DOI: https://doi.org/10.24171/j.phrp.2024.0013
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Immunoinformatics approach for design novel multi-epitope prophylactic and therapeutic vaccine based on capsid proteins L1 and L2 and oncoproteins E6 and E7 of human papillomavirus 16 and human papillomavirus 18 against cervical cancer
Osong Public Health Res Perspect. 2024;15(4):307-328.   Published online July 23, 2024
DOI: https://doi.org/10.24171/j.phrp.2024.0013
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