Designing of an mRNA vaccine against high-risk human papillomavirus targeting the E6 and E7 oncoproteins exploiting immunoinformatics and dynamic simulation

Abstract

Human papillomavirus 16 and human papillomavirus 18 have been associated with different life-threatening cancers, including cervical, lung, penal, vulval, vaginal, anal, and oropharyngeal cancers, while cervical cancer is the most prominent one. Several research studies have suggested that the oncoproteins E6 and E7 are the leading cause of cancers associated with the human papillomavirus infection. Therefore, we developed two mRNA vaccines (V1 and V2) targeting these oncoproteins. We used several bioinformatics tools to predict helper T lymphocyte, cytotoxic T lymphocyte, and B-cell epitopes derived from the proteins and assessed their antigenicity, allergenicity, and toxicity. Both vaccines were constructed using selected epitopes, linkers, and adjuvants. After that, the vaccines were applied for physicochemical properties, secondary and tertiary structure predictions, and subsequent docking and simulation analyses. Accordingly, vaccine 1 (V1) and vaccine 2 (V2) showed better hydrophilicity with the grand average hydropathicity score of -0.811 and -0.648, respectively. The secondary and tertiary structures of the vaccines were also deemed satisfactory, with high stability indicated by the Ramachandran plot (V1:94.5% and V2:87.1%) and Z scores (V1: -5.15 and V2: -4.1). Docking analysis revealed substantial affinity of the vaccines towards the toll-like receptor-2 (V1: -1159.3, V2: -1246.3) and toll-like receptor-4 (V1: -1109.3, V2: -1244.8) receptors. Molecular dynamic simulation validated structural integrity and indicated varying stability throughout the simulation. Codon optimization showed significant expression of the vaccines (V1:51.88% and V2:51.63%) in E. coli vectors. Furthermore, regarding immune stimulation, the vaccines elicited significant B-cell and T-cell responses, including sustained adaptive and innate immune responses. Finally, thermodynamic predictions indicated stable mRNA structures of the vaccines (V1: -502.60 kcal/mol and V2: -450.90 kcal/mol). The proposed vaccines designed effectively targeting human papillomavirus oncoproteins have demonstrated promising results via robust immune responses, suggesting their suitability for further clinical advancement, including in vitro and in vivo experiments.

Introduction

Human papillomaviruses (HPVs) are small double-stranded DNA viruses belonging to the papillomaviridae family that cause infections in human mucous epithelia. It is the most frequent sexually transmitted disease (STD) worldwide [1]. HPV has been associated with 5% of cancer cases, affecting various parts of the human body, including the lung, cervix, penis, vulva, vagina, anus, and oropharynx, and thereby associated with cervical, penal, vulval, vaginal, anal, and oropharyngeal cancer [2–5]. Additionally, there is evidence suggesting the involvement of HPV in conjunctival malignancies [3]. Cervical cancer is the most significant form of cancer caused by human HPV; more than 95% of cervical cancers have been associated with HPV [2]. According to the World Health Organization (WHO), it holds the 14th position among all types of cancers and is the 4th most prevalent cancer among women globally [6, 7]. Annually, an estimated 500,000 cases of cervical cancer have been identified, where nearly half of which are fatal [8]. The prevalence of STDs among young women typically ranges from around 30%, but in some demographics, it may reach as high as 64%. Interestingly, 50% of young women had contracted a cervical HPV infection within four years of their first sexual contact [9]. There is an enormous variation in the burden of cervical disease, with more than 85% of cases and fatalities occurring in countries with low and moderate incomes [10]. These disparities are mostly attributable to inadequate cervical cancer screening and treatment strategies [11]. Over the last several decades, a combination of surgery, radiation therapy, chemotherapy, and, more recently, immunotherapy have become the mainstay of cervical cancer treatment. Currently, the United States Food and Drug Administration (US-FDA) has approved three immunotherapy alternatives for treating cervical cancer, including Bevacizumab, Tisotumab vedotin, and Pembrolizumab [12]. However, these strategies lack efficacy, are highly costly, and, more importantly, have adverse effects, including fever, headache, skin rashes or itching, abdominal pain, nausea, cough, diarrhea, constipation, fatigue, loss of appetite, peripheral neuropathy, and hypothyroidism [13–15]. Hence, vaccination is the only practical approach for minimizing the risks of developing cervical cancer as well as for its prevention.

Meanwhile, up to 70% of oropharyngeal cancers have been attributed to HPV, and the incidence of oral squamous cell carcinomas (OSCCs) in both Europe and North America is on an upward trajectory [16]. Oropharyngeal cancers now surpass cervical cancer events in the United States and are the leading cancer associated with HPV [17]. Although there is still uncertainty about the involvement of HPV in oropharyngeal cancers, it is possible to identify HPV in a specific subset of these malignancies [18, 19]. There was a statistically significant association between increasing levels of anti-HPV-16 IgG antibody and oropharyngeal cancer, based on research conducted in South Africa [20]. The yearly worldwide frequency of squamous cell carcinoma of the anus caused by HPV was estimated to be 30,416 cases, while women accounted for two-thirds of these cases [21].

Currently, more than 200 strains of HPV have been identified, which belong to 29 genera, with most of them infecting humans [22]. Around half of these strains target the epithelium of the female genitalia. However, there are two types of genital papillomaviruses: low-risk and high-risk. Low-risk HPVs cause benign lesions, while high-risk HPVs can develop malignant lesions [23, 24]. Notably, many cervical cancers are found to have DNA from high-risk types of HPV [25, 26], and HPV 16 and HPV 18 have been reported as the leading cause of cervical cancer. The two transforming proteins (E6 and E7) encoded by these strains, function through their associations with the tumor suppressor proteins p53 and retinoblastoma (Rb), respectively [27]. The continual expression of E6 and E7 in tumors and derived cell lines, even years after the initial immortalizing events, reveals their crucial role in sustaining the altered phenotype [27, 28]. The immortalization of primary human keratinocytes requires the symbiotic actions of E6 and E7, with E6 blocking cell survival pathways and E7 encouraging cell proliferation [28, 29]. As part of the ubiquitin proteolytic process, E6 interacts with an accessory protein called E6-AP, allowing it to degrade p53 [30, 31]. On the other hand, the E7 proteins from high-risk HPVs have an affinity for Rb [32, 33] and other pocket proteins like p107 and p130 [34, 35], resulting in altered functions of these cell cycle regulators. While low-risk E7 proteins bind Rb with significantly lower affinities, low-risk E6 proteins cannot abolish p53 functions [36]. It has also been suggested that p53 plays a crucial role in preserving the stability of the genome. However, the E6 of the high-risk types inhibits all of these functions of p53, thus leading to uncontrolled cell cycle and cell immortalization [37, 38]. The role of the Rb gene, along with p53, is crucial in regulating the cell cycle and its checkpoints. During the S phase, the protein Rb forms a complex with the transcription factor E2F, thus phosphorylating itself and causing E2F to be released, which is necessary for DNA synthesis [39]. However, the interaction between E7 and Rb prevents the binding of Rb to E2F, leading to persistent activation of E2F and subsequent expression of the associated genes [40].

There are currently six authorized alternatives in the landscape of HPV vaccines, all of which fall under the category of virus-like particle (VLP) vaccines, including Cervarix^®, Cecolin^®, WalrinvaxV, GARDASIL^®, Cervavac^®, and GARDASIL9^® [41]. Nevertheless, although VLPs imitate viral structures, they do not possess genetic material for replication. This could result in less effective immune responses, especially in susceptible groups such as the elderly or those with weakened immune systems [41]. Considering these factors, mRNA-based therapeutics may address these challenges while improving the management of infectious diseases and cancer [42, 43]. Non-infectious and non-integrating mRNA vaccines offer advantages over conventional immunizations. Several mRNA vaccines have been designed targeting HPV, which also showed promising outcomes in clinical and pre-clinical trials [44–47].

With the advent of immunoinformatics tools, computational approaches for vaccine design have become more widespread in the post-genomics era [48, 49]. Immunoinformatics is a revolutionary strategy that offers many advantages for vaccine development [49–51]. It utilizes machine learning algorithms to promptly analyze substantial amount of structural, genomic, and proteomic data to identify prospective vaccine candidates [52, 53]. This approach facilitates the prediction of immune-stimulating epitopes and antigenic targets, optimizing vaccine component selection for enhanced safety and efficacy [49, 54]. This precision-oriented approach reduces the need for rigorous and expensive clinical trials, exploiting resources and expediting vaccine production schedules [52, 55]. Yet, a few hurdles need to be explored before applying immunoinformatics in vaccine manufacturing. Immunoinformatics depends on extensive data, yet data quality and quantity discrepancies may affect the precision of vaccine target predictions. Moreover, predicted targets may not reliably elicit intended immune responses or may provoke adverse effects [55–57]. Leveraging immunoinformatics in vaccine development requires interdisciplinary alliance, technological advancements, and continual research to address the challenges [53]. This research used immunoinformatics approaches to identify prospective vaccine targets from the proteome of HPV.

This work focuses on the immunoinformatics for developing mRNA vaccines to address the urgent public need for HPV vaccines. Two mRNA vaccines were designed against high-risk type HPVs (HPV 16 and HPV 18) targeting the E6 and E7 oncoproteins. Here, we utilized advanced immunoinformatics approaches to predict potential epitopes, including helper T lymphocyte (HTL), cytotoxic T lymphocyte (CTL), and B-cell epitopes derived from the E6 and E7 oncoproteins. After being assessed for antigenicity, allergenicity, and toxicity, the final vaccine was constructed using highly prioritized epitopes. Further physicochemical profiling, structural prediction, validation, molecular docking, simulations, in silico cloning, and immune simulations were performed to evaluate the vaccine’s stability, functionality, and suitability.

Methods

Sequence retrieval

All available amino acid sequences of the E6 and E7 proteins of HPV 16 and HPV 18 were retrieved from the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/) protein database in FASTA format [58]. The protein sequences were then aligned with MEGA X software to generate a consensus sequence. The consensus sequences of the proteins E6 and E7 were then applied for subsequent vaccine construction.

Helper T lymphocyte (HTL) binding epitope prediction

HTL binding or histocompatibility complex-I (MHC-I) epitopes of the selected proteins (E6 and E7) were predicted by the Immune Epitope Database (IEDB) [59, 60] and Net-MHC 4.0 (http://www.cbs.dtu.dk/services/NetMHC/) [61, 62] server. The IEDB is a comprehensive database that contains information on antibodies, B- and T-cell epitopes, MHC molecules, and MHC binding ligands in humans and animal species, including primates and mice [59, 62–74]. HTL epitope binding was forecasted with the IEDB’s recommended NetMHCpan 4.1 EL prediction method, utilizing a low percentile rank (<1.00) as the selection criteria. A lower percentile score indicates more affinity for the epitopes [75, 76]. The human allele reference sets were used to predict HTL binding. By analyzing ligand data and MHC-I binding affinities, the NetMHC 4.0 server can foretell how MHC-I will interact with peptides [59, 61, 73]. The server’s default parameter was employed in subsequent HTL epitope prediction. Finally, the selected epitopes were screened for physiological features including antigenicity, allergenicity, and toxicity by VaxiJen 2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) [77], AllerTOP v. 2.0 (https://www.ddg-pharmfac.net/AllerTOP/index.html) [78] and ToxinPred (https://webs.iiitd.edu.in/raghava/toxinpred/algo.php) [79] server, respectively. All parameters relating to the servers were left in their default settings.

Cytotoxic T lymphocyte (CTL) binding epitope prediction

CTL binding or histocompatibility complex-II (MHC-II) epitopes of the selected proteins (E6 and E7) were predicted using the IEDB [59, 60] and NetMHCIIpan 4.0 server (http://www.cbs.dtu.dk/services/NetMHCIIpan/) [60, 80]. CTL epitope binding was predicted via the IEDB’s NetMHCIIpan 4.1 EL prediction method, employing a low percentile rank cutoff (<1.00), with human allele reference sets used for CTL binding prediction [75, 76, 81]. The NetMHCIIpan 4.0 server (http://www.cbs.dtu.dk/services/NetMHCIIpan/) utilizes the Artificial Neural Networks (ANNs) for the MHC-II binding epitope prediction [60, 80]. The default parameter of the server was utilized in the prediction of CTL epitopes. Following epitope selection, the epitopes were further assessed for antigenicity, allergenicity, and toxicity by VaxiJen 2.0 [77], AllerTOP v. 2.0 [78], and ToxinPred server, respectively [79]. Finally, the epitopes were assessed for interferon-gamma (IFN-γ) induction by the IFNepitope server (https://webs.iiitd.edu.in/raghava/ifnepitope/design.php). We used the IFNepitope server’s Design Module and Hybrid (Motif + SVM) prediction technique to determine whether the expected HTL epitopes might induce IFN-gamma [75, 76]. The epitopes that passed all the parameters were then proceeded for the vaccine construction.

Linear B-cell epitope prediction

The linear B-cell epitopes of the protein of interest (E6 and E7) were predicted by the IEDB and the ABCpred (https://webs.iiitd.edu.in/raghava/abcpred/ABC_submission.html) server [82]. The IEDB server uses Hidden Markov Model (HMM) approach to estimate linear B-cell epitopes based on the sequence properties of the antigen [83]. Conversely, the ABCpred server utilizes an HMM and a propensity scale approach to compute the linear B-cell epitopes [82]. During the prediction, all the parameters were kept as default respective to the servers. The selected epitopes were, therefore, screened for antigenic, allergenic, and toxigenic characteristics by VaxiJen 2.0 [77], AllerTOP v. 2.0 [78], and ToxinPred server, respectively [79].

Vaccine mapping

The mapping of vaccine 1 (V1) and vaccine 2 (V2) was carried out by using the highly prioritized epitopes of the selected proteins (E6 and E7). As an immune stimulant, the adjuvants, heparin-binding hemagglutinin (HBHA) and 50S ribosomal protein L7/L12, were used in the V1 and V2, respectively. Suitable linkers, including EAAAK, AYY, AK, and KFER, were used in the final vaccine mapping [53, 84, 85]. These linkers facilitate the vaccine components’ proper folding, stability, and flexibility, augmenting the immunological response [86–88].

Evaluation of physicochemical properties

The physicochemical properties of the V1 and V2, including the molecular weight, the total number of amino acids, instability, aliphatic index, isoelectric point (pI), grand average of hydropathicity (GRAVY), the total number of positively and negatively charged residues, and the total number of atoms, were predicted by Expasy’s ProtParam server (http://web.expasy.org/protparam/) [89]. Following that, the solubility of the V1 and V2 was predicted by the SOLpro (https://scratch.proteomics.ics.uci.edu/) and SOSUI server (https://harrier.nagahama-i-bio.ac.jp/sosui/mobile/) [90–93]. The probable allergenic reactions of the V1 and V2 were assessed by AllergenFP v.1.0 (http://ddg-pharmfac.net/AllergenFP/), AllerCatPro v.2.0 (https://allercatpro.bii.a-star.edu.sg/) [94] and AlgPred (https://webs.iiitd.edu.in/raghava/algpred/submission.html) server. The antigenic features of the vaccines were also estimated by ANTIGENpro (http://scratch.proteomics.ics.uci.edu) [95] and VaxiJen 2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) server [77]. The threshold value was set up for this analysis using the default setting of 0.5.

Secondary structure prediction

The secondary structures of the V1 and V2 were predicted by the GOR4 (https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_gor4.html), SOPMA (https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html) and PSIPRED (http://bioinf.cs.ucl.ac.uk/psipred/) [96–99]. While predicting the secondary structure, the GOR4 utilizes information theory and Bayesian statistics [100]. The SOPMA, on the other hand, predicts a three-state illustration (alpha-helix, beta-sheet, and coil) of the secondary structure via a homology modeling approach [98]. Contrarily, the PSIPRED server utilizes two feed-forward neural networks and PSI-BLAST (protein-specific iterated basic local alignment search tool) to predict the secondary structure [101].

Tertiary structure prediction, refinement, and validation

The tertiary structures (3D) of the V1 and V2 were predicted by the I-TASSER server (https://zhanggroup.org/I-TASSER/). The server performs multiple threading alignments and iterative template fragment assembly simulations to predict the 3D structure of a protein [74, 102]. The 3D structures of the vaccines were applied for further structural refinement by the GalaxyWEB server [103]. Further structural validations of the V1 and V2 were carried out by the SAVES v6.0 server (https://saves.mbi.ucla.edu/), which defines the stereochemical quality of the predicted vaccine models through Ramachandran plot and ERRAT analysis [104–108]. Finally, we utilized the ProSA-web server (https://prosa.services.came.sbg.ac.at/prosa.php) to assess the structural accuracy of the predicted 3D model structure. The server provides a Z-score for a predicted 3D model structure, indicating the correctness and probable flaws of the model [57, 76, 109].

Prediction of discontinuous B-cell epitopes

The discontinuous B-cell epitopes of the V1 and V2 were predicted by the DiscoTope 2.0 (http://www.cbs.dtu.dk/services/DiscoTope/) [110] and Ellipro (http://tools.iedb.org/ellipro/) server [111]. The DiscoTope 2.0 server performs through the surface accessibility computation [110], where the ElliPro server (http://tools.iedb.org/ellipro/) utilizes a combination of three algorithms during discontinuous B-cell epitope prediction [111].

Disulfide engineering of the vaccines

Disulfide engineering is a technique used to introduce disulfide bonds within protein structures, enhancing their stability. These disulfide bonds strengthen the protein’s folded structure by reducing conformational entropy and increasing the free energy of the denatured state [112]. In this study, the Disulfide by Design 2.13 server (http://cptweb.cpt.wayne.edu/DbD2/) was employed to identify specific residue pairs within the vaccine construct that could potentially be mutated to cysteine, facilitating the formation of disulfide bonds. This strategic mutation can further stabilize the overall protein structure [113]. The server’s algorithm utilizes a geometric model based on native disulfide bonds to accurately calculate the χ3 torsion angle, using the 5th Cβ-Cβ distance as a reference. Given the variability in native disulfide bonds, the DbD2 server allows some flexibility in the Caf-Cβ-Sγ angle. DbD2 generates an estimated energy value for each potential disulfide bond, aiding in the ranking of candidates. Additionally, mutant PDB files can be produced for selected disulfide bonds for further analysis [76, 114].

Molecular docking analysis

Before molecular docking analysis, the 3D structure of human toll-like receptor-2 (TLR-2) (PDB: 2Z7X) and toll-like receptor-4 (TLR-4) (PDB: 3FXI) were retrieved from the PDB (Protein data bank) database (www.rcsb.org) [115]. Subsequently, the docking analysis of vaccine—TLR-2 (V1—TLR-2 and V2—TLR-2) and vaccine—TLR-4 (V1—TLR-4 and V2—TLR-4) were carried out by the ClusPro 2.0 (cluspro.bu.edu/login.php) server [116–119]. The ClusPro 2.0 server operates through a series of three continuous phases: first, it performs rigid body docking; next, it clusters the lowest energy structures; and finally, it refines the structures through energy minimization [76, 118]. The complex with the lowest energy score and highest docking efficiency was selected as the best-docked candidate. The docked complexes were further visualized and analyzed by the PyMOL and PDBsum (http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/Generate.html) server [84, 120].

Free energy calculation by molecular mechanics with generalized Born and surface area solvation (MM-GBSA)

The free energies associated with the vaccine and TLRs interactions (V1—TLR-2, V2—TLR-2, V1—TLR-4, and V2—TLR-4) were calculated by the MM-GBSA program of the HawkDock server [121–123]. The program utilizes molecular mechanics and the Generalised Born approaches to analyze intermolecular interactions, including van der Waals forces (VDW), electrostatic interactions (ELE), polar (GB), and non-polar (SA) components [121–123].

Molecular dynamic simulation

The GROningen MAchine for Chemical Simulations (GROMACS) (version 2022.3) [124] software was utilized for the molecular dynamic simulations of the vaccine and TLRs (V1—TLR-2, V1—TLR-4, V2—TLR-2 and V2—TLR-4) complexes along with the vaccine apo structures (V1 and V2). The GROMOS96 43a1 force field was applied for the simulation, where a cubic water box was built with an SPC water model. However, the system was neutralized using the NaCl. Finally, 100 nanoseconds (ns) molecular dynamic simulation was run using the equilibration type of isothermal-isochoric (NVT) and isobaric (NPT), temperature (K) of 300, pressure (bar) of 1.0, and a periodic boundary conditions and time integration step of 2 femtoseconds (fs). The trajectory data were analyzed at 100 picoseconds (ps) in the snapshot interval. After the successful simulation run, the root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), and hydrogen bond (H-bond) analysis were performed by the respective modules integrated within the GROMACS software.

Codon optimization and in silico cloning

Codon optimization of the vaccines was performed by the Java Codon Adaptation Tool (JCat) server (http://www.jcat.de/Start.jsp) utilizing a bacterial expression system, E. coli strain K12. The server evaluates the expression level of the vaccines through codon adaptation index (CAI) and GC contents. However, the CAI value ≥ 0.8 is considered a good score, while the best score should be ≥ 1.0, and the GC contents, ranging from 30 to 70%, are acceptable [125, 126]. The optimized gene sequences of the vaccines were cloned in E. coli plasmid vector pET-28a(+) using EcoRI, BamHI, and NdeI restriction sites. Finally, the optimized sequences of the vaccines were inserted into the plasmid vector pET-28a(+) using the SnapGene software.

Immune simulation

Using the C-ImmSim server, the immune simulations of the V1 and V2 were evaluated and predicted (https://kraken.iac.rm.cnr.it/C-IMMSIM/) [127]. The server uses a position-specific scoring matrix (PSSM) to predict a vaccine-mediated humoral and cellular immune response of a mammalian immune system [126, 128]. For proper immunization, the V1 and V2 were designed with three doses of the regime, which will be given at four-week intervals. The simulation run was carried out by the default parameters of the server, while the time steps were adjusted at 1 (day 1), 84 (day 28), and 168 (day 56) (a single step is equivalent to 8 hours of daily life) [85]. The recommended interval between two successive doses of most commercial immunizations is four weeks; thus, three shots must be delivered four weeks apart [76, 129]. Finally, the simulation volume was set to 50 with 1000 times the simulation steps. However, the random seed was chosen as the server’s default setting without adjoining lipopolysaccharides (LPS).

mRNA structure prediction

The secondary structures of both the V1 and V2 vaccines were predicted by the RNAfold (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) server [130]. During the prediction, the server provided thermodynamically derived minimum free energy (MFE) of the query mRNA structures [131, 132]. Before this analysis, the JCat optimized gene sequences (V1 and V2) were first transcribed into RNA sequences through the process of DNA>RNA conversion at http://biomodel.uah.es/en/lab/cybertory/analysis/trans.htm. Subsequently, these RNA sequences were applied to the RNAfold server for secondary structure prediction and validation.

Results

Sequence retrieval

After obtaining the protein sequences E6 and E7 of HPV 16 and HPV 18 from the NCBI database, the multiple sequence analysis (MSA) analysis provided two consensus FASTA sequences of the proteins. These consensus sequences were then applied for the subsequent vaccine development. Fig 1 illustrates an empirical overview of the entire vaccine design process.

[Figure omitted. See PDF.]

HTL binding epitope prediction

Both the IEDB and Net-MHC 4.0 server predicted several HTL peptides from which a total of twelve 9-mer length peptides were selected based on their affinity score and percentile rank (≤1.00) (Table 1). These peptide sequences were also predicted to be antigenic, non-allergic, and non-toxic and were applied for subsequent vaccine construction.

[Figure omitted. See PDF.]

CTL binding epitope prediction

The CTL binding epitopes of the selected proteins, the E6 and E7 proteins of HPV 16 and HPV 18, were predicted by the IEDB and NetMHCIIpan 4.0 server. Based on affinity and percentile score, twelve 15-mer length peptides were selected. The selected peptide sequences were also reported to have antigenic, non-allergenic, and non-toxic properties. No IL-10-producing capabilities of the selected epitopes were found, but these have IFN-γ inducing capabilities (Table 2).

[Figure omitted. See PDF.]

Linear B-cell epitope prediction

The linear B-cells of the selected proteins, the E6 and E7 proteins of HPV 16 and HPV 18, were predicted by the IEDB and the ABCpred servers. Twelve 16-mer epitopes were selected that have no toxic and allergenic properties but show antigenic properties (Table 3).

[Figure omitted. See PDF.]

Vaccine mapping

The selected epitopes of the proteins E6 and E7 were used in the final vaccine mapping (Tables 1–3). Two different vaccine candidates were mapped using the selected epitopes, adjuvants, and linkers. The adjuvant, HBHA was connected to the V1 with the EAAK linker, while the HTL, CTL, and linear B-cell epitopes were connected with each other by AYY, AK, and KFER linkers, respectively (Fig 2A). For the V2, 50S ribosomal protein L7/L12 adjuvant was connected with the EAAK linker, while the rest of the epitopes, including the HTL, CTL, and linear B-cell epitopes, were connected by AYY, AK, and KFER linkers, respectively (Fig 2B).

[Figure omitted. See PDF.]

The schematic presentation of the vaccines containing adjuvant (yellow color), epitopes (MHC-I-green, MHC-II- transparent orange, linear B-cell-light blue), and linkers (EAAAK- deep blue, AYY-light blue, AK-purple, and KFER-maroon).

Evaluation of physicochemical properties

The physicochemical properties of the V1 and V2 were obtained from the ExPASy ProtPram server. According to the server, V1 constituted a total number of 592 amino acids, while V2 constituted 523 amino acids. The molecular weight of the V1 and V2 were predicted to be 68551.58 Da and 60457.60 Da, respectively. The V1 has been reported as basic in nature, with an isoelectric point (pI) of 8.73, while V2 has been reported as acidic in nature, with a pI score of 6.62. The GRAVY of the V1 and V2 were predicted to be -0.811 and -0.648, respectively, indicating that both vaccines were predicted to be water-soluble (hydrophilic). The aliphatic index of the V1 was predicted to be 84.33, whereas it was predicted to be 78.26 for the V2. Both the vaccines, V1 and V2, were predicted to be unstable, with instability indices of 61.89 and 57.24, respectively (Table 4). Additionally, both the V1 and V2 vaccines were predicted to be water soluble, non-allergenic, and antigenic proteins (Table 4).

[Figure omitted. See PDF.]

Secondary structure prediction

Utilizing the three distinct servers (GOR4, SOPMA, and PSIPRED), the secondary structures of the vaccines (V1 and V2) were predicted and evaluated. The GOR4 server predicted 29.73% random coil, 61.15% alpha helix, and 9.12% extended strands (beta sheet) in V1; in comparison, the SOPMA server predicted random coil of 27.03%, an alpha helix of 66.05%, and extended strands of 6.93% (S1 Fig). For V2, the GOR4 server indicated that the secondary structure of the V2 has 35.76% random coil, 51.83% alpha helix, and 12.43% extended strands (beta sheet), while the SOPMA predicted 32,70% random coil, 57.93% alpha helix and 9.37% extended strands (S1 Fig). Additionally, the PSIPRED server provided the features of the vaccines’ secondary structure through three-state prediction (coil, helix, and strands) (Fig 3, S2 Fig).

[Figure omitted. See PDF.]

A larger bar indicates a higher confidence level, and the first bar (Conf) represents that degree of confidence in the forecast. In the second bar (Cart), the vaccine’s beta-sheet, helix, and coil structures are yellow, pink, and grey, respectively. The third bar (Pred) represents a structural feature, and the fourth bar (AA) represents an amino acid sequence.

Tertiary structure prediction, refinement, and validation

For the V1, out of the five models predicted by the I-TASSER server, the model with the greatest C-score of -0.86, TM-score of 0.51±0.15, and RMSD of 11.7±4.5Å was chosen for further investigation; in contrast, the V2 model with the greatest C-score of -0.61, TM-score of 0.64±0.13, and RMSD of 8.8±4.6Å was chosen. Subsequently, both the predicted models were then applied for structural refinement by the GalaxyWEB server. The refined 3D model of the V1 was given RMSD, MolProbity score, and Ramachandran’s favored region of 0.410, 1.674, and 97.3%, respectively. On the other hand, the refined 3D model of the V2 was found with RMSD, MolProbity score, and Ramachandran’s favored region of 0.525, 2.205, and 92.1%, respectively. These findings indicated that both proteins have well-stable 3D structures. According to the Ramachandran plot analysis, the V1 model had 94.5% amino acid residues in the most favored region, 4.2% in the additional allowed region, and 0.5% in the generously allowed region (Fig 4A). At the same time, the V2 had 87.1% amino acid residues in the most favored region, 9.6% in the additional allowed region, and 0.6% in the generously allowed region (Fig 4A). The Z score of the V1 and V2 were predicted to be -5.15 and -4.1, respectively (Fig 4B). Also, the ERRAT scores for the refined V1 and V2 were calculated as 92.771 and 83.497, respectively (Fig 4D).

[Figure omitted. See PDF.]

The refined 3D structures are evaluated through the Ramachandran plot (A), Z score (B), and ERRAT (D).

Prediction of discontinuous B-cell epitopes

The DiscoTope 2.0 server identified 290 and 252 discontinuous B-cell epitope residues in the V1 and V2 structures, respectively (Fig 5 and S1 Table). In the case of V1, the epitopes have different scoring ranges from 0.524 to 0.86 with an individual number of residues, whereas the epitopes from V2 have different scoring ranges from 0.519 to 0.79 (S1 Table).

[Figure omitted. See PDF.]

The discontinuous B-cell epitopes were illustrated as yellow-color surfaces, while the whole vaccine portions were displayed with grey sticks.

Disulfide engineering of the vaccines

Through disulfide engineering, it was discovered that V1 included 20 pairs of amino acid residues, but V2 contained 31 pairs of residues capable of establishing disulfide bonds. Between -87 and +97 degrees, χ3 peaks in 1505 native disulfide linkages in 331 non-homologous proteins were observed, with 90% of spontaneously produced bonds having an energy value below 2.2 kcal/mol [113]. Following the scrutiny of the χ3 and B-factor characteristics of the residue pairs based on energy, two pairs were chosen for V1 (LYS 178- TYR 418, ALA 201-HIS 390, and GLY 244- ALA 344), while three pairs were chosen for V2 (GLY 46-ASP 98, GLY 89-ASP 136, and LEU 188- ALA 200) (S3 Fig). With the addition of cysteine, these pairings were altered.

Molecular docking study

The docking analysis of the vaccines and human receptors (TLR-2 and TLR-4) was performed by the ClusPro 2.0 server. The server has generated 30 models for each complex, from which the models having the lowest energy scores of -1159.3, -1109.3, -1246.3, and -1244.8 were chosen for the “V1—TLR-2”, “V1—TLR-4”, “V2—TLR-2” and the “V2—TLR-4”, respectively (Fig 6; Table 5). Additionally, the center energy scores of the “V1—TLR-2”, “V1—TLR-4”, “V2—TLR-2” and “V2—TLR-4” were found to be -1159.3, -1109.3, -1140.8 and -1244.8, respectively (Fig 6; Table 5). However, the docked vaccine-TLRs complexes were visualized and analyzed by PyMOL and PDBsum. According to PDBsum, the “V1—TLR-2” complex has nine hydrogen bonds, 25 salt bridges, and 235 non-bond interactions (Fig 6A; Table 5), whereas the “V1—TLR-4” complex has 7 hydrogen bonds, 23 salt bridges and 224 non-bond interactions (Fig 6B; Table 5). Additionally, the “V2—TLR-2” complex has 8 hydrogen bonds, 17 salt bridges, and 231 non-bond interactions (Fig 6C; Table 5), whereas the “V2—TLR-4” complex has 7 hydrogen bonds, 38 salt bridges, and 421 non-bond interactions (Fig 6D; Table 5).

[Figure omitted. See PDF.]

The vaccines V1 and V2 and the receptors TLR-2 and TLR-4 are depicted as green, red, cyan, and purple, respectively. At the same time, their interactions are denoted with multiple colors, following the characteristics of the bonds.

[Figure omitted. See PDF.]

Free energy calculation by MM-GBSA

For the MM-GBSA analysis, we utilized the HawkDock server. This analysis revealed that the “V1—TLR-2” complex has the VDW, ELE, GB, and SA scores of -221.68, -1718.59, 1815.14, and -30.17 kcal/mol, respectively, and a total binding free energy of -155.29 (kcal/mol) (S3 Fig). Regarding the “V1—TLR-4” complex, the VDW, ELE, GB, and SA were calculated to be -186.44, -1068.74, 1182.46, and -23 kcal/mol, respectively with the total binding free energy of -95.72 kcal/mol (S4 Fig). On the other hand, the calculated MM-GBSA values for the "V2—TLR-2" complex was predicted to be -230.59 kcal/mol for VDW, -571.77 kcal/mol for ELE, 758.29 kcal/mol for GB, and -28.91 kcal/mol for SA. Following that, the complex was also reported with a total binding free energy of -72.97 kcal/mol (S4 Fig). Additionally, the V2—TLR-4 complex was analyzed, and the following MM-GBSA values were obtained: -331.1 kcal/mol for VDW energy, -342.91 kcal/mol for ELE energy, 530.07 kcal/mol for GB energy, and -44.74 kcal/mol for SA energy. Nevertheless, the complex was found to have a high binding free energy of -188.68 kcal/mol (S4 Fig).

Molecular dynamic simulation

RMSD analysis.

The RMSD of the vaccines (V1 and V2) and vaccine-TLR complexes (V1—TLR-2, V1—TLR-4, V2—TLR-2, and V2—TLR-4) structures were calculated to evaluate the stability of these complexes. Throughout the 100ns run, the RMSD values for the V1 and V1—TLR-4 steadily increased, while the RMSD values for V1—TLR-2 were drastically improved. But the RMSD of these complexes tended to decrease after a 100ns simulation run (Fig 7A). At the halfway of the simulation run (50ns), the differences in RMSD values of these complexes were clearly evident, which remained at the end of the 100ns simulation run. After the 100ns run, the average RMSD values for V1, V1—TLR-2, and V1—TLR-4 were calculated to be 0.106 nm, 0.116 nm, and 0.081 nm, respectively. These findings indicated that the V1 obtained a more stable structure when bound to TLR-4 than TLR-2 (Fig 7A). The RMSD values for the V2 and V2—TLR-2 were steadily increased, while the RMSD values for V2—TLR-2 showed a fluctuated plot; however, values started to decrease again after the 100 ns simulation run (Fig 7A). At the end of the complete simulation run, the average RMSD values for V2, V2—TLR-2, and V2—TLR-4 were calculated to be 0.737 nm, 0.738 nm, and 0.860 nm, respectively. Therefore, V2 was stable enough to be bound to TLR-2 rather than TLR-4 (Fig 7A).

[Figure omitted. See PDF.]

RMSF analysis.

Additionally, the RMSF of these complexes was used to assess regional flexibility. Throughout the simulation, the motility pattern of the V1—TLR-2 is significantly different than V1 and V1—TLR-4 (Fig 7B), especially at the C and N terminals. In its unbound state, the V1 residues exhibited an average RMSF value of 0.80 nm, indicating moderate flexibility. However, this dynamic behavior significantly changed when V1 interacted with TLRs. Upon binding to TLR-2, the average RMSF value of V1 decreased to 0.61 nm, suggesting that the interaction stabilized the V1 residues, reducing their flexibility. Similarly, when bound to TLR-4, the RMSF value was reported as 0.71 nm, again reflecting a reduction in flexibility compared to the unbound state, though to a lesser extent than with TLR-2. In the case of V2, the motility pattern of RMSF of the V2—TLR-4 was notably different from the V2 and V2-TLR-2 (Fig 7B). During the simulation, the V2 domain showed distinct fluctuation patterns depending on whether it was bound to TLR-2 or TLR-4. The residues of V2 bound to TLR-4 exhibited more abrupt and pronounced fluctuations, indicating significant flexibility and instability during the interaction. Conversely, in both the unbound state and when bound to TLR-2, the V2 residues maintained more steady fluctuations. At the end of the 100ns simulation run, the average RMSF values for V2 were measured. For the unbound, the average RMSF was 0.29 nm, reflecting moderate flexibility. When bound to TLR-2, the RMSF decreased to 0.25 nm, suggesting that the interaction stabilized the V2 residues and reduced their movement. In contrast, the binding of V2 to TLR-4 resulted in a significantly higher RMSF value of 0.56 nm, indicating increased fluctuation and flexibility (Fig 7B).

Rg analysis.

The Rg value of the V1—TLR-2 was higher than the Rg values of the V1 and V1—TLR-4 throughout the simulation run. Following the completion of the simulation, the Rg for the V1 was determined to be 2.96 nm. On the other hand, the Rg values obtained by the V1—TLR-2 and V1—TLR-4 complexes were 3.46 nm and 3.24 nm, respectively. Based on these findings, it can be inferred that the V1 exhibits a higher structural stability level when paired with TLR-4. Additionally, the Rg value of the V2—TLR-2 was higher than the Rg values of V2 and V2—TLR-4 (Fig 7C). After the simulation (100ns), the Rg for the V2 was measured as 2.96 nm, indicating the vaccine’s relatively compact structure in its unbound state. When V2 engaged with the TLR-2 and TLR-4, notable variations in Rg values were observed. The Rg value for the V2—TLR-2 complex spiked significantly to 3.79 nm, indicating a more expanded and less compact structure than the unbound V2. The V2—TLR-4 complex exhibited an Rg of 3.53 nm, suggesting a slightly more compact structure than V2—TLR-2, yet it remained more extended than the unbound V2 protein. The findings suggested that the interaction of V2 with various receptors affects its structural configuration, with TLR-2 causing a more significant structural expansion than TLR-4 (Fig 7C).

SASA analysis.

After a 100ns simulation run, the SASA plot of the V1, V1—TLR-2, and V1—TLR-4 were almost identical throughout the simulation run. After the 100ns simulation, V1—TLR-2 had the highest SASA value out of V1 and V1—TLR-4. At the end of the simulation, an average SASA value of 261.55 nm² was shown by the V1 itself. In contrast, the V1—TLR-2 and V1—TLR-4 complexes had significantly higher SASA values of 291.29 nm² and 275.51 nm², respectively. These findings demonstrated that the V1—TLR-2 complex was exposed to more solvent throughout the simulation, which could indicate that the V1 was more flexible or had a different shape when bound to TLR-2. While V1 singly was the most compact with the least solvent exposure, the intermediate SASA value of V1—TLR-4 showed a comparable but considerably less apparent impact (Fig 7D).

In the meantime, the SASA values of the V2 were consistently lower than those of its complexes with TLR-2 and TLR-4. The SASA quantifies the surface area of a biomolecule that is accessible to a solvent, frequently reflecting structural stability, folding, or possible interaction sites. During the simulation, the SASA values of the V2—TLR-2 and V2—TLR-4 complexes exhibited little variation, indicating no substantial difference in solvent exposure between the two complexes. A significant change occurred at the end of the complete simulation run. The V2—TLR-2 complex had the highest SASA value compared to the three configurations: V2 solely V2—TLR-2 and V2—TLR-4. The average SASA value of the V2 solely was 420.43 nm², indicating a more compact or less solvent-exposed conformation. Nevertheless, the formation of complexes between V2 and TLR-2, as well as TLR-4, resulted in a significant elevation in the SASA values. The V2—TLR-2 complex exhibited an average SASA of 662.76 nm², while the V2—TLR-4 complex had a lower, although still significant, SASA of 643.99 nm² (Fig 7D). The augmentation in SASA for the complexes suggested that the V2 undergoes structural modifications upon interaction with TLR-2 and TLR-4, likely revealing a greater surface area to the surrounding solvent.

H-bond analysis.

The simulation showed fluctuations in the number of hydrogen bonds in the docked complexes across different time intervals. At the beginning of the simulation (0 ns), the initial counts of hydrogen bonds were recorded as follows: V1—TLR-2 showed ten hydrogen bonds, V1—TLR-4 presented 11, V2—TLR-2 revealed 13, and V2—TLR-4 achieved a total of 16 hydrogen bonds. As the simulation progressed, significant changes were observed. At the 25 ns trajectory, the number of hydrogen bonds changed to 14 for V1—TLR-2, 16 for V1—TLR-4, 13 for V2—TLR-2, and a notable rise to 26 for V2—TLR-4. More alterations persisted during the simulation (S5 Fig). At the midpoint (50 ns), V1—TLR-2 showed an increase to 18 hydrogen bonds, whereas V1—TLR-4 stayed constant at 16. Conversely, V2—TLR-2 recognized a reduction to 12, while V2—TLR-4 significantly increased to 31 hydrogen bonds. At the end of a 100 ns simulation trajectory, the hydrogen bond analysis for two different ligand variations, V1 and V2, with TLR-2 and TLR-4 revealed notable patterns. The analysis showed that V1 formed 21 hydrogen bonds with TLR-2 and 13 hydrogen bonds with TLR-4. On the other hand, V2 demonstrated different binding characteristics, forming 15 hydrogen bonds with TLR-2 but a significantly higher number with TLR-4, recording 36 hydrogen bonds. (S5 Fig).

Codon optimization and in silico cloning

The codon optimization of the V1 and V2 vaccines was facilitated by the Java Codon Adaptation Tool (JCat) using E. coli strain K12. The server predicted optimized codon sequences of 1776 and 1569 nucleotide length for V1 and V2, respectively (Fig 8). Besides, the adapted sequences’ codon optimization index (CAI) values were calculated as 1.0 for each vaccine. At the same time, the average GC content of the adapted V1 and V2 sequences were calculated as 51.88% and 51.63%, respectively. Finally, the adapted vaccine sequences were inserted into the plasmid vector pET-28a(+), SnapGene software to establish recombinant plasmid sequences (Fig 8).

[Figure omitted. See PDF.]

SnapGene performed the expression of the vaccine-conjugated pET-28a(+) plasmid vector. The vaccine inserts are represented as a red circular portion within the plasmid vector backbone (black circle).

Immune simulation

After a successful three-dose vaccine regime, there was a notable increase in the B-cell population for both V1 (Fig 9A) and V2 (Fig 9B), and the total amounts of B-cells remained active, indicating a sustained year-long immunity (Fig 9A and 9B). The T cell responses, including T_H and TC, were also evident for both V1 and V2 vaccines. The study also showed elevated expression of active T_H on day 60, which gradually decreased but sustained for nearly a year (S6 Fig). Furthermore, active T_C exhibited high-level expression after the post-vaccination regime and remained active for a year around (Fig 9C and 9D).

[Figure omitted. See PDF.]

The evolution of B-cell (A, B), cytotoxic T-cell (C, D), and Mφ (E, F) populations after three successive injections of the vaccine.

The innate immune response, including Mφ, NK, and DC-mediated immune responses, was also evident for V1 and V2. Both vaccines, V1 and V2, showed elevated levels of Mφ responses until 100 days of post-vaccination (Fig 9E and 9F), while NK showed one-year sustainable immune responses (S6 Fig). However, both vaccines showed reduced levels of DC activity (S6 Fig).

mRNA structure prediction

The V1 mRNA exhibited an MFE score of -502.60 kcal/mol for optimum and -375.43 kcal/mol for centroid structure. Moreover, the V2 mRNA exhibited an MFE score of -450.90 kcal/mol for optimum and -329.52 kcal/mol for centroid structure. The thermodynamic free energy was predicted to be -534.60 kcal/mol and -475.73 kcal/mol, respectively. Further, the MFE structures of V1 and V2 were correlated with a frequency of 0.00% in each ensemble (Fig 10). Therefore, the mRNA structure of these designed vaccines will remain stable throughout the process of entry, transcription, and expression in the host [131, 132].

[Figure omitted. See PDF.]

The MFE structures of the V1 and V2 are represented with the base pair probabilities (A, B) and the positional entropy (C, D), while the centroid structures of the vaccines are represented with the base pair probabilities (E, F) and the positional entropy (G, H).

Discussion

Vaccination is a prerequisite for enhancing public health and boosting global well-being by protecting against HPV infection and its related health effects. There are six authorized HPV vaccines available, all of which are virus-like particles (VLPs) type vaccines, including three bivalent (Cervarix^®, Cecolin^®, and WalrinvaxV) [133–135], two quadrivalent (GARDASIL^® and Cervavac^®) [136, 137], and one ninevalent (GARDASIL9^®) vaccine [138–140]. The bivalent vaccines provide protection against HPV types 16 and 18, which have been associated with over 70% of cervical cancers [141–143]. Nevertheless, these vaccines cannot effectively cure pre-existing cancers since their mechanism of action involves the deployment of antibodies explicitly targeting the HPV capsid protein. This protein’s expression occurs before the virus’s release, making these vaccines less effective in preventing the formation of pre-existing lesions. Therapeutic vaccines, in contrast, elicit a cellular immune response instead of producing antibodies, which allows them to target and combat infected cells [144, 145].

Although VLPs imitate the molecular makeup of viruses, they do not possess the genetic material required for reproduction. As a result, they may not stimulate as strong of an immunological response as live attenuated or mRNA vaccines, especially in certain groups like the elderly or persons with weakened immune systems [41]. The advancements in vaccine research have shown that mRNA-based therapeutic agents, which are rapidly growing, may effectively tackle the challenges encountered in developing vaccines for infectious diseases and cancer [42, 43]. With its non-infectious and non-integrating nature, mRNA considered a superior vaccination choice compared to subunit, killed, live-attenuated, and DNA-based vaccines [44–47]. Also, the stability of mRNA in living cells can be influenced by various modifications and methods of delivery, as it is naturally broken down by cellular processes [44–47]. However, mRNA vaccines may be given by systemic delivery, which involves injecting the vaccine directly into the circulation, often through intravenous injections. Local injections, delivered directly at the target location, provide an alternative to systemic delivery and minimize adverse effects. Targeted delivery may be achieved by directly administering the medication into the specific tissue, such as by internodal injection. Hence, there is a diverse range of options for administering substances by intravenous, subcutaneous, intradermal, intramuscular, and intranodal routes [145, 146]. Multiple mRNA-based vaccines for cervical cancer are currently being experimented with in clinical and pre-clinical trials. In a recent study, an mRNA vaccine was developed targeting the E6 and E7 oncoproteins of HPV 16 and HPV 18. They found that the mRNA vaccine considerably triggered strong T-cell-mediated immune responses and substantially inhibited tumor development in both subcutaneous and orthotopic tumor-implanted mice models. Additionally, they discovered that the vaccine resulted in a large infiltration of immune cells into tumor tissues [147]. In another study, a notable mRNA-based vaccine was developed to target the HPV 16 late oncoproteins E6 and E7. In a preclinical model of HPV 16-associated lesion, the vaccine demonstrated a targeted adaptive immune response specific to the antigen [148]. Considering these, this study focuses on the reverse vaccinology technique to produce mRNA vaccines in response to the urgent public need for a vaccine against cervical cancer.

Here, we successfully developed two mRNA vaccines (V1 and V2) targeting the E6 and E7 oncoproteins of HPV 16 and HPV 18. The HTL, CTL, and B-cell epitopes were predicted and used in subsequent vaccine development using several bioinformatics tools. All the selected epitopes were further assessed for antigenicity, allergenicity, and toxicity. We then utilized several linkers and adjuvants to construct the mRNA vaccines with the selected epitopes. According to physicochemical properties, both the vaccines’ products were predicted as soluble proteins, which might be functionally stable under body conditions [127, 149]. The vaccines were also predicted to be basic in nature with the theoretical pI of 8.73 and 6.62 for the V1 and V2, respectively. The aliphatic index of the V1 and V2 were predicted to be 84.33 and 78.26, respectively, indicating the vaccines as hydrophobic proteins containing aliphatic side chains [127, 150]. The instability indices for vaccines V1 and V2 were expected to be 61.89 and 57.24, respectively, indicating their instability. The glitch was rectified during the assessment of their tertiary structures. It is crucial to consider the process of protein folding into its secondary and tertiary structures while developing an effective vaccine. Both the unfolded and folded proteins’ antigens are essential in protein-specific immune responses; hence, these are the prime targets for antibodies, which in turn mount in response to infections [151]. The predicted secondary and tertiary structures of the V1 and V2 were found satisfactory and reliable. The Ramachandran plot analysis also showed that most of the vaccines’ residues were within the preferred regions (94.5% for V1 and 87.1% for V2), conferring the structural integrity of the tertiary structures. Also, the Z score of the V1 and V2 indicated the stability of the vaccine structures with scores of -5.15 and -4.1, respectively, and ERRAT scores of the vaccines confirmed the quality of the V1 (92.771) and V2 (83.497) models.

To evaluate the possible association between the vaccines and TLRs on immune cells, a docking analysis was performed by Cluspro 2.0 utilizing human TLR-2 and TLR-4. According to this analysis, V1 exhibited a substantial affinity towards TLR-2 (lowest energy score of -1159.3 KJ/ml) and TLR-4 (lowest energy score of -1109.3 KJ/ml) receptors, where V2 exhibited a substantial affinity towards TLR-2 (lowest energy score of -1246.3 KJ/ml) and TLR-4 (lowest energy score of -1244.8 KJ/ml) receptors. According to the PBDsum, a total number of 25, 23, 17, and 38 hydrogen bonds were found within the “V1—TLR-2”, “V2—TLR-4”, “V2—TLR-2” and “V2—TLR-4” complexes, respectively.

Molecular dynamic simulation was also performed to validate the structural integrity of the vaccines and vaccine-TLR complexes. The analysis of RMSD values for V1 and V2 and their complexes with TLR-2 and TLR-4 indicated variable degrees of stability throughout the 100 ns simulation. While the RMSD values for V1 and V1—TLR-4 steadily increased over time, V1—TLR-2 exhibited a drastic initial increase, followed by a subsequent decrease in post-100 ns simulation time. The average RMSD values obtained at the end of the 100 ns simulation were 0.106 nm for V1, 0.116 nm for V1—TLR-2, and 0.081 nm for V1—TLR-4. The findings suggested that V1 adopted a more stable conformation when bound to TLR-4 than TLR-2. Conversely, the RMSD values for V2 and V2—TLR-2 showed steady growth, with V2—TLR-4’s fluctuation that eventually decreased after 100ns. The average RMSD for V2, V2—TLR-2, and V2—TLR-4 was 0.737 nm, 0.738 nm, and 0.860 nm, respectively. Therefore, the V2 was shown to be more stable when coupled with TLR-2 compared to TLR-4.

Furthermore, the RMSF analysis highlights distinct regional flexibility patterns, particularly evident in V1—TLR-2 compared to V1 and V1—TLR-4. With an average RMSF value of 0.80 nm, V1 was a bit flexible when it wasn’t tied to the TLRs. When V1 interacted with TLR-2, its flexibility was considerably reduced to 0.61 nm, which suggests that TLR-2 has a stabilizing impact on the V1 residues via its contact. With TLR-4, the RMSF value was lowered to 0.71 nm, reflecting a lesser degree of flexibility reduction than the TLR-2 interaction [152, 153]. On the other hand, V2—TLR-4 residues showed more altered RMSF values than V2 and V2—TLR-2. The motility pattern of V2 bound to TLR-4 exhibited more significant changes than its unbound state and its interaction with TLR-2, indicating enhanced flexibility and instability during the V2—TLR-4 interaction [154, 155]. Upon binding to TLR-2, V2 had reduced fluctuations, indicating a stabilizing influence of TLR-2 on V2 residues. The RMSF value decreased from 0.29 nm in the unbound state to 0.25 nm in the V2—TLR-2 complex, indicating that this interaction constrained the movement of V2, resulting in increased stability. In contrast, the interaction with TLR-4 significantly increased RMSF to 0.56 nm, indicating enhanced residue flexibility [152]. The increased flexibility in the V2—TLR-4 complex suggested a more unstable interaction, perhaps leading to functional consequences. The disparity in RMSF patterns between V2 associated with TLR-2 and TLR-4 indicated that TLR-2 more efficiently stabilizes the V2 domain, while TLR-4 binding leads to increased dynamic fluctuations and possible instability.

Additionally, the Rg values indicated higher structural compactness of V1 and V1—TLR-4 compared to V1—TLR-2, while V2—TLR-4 had a more fragile structure compared to V2 and V2—TLR-4. During the simulation, the Rg value of the V1—TLR-2 complex consistently surpassed that of both the unbound V1 and the V1—TLR-4 complex. The Rg for the unbound V1 was measured at 2.96 nm, but the Rg values for the V1—TLR-2 and V1—TLR-4 complexes were found to be 3.46 nm and 3.24 nm, respectively. The findings revealed that V1 has enhanced structural stability in the presence of TLR-4, suggesting a more efficient binding interaction. The Rg analysis for V2 concurrently revealed significant differences depending on its interaction with various TLRs. The unbound V2 had an Rg of 2.96 nm, indicating its very compact topology. Upon interaction with TLR-2, the Rg value increased to 3.79 nm, demonstrating a significant structural expansion that signifies a less compact shape relative to the unbound state. The V2—TLR-4 complex exhibited an Rg of 3.53 nm, indicating a more compact structure than V2—TLR-2 yet still more extended than the unbound V2. The significant enlargement seen in the V2—TLR-2 complex suggested that this receptor may elicit conformational alterations that disrupt the compact structure of V2 more than TLR-4 responses. In the post-100 ns simulation, the SASA values remained nearly identical for V1, V1—TLR-2, and V1—TLR-4, while V2 exhibited lower values compared to V2—TLR-2 and V2—TLR-4, indicating differences in surface accessibility. The V1—TLR-2 complex had the highest SASA value at 291.29 nm², surpassing V1 (261.55 nm²) and V1—TLR-4 (275.51 nm²). The heightened solvent exposure in the V1—TLR-2 complex suggests enhanced flexibility or a conformational alteration. Although V1 exhibited minimal solvent exposure, the V1—TLR-4 complex had an intermediate SASA value, indicating a lesser extent of structural alteration. Conversely, V2 consistently showed lower SASA values than its complexes with TLR-2 and TLR-4. Initially, the SASA values for the V2—TLR-2 and V2—TLR-4 complexes were quite similar, suggesting comparable solvent exposure. However, by the end of the simulation, the V2—TLR-2 complex revealed the highest SASA value of 662.76 nm², while the V2—TLR-4 complex recorded a substantial SASA of 643.99 nm². In contrast, the unbound V2 had a SASA of 420.43 nm². These results suggested that the binding of V2 to TLR-2 and TLR-4 leads to structural changes, increasing its exposure to the solvent.

The simulation run provided critical insights into the dynamic behavior of hydrogen bonds within the docked complexes, illustrating how molecular interactions evolve over time [48]. Initially, at 0 ns, the counts of hydrogen bonds were modest, with V1—TLR-2 at 10, V1—TLR-4 at 11, V2—TLR-2 at 13, and V2—TLR-4 at 16. As the simulation progressed to 25 ns, fluctuations in hydrogen bonding were observed. Notably, V2—TLR-4 exhibited a remarkable increase in hydrogen bonds, rising to 26, which may indicate enhanced stability or a stronger interaction with its ligand compared to the other complexes. The mid-simulation assessment at 50 ns highlighted continued variability, with V1—TLR-2 increasing its hydrogen bond count to 18 while V1—TLR-4 remained stable at 16. This stability in V1—TLR-4 might imply that the binding interactions are reaching an equilibrium state, while V1—TLR-2’s increase suggests ongoing adaptation in its binding environment. Conversely, V2—TLR-2’s reduction in hydrogen bonds to 12 indicates a potential weakening of its interaction, which may affect its overall stability and function. By the end of the simulation at 100 ns, the final counts revealed 21 hydrogen bonds for V1—TLR-2, 13 for V1—TLR-4, 15 for V2—TLR-2, and a striking increase to 36 for V2—TLR-4. The pronounced growth in hydrogen bonds for V2—TLR-4 suggests a strong affinity for its ligand, reinforcing the notion that this complex may represent a key target for further biochemical exploration [153, 156].

Codon optimization was employed to assess the expression of recombinant vaccines in the E. coli cloning vector, especially the K12 strain. The experimental results indicate that both vaccines showed substantial expression in the vector. The average GC content of the adapted V1 and V2 sequences were calculated as 51.88% and 51.63%, respectively, while both vaccines showed a CAI score of 1.0 each. After administering three doses of the vaccines, there was a significant increase in B-cell populations for both V1 and V2, indicating sustained immunity for a year. T-cell responses, including T_H and T_C, were also observed for both vaccines, with elevated expression of active T_H cells on day 60 and sustained T_C activity post-vaccination. Additionally, innate immune responses, including Mφ, NK, and DC responses, were evident for both vaccines. Elevated Mφ responses were observed up to 100 days post-vaccination, while NK exhibited sustained immune reactions over a year. However, both vaccines showed reduced DC activity. The secondary structure predictions of the V1 mRNA vaccine revealed an optimal MFE score of -502.60 kcal/mol and a centroid structure score of -375.43 kcal/mol. Similarly, for V2, the MFE score was -450.90 kcal/mol for the optimal structure and -329.52 kcal/mol for the centroid structure. The thermodynamic free energy predictions were -534.60 kcal/mol and -475.73 kcal/mol for V1 and V2, respectively. Additionally, the MFE structures of both vaccines exhibited a correlation frequency of 0.00% within the ensemble, indicating a unique and stable structural conformation for each. Consequently, the mRNA structures of V1 and V2 are poised to maintain stability during entry, transcription, and expression in the host, ensuring their efficacy and functionality.

However, a notable limitation of this study is the inability to extend the simulation beyond 100ns because of lacking access to suitable machine. A longer trajectory, such as a 200ns simulation, would provide a more thorough investigation of ligand-receptor interactions, perhaps uncovering more stable or variable binding patterns over time. This constraint may have hindered the comprehensive observation of equilibrium states or prolonged binding behaviors. Finally, this work emphasizes the ability of the two mRNA vaccines against HPV 16 and HPV 18 to stimulate robust humoral and cellular immune responses against the virus. Considering the facts and potential findings of the study, further in vitro and in vivo analyses are strongly recommended. Therefore, our research will now focus on conducting further laboratory experiments to evaluate the efficacy and safety of the designed vaccines.

Conclusion

We successfully designed two mRNA vaccines targeting the E6 and E7 oncoproteins of the high-risk HPVs: HPV 16 and HPV 18. Bioinformatics tools aided epitope selection, with subsequent structural assessments confirming stability and antigenicity. Docking analysis revealed strong interactions between vaccines and TLRs, crucial for immune activation. Molecular dynamics simulations highlighted the stability and flexibility of vaccine complexes. Codon optimization ensured efficient expression in E. coli vectors. After vaccination, sustained B-cell and T-cell responses were observed alongside innate immune activation. Secondary structure predictions confirmed stable mRNA structures, which are vital for vaccine efficacy. Overall, V1 and V2 vaccines exhibit promising characteristics for inducing robust and durable immune responses against HPV infections. Although this comprehensive evaluation provides crucial insights into the vaccines’ characteristics, in vitro, in vivo, and clinical trials are imperative to validate their efficacy, safety, and potential in combating HPV infection and preventing its associated cancer. This study will provide a solid foundation for further research, emphasizing the need for subsequent experimental validations and clinical investigations to ascertain the translational potential of these mRNA vaccines.

Supporting information

S1 Fig. The secondary structure of V1 and V2 predicted by SOPMA and GOR4.

https://doi.org/10.1371/journal.pone.0313559.s001

(TIF)

S2 Fig. The secondary structures of V1 and V2 predicted by PSIPRED.

The polarity of the vaccine is remarkable along with its hydrophobicity (A), whereas, the three-state structure prediction depicted the vaccine has enormous helical region (B).

https://doi.org/10.1371/journal.pone.0313559.s002

(TIF)

S3 Fig. Disulfide engineering of V1 and V2.

The wild models of the V1 (A) and V2 (B) contain no disulfide bond, while the mutant V1 (C) and V2 (D) models contain two pairs (LYS 178- TYR 418, ALA 201-HIS 390, and GLY 244- ALA 344) and three pairs (GLY 46-ASP 98, GLY 89-ASP 136, and LEU 188- ALA 200) disulfide bond.

https://doi.org/10.1371/journal.pone.0313559.s003

(TIF)

S4 Fig. The generalized Born and surface area solvation (MM-GBSA) analysis of the V1 and V2.

The illustration represents intermolecular interactions including van der Waals forces (VDW), electrostatic interactions (ELE), polar (GB), and non-polar (SA) components.

https://doi.org/10.1371/journal.pone.0313559.s004

(TIF)

S5 Fig. Hydrogen bond analysis.

Interchain hydrogen bonds between TLR-2 and V1, TLR-4 and V1, TLR-2 and V2, and TLR-4 and V2 were depicted in red, yellow, green, and orange, respectively.

https://doi.org/10.1371/journal.pone.0313559.s005

(TIF)

S6 Fig. Immune simulation analysis of the V1 and V2 including helper T cell (TH) (A, B), natural killer cell (NK) (C, D), and dendritic cell (DC) (E, F) responses.

https://doi.org/10.1371/journal.pone.0313559.s006

(TIF)

S1 Table. DiscoTope 2.0 predicted the discontinuous B-cell epitopes residues of the vaccine structure.

https://doi.org/10.1371/journal.pone.0313559.s007

(DOCX)

References

1. 1. Einstein MH, Schiller JT, Viscidi RP, Strickler HD, Coursaget P, Tan T, et al. Clinician’s guide to human papillomavirus immunology: knowns and unknowns. The Lancet Infectious diseases. 2009;9(6):347–56. Epub 2009/05/27. pmid:19467474.

* View Article

* PubMed/NCBI

* Google Scholar

2. 2. Lin S, Gao K, Gu S, You L, Qian S, Tang M, et al. Worldwide trends in cervical cancer incidence and mortality, with predictions for the next 15 years. Cancer. 2021;127(21):4030–9. Epub 2021/08/10. pmid:34368955.

* View Article

* PubMed/NCBI

* Google Scholar

3. 3. Ramberg IMS. Human papillomavirus-related neoplasia of the ocular adnexa. Acta ophthalmologica. 2022;100 Suppl 272(Suppl 272):3–33. Epub 2022/10/07. pmid:36203222.

* View Article

* PubMed/NCBI

* Google Scholar

4. 4. Karnosky J, Dietmaier W, Knuettel H, Freigang V, Koch M, Koll F, et al. HPV and lung cancer: A systematic review and meta-analysis. 2021;4(4):e1350. pmid:33624444

* View Article

* PubMed/NCBI

* Google Scholar

5. 5. Srinivasan M, Taioli E, Ragin CC. Human papillomavirus type 16 and 18 in primary lung cancers—a meta-analysis. Carcinogenesis. 2009;30(10):1722–8. Epub 2009/07/22. pmid:19620233.

* View Article

* PubMed/NCBI

* Google Scholar

6. 6. Brisson M, Drolet M. Global elimination of cervical cancer as a public health problem. The Lancet Oncology. 2019;20(3):319–21. Epub 2019/02/24. pmid:30795952.

* View Article

* PubMed/NCBI

* Google Scholar

7. 7. (WHO) WHO. Cervical Cancer 2024 [30 April 2024]. https://www.who.int/news-room/fact-sheets/detail/cervical-cancer#:~:text=Cervical%20cancer%20is%20the%20fourth,%2D%20and%20middle%2Dincome%20countries.

8. 8. Eiben GL, da Silva DM, Fausch SC, Le Poole IC, Nishimura MI, Kast WM. Cervical cancer vaccines: recent advances in HPV research. Viral immunology. 2003;16(2):111–21. Epub 2003/06/28. pmid:12828864.

* View Article

* PubMed/NCBI

* Google Scholar

9. 9. Brianti P, De Flammineis E, Mercuri SR. Review of HPV-related diseases and cancers. The new microbiologica. 2017;40(2):80–5. Epub 2017/04/04. pmid:28368072.

* View Article

* PubMed/NCBI

* Google Scholar

10. 10. Denny L, Prendiville W. Cancer of the cervix: Early detection and cost-effective solutions. International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics. 2015;131 Suppl 1:S28–32. Epub 2015/10/05. pmid:26433500.

* View Article

* PubMed/NCBI

* Google Scholar

11. 11. Denny L, de Sanjose S, Mutebi M, Anderson BO, Kim J, Jeronimo J, et al. Interventions to close the divide for women with breast and cervical cancer between low-income and middle-income countries and high-income countries. Lancet (London, England). 2017;389(10071):861–70. Epub 2016/11/07. pmid:27814963.

* View Article

* PubMed/NCBI

* Google Scholar

12. 12. (CRI) CRI. Immunotherapy For Cervical Cancer 2024 [30 April 2024]. https://www.cancerresearch.org/cancer-types/cervical-cancer.

13. 13. UK CR. Targeted and immunotherapy drugs for cervical cancer 2024 [30 April 2024]. https://www.cancerresearchuk.org/about-cancer/cervical-cancer/treatment/targeted-immunotherapy-drugs.

14. 14. UK CR. Bevacizumab (Avastin) 2024 [30 April 2024]. https://www.cancerresearchuk.org/about-cancer/treatment/drugs/bevacizumab.

15. 15. UK CR. Pembrolizumab (Keytruda) 2024 [30 April 2024]. https://www.cancerresearchuk.org/about-cancer/treatment/drugs/pembrolizumab.

16. 16. Gooi Z, Chan JY, Fakhry C. The epidemiology of the human papillomavirus related to oropharyngeal head and neck cancer. The Laryngoscope. 2016;126(4):894–900. Epub 2016/02/05. pmid:26845348.

* View Article

* PubMed/NCBI

* Google Scholar

17. 17. Nogues JC, Fassas S, Mulcahy C, Zapanta PE. Human Papillomavirus-Associated Head and Neck Cancer. Journal of the American Board of Family Medicine: JABFM. 2021;34(4):832–7. Epub 2021/07/28. pmid:34312276.

* View Article

* PubMed/NCBI

* Google Scholar

18. 18. Rajendra K, Sharma P. Viral Pathogens in Oesophageal and Gastric Cancer. Pathogens (Basel, Switzerland). 2022;11(4). Epub 2022/04/24. pmid:35456151.

* View Article

* PubMed/NCBI

* Google Scholar

19. 19. Li S, Luk HY, Xia C, Chen Z, Chan PKS, Boon SS. Oesophageal carcinoma: The prevalence of DNA tumour viruses and therapy. Tumour virus research. 2022;13:200231. Epub 2021/12/18. pmid:34920177.

* View Article

* PubMed/NCBI

* Google Scholar

20. 20. Sitas F, Urban M, Stein L, Beral V, Ruff P, Hale M, et al. The relationship between anti-HPV-16 IgG seropositivity and cancer of the cervix, anogenital organs, oral cavity and pharynx, oesophagus and prostate in a black South African population. Infectious agents and cancer. 2007;2:6. Epub 2007/03/03. pmid:17331260.

* View Article

* PubMed/NCBI

* Google Scholar

21. 21. Deshmukh AA, Damgacioglu H, Georges D, Sonawane K, Ferlay J, Bray F, et al. Global burden of HPV-attributable squamous cell carcinoma of the anus in 2020, according to sex and HIV status: A worldwide analysis. International journal of cancer. 2023;152(3):417–28. Epub 2022/09/03. pmid:36054026.

* View Article

* PubMed/NCBI

* Google Scholar

22. 22. Cubie HA. Diseases associated with human papillomavirus infection. Virology. 2013;445(1–2):21–34. Epub 2013/08/13. pmid:23932731.

* View Article

* PubMed/NCBI

* Google Scholar

23. 23. Howley PMJFv. Papillomavirinae: the viruses and their replication. 1996:2045–76.

24. 24. Thomas Jennifer T, Laimins Laimonis A. Human Papillomavirus Oncoproteins E6 and E7 Independently Abrogate the Mitotic Spindle Checkpoint. Journal of virology. 1998;72(2):1131–7. pmid:9445009

* View Article

* PubMed/NCBI

* Google Scholar

25. 25. zur Hausen H, de Villiers EM. Human papillomaviruses. Annual review of microbiology. 1994;48:427–47. Epub 1994/01/01. pmid:7826013.

* View Article

* PubMed/NCBI

* Google Scholar

26. 26. Schiffman MH. Epidemiology of cervical human papillomavirus infections. Current topics in microbiology and immunology. 1994;186:55–81. Epub 1994/01/01. pmid:8205846.

* View Article

* PubMed/NCBI

* Google Scholar

27. 27. Vousden KH. Interactions between papillomavirus proteins and tumor suppressor gene products. Advances in cancer research. 1994;64:1–24. Epub 1994/01/01. pmid:7879655.

* View Article

* PubMed/NCBI

* Google Scholar

28. 28. von Knebel Doeberitz M, Rittmüller C, zur Hausen H, Dürst M. Inhibition of tumorigenicity of cervical cancer cells in nude mice by HPV E6-E7 anti-sense RNA. International journal of cancer. 1992;51(5):831–4. Epub 1992/07/09. pmid:1319412.

* View Article

* PubMed/NCBI

* Google Scholar

29. 29. Hawley-Nelson P, Vousden KH, Hubbert NL, Lowy DR, Schiller JT. HPV16 E6 and E7 proteins cooperate to immortalize human foreskin keratinocytes. The EMBO journal. 1989;8(12):3905–10. Epub 1989/12/01. pmid:2555178.

* View Article

* PubMed/NCBI

* Google Scholar

30. 30. Scheffner M, Huibregtse JM, Vierstra RD, Howley PM. The HPV-16 E6 and E6-AP complex functions as a ubiquitin-protein ligase in the ubiquitination of p53. Cell. 1993;75(3):495–505. Epub 1993/11/05. pmid:8221889.

* View Article

* PubMed/NCBI

* Google Scholar

31. 31. Scheffner M, Werness BA, Huibregtse JM, Levine AJ, Howley PM. The E6 oncoprotein encoded by human papillomavirus types 16 and 18 promotes the degradation of p53. Cell. 1990;63(6):1129–36. Epub 1990/12/21. pmid:2175676.

* View Article

* PubMed/NCBI

* Google Scholar

32. 32. Münger K, Werness BA, Dyson N, Phelps WC, Harlow E, Howley PM. Complex formation of human papillomavirus E7 proteins with the retinoblastoma tumor suppressor gene product. The EMBO journal. 1989;8(13):4099–105. Epub 1989/12/20. pmid:2556261.

* View Article

* PubMed/NCBI

* Google Scholar

33. 33. Dyson N, Howley PM, Münger K, Harlow E. The human papilloma virus-16 E7 oncoprotein is able to bind to the retinoblastoma gene product. Science (New York, NY). 1989;243(4893):934–7. Epub 1989/02/17. pmid:2537532.

* View Article

* PubMed/NCBI

* Google Scholar

34. 34. Davies R, Hicks R, Crook T, Morris J, Vousden K. Human papillomavirus type 16 E7 associates with a histone H1 kinase and with p107 through sequences necessary for transformation. Journal of virology. 1993;67(5):2521–8. Epub 1993/05/01. pmid:8386265.

* View Article

* PubMed/NCBI

* Google Scholar

35. 35. Dyson N, Guida P, Münger K, Harlow EJJov. Homologous sequences in adenovirus E1A and human papillomavirus E7 proteins mediate interaction with the same set of cellular proteins. 1992;66(12):6893–902.

* View Article

* Google Scholar

36. 36. Scheffner M, Romanczuk H, Münger K, Huibregtse JM, Mietz JA, Howley PM. Functions of human papillomavirus proteins. Current topics in microbiology and immunology. 1994;186:83–99. Epub 1994/01/01. pmid:8205847.

* View Article

* PubMed/NCBI

* Google Scholar

37. 37. Lechner MS, Laimins LA. Inhibition of p53 DNA binding by human papillomavirus E6 proteins. Journal of virology. 1994;68(7):4262–73. Epub 1994/07/01. pmid:8207801.

* View Article

* PubMed/NCBI

* Google Scholar

38. 38. Lechner MS, Mack DH, Finicle AB, Crook T, Vousden KH, Laimins LA. Human papillomavirus E6 proteins bind p53 in vivo and abrogate p53-mediated repression of transcription. The EMBO journal. 1992;11(8):3045–52. Epub 1992/08/01. pmid:1379175.

* View Article

* PubMed/NCBI

* Google Scholar

39. 39. Weinberg RA. The retinoblastoma protein and cell cycle control. Cell. 1995;81(3):323–30. Epub 1995/05/05. pmid:7736585.

* View Article

* PubMed/NCBI

* Google Scholar

40. 40. Cahilly-Snyder L, Yang-Feng T, Francke U, George DL. Molecular analysis and chromosomal mapping of amplified genes isolated from a transformed mouse 3T3 cell line. Somatic cell and molecular genetics. 1987;13(3):235–44. Epub 1987/05/01. pmid:3474784.

* View Article

* PubMed/NCBI

* Google Scholar

41. 41. Williamson AL. Recent Developments in Human Papillomavirus (HPV) Vaccinology. Viruses. 2023;15(7). Epub 2023/07/29. pmid:37515128.

* View Article

* PubMed/NCBI

* Google Scholar

42. 42. Mohammadi Y, Nezafat N, Negahdaripour M, Eskandari S, Zamani M. In silico design and evaluation of a novel mRNA vaccine against BK virus: a reverse vaccinology approach. Immunologic Research. 2023;71(3):422–41. pmid:36580228

* View Article

* PubMed/NCBI

* Google Scholar

43. 43. Pardi N, Hogan MJ, Porter FW, Weissman D. mRNA vaccines—a new era in vaccinology. Nature reviews Drug discovery. 2018;17(4):261–79. Epub 2018/01/13. pmid:29326426.

* View Article

* PubMed/NCBI

* Google Scholar

44. 44. Guan S, Rosenecker J. Nanotechnologies in delivery of mRNA therapeutics using nonviral vector-based delivery systems. Gene therapy. 2017;24(3):133–43. Epub 2017/01/18. pmid:28094775.

* View Article

* PubMed/NCBI

* Google Scholar

45. 45. Karikó K, Muramatsu H, Welsh FA, Ludwig J, Kato H, Akira S, et al. Incorporation of pseudouridine into mRNA yields superior nonimmunogenic vector with increased translational capacity and biological stability. Molecular therapy: the journal of the American Society of Gene Therapy. 2008;16(11):1833–40. Epub 2008/09/18. pmid:18797453.

* View Article

* PubMed/NCBI

* Google Scholar

46. 46. Kauffman KJ, Webber MJ, Anderson DG. Materials for non-viral intracellular delivery of messenger RNA therapeutics. Journal of controlled release: official journal of the Controlled Release Society. 2016;240:227–34. Epub 2016/01/01. pmid:26718856.

* View Article

* PubMed/NCBI

* Google Scholar

47. 47. Thess A, Grund S, Mui BL, Hope MJ, Baumhof P, Fotin-Mleczek M, et al. Sequence-engineered mRNA Without Chemical Nucleoside Modifications Enables an Effective Protein Therapy in Large Animals. Molecular therapy: the journal of the American Society of Gene Therapy. 2015;23(9):1456–64. Epub 2015/06/09. pmid:26050989.

* View Article

* PubMed/NCBI

* Google Scholar

48. 48. Rani NA, Robin TB, Prome AA, Ahmed N, Moin AT, Patil RB, et al. Development of multi epitope subunit vaccines against emerging carp viruses Cyprinid herpesvirus 1 and 3 using immunoinformatics approach. Scientific Reports. 2024;14(1):11783. pmid:38782944

* View Article

* PubMed/NCBI

* Google Scholar

49. 49. Ahmad S, Demneh FM, Rehman B, Almanaa TN, Akhtar N, Pazoki-Toroudi H, et al. In silico design of a novel multi-epitope vaccine against HCV infection through immunoinformatics approaches. International journal of biological macromolecules. 2024;267:131517. pmid:38621559

* View Article

* PubMed/NCBI

* Google Scholar

50. 50. Ahmad S, Nazarian S, Alizadeh A, Pashapour Hajialilou M, Tahmasebian S, Alharbi M, et al. Computational design of a multi-epitope vaccine candidate against Langya henipavirus using surface proteins. Journal of Biomolecular Structure and Dynamics. 1–18. pmid:37713338

* View Article

* PubMed/NCBI

* Google Scholar

51. 51. Ullah A, Rehman B, Khan S, Almanaa TN, Waheed Y, Hassan M, et al. An In Silico Multi-epitopes Vaccine Ensemble and Characterization Against Nosocomial Proteus penneri. Molecular Biotechnology. 2023. pmid:37934390

* View Article

* PubMed/NCBI

* Google Scholar

52. 52. Rawat SS, Keshri AK, Kaur R, Prasad A. Immunoinformatics Approaches for Vaccine Design: A Fast and Secure Strategy for Successful Vaccine Development. Vaccines. 2023;11(2). Epub 2023/03/01. pmid:36851099.

* View Article

* PubMed/NCBI

* Google Scholar

53. 53. Parvin R, Habib Ullah Masum M, Ferdous J, Mahdeen AA, Shafiqul Islam Khan M. Designing of a chimeric multiepitope vaccine against bancroftian lymphatic filariasis through immunoinformatics approaches. PloS one. 2024;19(9):e0310398. pmid:39298468

* View Article

* PubMed/NCBI

* Google Scholar

54. 54. Farhani I, Yamchi A, Madanchi H, Khazaei V, Behrouzikhah M, Abbasi H, et al. Designing a Multi-epitope Vaccine against the SARS-CoV-2 Variant based on an Immunoinformatics Approach. Current Computer-Aided Drug Design. 2024;20(3):274–90. pmid:37309762

* View Article

* PubMed/NCBI

* Google Scholar

55. 55. Oli AN, Obialor WO, Ifeanyichukwu MO, Odimegwu DC, Okoyeh JN, Emechebe GO, et al. Immunoinformatics and Vaccine Development: An Overview. ImmunoTargets and therapy. 2020;9:13–30. Epub 2020/03/13. pmid:32161726.

* View Article

* PubMed/NCBI

* Google Scholar

56. 56. Joon S, Singla RK, Shen B. Vaccines and Immunoinformatics for Vaccine Design. In: Shen B, editor. Translational Informatics: Prevention and Treatment of Viral Infections. Singapore: Springer Nature Singapore; 2022. p. 95–110.

57. 57. Sippl MJ. Recognition of errors in three-dimensional structures of proteins. Proteins. 1993;17(4):355–62. Epub 1993/12/01. pmid:8108378.

* View Article

* PubMed/NCBI

* Google Scholar

58. 58. Sayers EW, Bolton EE, Brister JR, Canese K, Chan J, Comeau DC, et al. Database resources of the national center for biotechnology information. Nucleic acids research. 2022;50(D1):D20–d6. Epub 2021/12/02. pmid:34850941.

* View Article

* PubMed/NCBI

* Google Scholar

59. 59. Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M. NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. Journal of immunology (Baltimore, Md: 1950). 2017;199(9):3360–8. Epub 2017/10/06. pmid:28978689.

* View Article

* PubMed/NCBI

* Google Scholar

60. 60. Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic acids research. 2020;48(W1):W449–w54. Epub 2020/05/15. pmid:32406916.

* View Article

* PubMed/NCBI

* Google Scholar

61. 61. Andreatta M, Nielsen M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics. 2016;32(4):511–7. Epub 2015/10/31. pmid:26515819.

* View Article

* PubMed/NCBI

* Google Scholar

62. 62. Nielsen M, Lundegaard C, Worning P, Lauemøller SL, Lamberth K, Buus S, et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein science: a publication of the Protein Society. 2003;12(5):1007–17. Epub 2003/04/30. pmid:12717023.

* View Article

* PubMed/NCBI

* Google Scholar

63. 63. Abdelmageed MI, Abdelmoneim AH, Mustafa MI, Elfadol NM, Murshed NS, Shantier SW, et al. Design of a Multiepitope-Based Peptide Vaccine against the E Protein of Human COVID-19: An Immunoinformatics Approach. BioMed research international. 2020;2020:2683286. Epub 2020/05/29. pmid:32461973.

* View Article

* PubMed/NCBI

* Google Scholar

64. 64. Buus S, Lauemøller SL, Worning P, Kesmir C, Frimurer T, Corbet S, et al. Sensitive quantitative predictions of peptide-MHC binding by a ’Query by Committee’ artificial neural network approach. Tissue Antigens. 2003;62(5):378–84. Epub 2003/11/18. pmid:14617044.

* View Article

* PubMed/NCBI

* Google Scholar

65. 65. Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, et al. Immune epitope database analysis resource. Nucleic acids research. 2012;40(Web Server issue):W525–30. Epub 2012/05/23. pmid:22610854.

* View Article

* PubMed/NCBI

* Google Scholar

66. 66. Kim Y, Sidney J, Pinilla C, Sette A, Peters B. Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. BMC bioinformatics. 2009;10:394. Epub 2009/12/02. pmid:19948066.

* View Article

* PubMed/NCBI

* Google Scholar

67. 67. Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11. Nucleic acids research. 2008;36(Web Server issue):W509–12. Epub 2008/05/09. pmid:18463140.

* View Article

* PubMed/NCBI

* Google Scholar

68. 68. Lundegaard C, Lund O, Nielsen M. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Bioinformatics. 2008;24(11):1397–8. Epub 2008/04/17. pmid:18413329.

* View Article

* PubMed/NCBI

* Google Scholar

69. 69. Moutaftsi M, Peters B, Pasquetto V, Tscharke DC, Sidney J, Bui HH, et al. A consensus epitope prediction approach identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus. Nat Biotechnol. 2006;24(7):817–9. Epub 2006/06/13. pmid:16767078.

* View Article

* PubMed/NCBI

* Google Scholar

70. 70. Peters B, Sette A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC bioinformatics. 2005;6:132. Epub 2005/06/02. pmid:15927070.

* View Article

* PubMed/NCBI

* Google Scholar

71. 71. Sidney J, Assarsson E, Moore C, Ngo S, Pinilla C, Sette A, et al. Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res. 2008;4:2. Epub 2008/01/29. pmid:18221540.

* View Article

* PubMed/NCBI

* Google Scholar

72. 72. Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic acids research. 2019;47(D1):D339–d43. Epub 2018/10/26. pmid:30357391.

* View Article

* PubMed/NCBI

* Google Scholar

73. 73. Yazdani Z, Rafiei A, Irannejad H, Yazdani M, Valadan R. Designing a novel multiepitope peptide vaccine against melanoma using immunoinformatics approach. Journal of biomolecular structure & dynamics. 2022;40(7):3312–24. Epub 2020/11/24. pmid:33226282.

* View Article

* PubMed/NCBI

* Google Scholar

74. 74. Zhang Q, Wang P, Kim Y, Haste-Andersen P, Beaver J, Bourne PE, et al. Immune epitope database analysis resource (IEDB-AR). Nucleic acids research. 2008;36(Web Server issue):W513–8. Epub 2008/06/03. pmid:18515843.

* View Article

* PubMed/NCBI

* Google Scholar

75. 75. Hossain MS, Hossan MI, Mizan S, Moin AT, Yasmin F, Akash A-S, et al. Immunoinformatics approach to designing a multi-epitope vaccine against Saint Louis Encephalitis Virus. Informatics in Medicine Unlocked. 2021;22:100500.

* View Article

* Google Scholar

76. 76. Araf Y, Moin AT, Timofeev VI, Faruqui NA, Saiara SA, Ahmed N, et al. Immunoinformatic Design of a Multivalent Peptide Vaccine Against Mucormycosis: Targeting FTR1 Protein of Major Causative Fungi. 2022;13. pmid:35720422

* View Article

* PubMed/NCBI

* Google Scholar

77. 77. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC bioinformatics. 2007;8:4. Epub 2007/01/09. pmid:17207271.

* View Article

* PubMed/NCBI

* Google Scholar

78. 78. Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v.2—a server for in silico prediction of allergens. Journal of molecular modeling. 2014;20(6):2278. Epub 2014/06/01. pmid:24878803.

* View Article

* PubMed/NCBI

* Google Scholar

79. 79. Sharma N, Naorem LD, Jain S, Raghava GPS. ToxinPred2: an improved method for predicting toxicity of proteins. Briefings in bioinformatics. 2022;23(5). Epub 2022/05/21. pmid:35595541.

* View Article

* PubMed/NCBI

* Google Scholar

80. 80. Reynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Nielsen M. Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data. Journal of proteome research. 2020;19(6):2304–15. Epub 2020/04/21. pmid:32308001.

* View Article

* PubMed/NCBI

* Google Scholar

81. 81. Masum MHU, Ferdous J, Lokman S, Siddiki AZ. Designing of a multiepitope-based chimeric vaccine against dengue virus serotype 3 (DENV-3) through next generation reverse vaccinology approaches. Informatics in Medicine Unlocked. 2024;44:101422.

* View Article

* Google Scholar

82. 82. Singh H, Ansari HR, Raghava GP. Improved method for linear B-cell epitope prediction using antigen’s primary sequence. PloS one. 2013;8(5):e62216. Epub 2013/05/15. pmid:23667458 This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

* View Article

* PubMed/NCBI

* Google Scholar

83. 83. Emini EA, Hughes JV, Perlow DS, Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. Journal of virology. 1985;55(3):836–9. Epub 1985/09/01. pmid:2991600.

* View Article

* PubMed/NCBI

* Google Scholar

84. 84. Rahman MM, Masum MHU, Talukder A, Akter R. An in silico reverse vaccinology approach to design a novel multiepitope peptide vaccine for non-small cell lung cancers. Informatics in Medicine Unlocked. 2023;37:101169.

* View Article

* Google Scholar

85. 85. Masum MHU, Wajed S, Hossain MI, Moumi NR, Talukder A, Rahman MM. An mRNA vaccine for pancreatic cancer designed by applying in silico immunoinformatics and reverse vaccinology approaches. PloS one. 2024;19(7):e0305413. Epub 2024/07/08. pmid:38976715.

* View Article

* PubMed/NCBI

* Google Scholar

86. 86. Arai R, Ueda H, Kitayama A, Kamiya N, Nagamune T. Design of the linkers which effectively separate domains of a bifunctional fusion protein. Protein engineering. 2001;14(8):529–32. Epub 2001/10/02. pmid:11579220.

* View Article

* PubMed/NCBI

* Google Scholar

87. 87. 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. Computers in Biology and Medicine. 2016;76:24–9. pmid:27393958

* View Article

* PubMed/NCBI

* Google Scholar

88. 88. George RA, Heringa J. An analysis of protein domain linkers: their classification and role in protein folding. Protein engineering. 2002;15(11):871–9. Epub 2003/01/23. pmid:12538906.

* View Article

* PubMed/NCBI

* Google Scholar

89. 89. Wilkins MR, Gasteiger E, Bairoch A, Sanchez JC, Williams KL, Appel RD, et al. Protein identification and analysis tools in the ExPASy server. Methods in molecular biology (Clifton, NJ). 1999;112:531–52. Epub 1999/02/23. pmid:10027275.

* View Article

* PubMed/NCBI

* Google Scholar

90. 90. Hirokawa T, Boonćchieng S, Mitaku SJB. SOSUI: classification and secondary structure prediction system for membrane proteins. 1998;14 4:378–9.

* View Article

* Google Scholar

91. 91. Magnan CN, Randall A, Baldi P. SOLpro: accurate sequence-based prediction of protein solubility. Bioinformatics. 2009;25(17):2200–7. %J Bioinformatics. pmid:19549632

* View Article

* PubMed/NCBI

* Google Scholar

92. 92. Mitaku S, Hirokawa T. Physicochemical factors for discriminating between soluble and membrane proteins: hydrophobicity of helical segments and protein length. Protein engineering. 1999;12(11):953–7. Epub 1999/12/10. pmid:10585500.

* View Article

* PubMed/NCBI

* Google Scholar

93. 93. Mitaku S, Hirokawa T, Tsuji T. Amphiphilicity index of polar amino acids as an aid in the characterization of amino acid preference at membrane-water interfaces. Bioinformatics. 2002;18(4):608–16. Epub 2002/05/23. pmid:12016058.

* View Article

* PubMed/NCBI

* Google Scholar

94. 94. Maurer-Stroh S, Krutz NL, Kern PS, Gunalan V, Nguyen MN, Limviphuvadh V, et al. AllerCatPro—prediction of protein allergenicity potential from the protein sequence. Bioinformatics. 2019;35(17):3020–7. %J Bioinformatics. pmid:30657872

* View Article

* PubMed/NCBI

* Google Scholar

95. 95. Dhanda SK, Vir P, Raghava GP. Designing of interferon-gamma inducing MHC class-II binders. Biology direct. 2013;8:30. Epub 2013/12/07. pmid:24304645.

* View Article

* PubMed/NCBI

* Google Scholar

96. 96. Buchan DWA, Jones DT. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic acids research. 2019;47(W1):W402–w7. Epub 2019/06/30. pmid:31251384.

* View Article

* PubMed/NCBI

* Google Scholar

97. 97. Combet C, Blanchet C, Geourjon C, Deléage G. NPS@: network protein sequence analysis. Trends in biochemical sciences. 2000;25(3):147–50. Epub 2000/03/01. pmid:10694887.

* View Article

* PubMed/NCBI

* Google Scholar

98. 98. Geourjon C, Deléage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Computer applications in the biosciences: CABIOS. 1995;11(6):681–4. Epub 1995/12/01. pmid:8808585.

* View Article

* PubMed/NCBI

* Google Scholar

99. 99. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. Journal of molecular biology. 1999;292(2):195–202. Epub 1999/09/24. pmid:10493868.

* View Article

* PubMed/NCBI

* Google Scholar

100. 100. Garnier J, Gibrat JF, Robson B. GOR method for predicting protein secondary structure from amino acid sequence. Methods in enzymology. 1996;266:540–53. Epub 1996/01/01. pmid:8743705.

* View Article

* PubMed/NCBI

* Google Scholar

101. 101. McGuffin LJ, Bryson K, Jones DT. The PSIPRED protein structure prediction server. Bioinformatics (Oxford, England). 2000;16(4):404–5. Epub 2000/06/27. pmid:10869041.

* View Article

* PubMed/NCBI

* Google Scholar

102. 102. Ghahremanifard P, Afzali F, Rostami A, Nayeri Z, Bambai B, Minuchehr Z. Designing a Novel Multi-epitope T Vaccine for “Targeting Protein for Xklp-2” (TPX2) in Hepatocellular Carcinoma Based on Immunoinformatics Approach. International Journal of Peptide Research and Therapeutics. 2020;26.

* View Article

* Google Scholar

103. 103. Ko J, Park H, Heo L, Seok C. GalaxyWEB server for protein structure prediction and refinement. Nucleic acids research. 2012;40(Web Server issue):W294–7. Epub 2012/06/01. pmid:22649060.

* View Article

* PubMed/NCBI

* Google Scholar

104. 104. Laskowski R, Macarthur MW, Moss DS, Thornton J. PROCHECK: A program to check the stereochemical quality of protein structures. Journal of Applied Crystallography. 1993;26:283–91.

* View Article

* Google Scholar

105. 105. Laskowski RA, MacArthur MW, Thornton JMJITfC. PROCHECK: validation of protein‐structure coordinates. 2012:684–7.

106. 106. Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton JM. AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. Journal of biomolecular NMR. 1996;8(4):477–86. Epub 1996/12/01. pmid:9008363.

* View Article

* PubMed/NCBI

* Google Scholar

107. 107. Morris AL, MacArthur MW, Hutchinson EG, Thornton JM. Stereochemical quality of protein structure coordinates. Proteins. 1992;12(4):345–64. Epub 1992/04/01. pmid:1579569.

* View Article

* PubMed/NCBI

* Google Scholar

108. 108. Talukder A, Rahman MM, Masum MHU. Biocomputational characterisation of MBO_200107 protein of Mycobacterium tuberculosis variant caprae: a molecular docking and simulation study. Journal of Biomolecular Structure and Dynamics. 2023;41(15):7204–23. pmid:36039775

* View Article

* PubMed/NCBI

* Google Scholar

109. 109. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic acids research. 2007;35(Web Server issue):W407–10. Epub 2007/05/23. pmid:17517781.

* View Article

* PubMed/NCBI

* Google Scholar

110. 110. Kringelum JV, Lundegaard C, Lund O, Nielsen M. Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS computational biology. 2012;8(12):e1002829. Epub 2013/01/10. pmid:23300419.

* View Article

* PubMed/NCBI

* Google Scholar

111. 111. Ponomarenko J, Bui HH, Li W, Fusseder N, Bourne PE, Sette A, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC bioinformatics. 2008;9:514. Epub 2008/12/06. pmid:19055730.

* View Article

* PubMed/NCBI

* Google Scholar

112. 112. Flory PJ. Theory of elastic mechanisms in fibrous proteins. JJotACS 1956;78(20):5222–35.

* View Article

* Google Scholar

113. 113. Craig DB, Dombkowski AA. Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins. BMC bioinformatics. 2013;14:346. Epub 2013/12/03. pmid:24289175.

* View Article

* PubMed/NCBI

* Google Scholar

114. 114. Dombkowski AA, Sultana KZ, Craig DB. Protein disulfide engineering. FEBS letters. 2014;588(2):206–12. Epub 2013/12/03. pmid:24291258.

* View Article

* PubMed/NCBI

* Google Scholar

115. 115. Shahik SM, Salauddin A, Hossain MS, Noyon SH, Moin AT, Mizan S, et al. Screening of novel alkaloid inhibitors for vascular endothelial growth factor in cancer cells: an integrated computational approach. Genomics & informatics. 2021;19(1):e6. Epub 2021/04/13. pmid:33840170.

* View Article

* PubMed/NCBI

* Google Scholar

116. 116. Desta IT, Porter KA, Xia B, Kozakov D, Vajda S. Performance and Its Limits in Rigid Body Protein-Protein Docking. Structure (London, England: 1993). 2020;28(9):1071–81.e3. Epub 2020/07/11. pmid:32649857.

* View Article

* PubMed/NCBI

* Google Scholar

117. 117. Kozakov D, Beglov D, Bohnuud T, Mottarella SE, Xia B, Hall DR, et al. How good is automated protein docking? Proteins. 2013;81(12):2159–66. Epub 2013/09/03. pmid:23996272.

* View Article

* PubMed/NCBI

* Google Scholar

118. 118. Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, et al. The ClusPro web server for protein-protein docking. Nature protocols. 2017;12(2):255–78. Epub 2017/01/13. pmid:28079879.

* View Article

* PubMed/NCBI

* Google Scholar

119. 119. Vajda S, Yueh C, Beglov D, Bohnuud T, Mottarella SE, Xia B, et al. New additions to the ClusPro server motivated by CAPRI. Proteins. 2017;85(3):435–44. Epub 2016/12/10. pmid:27936493.

* View Article

* PubMed/NCBI

* Google Scholar

120. 120. Laskowski RA, Jabłońska J, Pravda L, Vařeková RS, Thornton JM. PDBsum: Structural summaries of PDB entries. Protein science: a publication of the Protein Society. 2018;27(1):129–34. Epub 2017/09/07. pmid:28875543.

* View Article

* PubMed/NCBI

* Google Scholar

121. 121. Chen F, Sun H, Wang J, Zhu F, Liu H, Wang Z, et al. Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes. RNA (New York, NY). 2018;24(9):1183–94. Epub 2018/06/23. pmid:29930024.

* View Article

* PubMed/NCBI

* Google Scholar

122. 122. Hou T, Wang J, Li Y, Wang W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. Journal of chemical information and modeling. 2011;51(1):69–82. Epub 2010/12/02. pmid:21117705.

* View Article

* PubMed/NCBI

* Google Scholar

123. 123. Sun H, Li Y, Tian S, Xu L, Hou T. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Physical chemistry chemical physics: PCCP. 2014;16(31):16719–29. Epub 2014/07/08. pmid:24999761.

* View Article

* PubMed/NCBI

* Google Scholar

124. 124. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1–2:19–25.

* View Article

* Google Scholar

125. 125. Ali M, Pandey RK, Khatoon N, Narula A, Mishra A, Prajapati VK. Exploring dengue genome to construct a multi-epitope based subunit vaccine by utilizing immunoinformatics approach to battle against dengue infection. Scientific reports. 2017;7(1):9232. Epub 2017/08/25. pmid:28835708.

* View Article

* PubMed/NCBI

* Google Scholar

126. 126. Bibi S, Ullah I, Zhu B, Adnan M, Liaqat R, Kong WB, et al. In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology. Sci Rep. 2021;11(1):1249. Epub 2021/01/15. pmid:33441913.

* View Article

* PubMed/NCBI

* Google Scholar

127. 127. Rapin N, Lund O, Bernaschi M, Castiglione F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PloS one. 2010;5(4):e9862. Epub 2010/04/27. pmid:20419125.

* View Article

* PubMed/NCBI

* Google Scholar

128. 128. Sanami S, Zandi M, Pourhossein B, Mobini GR, Safaei M, Abed A, et al. Design of a multi-epitope vaccine against SARS-CoV-2 using immunoinformatics approach. International journal of biological macromolecules. 2020;164:871–83. Epub 2020/07/19. pmid:32682041.

* View Article

* PubMed/NCBI

* Google Scholar

129. 129. Castiglione F, Mantile F, De Berardinis P, Prisco A. How the Interval between Prime and Boost Injection Affects the Immune Response in a Computational Model of the Immune System. 2012;2012(1):842329. pmid:22997539

* View Article

* PubMed/NCBI

* Google Scholar

130. 130. Gruber AR, Lorenz R, Bernhart SH, Neuböck R, Hofacker IL. The Vienna RNA Websuite. Nucleic acids research. 2008;36(suppl_2):W70–W4. %J Nucleic Acids Research. pmid:18424795

* View Article

* PubMed/NCBI

* Google Scholar

131. 131. Lorenz R, Bernhart SH, Höner zu Siederdissen C, Tafer H, Flamm C, Stadler PF, et al. ViennaRNA Package 2.0. Algorithms for Molecular Biology. 2011;6(1):26. pmid:22115189

* View Article

* PubMed/NCBI

* Google Scholar

132. 132. Motamedi H, Alvandi A, Fathollahi M, Ari MM, Moradi S, Moradi J, et al. In silico designing and immunoinformatics analysis of a novel peptide vaccine against metallo-beta-lactamase (VIM and IMP) variants. PloS one. 2023;18(7):e0275237. pmid:37471423

* View Article

* PubMed/NCBI

* Google Scholar

133. 133. Harper DM, Franco EL, Wheeler CM, Moscicki AB, Romanowski B, Roteli-Martins CM, et al. Sustained efficacy up to 4.5 years of a bivalent L1 virus-like particle vaccine against human papillomavirus types 16 and 18: follow-up from a randomised control trial. Lancet (London, England). 2006;367(9518):1247–55. Epub 2006/04/25. pmid:16631880.

* View Article

* PubMed/NCBI

* Google Scholar

134. 134. Harper DM, Franco EL, Wheeler C, Ferris DG, Jenkins D, Schuind A, et al. Efficacy of a bivalent L1 virus-like particle vaccine in prevention of infection with human papillomavirus types 16 and 18 in young women: a randomised controlled trial. Lancet (London, England). 2004;364(9447):1757–65. Epub 2004/11/16. pmid:15541448.

* View Article

* PubMed/NCBI

* Google Scholar

135. 135. Paavonen J, Jenkins D, Bosch FX, Naud P, Salmerón J, Wheeler CM, et al. Efficacy of a prophylactic adjuvanted bivalent L1 virus-like-particle vaccine against infection with human papillomavirus types 16 and 18 in young women: an interim analysis of a phase III double-blind, randomised controlled trial. Lancet (London, England). 2007;369(9580):2161–70. Epub 2007/07/03. pmid:17602732.

* View Article

* PubMed/NCBI

* Google Scholar

136. 136. Garland SM, Hernandez-Avila M, Wheeler CM, Perez G, Harper DM, Leodolter S, et al. Quadrivalent vaccine against human papillomavirus to prevent anogenital diseases. The New England journal of medicine. 2007;356(19):1928–43. Epub 2007/05/15. pmid:17494926.

* View Article

* PubMed/NCBI

* Google Scholar

137. 137. Villa LL, Costa RL, Petta CA, Andrade RP, Ault KA, Giuliano AR, et al. Prophylactic quadrivalent human papillomavirus (types 6, 11, 16, and 18) L1 virus-like particle vaccine in young women: a randomised double-blind placebo-controlled multicentre phase II efficacy trial. The Lancet Oncology. 2005;6(5):271–8. Epub 2005/05/03. pmid:15863374.

* View Article

* PubMed/NCBI

* Google Scholar

138. 138. Giuliano AR, Joura EA, Garland SM, Huh WK, Iversen OE, Kjaer SK, et al. Nine-valent HPV vaccine efficacy against related diseases and definitive therapy: comparison with historic placebo population. Gynecologic oncology. 2019;154(1):110–7. Epub 2019/04/16. pmid:30982556.

* View Article

* PubMed/NCBI

* Google Scholar

139. 139. Yang DY, Bracken K. Update on the new 9-valent vaccine for human papillomavirus prevention. Canadian family physician Medecin de famille canadien. 2016;62(5):399–402. Epub 2016/06/04. pmid:27255620.

* View Article

* PubMed/NCBI

* Google Scholar

140. 140. Joura EA, Giuliano AR, Iversen OE, Bouchard C, Mao C, Mehlsen J, et al. A 9-valent HPV vaccine against infection and intraepithelial neoplasia in women. The New England journal of medicine. 2015;372(8):711–23. Epub 2015/02/19. pmid:25693011.

* View Article

* PubMed/NCBI

* Google Scholar

141. 141. Clifford GM, de Vuyst H, Tenet V, Plummer M, Tully S, Franceschi S. Effect of HIV Infection on Human Papillomavirus Types Causing Invasive Cervical Cancer in Africa. Journal of acquired immune deficiency syndromes (1999). 2016;73(3):332–9. Epub 2016/06/23. pmid:27331659.

* View Article

* PubMed/NCBI

* Google Scholar

142. 142. Pimenta JM, Galindo C, Jenkins D, Taylor SM. Estimate of the global burden of cervical adenocarcinoma and potential impact of prophylactic human papillomavirus vaccination. BMC cancer. 2013;13:553. Epub 2013/11/23. pmid:24261839.

* View Article

* PubMed/NCBI

* Google Scholar

143. 143. Lin C, Franceschi S, Clifford GM. Human papillomavirus types from infection to cancer in the anus, according to sex and HIV status: a systematic review and meta-analysis. The Lancet Infectious diseases. 2018;18(2):198–206. Epub 2017/11/22. pmid:29158102.

* View Article

* PubMed/NCBI

* Google Scholar

144. 144. Hancock G, Hellner K, Dorrell L. Therapeutic HPV vaccines. Best practice & research Clinical obstetrics & gynaecology. 2018;47:59–72. Epub 2017/11/08. pmid:29108943.

* View Article

* PubMed/NCBI

* Google Scholar

145. 145. Iqbal L, Jehan M, Azam S. Advancements in mRNA Vaccines: A Promising Approach for Combating Human Papillomavirus-Related Cancers. Cancer control: journal of the Moffitt Cancer Center. 2024;31:10732748241238629. Epub 2024/03/11. pmid:38462683.

* View Article

* PubMed/NCBI

* Google Scholar

146. 146. Nitika , Wei J, Hui AM. The Delivery of mRNA Vaccines for Therapeutics. Life (Basel, Switzerland). 2022;12(8). Epub 2022/08/27. pmid:36013433.

* View Article

* PubMed/NCBI

* Google Scholar

147. 147. Lee S, Yoon H, Hong SH, Kwon SP, Hong JJ, Kwak HW, et al. mRNA-HPV vaccine encoding E6 and E7 improves therapeutic potential for HPV-mediated cancers via subcutaneous immunization. 2023;95(12):e29309. pmid:38100632

* View Article

* PubMed/NCBI

* Google Scholar

148. 148. Zhou K, Yuzhakov O, Behloul N, Wang D, Bhagat L, Chu D, et al. HPV16 E6/E7 -based mRNA vaccine is therapeutic in mice bearing aggressive HPV-positive lesions. Frontiers in immunology. 2023;14:1213285. Epub 2023/07/28. pmid:37503351.

* View Article

* PubMed/NCBI

* Google Scholar

149. 149. Khatoon N, Pandey RK, Prajapati VK. Exploring Leishmania secretory proteins to design B and T cell multi-epitope subunit vaccine using immunoinformatics approach. Scientific reports. 2017;7(1):8285. Epub 2017/08/16. pmid:28811600.

* View Article

* PubMed/NCBI

* Google Scholar

150. 150. Meza B, Ascencio F, Sierra-Beltrán AP, Torres J, Angulo C. A novel design of a multi-antigenic, multistage and multi-epitope vaccine against Helicobacter pylori: An in silico approach. Infection, genetics and evolution: journal of molecular epidemiology and evolutionary genetics in infectious diseases. 2017;49:309–17. Epub 2017/02/12. pmid:28185986.

* View Article

* PubMed/NCBI

* Google Scholar

151. 151. Scheiblhofer S, Laimer J, Machado Y, Weiss R, Thalhamer J. Influence of protein fold stability on immunogenicity and its implications for vaccine design. Expert review of vaccines. 2017;16(5):479–89. Epub 2017/03/16. pmid:28290225.

* View Article

* PubMed/NCBI

* Google Scholar

152. 152. Moin AT, Rani NA, Patil RB, Robin TB, Ullah MA, Rahim Z, et al. In-silico formulation of a next-generation polyvalent vaccine against multiple strains of monkeypox virus and other related poxviruses. PloS one. 2024;19(5):e0300778. pmid:38758816

* View Article

* PubMed/NCBI

* Google Scholar

153. 153. Mustapha T, B S, Zubair T, Patil RB, Bhongade BA, Sangshetti JN, et al. In vitro and in silico investigation of effects of antimicrobial peptides from Solanaceae plants against rice sheath blight pathogen Rhizoctinia solani. PloS one. 2024;19(6):e0302440. pmid:38870165

* View Article

* PubMed/NCBI

* Google Scholar

154. 154. Khanam M, Moin AT, Ahmed KA, Patil RB, Ripon Khalipha AB, Ahmed N, et al. Computational modeling of potential milciclib derivatives inhibitor-CDK2 binding through global docking and accelerated molecular dynamics simulations. Informatics in Medicine Unlocked. 2022;33:101069.

* View Article

* Google Scholar

155. 155. Moin Abu T, Patil Rajesh B, Tabassum T, Araf Y, Ullah Md A, Snigdha Hafsa J, et al. Immunoinformatics Approach to Design Novel Subunit Vaccine against the Epstein-Barr Virus. Microbiology Spectrum. 2022;10(5):e01151–22. pmid:36094198

* View Article

* PubMed/NCBI

* Google Scholar

156. 156. Moin AT, Robin TB, Patil RB, Rani NA, Prome AA, Sakif TI, et al. Antifungal plant flavonoids identified in silico with potential to control rice blast disease caused by Magnaporthe oryzae. PloS one. 2024;19(4):e0301519. pmid:38578751

* View Article

* PubMed/NCBI

* Google Scholar

Citation: Rahman MM, Masum MHU, Parvin R, Das SC, Talukder A (2025) Designing of an mRNA vaccine against high-risk human papillomavirus targeting the E6 and E7 oncoproteins exploiting immunoinformatics and dynamic simulation. PLoS ONE 20(1): e0313559. https://doi.org/10.1371/journal.pone.0313559

About the Authors:

Md. Mijanur Rahman

Roles: Conceptualization, Project administration, Supervision, Validation, Writing – review & editing

¶‡ MMR and MHUM are co-first authors on this work.

Affiliations: Department of Microbiology, Noakhali Science and Technology University, Noakhali, Bangladesh, Microbiology, Cancer and Bioinformatics Research Group, Noakhali Science and Technology University, Noakhali, Bangladesh, School of Pharmacy and Medical Sciences, Griffith University, Queensland, Australia

Md. Habib Ullah Masum

Roles: Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

¶‡ MMR and MHUM are co-first authors on this work.

Affiliations: Department of Microbiology, Noakhali Science and Technology University, Noakhali, Bangladesh, Microbiology, Cancer and Bioinformatics Research Group, Noakhali Science and Technology University, Noakhali, Bangladesh, Department of Genomics and Bioinformatics, Faculty of Biotechnology and Genetic Engineering, Chattogram Veterinary and Animal Sciences University, Khulshi, Chittagong, Bangladesh

Rehana Parvin

Roles: Validation, Writing – review & editing

Affiliation: Department of Pathology and Parasitology, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University, Khulshi, Chittagong, Bangladesh

Shuvo Chandra Das

Roles: Methodology, Validation, Writing – review & editing

Affiliation: Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh

Asma Talukder

Roles: Methodology, Project administration, Supervision, Validation, Writing – review & editing

E-mail: [email protected]

Affiliations: Microbiology, Cancer and Bioinformatics Research Group, Noakhali Science and Technology University, Noakhali, Bangladesh, School of Pharmacy and Medical Sciences, Griffith University, Queensland, Australia, Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh

ORICD: https://orcid.org/0000-0002-5647-9212

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

References

1. Einstein MH, Schiller JT, Viscidi RP, Strickler HD, Coursaget P, Tan T, et al. Clinician’s guide to human papillomavirus immunology: knowns and unknowns. The Lancet Infectious diseases. 2009;9(6):347–56. Epub 2009/05/27. pmid:19467474.

2. Lin S, Gao K, Gu S, You L, Qian S, Tang M, et al. Worldwide trends in cervical cancer incidence and mortality, with predictions for the next 15 years. Cancer. 2021;127(21):4030–9. Epub 2021/08/10. pmid:34368955.

3. Ramberg IMS. Human papillomavirus-related neoplasia of the ocular adnexa. Acta ophthalmologica. 2022;100 Suppl 272(Suppl 272):3–33. Epub 2022/10/07. pmid:36203222.

4. Karnosky J, Dietmaier W, Knuettel H, Freigang V, Koch M, Koll F, et al. HPV and lung cancer: A systematic review and meta-analysis. 2021;4(4):e1350. pmid:33624444

5. Srinivasan M, Taioli E, Ragin CC. Human papillomavirus type 16 and 18 in primary lung cancers—a meta-analysis. Carcinogenesis. 2009;30(10):1722–8. Epub 2009/07/22. pmid:19620233.

6. Brisson M, Drolet M. Global elimination of cervical cancer as a public health problem. The Lancet Oncology. 2019;20(3):319–21. Epub 2019/02/24. pmid:30795952.

7. (WHO) WHO. Cervical Cancer 2024 [30 April 2024]. https://www.who.int/news-room/fact-sheets/detail/cervical-cancer#:~:text=Cervical%20cancer%20is%20the%20fourth,%2D%20and%20middle%2Dincome%20countries.

8. Eiben GL, da Silva DM, Fausch SC, Le Poole IC, Nishimura MI, Kast WM. Cervical cancer vaccines: recent advances in HPV research. Viral immunology. 2003;16(2):111–21. Epub 2003/06/28. pmid:12828864.

9. Brianti P, De Flammineis E, Mercuri SR. Review of HPV-related diseases and cancers. The new microbiologica. 2017;40(2):80–5. Epub 2017/04/04. pmid:28368072.

10. Denny L, Prendiville W. Cancer of the cervix: Early detection and cost-effective solutions. International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics. 2015;131 Suppl 1:S28–32. Epub 2015/10/05. pmid:26433500.

11. Denny L, de Sanjose S, Mutebi M, Anderson BO, Kim J, Jeronimo J, et al. Interventions to close the divide for women with breast and cervical cancer between low-income and middle-income countries and high-income countries. Lancet (London, England). 2017;389(10071):861–70. Epub 2016/11/07. pmid:27814963.

12. (CRI) CRI. Immunotherapy For Cervical Cancer 2024 [30 April 2024]. https://www.cancerresearch.org/cancer-types/cervical-cancer.

13. UK CR. Targeted and immunotherapy drugs for cervical cancer 2024 [30 April 2024]. https://www.cancerresearchuk.org/about-cancer/cervical-cancer/treatment/targeted-immunotherapy-drugs.

14. UK CR. Bevacizumab (Avastin) 2024 [30 April 2024]. https://www.cancerresearchuk.org/about-cancer/treatment/drugs/bevacizumab.

15. UK CR. Pembrolizumab (Keytruda) 2024 [30 April 2024]. https://www.cancerresearchuk.org/about-cancer/treatment/drugs/pembrolizumab.

16. Gooi Z, Chan JY, Fakhry C. The epidemiology of the human papillomavirus related to oropharyngeal head and neck cancer. The Laryngoscope. 2016;126(4):894–900. Epub 2016/02/05. pmid:26845348.

17. Nogues JC, Fassas S, Mulcahy C, Zapanta PE. Human Papillomavirus-Associated Head and Neck Cancer. Journal of the American Board of Family Medicine: JABFM. 2021;34(4):832–7. Epub 2021/07/28. pmid:34312276.

18. Rajendra K, Sharma P. Viral Pathogens in Oesophageal and Gastric Cancer. Pathogens (Basel, Switzerland). 2022;11(4). Epub 2022/04/24. pmid:35456151.

19. Li S, Luk HY, Xia C, Chen Z, Chan PKS, Boon SS. Oesophageal carcinoma: The prevalence of DNA tumour viruses and therapy. Tumour virus research. 2022;13:200231. Epub 2021/12/18. pmid:34920177.

20. Sitas F, Urban M, Stein L, Beral V, Ruff P, Hale M, et al. The relationship between anti-HPV-16 IgG seropositivity and cancer of the cervix, anogenital organs, oral cavity and pharynx, oesophagus and prostate in a black South African population. Infectious agents and cancer. 2007;2:6. Epub 2007/03/03. pmid:17331260.

21. Deshmukh AA, Damgacioglu H, Georges D, Sonawane K, Ferlay J, Bray F, et al. Global burden of HPV-attributable squamous cell carcinoma of the anus in 2020, according to sex and HIV status: A worldwide analysis. International journal of cancer. 2023;152(3):417–28. Epub 2022/09/03. pmid:36054026.

22. Cubie HA. Diseases associated with human papillomavirus infection. Virology. 2013;445(1–2):21–34. Epub 2013/08/13. pmid:23932731.

23. Howley PMJFv. Papillomavirinae: the viruses and their replication. 1996:2045–76.

24. Thomas Jennifer T, Laimins Laimonis A. Human Papillomavirus Oncoproteins E6 and E7 Independently Abrogate the Mitotic Spindle Checkpoint. Journal of virology. 1998;72(2):1131–7. pmid:9445009

25. zur Hausen H, de Villiers EM. Human papillomaviruses. Annual review of microbiology. 1994;48:427–47. Epub 1994/01/01. pmid:7826013.

26. Schiffman MH. Epidemiology of cervical human papillomavirus infections. Current topics in microbiology and immunology. 1994;186:55–81. Epub 1994/01/01. pmid:8205846.

27. Vousden KH. Interactions between papillomavirus proteins and tumor suppressor gene products. Advances in cancer research. 1994;64:1–24. Epub 1994/01/01. pmid:7879655.

28. von Knebel Doeberitz M, Rittmüller C, zur Hausen H, Dürst M. Inhibition of tumorigenicity of cervical cancer cells in nude mice by HPV E6-E7 anti-sense RNA. International journal of cancer. 1992;51(5):831–4. Epub 1992/07/09. pmid:1319412.

29. Hawley-Nelson P, Vousden KH, Hubbert NL, Lowy DR, Schiller JT. HPV16 E6 and E7 proteins cooperate to immortalize human foreskin keratinocytes. The EMBO journal. 1989;8(12):3905–10. Epub 1989/12/01. pmid:2555178.

30. Scheffner M, Huibregtse JM, Vierstra RD, Howley PM. The HPV-16 E6 and E6-AP complex functions as a ubiquitin-protein ligase in the ubiquitination of p53. Cell. 1993;75(3):495–505. Epub 1993/11/05. pmid:8221889.

31. Scheffner M, Werness BA, Huibregtse JM, Levine AJ, Howley PM. The E6 oncoprotein encoded by human papillomavirus types 16 and 18 promotes the degradation of p53. Cell. 1990;63(6):1129–36. Epub 1990/12/21. pmid:2175676.

32. Münger K, Werness BA, Dyson N, Phelps WC, Harlow E, Howley PM. Complex formation of human papillomavirus E7 proteins with the retinoblastoma tumor suppressor gene product. The EMBO journal. 1989;8(13):4099–105. Epub 1989/12/20. pmid:2556261.

33. Dyson N, Howley PM, Münger K, Harlow E. The human papilloma virus-16 E7 oncoprotein is able to bind to the retinoblastoma gene product. Science (New York, NY). 1989;243(4893):934–7. Epub 1989/02/17. pmid:2537532.

34. Davies R, Hicks R, Crook T, Morris J, Vousden K. Human papillomavirus type 16 E7 associates with a histone H1 kinase and with p107 through sequences necessary for transformation. Journal of virology. 1993;67(5):2521–8. Epub 1993/05/01. pmid:8386265.

35. Dyson N, Guida P, Münger K, Harlow EJJov. Homologous sequences in adenovirus E1A and human papillomavirus E7 proteins mediate interaction with the same set of cellular proteins. 1992;66(12):6893–902.

36. Scheffner M, Romanczuk H, Münger K, Huibregtse JM, Mietz JA, Howley PM. Functions of human papillomavirus proteins. Current topics in microbiology and immunology. 1994;186:83–99. Epub 1994/01/01. pmid:8205847.

37. Lechner MS, Laimins LA. Inhibition of p53 DNA binding by human papillomavirus E6 proteins. Journal of virology. 1994;68(7):4262–73. Epub 1994/07/01. pmid:8207801.

38. Lechner MS, Mack DH, Finicle AB, Crook T, Vousden KH, Laimins LA. Human papillomavirus E6 proteins bind p53 in vivo and abrogate p53-mediated repression of transcription. The EMBO journal. 1992;11(8):3045–52. Epub 1992/08/01. pmid:1379175.

39. Weinberg RA. The retinoblastoma protein and cell cycle control. Cell. 1995;81(3):323–30. Epub 1995/05/05. pmid:7736585.

40. Cahilly-Snyder L, Yang-Feng T, Francke U, George DL. Molecular analysis and chromosomal mapping of amplified genes isolated from a transformed mouse 3T3 cell line. Somatic cell and molecular genetics. 1987;13(3):235–44. Epub 1987/05/01. pmid:3474784.

41. Williamson AL. Recent Developments in Human Papillomavirus (HPV) Vaccinology. Viruses. 2023;15(7). Epub 2023/07/29. pmid:37515128.

42. Mohammadi Y, Nezafat N, Negahdaripour M, Eskandari S, Zamani M. In silico design and evaluation of a novel mRNA vaccine against BK virus: a reverse vaccinology approach. Immunologic Research. 2023;71(3):422–41. pmid:36580228

43. Pardi N, Hogan MJ, Porter FW, Weissman D. mRNA vaccines—a new era in vaccinology. Nature reviews Drug discovery. 2018;17(4):261–79. Epub 2018/01/13. pmid:29326426.

44. Guan S, Rosenecker J. Nanotechnologies in delivery of mRNA therapeutics using nonviral vector-based delivery systems. Gene therapy. 2017;24(3):133–43. Epub 2017/01/18. pmid:28094775.

45. Karikó K, Muramatsu H, Welsh FA, Ludwig J, Kato H, Akira S, et al. Incorporation of pseudouridine into mRNA yields superior nonimmunogenic vector with increased translational capacity and biological stability. Molecular therapy: the journal of the American Society of Gene Therapy. 2008;16(11):1833–40. Epub 2008/09/18. pmid:18797453.

46. Kauffman KJ, Webber MJ, Anderson DG. Materials for non-viral intracellular delivery of messenger RNA therapeutics. Journal of controlled release: official journal of the Controlled Release Society. 2016;240:227–34. Epub 2016/01/01. pmid:26718856.

47. Thess A, Grund S, Mui BL, Hope MJ, Baumhof P, Fotin-Mleczek M, et al. Sequence-engineered mRNA Without Chemical Nucleoside Modifications Enables an Effective Protein Therapy in Large Animals. Molecular therapy: the journal of the American Society of Gene Therapy. 2015;23(9):1456–64. Epub 2015/06/09. pmid:26050989.

48. Rani NA, Robin TB, Prome AA, Ahmed N, Moin AT, Patil RB, et al. Development of multi epitope subunit vaccines against emerging carp viruses Cyprinid herpesvirus 1 and 3 using immunoinformatics approach. Scientific Reports. 2024;14(1):11783. pmid:38782944

49. Ahmad S, Demneh FM, Rehman B, Almanaa TN, Akhtar N, Pazoki-Toroudi H, et al. In silico design of a novel multi-epitope vaccine against HCV infection through immunoinformatics approaches. International journal of biological macromolecules. 2024;267:131517. pmid:38621559

50. Ahmad S, Nazarian S, Alizadeh A, Pashapour Hajialilou M, Tahmasebian S, Alharbi M, et al. Computational design of a multi-epitope vaccine candidate against Langya henipavirus using surface proteins. Journal of Biomolecular Structure and Dynamics. 1–18. pmid:37713338

51. Ullah A, Rehman B, Khan S, Almanaa TN, Waheed Y, Hassan M, et al. An In Silico Multi-epitopes Vaccine Ensemble and Characterization Against Nosocomial Proteus penneri. Molecular Biotechnology. 2023. pmid:37934390

52. Rawat SS, Keshri AK, Kaur R, Prasad A. Immunoinformatics Approaches for Vaccine Design: A Fast and Secure Strategy for Successful Vaccine Development. Vaccines. 2023;11(2). Epub 2023/03/01. pmid:36851099.

53. Parvin R, Habib Ullah Masum M, Ferdous J, Mahdeen AA, Shafiqul Islam Khan M. Designing of a chimeric multiepitope vaccine against bancroftian lymphatic filariasis through immunoinformatics approaches. PloS one. 2024;19(9):e0310398. pmid:39298468

54. Farhani I, Yamchi A, Madanchi H, Khazaei V, Behrouzikhah M, Abbasi H, et al. Designing a Multi-epitope Vaccine against the SARS-CoV-2 Variant based on an Immunoinformatics Approach. Current Computer-Aided Drug Design. 2024;20(3):274–90. pmid:37309762

55. Oli AN, Obialor WO, Ifeanyichukwu MO, Odimegwu DC, Okoyeh JN, Emechebe GO, et al. Immunoinformatics and Vaccine Development: An Overview. ImmunoTargets and therapy. 2020;9:13–30. Epub 2020/03/13. pmid:32161726.

56. Joon S, Singla RK, Shen B. Vaccines and Immunoinformatics for Vaccine Design. In: Shen B, editor. Translational Informatics: Prevention and Treatment of Viral Infections. Singapore: Springer Nature Singapore; 2022. p. 95–110.

57. Sippl MJ. Recognition of errors in three-dimensional structures of proteins. Proteins. 1993;17(4):355–62. Epub 1993/12/01. pmid:8108378.

58. Sayers EW, Bolton EE, Brister JR, Canese K, Chan J, Comeau DC, et al. Database resources of the national center for biotechnology information. Nucleic acids research. 2022;50(D1):D20–d6. Epub 2021/12/02. pmid:34850941.

59. Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M. NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. Journal of immunology (Baltimore, Md: 1950). 2017;199(9):3360–8. Epub 2017/10/06. pmid:28978689.

60. Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic acids research. 2020;48(W1):W449–w54. Epub 2020/05/15. pmid:32406916.

61. Andreatta M, Nielsen M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics. 2016;32(4):511–7. Epub 2015/10/31. pmid:26515819.

62. Nielsen M, Lundegaard C, Worning P, Lauemøller SL, Lamberth K, Buus S, et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein science: a publication of the Protein Society. 2003;12(5):1007–17. Epub 2003/04/30. pmid:12717023.

63. Abdelmageed MI, Abdelmoneim AH, Mustafa MI, Elfadol NM, Murshed NS, Shantier SW, et al. Design of a Multiepitope-Based Peptide Vaccine against the E Protein of Human COVID-19: An Immunoinformatics Approach. BioMed research international. 2020;2020:2683286. Epub 2020/05/29. pmid:32461973.

64. Buus S, Lauemøller SL, Worning P, Kesmir C, Frimurer T, Corbet S, et al. Sensitive quantitative predictions of peptide-MHC binding by a ’Query by Committee’ artificial neural network approach. Tissue Antigens. 2003;62(5):378–84. Epub 2003/11/18. pmid:14617044.

65. Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, et al. Immune epitope database analysis resource. Nucleic acids research. 2012;40(Web Server issue):W525–30. Epub 2012/05/23. pmid:22610854.

66. Kim Y, Sidney J, Pinilla C, Sette A, Peters B. Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. BMC bioinformatics. 2009;10:394. Epub 2009/12/02. pmid:19948066.

67. Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11. Nucleic acids research. 2008;36(Web Server issue):W509–12. Epub 2008/05/09. pmid:18463140.

68. Lundegaard C, Lund O, Nielsen M. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Bioinformatics. 2008;24(11):1397–8. Epub 2008/04/17. pmid:18413329.

69. Moutaftsi M, Peters B, Pasquetto V, Tscharke DC, Sidney J, Bui HH, et al. A consensus epitope prediction approach identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus. Nat Biotechnol. 2006;24(7):817–9. Epub 2006/06/13. pmid:16767078.

70. Peters B, Sette A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC bioinformatics. 2005;6:132. Epub 2005/06/02. pmid:15927070.

71. Sidney J, Assarsson E, Moore C, Ngo S, Pinilla C, Sette A, et al. Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res. 2008;4:2. Epub 2008/01/29. pmid:18221540.

72. Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic acids research. 2019;47(D1):D339–d43. Epub 2018/10/26. pmid:30357391.

73. Yazdani Z, Rafiei A, Irannejad H, Yazdani M, Valadan R. Designing a novel multiepitope peptide vaccine against melanoma using immunoinformatics approach. Journal of biomolecular structure & dynamics. 2022;40(7):3312–24. Epub 2020/11/24. pmid:33226282.

74. Zhang Q, Wang P, Kim Y, Haste-Andersen P, Beaver J, Bourne PE, et al. Immune epitope database analysis resource (IEDB-AR). Nucleic acids research. 2008;36(Web Server issue):W513–8. Epub 2008/06/03. pmid:18515843.

75. Hossain MS, Hossan MI, Mizan S, Moin AT, Yasmin F, Akash A-S, et al. Immunoinformatics approach to designing a multi-epitope vaccine against Saint Louis Encephalitis Virus. Informatics in Medicine Unlocked. 2021;22:100500.

76. Araf Y, Moin AT, Timofeev VI, Faruqui NA, Saiara SA, Ahmed N, et al. Immunoinformatic Design of a Multivalent Peptide Vaccine Against Mucormycosis: Targeting FTR1 Protein of Major Causative Fungi. 2022;13. pmid:35720422

77. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC bioinformatics. 2007;8:4. Epub 2007/01/09. pmid:17207271.

78. Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v.2—a server for in silico prediction of allergens. Journal of molecular modeling. 2014;20(6):2278. Epub 2014/06/01. pmid:24878803.

79. Sharma N, Naorem LD, Jain S, Raghava GPS. ToxinPred2: an improved method for predicting toxicity of proteins. Briefings in bioinformatics. 2022;23(5). Epub 2022/05/21. pmid:35595541.

80. Reynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Nielsen M. Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data. Journal of proteome research. 2020;19(6):2304–15. Epub 2020/04/21. pmid:32308001.

81. Masum MHU, Ferdous J, Lokman S, Siddiki AZ. Designing of a multiepitope-based chimeric vaccine against dengue virus serotype 3 (DENV-3) through next generation reverse vaccinology approaches. Informatics in Medicine Unlocked. 2024;44:101422.

82. Singh H, Ansari HR, Raghava GP. Improved method for linear B-cell epitope prediction using antigen’s primary sequence. PloS one. 2013;8(5):e62216. Epub 2013/05/15. pmid:23667458 This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

83. Emini EA, Hughes JV, Perlow DS, Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. Journal of virology. 1985;55(3):836–9. Epub 1985/09/01. pmid:2991600.

84. Rahman MM, Masum MHU, Talukder A, Akter R. An in silico reverse vaccinology approach to design a novel multiepitope peptide vaccine for non-small cell lung cancers. Informatics in Medicine Unlocked. 2023;37:101169.

85. Masum MHU, Wajed S, Hossain MI, Moumi NR, Talukder A, Rahman MM. An mRNA vaccine for pancreatic cancer designed by applying in silico immunoinformatics and reverse vaccinology approaches. PloS one. 2024;19(7):e0305413. Epub 2024/07/08. pmid:38976715.

86. Arai R, Ueda H, Kitayama A, Kamiya N, Nagamune T. Design of the linkers which effectively separate domains of a bifunctional fusion protein. Protein engineering. 2001;14(8):529–32. Epub 2001/10/02. pmid:11579220.

87. 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. Computers in Biology and Medicine. 2016;76:24–9. pmid:27393958

88. George RA, Heringa J. An analysis of protein domain linkers: their classification and role in protein folding. Protein engineering. 2002;15(11):871–9. Epub 2003/01/23. pmid:12538906.

89. Wilkins MR, Gasteiger E, Bairoch A, Sanchez JC, Williams KL, Appel RD, et al. Protein identification and analysis tools in the ExPASy server. Methods in molecular biology (Clifton, NJ). 1999;112:531–52. Epub 1999/02/23. pmid:10027275.

90. Hirokawa T, Boonćchieng S, Mitaku SJB. SOSUI: classification and secondary structure prediction system for membrane proteins. 1998;14 4:378–9.

91. Magnan CN, Randall A, Baldi P. SOLpro: accurate sequence-based prediction of protein solubility. Bioinformatics. 2009;25(17):2200–7. %J Bioinformatics. pmid:19549632

92. Mitaku S, Hirokawa T. Physicochemical factors for discriminating between soluble and membrane proteins: hydrophobicity of helical segments and protein length. Protein engineering. 1999;12(11):953–7. Epub 1999/12/10. pmid:10585500.

93. Mitaku S, Hirokawa T, Tsuji T. Amphiphilicity index of polar amino acids as an aid in the characterization of amino acid preference at membrane-water interfaces. Bioinformatics. 2002;18(4):608–16. Epub 2002/05/23. pmid:12016058.

94. Maurer-Stroh S, Krutz NL, Kern PS, Gunalan V, Nguyen MN, Limviphuvadh V, et al. AllerCatPro—prediction of protein allergenicity potential from the protein sequence. Bioinformatics. 2019;35(17):3020–7. %J Bioinformatics. pmid:30657872

95. Dhanda SK, Vir P, Raghava GP. Designing of interferon-gamma inducing MHC class-II binders. Biology direct. 2013;8:30. Epub 2013/12/07. pmid:24304645.

96. Buchan DWA, Jones DT. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic acids research. 2019;47(W1):W402–w7. Epub 2019/06/30. pmid:31251384.

97. Combet C, Blanchet C, Geourjon C, Deléage G. NPS@: network protein sequence analysis. Trends in biochemical sciences. 2000;25(3):147–50. Epub 2000/03/01. pmid:10694887.

98. Geourjon C, Deléage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Computer applications in the biosciences: CABIOS. 1995;11(6):681–4. Epub 1995/12/01. pmid:8808585.

99. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. Journal of molecular biology. 1999;292(2):195–202. Epub 1999/09/24. pmid:10493868.

100. Garnier J, Gibrat JF, Robson B. GOR method for predicting protein secondary structure from amino acid sequence. Methods in enzymology. 1996;266:540–53. Epub 1996/01/01. pmid:8743705.

101. McGuffin LJ, Bryson K, Jones DT. The PSIPRED protein structure prediction server. Bioinformatics (Oxford, England). 2000;16(4):404–5. Epub 2000/06/27. pmid:10869041.

102. Ghahremanifard P, Afzali F, Rostami A, Nayeri Z, Bambai B, Minuchehr Z. Designing a Novel Multi-epitope T Vaccine for “Targeting Protein for Xklp-2” (TPX2) in Hepatocellular Carcinoma Based on Immunoinformatics Approach. International Journal of Peptide Research and Therapeutics. 2020;26.

103. Ko J, Park H, Heo L, Seok C. GalaxyWEB server for protein structure prediction and refinement. Nucleic acids research. 2012;40(Web Server issue):W294–7. Epub 2012/06/01. pmid:22649060.

104. Laskowski R, Macarthur MW, Moss DS, Thornton J. PROCHECK: A program to check the stereochemical quality of protein structures. Journal of Applied Crystallography. 1993;26:283–91.

105. Laskowski RA, MacArthur MW, Thornton JMJITfC. PROCHECK: validation of protein‐structure coordinates. 2012:684–7.

106. Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton JM. AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. Journal of biomolecular NMR. 1996;8(4):477–86. Epub 1996/12/01. pmid:9008363.

107. Morris AL, MacArthur MW, Hutchinson EG, Thornton JM. Stereochemical quality of protein structure coordinates. Proteins. 1992;12(4):345–64. Epub 1992/04/01. pmid:1579569.

108. Talukder A, Rahman MM, Masum MHU. Biocomputational characterisation of MBO_200107 protein of Mycobacterium tuberculosis variant caprae: a molecular docking and simulation study. Journal of Biomolecular Structure and Dynamics. 2023;41(15):7204–23. pmid:36039775

109. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic acids research. 2007;35(Web Server issue):W407–10. Epub 2007/05/23. pmid:17517781.

110. Kringelum JV, Lundegaard C, Lund O, Nielsen M. Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS computational biology. 2012;8(12):e1002829. Epub 2013/01/10. pmid:23300419.

111. Ponomarenko J, Bui HH, Li W, Fusseder N, Bourne PE, Sette A, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC bioinformatics. 2008;9:514. Epub 2008/12/06. pmid:19055730.

112. Flory PJ. Theory of elastic mechanisms in fibrous proteins. JJotACS 1956;78(20):5222–35.

113. Craig DB, Dombkowski AA. Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins. BMC bioinformatics. 2013;14:346. Epub 2013/12/03. pmid:24289175.

114. Dombkowski AA, Sultana KZ, Craig DB. Protein disulfide engineering. FEBS letters. 2014;588(2):206–12. Epub 2013/12/03. pmid:24291258.

115. Shahik SM, Salauddin A, Hossain MS, Noyon SH, Moin AT, Mizan S, et al. Screening of novel alkaloid inhibitors for vascular endothelial growth factor in cancer cells: an integrated computational approach. Genomics & informatics. 2021;19(1):e6. Epub 2021/04/13. pmid:33840170.

116. Desta IT, Porter KA, Xia B, Kozakov D, Vajda S. Performance and Its Limits in Rigid Body Protein-Protein Docking. Structure (London, England: 1993). 2020;28(9):1071–81.e3. Epub 2020/07/11. pmid:32649857.

117. Kozakov D, Beglov D, Bohnuud T, Mottarella SE, Xia B, Hall DR, et al. How good is automated protein docking? Proteins. 2013;81(12):2159–66. Epub 2013/09/03. pmid:23996272.

118. Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, et al. The ClusPro web server for protein-protein docking. Nature protocols. 2017;12(2):255–78. Epub 2017/01/13. pmid:28079879.

119. Vajda S, Yueh C, Beglov D, Bohnuud T, Mottarella SE, Xia B, et al. New additions to the ClusPro server motivated by CAPRI. Proteins. 2017;85(3):435–44. Epub 2016/12/10. pmid:27936493.

120. Laskowski RA, Jabłońska J, Pravda L, Vařeková RS, Thornton JM. PDBsum: Structural summaries of PDB entries. Protein science: a publication of the Protein Society. 2018;27(1):129–34. Epub 2017/09/07. pmid:28875543.

121. Chen F, Sun H, Wang J, Zhu F, Liu H, Wang Z, et al. Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes. RNA (New York, NY). 2018;24(9):1183–94. Epub 2018/06/23. pmid:29930024.

122. Hou T, Wang J, Li Y, Wang W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. Journal of chemical information and modeling. 2011;51(1):69–82. Epub 2010/12/02. pmid:21117705.

123. Sun H, Li Y, Tian S, Xu L, Hou T. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Physical chemistry chemical physics: PCCP. 2014;16(31):16719–29. Epub 2014/07/08. pmid:24999761.

124. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1–2:19–25.

125. Ali M, Pandey RK, Khatoon N, Narula A, Mishra A, Prajapati VK. Exploring dengue genome to construct a multi-epitope based subunit vaccine by utilizing immunoinformatics approach to battle against dengue infection. Scientific reports. 2017;7(1):9232. Epub 2017/08/25. pmid:28835708.

126. Bibi S, Ullah I, Zhu B, Adnan M, Liaqat R, Kong WB, et al. In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology. Sci Rep. 2021;11(1):1249. Epub 2021/01/15. pmid:33441913.

127. Rapin N, Lund O, Bernaschi M, Castiglione F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PloS one. 2010;5(4):e9862. Epub 2010/04/27. pmid:20419125.

128. Sanami S, Zandi M, Pourhossein B, Mobini GR, Safaei M, Abed A, et al. Design of a multi-epitope vaccine against SARS-CoV-2 using immunoinformatics approach. International journal of biological macromolecules. 2020;164:871–83. Epub 2020/07/19. pmid:32682041.

129. Castiglione F, Mantile F, De Berardinis P, Prisco A. How the Interval between Prime and Boost Injection Affects the Immune Response in a Computational Model of the Immune System. 2012;2012(1):842329. pmid:22997539

130. Gruber AR, Lorenz R, Bernhart SH, Neuböck R, Hofacker IL. The Vienna RNA Websuite. Nucleic acids research. 2008;36(suppl_2):W70–W4. %J Nucleic Acids Research. pmid:18424795

131. Lorenz R, Bernhart SH, Höner zu Siederdissen C, Tafer H, Flamm C, Stadler PF, et al. ViennaRNA Package 2.0. Algorithms for Molecular Biology. 2011;6(1):26. pmid:22115189

132. Motamedi H, Alvandi A, Fathollahi M, Ari MM, Moradi S, Moradi J, et al. In silico designing and immunoinformatics analysis of a novel peptide vaccine against metallo-beta-lactamase (VIM and IMP) variants. PloS one. 2023;18(7):e0275237. pmid:37471423

133. Harper DM, Franco EL, Wheeler CM, Moscicki AB, Romanowski B, Roteli-Martins CM, et al. Sustained efficacy up to 4.5 years of a bivalent L1 virus-like particle vaccine against human papillomavirus types 16 and 18: follow-up from a randomised control trial. Lancet (London, England). 2006;367(9518):1247–55. Epub 2006/04/25. pmid:16631880.

134. Harper DM, Franco EL, Wheeler C, Ferris DG, Jenkins D, Schuind A, et al. Efficacy of a bivalent L1 virus-like particle vaccine in prevention of infection with human papillomavirus types 16 and 18 in young women: a randomised controlled trial. Lancet (London, England). 2004;364(9447):1757–65. Epub 2004/11/16. pmid:15541448.

135. Paavonen J, Jenkins D, Bosch FX, Naud P, Salmerón J, Wheeler CM, et al. Efficacy of a prophylactic adjuvanted bivalent L1 virus-like-particle vaccine against infection with human papillomavirus types 16 and 18 in young women: an interim analysis of a phase III double-blind, randomised controlled trial. Lancet (London, England). 2007;369(9580):2161–70. Epub 2007/07/03. pmid:17602732.

136. Garland SM, Hernandez-Avila M, Wheeler CM, Perez G, Harper DM, Leodolter S, et al. Quadrivalent vaccine against human papillomavirus to prevent anogenital diseases. The New England journal of medicine. 2007;356(19):1928–43. Epub 2007/05/15. pmid:17494926.

137. Villa LL, Costa RL, Petta CA, Andrade RP, Ault KA, Giuliano AR, et al. Prophylactic quadrivalent human papillomavirus (types 6, 11, 16, and 18) L1 virus-like particle vaccine in young women: a randomised double-blind placebo-controlled multicentre phase II efficacy trial. The Lancet Oncology. 2005;6(5):271–8. Epub 2005/05/03. pmid:15863374.

138. Giuliano AR, Joura EA, Garland SM, Huh WK, Iversen OE, Kjaer SK, et al. Nine-valent HPV vaccine efficacy against related diseases and definitive therapy: comparison with historic placebo population. Gynecologic oncology. 2019;154(1):110–7. Epub 2019/04/16. pmid:30982556.

139. Yang DY, Bracken K. Update on the new 9-valent vaccine for human papillomavirus prevention. Canadian family physician Medecin de famille canadien. 2016;62(5):399–402. Epub 2016/06/04. pmid:27255620.

140. Joura EA, Giuliano AR, Iversen OE, Bouchard C, Mao C, Mehlsen J, et al. A 9-valent HPV vaccine against infection and intraepithelial neoplasia in women. The New England journal of medicine. 2015;372(8):711–23. Epub 2015/02/19. pmid:25693011.

141. Clifford GM, de Vuyst H, Tenet V, Plummer M, Tully S, Franceschi S. Effect of HIV Infection on Human Papillomavirus Types Causing Invasive Cervical Cancer in Africa. Journal of acquired immune deficiency syndromes (1999). 2016;73(3):332–9. Epub 2016/06/23. pmid:27331659.

142. Pimenta JM, Galindo C, Jenkins D, Taylor SM. Estimate of the global burden of cervical adenocarcinoma and potential impact of prophylactic human papillomavirus vaccination. BMC cancer. 2013;13:553. Epub 2013/11/23. pmid:24261839.

143. Lin C, Franceschi S, Clifford GM. Human papillomavirus types from infection to cancer in the anus, according to sex and HIV status: a systematic review and meta-analysis. The Lancet Infectious diseases. 2018;18(2):198–206. Epub 2017/11/22. pmid:29158102.

144. Hancock G, Hellner K, Dorrell L. Therapeutic HPV vaccines. Best practice & research Clinical obstetrics & gynaecology. 2018;47:59–72. Epub 2017/11/08. pmid:29108943.

145. Iqbal L, Jehan M, Azam S. Advancements in mRNA Vaccines: A Promising Approach for Combating Human Papillomavirus-Related Cancers. Cancer control: journal of the Moffitt Cancer Center. 2024;31:10732748241238629. Epub 2024/03/11. pmid:38462683.

146. Nitika , Wei J, Hui AM. The Delivery of mRNA Vaccines for Therapeutics. Life (Basel, Switzerland). 2022;12(8). Epub 2022/08/27. pmid:36013433.

147. Lee S, Yoon H, Hong SH, Kwon SP, Hong JJ, Kwak HW, et al. mRNA-HPV vaccine encoding E6 and E7 improves therapeutic potential for HPV-mediated cancers via subcutaneous immunization. 2023;95(12):e29309. pmid:38100632

148. Zhou K, Yuzhakov O, Behloul N, Wang D, Bhagat L, Chu D, et al. HPV16 E6/E7 -based mRNA vaccine is therapeutic in mice bearing aggressive HPV-positive lesions. Frontiers in immunology. 2023;14:1213285. Epub 2023/07/28. pmid:37503351.

149. Khatoon N, Pandey RK, Prajapati VK. Exploring Leishmania secretory proteins to design B and T cell multi-epitope subunit vaccine using immunoinformatics approach. Scientific reports. 2017;7(1):8285. Epub 2017/08/16. pmid:28811600.

150. Meza B, Ascencio F, Sierra-Beltrán AP, Torres J, Angulo C. A novel design of a multi-antigenic, multistage and multi-epitope vaccine against Helicobacter pylori: An in silico approach. Infection, genetics and evolution: journal of molecular epidemiology and evolutionary genetics in infectious diseases. 2017;49:309–17. Epub 2017/02/12. pmid:28185986.

151. Scheiblhofer S, Laimer J, Machado Y, Weiss R, Thalhamer J. Influence of protein fold stability on immunogenicity and its implications for vaccine design. Expert review of vaccines. 2017;16(5):479–89. Epub 2017/03/16. pmid:28290225.

152. Moin AT, Rani NA, Patil RB, Robin TB, Ullah MA, Rahim Z, et al. In-silico formulation of a next-generation polyvalent vaccine against multiple strains of monkeypox virus and other related poxviruses. PloS one. 2024;19(5):e0300778. pmid:38758816

153. Mustapha T, B S, Zubair T, Patil RB, Bhongade BA, Sangshetti JN, et al. In vitro and in silico investigation of effects of antimicrobial peptides from Solanaceae plants against rice sheath blight pathogen Rhizoctinia solani. PloS one. 2024;19(6):e0302440. pmid:38870165

154. Khanam M, Moin AT, Ahmed KA, Patil RB, Ripon Khalipha AB, Ahmed N, et al. Computational modeling of potential milciclib derivatives inhibitor-CDK2 binding through global docking and accelerated molecular dynamics simulations. Informatics in Medicine Unlocked. 2022;33:101069.

155. Moin Abu T, Patil Rajesh B, Tabassum T, Araf Y, Ullah Md A, Snigdha Hafsa J, et al. Immunoinformatics Approach to Design Novel Subunit Vaccine against the Epstein-Barr Virus. Microbiology Spectrum. 2022;10(5):e01151–22. pmid:36094198

156. Moin AT, Robin TB, Patil RB, Rani NA, Prome AA, Sakif TI, et al. Antifungal plant flavonoids identified in silico with potential to control rice blast disease caused by Magnaporthe oryzae. PloS one. 2024;19(4):e0301519. pmid:38578751