https://orcid.org/0000-0002-5249-209X
Figures
Abstract
Significant progress has been made in HIV-1 research; however, researchers have not yet achieved the objective of eradicating HIV-1 infection. Accordingly, in this study, eucaryotic and procaryotic in silico vaccines were developed for HIV-Gag polyproteins from 100 major HIV subtypes and CRFs using immunoinformatic techniques to simulate immune responses in mice and humans. The epitopes located in the conserved domains of the Gag polyprotein were evaluated for allergenicity, antigenicity, immunogenicity, toxicity, homology, topology, and IFN-γ induction. Adjuvants, linkers, CTLs, HTLs, and BCL epitopes were incorporated into the vaccine models. Strong binding affinities were detected between HLA/MHC alleles, TLR-2, TLR-3, TLR-4, TLR-7, and TLR-9, and vaccine models. Immunological simulation showed that innate and adaptive immune cells elicited active and consistent responses. The human vaccine model was matched with approximately 93.91% of the human population. The strong binding of the vaccine to MHC/HLA and TLR molecules was confirmed through molecular dynamic stimulation. Codon optimization ensured the successful translation of the designed constructs into human cells and E. coli hosts. We believe that the HIV-1 Gag vaccine formulated in our research can reduce the challenges faced in developing an HIV-1 vaccine. Nevertheless, experimental verification is necessary to confirm the effectiveness of these vaccines in these models.
Citation: Hashempour A, Khodadad N, Bemani P, Ghasemi Y, Akbarinia S, Bordbari R, et al. (2024) Design of multivalent-epitope vaccine models directed toward the world’s population against HIV-Gag polyprotein: Reverse vaccinology and immunoinformatics. PLoS ONE 19(9): e0306559. https://doi.org/10.1371/journal.pone.0306559
Editor: Anoop Kumar, National Institute of Biologicals (NIB), Ministry of Health & Family Welfare, Government of India, INDIA
Received: February 27, 2024; Accepted: June 18, 2024; Published: September 27, 2024
Copyright: © 2024 Hashempour et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting information.
Funding: This project was funded by Shiraz University of Medical Sciences, Shiraz, Iran (grant number 29467). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
Despite a global reduction in deaths caused by harmful pathogens, infectious diseases continue to impose significant catastrophes on human civilization worldwide [1–5]. Significant advances have been made in the treatment of infections; however, the burdens of sickness and death continue to increase substantially [6–8]. The implementation of new detection techniques, vaccines, medications, and improved hygiene has decreased the occurrence of infections and related fatalities [9–11]; nonetheless, continuing to invest in existing strategies to explore innovative therapeutic approaches is crucial [12–15]. For example, various studies have been conducted to find effective treatments or potential vaccine candidates to either eliminate or control the global health crisis caused by human immunodeficiency virus (HIV)-1, which causes acquired immunodeficiency syndrome (AIDS). Antiretroviral therapy (ART) decreases the transmission of HIV and slows the transmission of AIDS, leading to prolonged life expectancy [16]. Nevertheless, ARTs do not eradicate HIV from the body, necessitating the development of a potent vaccine to overcome numerous obstacles in the suppression of HIV infection.
Various vaccine techniques are employed to control HIV infection and AIDs, among which the conventional methods are old strategies that utilize either weakened or inactivated HIV [17]. At present, an effective method for vaccine development involves the use of advanced bioinformatic techniques to examine and predict virological and immunological results [18–20]. Multiepitope vaccines targeting several human tumor viruses have gained significant global attention, as exemplified by increased interest worldwide [21]. In addition, bioinformatics methods possess numerous advantages, such as effectiveness in preclinical trials, safety, target specificity, and simplicity of production. In other words, such valuable techniques employ computational methods such as molecular docking and molecular dynamics (MD) simulations to analyze vast quantities of biomedical data produced by different software programs, aiming to identify potential vaccine targets [22]. However, various challenges hinder the development of an ideal vaccine against HIV. These include 1) multiple funding issues associated with vaccine development; 2) the rapid mutation rate of the HIV genome due to replication, resulting in the emergence of new subtypes, circulating recombinant forms (CRFs), and strains; 3) the absence of a suitable animal model for studying HIV behaviors; 4) the establishment of a latent reservoir by HIV-1 [23], the diverse modes of HIV transmission to humans; and [24] the inability of current vaccines to stimulate both humoral and cellular responses, as well as their failure to produce broadly neutralizing antibodies [25]. This is why the HIV vaccine fails to generate an effective immune response in the global human population. For instance, one of the highly effective vaccines developed, RV144, only leads to 31.2% protection against HIV [26]. According to previous findings, the Gag gene contains numerous highly immunogenic protective epitopes, making such important polyproteins key targets for cytotoxic T lymphocytes (CTLs) involved in the control of HIV replication and suppression [27]. For example, in the AIDS Clinical Trials Group (ACTG), patients with HIV-1 infection were vaccinated with rAd5 HIV-1 Gag, which resulted in an increase in the activity of HIV-specific CD4 and CD8 cells [28]. Furthermore, administering DNA vaccines containing identical epitopes found in SIV p27Gag to the SIV/macaque model resulted in a robust immune response [29].
This is the first study to suggest in silico vaccine constructs for Gag-HIV polyproteins using bioinformatics software. Another novelty of this report is that the suggested eukaryotic and prokaryotic models of vaccines are compatible with the main HIV subtypes and CRFs and induce both cellular and humoral immune responses in computer-based immune responses in mouse and human hosts. To achieve this goal, we obtained a total of one hundred full-length HIV-1 sequences of the major HIV subtypes and CRFs from the Los Alamos National Laboratory (LANL) database. Subsequently, consensus sequences of the Gag polyproteins were generated. Multiple servers have been used to predict and evaluate thousands of CTL, helper T lymphocyte (HTL), and B-cell (BCL) epitopes based on various distinctive criteria, including topology, antigenicity, allergenicity, homology, population coverage, immunogenicity, toxicity, and induction of interferon-gamma (IFN-γ). Among the thousands of predicted epitopes, only those located in the conserved domains of the Gag polyprotein were considered candidates for inclusion in the vaccine construct sequence. The final sequence of the vaccine constructs was evaluated for structural characteristics, including secondary and tertiary structures, and molecular dynamics and molecular docking studies were conducted on Toll-like receptors (TLRs) 2, 3, 4, 7, and 9, which confirmed the long-term effectiveness of the vaccine model. To stimulate appropriate proinflammatory cytokines, such as IFN-ɣ, and the innate response through TLR docking, suitable adjuvants, including the C-terminal invasin sequence of Yersinia, beta defensin-3, and the pan-HLA DR-binding epitope (PADRE), were incorporated into the vaccine construct. Finally, codon-optimized vaccine sequence constructs were cloned and inserted into the pET-30a (+) plasmid and pcDNA3.1 (+) using SnapGene software to efficiently express the vaccine constructs in bacteria and human hosts, respectively.
2. Materials and methods
2.1. Data retrieval and sequence alignment
Up to 4th June 2023, one hundred full-length HIV genomes of the most common HIV subtypes and CRFs submitted to the LANL HIV sequence database were B, C, A, AE, D, G, F, AG, BC, O, BF, and CRF35-AD. Using CLC-sequence Viewer Software (version CLC Genomics Workbench 20) [1], GAG amino acid sequences were obtained from the mentioned subtypes and CRFs and aligned to generate the consensus sequence that was considered for further analysis. Intersequence homology was examined by CLC-sequence Viewer considering the following parameters: gap opening cost: 10 and gap extension cost: 1.0.
2.2. Conserved domain analysis
To design a more effective vaccine construct, NCBI CDD-BLAST was used to define the conserved domains of the Gag sequence to select the most promising epitopes from the mentioned domains.
2.3. Prediction of biophysical and biochemical features
The ProtParam tool was used for the purpose of analyzing a variety of physicochemical properties of the consensus Gag sequence. These properties include the number of amino acids, molecular weight, theoretical isoelectric point (pI), estimated half-life (both in vitro and in vivo), as determined by the N-end rule, aliphatic index, and grand average of hydropathy (GRAVY) index of the vaccine construct [30–32].
2.4. Prediction and selection of linear BCL epitopes
The BCL epitope, also known as the antigenic determinant, is precisely defined as the specific portion of the antigen that is recognized by the BCL receptor or its soluble form, namely, antibodies, which are secreted following the activation of BCLs [33–35]. BCL epitopes can be categorized as either conformational, which are also referred to as discontinuous, or linear, also known as continuous. In the case of proteins, conformational BCL epitopes encompass residues that are not sequential in the primary structure but are in close proximity within the three-dimensional structure of the antigen [35, 36]. Conversely, linear BCL epitopes consist of consecutive amino acid residues. These BCL epitopes can be identified by antibodies independently of the surrounding protein context and can be used as a substitute for the entire protein in antibody production [35]. Multiple approaches and techniques are available for the prediction of linear BCL epitopes [35–40]. Some of these techniques employ amino acid propensity scales that illustrate the physicochemical characteristics of BCL epitopes. These techniques include ABCpred, Bepipred, Emini, Karplus, and Parker [41].
2.4.1. ABCpred.
The ABCpred server was used to predict the linear BCL epitopes specific to the humoral immune response of the aforementioned Gag protein. This prediction was accomplished through the utilization of an artificial neural network, which is the basis of the algorithm employed by the server. The neural network consists of 700 BCl epitopes and 700 non-BCL epitopes, each with a maximum length of 20 residues, for training and testing purposes [42, 43]. The server has demonstrated an accuracy of 65.93% using this recurrent neural network. Consequently, the FASTA sequence of the Gag protein was used to predict linear BCL epitopes with a threshold of 0.51 and a window length of 10 [44].
2.4.2. BepiPred-2.0.
The BepiPred-2.0 method relies on a random forest algorithm that has been trained on epitopes annotated from antibody-antigen protein structures [45]. This novel approach has been shown to outperform other available tools for predicting epitopes based on sequence. The superiority of this method was demonstrated by evaluating epitope data derived from solved 3D structures, as well as a large collection of linear epitopes obtained from the Immune Epitope Database (IEDB). The results of the method are presented in a user-friendly and informative manner, catering to both computer-savvy and nonexpert users [46].
2.4.3. Emini surface accessibility.
The Emini surface accessibility prediction method utilizes the calculation of surface probability to increase confidence in provisional alignment for sequence comparison [47]. This prediction is based on the method proposed by Garnier et al. [48] and the Chou and Fasman method [49]. Furthermore, this method assumes the absence of significant internal deletions or insertions. The IEDB database was used to predict surface accessibility using the default threshold value for each protein [50].
2.4.4. Karplus flexibility.
The Karplus flexibility scale method constructs a flexibility scale based on the mobility of protein segments. This scale is derived from the known temperature B factors of the α-carbons of 31 proteins with known structures. The calculation based on this flexibility scale is similar to the classical calculation, with the exception that the center is the first amino acid of the window length of six amino acids, and three scales are used to describe flexibility instead of just one [51].
2.4.5. Parker hydrophilicity.
The Parker hydrophilicity prediction method constructs a hydrophilic scale based on the retention times of peptides during high-performance liquid chromatography (HPLC) on a reversed-phase column. The epitope region was analyzed using a window of seven residues [41]. Finally, the epitopes were screened through the following filters:
2.5. Selection of top epitopes
Thousands of epitopes were suggested for the Gag sequence by different methods; however, the selection of high-quality epitopes was based on the combination of filters used in this study.
2.5.1. Antigenicity evaluation.
Epitopes that bind to immune cells with strong binding affinity exhibit high antigenicity [52]. The VaxiJen v2.0 server (Table 1g) with a threshold level of 0.4 was utilized to assess the antigenicity of an epitope [52], and any epitopes that did not meet this requirement were discarded. VaxiJen’s prognostications regarding antigenicity are based on the application of autocross covariance transformations to protein sequences, resulting in standardized vectors of principal amino acid characteristics [53, 54].
2.5.2. Prediction of toxicity.
The ToxinPred webserver and CSM-Toxin webserver (Table 1g) were used to identify the toxicity of the epitopes and choose nontoxic epitopes [55]. Based on quantitative matrices and machine learning, the toxicity of the epitopes was predicted by CSM-Toxin [56] and ToxinPred with an accuracy of 94.50% [53]. This server detects certain amino acid residues that are located at specific positions on toxic peptides.
2.5.3. Allergen prediction.
The epitope candidates should not be allergenic; therefore, the potential allergenicity of the vaccine was assessed using and AllerTOP V2.0 (Table 1g) [57]. These server employs an autocovariance (ACC) transformation technique to convert protein sequences into uniform vectors of equal length. Using the K-nearest neighbor algorithm, proteins were classified based on a training set comprising 2210 allergens and 2210 nonallergens from diverse species [44] also AllergenFP v.1.0 [58] was the other webserver used for predicting the allergenicity.
2.5.4. Analysis of homology.
With the use of the peptide match server (Table 1g), peptides can be matched with the complete UniProtKB human proteome in a timely and precise manner. To prevent the risk of host autoimmune diseases and cross-reactions, each epitope was subjected to additional scrutiny through the peptide match server [59].
2.5.5. Topology forecasting.
Because the majority of amino acids present in the transmembrane region of proteins are hydrophobic, determining the transmembrane sequence of vaccines is crucial. To accomplish this goal, the DeepTMHMM server (Table 1g), which is considered to be the most comprehensive and best-performing method for predicting the topology of both alpha-helical and beta-barrel transmembrane proteins, was utilized to identify the transmembrane domains of the vaccine [60].
2.5.6. Predicting IFNγ-inducing epitopes.
Based on the amino acid composition, positional conservation of residues, and peptide length, the IFNepitope server (Table 1g), which employs motif-based prediction and a hybrid approach of both machine learning methods, predicts the ability of the epitopes to release the IFNγ cytokine [53].
2.5.7. Immunogenicity prediction.
The immunogenicity of the epitopes was determined by the IEDB Class I Immunogenicity Tool (Table 1g), which predicts the immunogenicity of the epitope based on nonanchor position amino acids that play a less significant role in the binding affinity of the peptide [53].
2.6. Prediction and selection of the HTL epitope
With the utilization of the IEDB major histocompatibility complex (MHC) II binding server, a set of 15-mer epitopes was identified for mouse alleles (H2-IAb, H2-IAd, H2-IEd) based on a selected percentile rank of less than 10. To further refine the selection, various programs, such as VaxiJen v2.0, Immunogenicity, AllerTOP v.2.0, ToxinPred, TMHMM-2.0, topology, and PIR peptide matching, were used for filtration purposes. Subsequently, the qualified epitopes were subjected to screening through the IFNepitope webserver, which specializes in the identification of epitopes that induce IFN-γ production. Furthermore, these selected epitopes were cross-validated with the IEDB class II immunogenicity server, which is specifically designed for human leukocyte antigen (HLA) class II binding analysis [61]. Finally, the epitopes that exhibited the highest HLA class-II coverage were prioritized for population coverage assessment.
2.7. Prediction and selection of the CTL epitope
The IEDB MHC I server was used to forecast CTL epitopes. The main requirement was the IEDB Recommendation 2020.09 (NetMHCpanEL4.1), while the additional requirement encompassed epitopes of all human HLA alleles. Epitopes possessing a percentile level less than 0.5 were deemed suitable for the ensuing analysis stage. Subsequently, the Class I immunogenicity server was used to scrutinize the immunogenicity of these CTL epitopes, and only those epitopes with percentile levels less than 0.5 and immune scores greater than 1 were selected for further examination. To conclude, the VaxiJen v2.0 server was utilized to forecast antigenicity, employing a threshold value of 0.4, as referenced in a prior investigation [62]. Ultimately, CTL epitopes displaying immunological advantages were harnessed in the fabrication of a multiepitope vaccine.
2.8. Population coverage
The variety of HLA molecules is truly impressive, as more than a thousand unique allelic variants have been identified. The frequencies of these HLA alleles display notable differences among diverse ethnic groups. Therefore, the selection of different epitopes with varied HLA-binding specificities is of utmost importance when undertaking the design and development of T-cell epitope-based diagnostics or vaccines. This methodology will lead to increased coverage of the patient population, guaranteeing efficacy and inclusiveness [63]. The main purpose of the coverage analysis was to evaluate the suitability of selected epitope candidates for extensive populations [64]. A list of selected epitopes (CTL and HTL) and the corresponding alleles is provided in S1 and S2 Tables. A thorough examination was performed, wherein we collected data concerning population coverage, average epitope hits, and the proportion of the population constituting 90% (PC90), categorized by country and geographical region. Our findings included 115 countries and 16 geographically different regions [65].
2.9. Construction of a multiepitope subunit vaccine
In the final vaccine construct, the qualified epitopes of the Gag polyprotein that were located in conserved domains overlapped with other epitopes and were located in the conserved domains of the Gag polyprotein were included in the sequence of the vaccine construct. To protect the construct from degradation [66] and stimulate robust immune reactions, especially mucosal immune responses toward HIV [67], the beta defensin-3 adjuvant (GIINTLQKYYCRVRGGRCAVLSCLPKEEQIGKCSTRGRKCCRRKK) was incorporated at the N-terminus of the vaccine sequence. To overcome issues caused by highly polymorphic HLA class II alleles, the EAAAK linker and universal PADRE (AKFVAAWTLKAAA) were added [68] after the addition of the beta defensin-3 adjuvant to the vaccine construct. CTL epitopes were joined using GGGS linkers, HTL epitopes were joined using GPGPG linkers, and BCL epitopes were joined using KK linkers. The C-terminal invasin sequence of Yersinia (TAKSKKFPSYTATYQF) was added to the C-terminus of the construct via the EGGE linker. The EAAAK linker is considered a rigid linker for separating adjuvants from other parts of the vaccine sequence because of the special accessibility of the adjuvants and their interaction with TLRs. Furthermore, the GGGS, GPGP, KK, and EGGE linkers provide flexibility for fusing the epitopes [69]. These linkers were used to ensure the effective separation of individual epitopes and to inhibit the formation of junctional/neoepitopes and improve processing. Notably, the junctional epitopes hinder the efficacy of the selected epitopes [70]. The sequence of the vaccine model was screened through various servers, including VaxiJen, AllerTOP v.2.0, ToxinPred2 (Table 1g), TMHMM, and the peptide match server [59, 71].
2.10. Prediction of physicochemical and immunogenic properties
The ProtParam tool was used to analyze a variety of physicochemical properties. These properties include the number of amino acids, molecular weight, theoretical isoelectric point (pI), and estimated half-life (both in vitro and in vivo), as determined by the N-end rule, the aliphatic index, and the GRAVY index of the vaccine construct [30, 31, 72].
2.11. Secondary structure prediction
2.11.1. SOPMA.
The secondary structures of Gag were predicted using the online server SOPMA. All of the parameters used in SOPMA were configured to their default values. These default values include setting the number of conformational states to four, namely, helix, sheet, turn, and coil. The similarity threshold was set to eight, and the window width was set to 17 [31].
2.11.2. RNAfold.
Another instrument employed for the assessment of the secondary configuration of the DNA vaccine structures was RNAfold. The RNAfold server offers the most fundamental and widely utilized function. To utilize this service, the user must enter a single sequence in DNA/RNA format into a designated text field on the input form. In the simplest scenario, the server employs the classic Zuker and Stiegler algorithm [73] to predict only the minimum free energy (MFE) structure of the given sequence. Additionally, the server has the capability to calculate equilibrium base pairing probabilities using John McCaskill’s partition function algorithm [74]. By default, the RNA energy parameters from the Turner group [75] were utilized. The output of the fold server consists of a static HTML page that presents the predicted MFE structure as a string in brackets. It also includes links to plots that are generated for visualization purposes. There are three types of plots that can be produced. The predicted MFE structure is depicted as a conventional secondary structure graph using the naview layout method [76].
2.12. 3D modeling, refinement, and validation of the vaccine construct
To simulate the tertiary structures of the vaccine constructs, the I-TASSER server was used to produce five model sequences [55]. Subsequently, the optimized crude 3D models of the vaccine sequence were subsequently submitted to the GalaxyWEB server to reconstruct unreliable termini or loops of the initial model structures, thereby generating five refined models [40]. These models were established based on diverse parameters including GDT-HA, RMSD, MolProbity, Clash score, Poor rotamers, and Rama favored, serving as the foundation for template selection, sequence alignment, model construction, and enhancement. Consequently, additional online tools namely ERRAT [77], ProSA-Web [78], and RAMPAGE [79] were employed to identify the optimal model in this investigation. The ERRAT tool evaluates the general quality factor by considering the number of nonbonded interactions between various atomic types within a specific distance of 0.35 nm, with models scoring above 85 deemed favorable. Conversely, the RAMPAGE tool employs a Ramachandran plot to evaluate the stereochemical quality of the protein structure, aiming for a higher number of residues in the favored region and lower residues in the disallowed region. Furthermore, the Z score provided by the ProSA-Web server serves as an indicator of the overall quality of the model, where a positive Z score highlights potential structural issues or errors [24, 31].
2.13. Prediction of discontinuous BCL epitopes
A considerable number of BCL epitopes are not known to form a continuous sequence in the amino acid composition of a protein. These noncontinuous BCL epitopes assume a crucial function in biological operations [80]. ElliPro employs a trinity of algorithms to forecast disjunct epitopes. The program approximates the shape of the protein as an ellipsoid, computes the residue protrusion index (PI), and clusters the adjacent residues according to their PI values. ElliPro subsequently generates a PI score for every anticipated epitope [81]. In this particular investigation, the ElliPro server was used to forecast the noncontinuous BCL epitopes with a screening threshold of 0.5 for the vaccine [82].
2.14. Molecular docking
The process of presenting peptides derived from pathogens to T cells is strongly dependent on MHC molecules [83]. To evaluate the molecular docking process and the interaction performance between the expected CTL and HTL epitopes and their corresponding binding alleles, AutoDockVina software was used. To achieve this objective, we utilized the protein database (PDB) website to extract the PDB files of the most common alleles (MHC molecules) (refer to S1 and S2 Tables for more details). To determine the three-dimensional structure of these epitopes as ligands, we used the PEP-FOLD 4.0 server [84, 85].
2.15. Protein‒protein docking between TLR 2, 3, 4, 7, and 9 and the vaccine construct
It is of utmost importance to establish a stable correlation between a prospective vaccine and an immune receptor to initiate efficacious immune reactions. TLRs play a critical role in the initial protection against pathogens and serve as pivotal connectors between innate and adaptive immunity [83]. To achieve the desired immune response, it is crucial to evaluate the ability of the vaccine construct to attach to the immune receptor. Molecular docking, a computational technique, can be utilized to predict the binding affinity between the Gag vaccine construct and immune receptors, as well as the creation of interaction complexes [85]. Therefore, we performed molecular docking of the Gag vaccine construct to TLRs (TLR-2, TLR-3, TLR-4, TLR-7, and TLR-9) using the online server ClusPro 2.0. The PDB files for TLR-2 (PDB ID: 2Z7X), TLR-3 (PDB ID: 1ZIW), TLR-4 (PDB ID: 3FXI), TLR-7 (PDB ID: 5GMF), and TLR-9 (PDB ID: 3 WPB) were obtained from the RCSB database [24].
2.16. Molecular dynamic simulation
The use of MD simulation is widely recognized as a powerful method for studying biological systems at the molecular level [86]. In this study, we employed Linux-based GROMACS software to investigate the efficacy of the designed Gag vaccine against the TLR-2, TLR-3, TLR-4, TLR-7, and TLR-9 systems. The GROMACS tool can be implemented with a diverse range of force fields, including AMBER, OPLS, GROMOS, and CHARMM. For our research, the simulation was conducted using the GROMACS 2018—x series (version 05), employing the OPLS-AA force field (Optimized Potential for Liquid Simulation). During the preliminary stage of preparation, the Gag vaccine-TLR complexes were subjected to the integration of OPLS-AA force field parameters. As a result of this procedure, coordinate and topology files for the complex system are produced. The system was then solvated using the transferable intermolecular potential 3P (TIP3P) water model, followed by neutralization with Cl ions to ensure that the structural and topological coordinates remained stable [83]. Then, a process of energy minimization was performed, resulting in the final structure obtained through energy minimization (EM) [31] results were visualized using Excel software. The NVT ensemble equilibration lasted 100 ps and required 50,000 steps to reach the desired temperature. This process enabled the generation of velocities, allowing the simulation to run at different speeds. A 50,000-step NPT ensemble was then used to examine the density, potential, pressure, and temperature of the stabilized Gag vaccine construct throughout the process. After equilibration, a 100 ns, 50,000,000 step MD simulation was performed on the construct. The root mean square deviation (RMSD) of the backbone energy is minimized after MD simulation, and the results are presented in the form of a graph. Additionally, the radius of gyration (Rg), density plots, hydrogen bonding, and performance of the root mean square fluctuation (RMSF) protocol during MD simulations were analyzed.
2.17. Immune stimulation
We employed the C-IMMSIM online tool to analyze the immune stimulation effect of the vaccine on HIV-1. The strength of the interaction between a specific peptide-HLA complex and a T-cell receptor was assessed using the Miyazawa-Jernigan residue-residue potential tool [87]. C-IMMSIM was employed with its default settings [31], except for host HLA selection. The chosen HLA types were HLA-A*2402, HLA-A*3001, HLA-B*0701, HLA-B*4001, HLA-DRB1*1101, and HLA-DRB1*0101, as they have higher genotypic frequencies in the global population according to the IEDB analysis server.
2.18. Codon optimization and in silico cloning
Both the human and mouse immune systems were compatible with the epitopes of the vaccine model; therefore, eukaryotic and prokaryotic expression vectors were designed. A Kozak sequence that controls translation initiation and contains a start codon was added to the N-terminus of the vaccine protein of the eukaryotic expression vector vaccine. To increase the expression of the protein, codon optimization was performed for the Homo sapiens expression system using the VectorBuilder server (Table 1k). The cleavage sites of the BamHI and EcoRV restriction enzymes were excluded from the optimized cDNA sequence. Using SnapGene v7.1.1., the highly efficient reverse-transcribed nucleotide sequence, with a suitable GC content and CAI, was inserted into the pcDNA3.1(+) vector between the BamHI and EcoRV restriction sites under the control of the CMV promoter. Similarly, the prokaryotic vaccine construct was cloned in silico into the pET-30a (+) plasmid using the EcoRI and EcoRV restriction enzymes and codon optimization in the K12 strain of E. coli. In comparison to the pET-28a (+) plasmid, the pET-30a (+) plasmid contained a greater number of restriction sites, thereby conferring a distinct advantage in facilitating the expression of the target protein. The flowchart of the Gag vaccine design is shown in Fig 1.
The immunoinformatic techniques used in the development of the eukaryotic and prokaryotic in silico vaccines were the same, except for some techniques mentioned in the S3 Table.
3. Results
3.1. Protein sequence retrieval and alignment
A map of the distribution of the HIV subtypes and CRFs identified in the LANL database and the accession numbers of the full-length HIV sequence subtypes and CRFs used in the present study are shown in S1 Fig and S4 Table, respectively. The consensus sequence of Gag obtained one hundred main subtypes and CRFs was MGARASVLSGGKLDRWEKIRLRPGGKKKYRLKHIVWASRELERFAVNPGLLETSEGCRQILGQLQPALQTGSEELKSLYNTVATLYCVHQRIDVKDTKEALDKIEEEQNKSKKKAQQAAADTGNSSQVSQNYPIVQNLQGQMVHQAISPRTLNAWVKVIEEKAFSPEVIPMFSALSEGATPQDLNTMLNTVGGHQAAMQMLKETINEEAAEWDRLHPVHAGPIAPGQMREPRGSDIAGTTSTLQEQIGWMTSNPPIPVGEIYKRWIILGLNKIVRMYSPVSILDIRQGPKEPFRDYVDRFYKTLRAEQATQEVKNWMTETLLVQNANPDCKTILKALGPGATLEEMMTACQGVGGPGHKARVLAEAMSQVTNSNTIMMQRGNFRNQRKTVKCFNCGKEGHIARNCRAPRKKGCWKCGKEGHQMKDCTERQANFLGKIWPSHKGRPGNFLQSRPEPTAPPESFRFGEETTTPSQKQEPIDKELYPLASLKSLFGNDPSSQ.
3.2. Identification of conserved domains
The results showed that the HIV-1 Gag polyproteins contained five conserved domains comprising Gag_p17, Gag_p24_C, Gag_p6, the PTZ00368 superfamily, and Gag_p24, which are parts of the HIV nucleocapsid (Table 2). Epitopes located in the Gag conserved domains and passed through all the filters were qualified candidates for consideration in the vaccine construct.
3.3. Identification of the biophysical and biochemical features of HIV genes
The ExPASy server’s ProtParam tool successfully predicted distinct physicochemical attributes of the Gag protein. S5 Table shows the findings of various physicochemical parameters pertaining to the Gag protein.
3.4. Linear BCL epitope prediction
Multiple methods were employed to identify the most proficient linear BCL epitopes for the ORF Gag protein. Among the plethora of suggested epitopes, only a finite number possessing desirable attributes, including antigenicity, no signal sequence, nontoxicity, nonallergen, and nonhomology to human proteins, were meticulously chosen (see Table 3, S6 Table, and Fig 2).
The purple color displays each linear B-cell epitope, such as amino acids 8–116, with a score ranging from 0.50–0.78.
3.5. HTL epitope prediction
Epitopes that were suggested by both the IEDB MHC II and HLA binding webservers and possessed the capability to adhere to at least one mouse H-2-I allele and one human allele were subjected to a series of filters. These epitopes, which can bind to both human and mouse alleles concurrently, have the potential to elicit immune responses in both mice and humans. In the end, only those epitopes that were antigenic, lacked a signal sequence, were nonallergenic, nontoxic, nonhomologous and devoid of signal sequences that could induce IFN-γ responses were considered in the vaccine model (Table 4 and S1 Table).
3.6. CTL epitope prediction
The NetCTL 1.2 server puts a large number of epitopes for the Gag protein targeting 12 MHC supertypes in the human population. The restricted epitopes were shown to be immunogenic, antigenic, nontoxic, nonallergenic, and nonhomologous to the human proteome and possessed suitable topology. Furthermore, epitopes situated in conserved domains were taken into account in the final formulation of the vaccine (Table 5 and S2 Table).
3.7. Population coverage
We used the IEDB population coverage tool to analyze the population coverage of 18 CTL epitopes and 7 HTL epitopes and their corresponding HLA alleles in 16 different regions around the world. The European nations exhibited the most elevated occurrence of the MHCI and II alleles, encompassing 99.92% of the Gag gene. On the other hand, combined alleles found in Central American countries were the least widespread (44.99%) among the Gag genes. Furthermore, when studying the impact of these epitopes on the population, 93.91% of the global population was revealed to be covered when considering both MHC class I and II. Fig 3, Table 1o, and S7 Table provide a visual representation of these data. The results regarding population coverage indicate that HIV-1 Gag vaccine candidates can combat the worldwide occurrence of HIV infection.
3.8. Vaccine construction
As illustrated in Fig 4a, a vaccine sequence consisting of multiple epitopes was designed by combining seven HTL epitopes, eighteen CTL epitopes, and seven BCLs (S8 Table). To ensure the efficient presentation of epitopes and maximum immunity in the body, each HTL, BCL, and CTL epitope was separated from the others via the GPGPG, KK, and GGGS linkers, respectively.
Beta defensin-3, a TLR4 agonist, was added at the beginning of the construct sequence that was attached to the PADRE sequence by the EAAAK linker, but this sequence was separated from CTL epitopes by the GGGS linker to guarantee maximum MHC-II allele coverage and induction of the CTL response. Finally, the C-terminal invasin sequence of Yersinia was incorporated into the vaccine model, to which the BCL epitopes were linked using an EGGE linker. Based on various servers, the vaccine sequence (GGSVLSGGKLDRGGGSIRLRPGGKKGGGSRLRPGGKKKGGGSKKYRLKHIVGGGSKYRLKHIVWGGGSLKHIVWASRGGGSRFAVNPGLLGGGSATLYCVHQRGGGSTLYCVHQRIGGGSNSSQVSQNYGGGSHQAISPRTLGGGSSEGATPQDLGGGSATPQDLNTMGGGSAEWDRLHPVGGGSNPPIPVGEIGGGSQATQEVKNWGGGSANPDCKTILGPGPGKIRLRPGGKKKYRLKGPGPGIRLRPGGKKKYRLKHGPGPGRLRPGGKKKYRLKHIGPGPGLRPGGKKKYRLKHIVGPGPGGKKKYRLKHIVWASRGPGPGYCVHQRIDVKDTKEAGPGPGSPEVIPMFSALSEGAKKLSGGKLDRWEKIRLRPKKRWEKIRLRPGGKKKYRKKGQLQPALQTGSEELKSKKQAAADTGNSSQVSQNYKKEEAAEWDRLHPVHAGPKKFRFGEETTTPSQKQEPKKTTPSQKQEPIDKELYPEGGETAKSKKFPSYTATYQF) appears to be safe for in vitro and in vivo studies. Moreover, the vaccine models were found to be nonhomologous to the human proteome; nontoxic, antigenic, and nonallergenic; and without any signal sequence (S9 and S10 Tables).
3.9. Secondary and tertiary structure prediction, refinement, and validation
3.9.1. SOPMA.
Table 1b shows a variety of values pertaining to the anticipated parameters crucial for determining the secondary structures of the Gag protein, all of which were obtained through the utilization of the SOPMA online server. The secondary structure of the aforementioned protein exhibited areas characterized by alpha helix (45.49%) formations, with the exception of the transactivator protein, as well as elongated strands (7.62%), beta turns (4.41%), and random coil (42.48%) conformations (S11 Table).
3.9.2. RNAfold.
The structure of the mRNA vaccine was deduced through the use of the RNAfold platform. Additionally, the computation of the structures’ free energy was performed using this specific website. As an initial step, the codons that were optimized for utilization in the vaccine were selected. The mRNA vaccine displayed a minimal free energy of -545.80 kcal/mol during production, while the energy of its secondary centroid structure was recorded as -384.01 kcal/mol. The outcomes suggest that the structure of the mRNA vaccine is expected to exhibit stability. The optimal secondary structure is shown in dot-bracket notation, with a minimum free energy of -545.80 kcal/mol. The free energy of the thermodynamic ensemble is -569.08 kcal/mol. The frequency of the MFE structure in the ensemble is 0.00%. The ensemble diversity is 464.70. centroid secondary structure is shown in dot-bracket notation with a minimum free energy of -384.01 kcal/mol.
3.10. Tertiary structure prediction, refinement, and validation
To generate the 3D model of the vaccine, the I-TASSER server employed comparative modeling techniques, resulting in the production of a total of 5 vaccine construct models. The reliability of each model was assessed using the C-score, which quantifies the precision of the suggested models. As a result, the model with the most favorable C-score was chosen for further refinement. The refinement process of the selected model involved the utilization of the GalaxyRefine server (Fig 4b–4e). Additionally, quality analysis of the ProSA web server demonstrated an improved Ramachandran plot, wherein the majority of residues were located in the most favored region (Fig 4d). Overall, the model exhibiting high-quality three-dimensional characteristics was deemed suitable because of the 3D structure of the vaccine.
3.11. Prediction of discontinuous B-bell epitopes
The existence of efficacious BCL epitopes plays a crucial role in the framework of vaccines, as it facilitates the elicitation of humoral immunity against exogenous pathogens [88]. The ElliPro tool, employed at the IEDB, successfully detected a single epitope within the vaccine construct. This epitope is characterized by a residual position ranging from 62 to 77 and has a score of 0.679. (Table 6, S12 Table and Fig 5).
3.12. Molecular docking of the T-cell epitopes with MHC molecules
We performed a molecular docking analysis in our study to investigate how individual T-cell epitopes interact with their corresponding alleles. The details of these interactions can be found in S1 and S2 Tables. The data in Table 7 demonstrate that the binding affinity between CTL epitopes and the corresponding alleles ranged from -8.6 to -6.1 kcal/mol. Conversely, the binding affinity range between the selected HTL epitopes and their alleles, as indicated by the AutoDock Vina docking results, was between -6.7 and -5.5 kcal/mol. The data revealed that the CTL epitope (NSSQVSQNY) and the HTL epitope (IRLRPGGKKKYRLKH) of the Gag gene exhibited the highest affinity for binding when paired with their corresponding MHC alleles (HLA-A*01:01 and HLA-DRB1*0101).
3.13. Protein‒protein docking between TLRs and vaccine constructs
ClusPro 2.0 was utilized to investigate the interaction between TLRs and the Gag vaccine structure via protein‒protein docking. A set of 30 models was generated for each docking, and the best energy scores were used to select the models that effectively engaged with the receptor. The docking results showed that the Gag vaccine construct had the lowest energy scores when interacting with TLR-2, TLR-3, TLR-4, TLR-7 or TLR-9, with recorded values of -1106.9, -1336.9, -1287.4, -1317.8, and -1272.7, respectively (as stated in S13 Table. The complex formation data obtained by docking indicated that the Gag vaccine construct, as designed, exhibited the highest affinity for binding with all five TLRs at the lowest magnitude (as shown in Fig 6a–6e).
Docked complex of the Gag vaccine construct with a) TLR-2, b) TLR-3, c) TLR-4, d) TLR-7, and e) TLR-9. The 3D molecule of the vaccine is shown in green and red according to the ClusPro server.
3.14. Molecular dynamic analysis
The results of molecular dynamics simulations of Gag vaccine docking complexes with TLR-2, TLR-3, TLR-4, TLR-7, and TLR-9 are shown in Fig 7(A)–7(E). It was predicted that the simulations could determine the movement of atoms and molecules within the vaccine structure. Furthermore, the HIV-1/Gag vaccine construct was evaluated via various calculations, including density assessment, energy minimization, pressure evaluation, potential energy determination, temperature analysis, and Rg computation. By examining the trajectory produced during a 100 ns simulation, we acquired the RMSD and RMSF values. The Gag vaccine, which was designed to include TLRs (specifically TLR-2, TLR-3, TLR-4, TLR-7, and TLR-9), demonstrated RMSD values of 0.95 nm, 0.91 nm, 0.97 nm, 0.98 nm, and 0.96 nm, respectively, indicating its stability. The average RMSD value for the vaccine during the simulation was 0.95 nm [Fig 7(A1)–7(E1)]. Furthermore, the average RMSF value for the complex of the vaccine construct and the TLRs was 0.47 nm, confirming the overall stability of the protein structure throughout the MD simulation [Fig 7(A2)–7(E2)]. Rg demonstrated the structural stability of the construct during the MD simulation [Fig 7(A3)–7(E3)]. This finding implies that the flexibility and stability of the vaccine-TLR complex are balanced, which is beneficial for immune receptor activity.
Molecular dynamics simulation analysis of A) TLR-2, B) TLR-3, C) TLR-4, D) TLR-7, and E) TLR-9 with Gag vaccine constructs. RMSD results (A1-E1); RMSF results (A2-E2); Rg results (A3-E3).
3.15. Immune stimulation
Fig 8A–8J displays the outcomes of the immune simulation for the Gag vaccine design. Following the administration of the vaccine, various immune responses, including the production of IgM and IgG, the concentration of cytokines, the levels of BCL and Th-cell populations, and specifically, the generation of IgM+IgG antibody titers, were observed. Furthermore, the vaccine enhanced the population of IL-2 cells, memory cells, and Th cells. In addition, we noticed an increase in the quantity of dendritic cells, macrophages (MAs), and NK cells when the vaccine we created was used to stimulate the immune system. The immune simulation results showed that innate immunity and cytokine-based immune regulation are involved. MAs, a part of the innate immune system, are crucial for triggering an inflammatory reaction when exposed to antigens. As a result, their activation leads to the release of certain cytokines, such as IL-12 and IL-18, which in turn stimulate the adaptive immune response and the production of IFN-γ to promote immune activation. Consequently, these potential options can elicit effective immune responses.
3.16. In silico cloning of the vaccine candidates
The GC content and CAI of the optimized cDNA derived from humans and the K12 strain of E. coli were determined to be 59.99% and 0.89 and 59.99% and 0.92, respectively. The ideal value for the GC content is 30–70%, and the CAI is 1 [89]. The results demonstrated the successful and efficient expression of cDNA sequences from vaccine models in both eukaryotic and prokaryotic systems. The optimized sequences, identified in S14 Table, were subsequently inserted into the pcDNA3.1(+) vector and pET-30a(+) plasmid under the control of the CMV promoter using SnapGene v7.1.1 (Fig 9A1). To construct the pcDNA3.1(+) vector containing the HIV-Gag vaccine model, a BamHI restriction site was added at the 5’ end of the vaccine sequence, followed by the insertion of the Kozak sequence (Fig 9A2). A stop codon (TAA) was added at the 3’ end of the optimized codon, along with an EcoRV restriction site. To prepare pET-30a(+), which contains the sequence of the HIV vaccine construct. EcoRI and EcoRV restriction sites were inserted into the N- and C-termini of the optimized vaccine sequences, respectively.
4. Discussion
Given the increasing incidence and spread of emerging and re-emerging infections, the low effectiveness and high cost of many drugs, scientists are exploring new methods, namely, vaccination, to provide a cost-effective and effective cure or eradicate pathogens [90]. In addition, immunization against pathogens protects individuals from acquiring the disease and facing complications, resulting in wellness and enhanced physical conditions in human [91–93]. Recently, bioinformatic tools have been extensively employed in analyzing medical databases, leading to a considerable generation of experimental data intended for the development of novel vaccines [82]. Progress in the domains of reverse vaccinology, computational biology, and immunoinformatic presents an ideal opportunity to facilitate the design of vaccines that are not only safe and effective but also reduce the time and cost involved [94–96]. Furthermore, genomic and proteomic data are utilized to investigate potential epitopes for the development of long-lasting immunogenic subunit vaccines [97–99]. For example, several studies have focused on the development of multiepitope vaccines for different diseases caused by viruses such as bovine leukemia virus [100], dengue [101], Kaposi sarcoma [102], SARS-CoV-2 [103, 104], West Nile virus [86], and monkey poxvirus [83]. Therefore, a multiepitope vaccine that can effectively stimulate safe and strong immune responses is suggested for numerous infections [59, 105]. The complexity of comprehending the human immune response to the HIV pathogen poses a significant obstacle in the search for vaccines [85]. Pandey et al. applied innovative immunoinformatic methods in their research to develop a multi-epitope subunit vaccine for HIV. The vaccine includes B-cell and T-cell epitopes that can trigger both humoral and cell-mediated immune responses. It was determined that the vaccine is non-allergenic, safe, and has the potential to stimulate an immune response. The vaccine design exhibited strong interaction with TLR-3, an immune receptor that activates immune cells. In silico cloning was carried out to confirm the expression of the vaccine in microbial systems [44]. Habib et al. conducted a study in which they designed a vaccine for HIV-1 using bioinformatic tools, molecular docking, and MD simulations. The vaccine construct consisted of 315 amino acids and had the potential for HIV-1 prevention. Different prediction tools were utilized to examine the physiochemical properties and secondary structure of the vaccine, suggesting stability and possible immunogenicity. The researchers also looked into proteasomal cleavage and transporter-associated antigen processing to improve epitope specificity and sensitivity in the design of the vaccine [106]. The ineffectiveness of HIV vaccines can be attributed to their inability to induce helper T-cell and cellular responses, as demonstrated in trials of the in silico vaccine EP HIV-1090 [25]. Additionally, these vaccines have proven to be ineffective against rapidly mutating HIV infection [44]. Trials on BALB/c mice have shown that multiepitope vaccines are incapable of producing broadly neutralizing antibodies [107]. HIV vaccines fail to activate the desired innate immune response or induce appropriate cytokines. Furthermore, the effectiveness of the HIV vaccine is constrained by its capacity to stimulate the immune system in response to a few HIV genotypes. In a study carried out by Pandey et al., the designed vaccine-induced immune responses only against specific subtypes of HIV, namely, C and B [44]. Moreover, HIV-1 has a reputation for substantial genetic heterogeneity, thereby resulting in notable dissimilarities among the protein sequences of different HIV subtypes and CRFs. The effectiveness of vaccine constructs can be improved globally by considering the sequences of the major HIV subtypes and CRFs [108]. To address this issue, for the first time, new in silico vaccine models against the full length of the HIV-Gag genome were developed that were compatible with the main HIV subtypes and CRFS.
The designed vaccine constructs present promising avenues to address the specific challenges faced in controlling HIV infection including: 1) Selection of qualified epitopes from Gag conserved domains to target immunodominant responses to overcome the rapid mutation rate of HIV and enhance the ability to inhibit multiple subtypes and CRFs of HIV-1 by stimulation of the broadly neutralizing antibodies and cellular responses. 2) Induction of stronger cellular, humoral, and innate immune responses compared to vaccines without beta defensin-3 which is as TLR3 adjuvant an adjuvant. 3) Amplification of the immune response against HIV by incorporating several sequences, including beta defensin-3, universal PADRE, and C-terminal invasin sequence of Yersinia. 4) Selection of epitopes that were immunogenic, antigenic, nontoxic, nonallergenic, and nonhomologous to the human proteome and possessed suitable topology that were joint together with elements such as linkers for optimal epitope presentation. 5) Designing a new vaccine model ensuring broader global population coverage based on a consensus sequence of various HIV subtypes and CRFs.
This study was performed with the aim of developing a secure vaccine suitable for both human and mouse hosts. In particular, the research team obtained a collection of one hundred complete HIV gene sequences belonging to the major subtypes and CRFs from the LANL. Using immunoinformatic methods, we identified potential CTL, HTL, and BCL epitopes within the full-length Gag sequence. Given the pivotal role that Gag plays in the intricate life cycle of HIV-1, Gag is the logical focus of vaccine development [109]. For example, the HIV-Gag protein has various functions in the replication and life cycle. It participates in the formation of the viral capsid, transportation to the plasma membrane, interaction with host factors, and packaging of the viral genome [110, 111]. In this regard, a consensus Gag sequence utilized for vaccine design was derived from 100 Gag sequences representing the most prevalent subtypes and CRFs. In the context of designing epitope-based vaccines, the utilization of highly conserved epitopes can confer a wider range of immune protection against various subtypes, CRFs and even different strains. This notion is supported by previous research [85]. Hence, the final epitopes were chosen based on conserved regions within the Gag protein to generate a more robust and comprehensive immune response against the major subtypes and CRFs. A protein domain is characterized as the autonomously evolved, operative, and structurally preserved segment of a protein sequence that serves as a functional and structural representation of the corresponding protein. Consequently, the conserved domain may serve as an innovative target for the development of vaccines [85].The epitopes that were examined in this investigation have the capacity to generate neutralizing antibodies targeting the HIV-Gag gene. Furthermore, they also have the ability to elicit a suitable immune response involving the production of IFN-ɣ and activation of the innate immune system by interacting with the Toll-like receptors TLR-2, 3, 4, 7, and 9.
BCL epitopes play a significant role in the development of an immune response capable of combating viral infections. These epitopes possess distinct characteristics that enable BCLs to detect and trigger immune responses specific to viral infections [76, 112]. In this study, five different methods were utilized to identify linear BCL epitopes, followed by a screening process. The ElliPro server was also employed to predict both the conformation and linear BCL epitopes of the vaccine construct. Taken together, these findings indicate that the vaccine model has the capacity to induce humoral immunity and effectively identify HIV infection.
Protection against HIV is closely linked to T cells, which can stimulate various cytokines [113]. The presentation of antigens to CTLs through MHC-I/HLA-I is a critical step in triggering the immune response and generating memory cells to combat diseases. These CTLs are essential in the MHC-I-mediated immune response because they are responsible for identifying and eliminating damaged, virus-infected, or cancerous cells through the epitopes presented by the MHC-I/HLAI molecules on the cell surface. For this purpose, MHC-I/HLAI epitopes must possess significant immunogenicity to activate CD8+ T lymphocytes [114]. The results of these studies indicate that T cells that can stimulate the production of various cytokines have a significant impact on the suppression of HIV infection and could be vital in the development of efficacious vaccines [115].
Here, Gag was targeted by CTLs to find immunogenic CD8+ T-cell epitopes. The selected epitopes that can bind to both mouse and human alleles were passed through various filters and ultimately included in the vaccine construct. Detailed information can be found in S2 and S9 Tables. Another objective of this report was to evaluate potential HTL epitopes that can stimulate the production of IFN-ɣ and thereby initiate strong HTL immune responses through vaccination. The outcome of infectious diseases and the effectiveness of vaccines are influenced by various factors, including the induction of CD4+ cells, which can affect the virologic, host physiological, or molecular level [93, 112, 114, 116–119]. HTLs can acquire Th1 or Th2 phenotypes and activate immune responses, thereby stimulating macrophages, natural killer cells, and CD8+ T cells [99, 120, 121]. Moreover, HTLs are responsible for inducing robust humoral and cellular responses by facilitating the optimal expansion and maintenance of CD8+ T cells [122]. Therefore, HIV-1 clinical trials have revealed that CD4+ T-cell populations are suitable candidates for use in vaccine models, as such cells can stimulate strong immune responses against HIV infection [123]. In this study, highly qualified HTL epitopes were identified through advanced computational biology techniques.
In this report, we screened all the primary epitopes using various filters. To evaluate the antigenicity of each epitope and vaccine construct, VaxiJen v2.0 was used to determine the antigens that induce the immune system in nature. Another factor evaluated was immunogenicity, which refers to antigens’ ability to stimulate the immune system without binding to T cells. The antigenic and immunogenic features of vaccine constructs and epitopes are essential in designing efficient vaccines [124]. ToxinPred and ToxinPred2 servers (Table 1g) were applied to assess the toxicity of the peptide and vaccine construct, respectively. According to our data, the finalized epitopes and the sequence of the vaccine construct were nontoxic.
Another quality factor that was screened was non-allergenicity. A safe human vaccine model should not induce an allergenic reaction resulting in skin rash, sneezing, wheezing, or swelling of the mucous membrane [57]. Finally, the presence of signal peptides and transmembrane helices within the epitopes was estimated using the DeepTMHMM server to exclude epitopes that contained either signal peptides or transmembrane helices [125]. To increase the efficacy of the vaccine constructs, specific features, such as the Kozak sequence found exclusively in eukaryote expression vectors and the beta-defensin, PADRE, and invasin sequences, were incorporated into both prokaryotic and eukaryotic expression vectors.
From the standpoint of a public health program, the effectiveness of a vaccine depends on various factors, including its specificity and extent of population coverage [126]. The analysis of population coverage verified that utilizing the last 25 epitopes led to 93.91% coverage of the global population. These findings suggest that creating a vaccine with multiple epitopes could offer enhanced protection against HIV-1 infection. Notably, the MHC class combined epitopes exhibited the highest coverage in Europe (99.92%) and North America (99.79%). Conversely, Central America had the lowest coverage, with only 44.99% of the population. The reason for the lower population affected by HIV-1 in Central America compared to other parts of the world is mainly due to the prevalence of subtype B in the region, which makes up 98.9% of the sequences [127]. This high level of consistency indicates that HIV-1 subtype B was likely introduced to Central America only once, resulting in a more contained epidemic with separate outbreaks in each country [128]. On the other hand, regions like Latin America have a variety of HIV-1 subtypes and recombinants, which can affect coverage rates due to the complexity of the virus [129, 130]. Furthermore, the lack of visibility of the HIV epidemic among indigenous populations in Latin America also adds to the difficulty in achieving broader population coverage [131]. Together, these factors underscore the distinctive genetic diversity and epidemiological dynamics of HIV-1 in Central America, contributing to the lower coverage rates compared to more diverse regions globally. As a result, the constructed Gag vaccine provides widespread coverage for a vast number of people worldwide.
After all the required criteria were met, 32 epitopes were attached together using a variety of linkers in the vaccine construct. The utilization of the EAAAK linker in the designed vaccine ensures the separation of epitopes within the host [57, 70]. Moreover, the adjuvant was attached to the multiepitope vaccine through the EAAAK linker to minimize potential interactions among functional domains of the vaccine model [132]. To separate the CTL epitopes from one another, as well as from the PADRE sequence, a GGGS linker was used. The KK linker was utilized to bind BCL epitopes, ensuring the preservation of independent immunogenic responses [133]. The linkers GGGS, GPGPG, K, EAAAK, and EGGE have been extensively utilized in various bioinformatic methods for the development of multiepitope vaccines for viruses [94, 99]. Accordingly, the abovementioned linkers were used to join different sequences of the vaccine candidates.
In this study, adjuvants, as immunological and pharmacological substances, were added to the vaccine to stimulate and strengthen the adaptive immune system [134]. Defensins mediate a number of immune responses, including cell maturation, which generally leads to innate immunity [135]. In particular, defensins recruit naïve T cells and immature dendritic cells to specific sites of infection via the CCR6 receptor to trigger an adaptive immune response and exert anti-HIV-1 activities and immunomodulatory effects [136]. Another adjuvant attached to the construct was PADRE, which stimulates the innate and humoral immune systems. It also reaches a larger population since such a peptide overcomes barriers, as indicated by the high diversity among HLA molecules [135]. According to previous studies, PADRE improves the presentation of epitopes that generate specific CTLs as vaccines for hepatitis B virus infection. Furthermore, it enhances the immune response to human papillomavirus vaccines by inducing a robust CD8+ response [137, 138]. To ensure the stable expression of vaccine constructs in human cells, the Kozak sequence upstream of optimized cDNA should be incorporated for the recognition of mRNA by ribosomes [139]. In the present report, the Kozak sequence was included in the vaccine model to increase the expression of the vaccine protein in human cells. In addition, the desirable properties of the designed vaccine were determined based on its physicochemical parameters.
Furthermore, the findings obtained from the secondary and tertiary structures of the vaccine model demonstrated a high level of immunogenicity. This result signifies the potential of the designed vaccine construct as a promising candidate for the development of an efficacious vaccine. In addition, the physicochemical parameters of the vaccine model illustrated the desirable properties of the construct.
The validation parameters for evaluating the accuracy of the 3D structure of the designed vaccine construct revealed that the improved 3D model of the HIV vaccine construct exhibited a favorable level of quality. Notably, the interaction between the T-cell epitopes and MHC molecules was examined through molecular docking. The docking findings indicated that the T-cell epitopes could effectively attach to MHC molecules, resulting in recognition by antigen-presenting cells. Molecular docking analysis revealed that the designed HIV-1 Gag vaccine exhibited strong affinity for TLRs (TLR-2, TLR-3, TLR-4, TLR-7, and TLR-9). These findings confirm that the immune system of humans and mice can be used to efficiently identify multiepitope vaccines, which can lead to consistent and potent immune responses. Previous investigations have established that TLRs assume a vital function in the beginning of innate immune responses due to their possession of the capacity to recognize pathogens and stimulate the evolution of the adaptive immune system [140]. For example, TLR-2 and TLR-4 identifies viral structural proteins and induces the generation of inflammatory cytokines, while TLR-3 stimulates the initiation of dendritic cell activation mediated by HIV-1 [85]. Unexpectedly, the time factor plays a significant role in the response of TLR7 to HIV. During the initial phase of HIV infection, the activation of TLR7 leads to increased production of type I IFNs and a decrease in the expression of HIV coreceptors CCR5 and CXCR4 in CD4+ T cells [141]. This implies that stimulation of TLR7 inhibits the production of HIV and enhances the antiviral response of activated T cells, macrophages, and monocytes by inducing the production of type I IFNs [141, 142]. On the other hand, TLR3 has the ability to detect both double-stranded RNA (dsRNA) and single-stranded RNA (ssRNA) [143, 144]. Additionally, TLRs are capable of binding to polyproteins and epitopes [145–147]. For instance, TLR3 functions as a receptor for developing a vaccine that can target and identify the virus [94, 148]. The vaccine contains a TLR3 agonist known as β-defensin, which is connected to the N-terminus with an EAAAK linker [100]. TLR4 can activate immune responses against viruses by identifying proteins on the virus surface [59]. According to earlier studies, HIV can activate certain receptors (TLR-2, -3, -4, -7, and -9), which in turn activate downstream pathways that lead to the generation of proinflammatory cytokines. These cytokines play a role in fighting against HIV infection [24].
In this research, both prokaryotic and eukaryotic hosts were chosen for the production of the vaccine protein. Codon optimization was another step in this study. Each amino acid can be represented by multiple codons due to codon degeneracy, and the selection of synonymous codon pairs varies across different species. In other words, particular organisms or species tend to favor certain specific synonymous codons known as optimal codons [149]. As a result, codon optimization was conducted in both hosts to increase the efficiency of transcription and translation. This was achieved through an analysis of the total GC content as well as the codon adaptation index of the DNA sequence [150].
The stability of the vaccine-TLR complex was verified through MD simulation analysis, which included diverse environmental conditions, including changes in pressure and temperature. The RMSD analysis of the docked complexes revealed minimal deviation, indicating a stable interaction between the vaccine construct and TLR molecules. The RMSF graphs for the docked protein complexes displayed consistent levels, showcasing the flexibility of the side chains. Finally, the Rg plots for the docked complexes exhibited a steady curve, indicating stable vaccine-receptor complexes. Hence, the initial trajectory assessment, which involved calculating Rg, RMSD, RMSF, and hydrogen bonds, all supported the exceptional stability of the vaccine-TLR complexes in a biological context. Moreover, compared to those of receptor-vaccine complexes, the vaccine TLR4 construct displayed the highest level of stability when exposed to natural conditions. Using an immune simulator server, we predicted how the immune system would respond to three injections of the candidate Gag vaccine. Our analysis of the cytokine simulation plot showed an increase in IFN-γ, IL-2, TGFβ, IL-10, and IL-12 levels, similar to what Sher et al. [31] observed in the design of an HIV-1 multiepitope vaccine (Fig 8J). This indicates that antigen-presenting cells were appropriately activated, leading to a high production of memory cells from BCLs and T cells. The cytokines produced by Th memory cells also help control and clear antigens. Furthermore, the persistence of long-term memory after three injections confirmed the effectiveness of our candidate vaccine.
Here, there are more explanation how the selected epitopes from the Gag polyproteins contribute to the overall efficacy of the vaccine. The efficacy of vaccines is significantly influenced by the selection and presentation of epitopes that are recognized by the immune system. The computational approaches towards vaccine design against Gag-HIV gene commenced with identifying highly antigenic, immunogenic, and non-allergenic epitopes specific to CTLs, HTLs, and linear and conventional B lymphocytes derived from virus whole Gag protein sequences. This step underscores the critical role these epitopes play in eliciting a robust immune response by targeting different arms of the adaptive immune system—CTLs for cellular immunity, HTLs for assisting other cells in the immune response including B cells for antibody production, and B lymphocytes directly responsible for antibody production. Following epitope selection, linkers were employed to connect these epitopes cohesively while adjuvants were introduced at the construct to enhance immunogenicity further. This strategy illustrates how selected epitopes contribute not only through their inherent properties but also through strategic assembly enhancing overall vaccine effectiveness. Moreover, molecular docking and dynamics simulations revealed strong and stable binding interactions between these vaccine candidates and human TLRs. The interactions are pivotal since TLRs play a crucial role in innate immunity as one of their primary sensors triggering downstream signaling pathways leading to inflammatory responses—a foundational step towards adaptive immunity activation where epitope-specific responses are generated. Additionally, computer-aided immune simulation predicted real-life-like immune responses upon administration, highlighting how these carefully chosen epitopes could potentially translate into effective real-world outcomes. Finally, yet importantly, codon optimization was performed on these vaccine candidates, facilitating their cloning into both prokaryotic and eukaryotic vectors for future experimental validation.
To the best of our knowledge, there has been no research on the specific targeting of Gag polyproteins to determine the BCL, CTL, and HLA epitopes to develop an in-silico vaccine. The study available, conducted by Manalu et al. in 2023 [151], focused on a small part of the Gag polyproteins. In this research, various immunoinformatic techniques were used to determine whether the p17 matrix protein could serve as a target for designing an in-silico vaccine against HIV infection. Christy et al utilized immunoinformatic methods to find only the CD4+ epitopes present in the Gag protein restricted to the HLA-DRB1*07 allele, which is widely prevalent among the Indian population [152]. Multiple experimental studies have been conducted to examine various proteins encoded by the Gag polyprotein sequence of HIV and simian immunodeficiency viruses (SIVs). In fact, SIVs are diverse viruses that naturally infect a variety of African primates, making them valuable animal models for studying the natural steps of HIV infection and disease [153]. Benlahrech et al. explored two approaches to assess the effectiveness of the CD8+ T-cell response using SIV Gag vaccine models [153, 154]. In 2023, Tarres-Freixas et al. reported that immunogenicity is enhanced by Gag-based virus-like particles (VLPs) compared with the soluble Gag protein [155].
The evaluation of bioinformatic vaccine efficacy is an intricate process that involves multiple stages, including antigen selection, immune response prediction, vaccine formulation design, and assessment through both computational simulations followed by rigorous experimental validations both in vitro/in vivo settings before proceeding towards clinical trials where actual effectiveness can be gauged among populations at risk [156]. For example, algorithms have been developed to predict T-cell epitopes’ ability to bind to MHC molecules and B-cell epitope recognition patterns. These predictions are important to designing a vaccine that elicits strong cellular immunity and humoral responses [157]. Additionally, computer models are used to simulate the dynamics between different parts of vaccines, such as adjuvants, with the immune system. This helps improve effectiveness while maintaining safety standards [158].
Planned experimental studies on HIV bioinformatic vaccines should underscore a multidisciplinary approach combining virology, immunology, bioinformatics, and biostatistics to improve our understanding of HIV’s interaction with the human immune system. Here, some suggestions can be considered: 1. Development of safe and effective HIV vaccines by evaluating multiple vaccine approaches in vivo that elicit cross-reactive humoral and cellular responses 2. Induction of specific cellular and humoral immune responses through vaccination without adverse effects and ensuring broad protection across all major global HIV strains. 3. Utilization of advanced statistical models in evaluating longitudinal changes post-vaccination. 4. Conduction of clinical trials studying in both adult and infant populations. In other words, the final goal lies within human trials, where real-world efficacy is assessed against circulating HIV subtypes and CRFs in diverse epidemiological settings to address the HIV pandemic.
The main focus of this research was to identify epitope-based vaccines that can trigger the infiltration of various immune cells and stimulate chemokine production in both humans and mice, as proven through the use of a molecular simulation technique. Diverse factors of the vaccine construct, including antigenicity, stability, thermostability, non-allergenicity, and hydrophilicity, were evaluated via immunoinformatic approaches. Immune stimulation has been shown to promote the development of memory cells via the activation of dendritic cells and macrophages in a vaccine model. Overall, the proposed vaccine models revealed great potential as vaccine candidates for controlling HIV infection. Three levels of research can be conducted, related to the present report: 1) The suggested vaccine models should be developed in a wet laboratory, 2) the efficacy of vaccine models should be checked on different HIV virus subtypes and CRFs, and 3) further investigations of vaccine efficacy can be designed and run-in clinical trials.
5. Conclusions
Therapeutic measures aimed at preventing and eradicating HIV-1 infection necessitate innovative pharmaceuticals and immunization vaccines that can protect individuals at risk of pathogens and those already infected.
For the first time, in silico vaccine models against the HIV-Gag polyprotein against major HIV subtypes and CRFs were designed. A vast number of epitopes were predicted and screened using different parameters, and the qualified epitopes located in the conserved domains of the Gag sequence were selected for joining via appropriate linkers in vaccine models. The designed vaccines also contained suitable adjuvants to vigorously stimulate the immune response and reduce HLA polymorphisms in the population. The confirmation of the high level of expression of the vaccine models in both human cells and E. coli bacteria was achieved through the implementation of codon optimization. Furthermore, the vaccine constructs exhibited favorable affinity for the immune receptor TLR-2, TLR-3, TLR-4, TLR-7, and TLR-9. The final constructs were able to stimulate immune cells vigorously, which was confirmed using the immune stimulation method. Experimental analysis should be carried out in a wet laboratory setting to ascertain the effectiveness of the designed vaccine models.
Supporting information
S1 Table. A list of HTL-selected epitopes and identified MHC alleles in the Gag gene of HIV-1.
https://doi.org/10.1371/journal.pone.0306559.s001
(DOCX)
S2 Table. A list of CTL-selected epitopes and identified MHC alleles in the Gag gene of HIV-1.
https://doi.org/10.1371/journal.pone.0306559.s002
(DOCX)
S3 Table. The techniques that were different in the development of vaccines in eukaryotic and prokaryotic hosts.
https://doi.org/10.1371/journal.pone.0306559.s003
(DOCX)
S4 Table. Accession numbers of 100 full-length HIV-1 sequences of the most predominant subtypes and CRFs.
https://doi.org/10.1371/journal.pone.0306559.s004
(DOCX)
S5 Table. ProtParam results for the HIV Gag protein and vaccine construct.
https://doi.org/10.1371/journal.pone.0306559.s005
(DOCX)
S6 Table. The residue data and scores of the predicted linear BCL epitopes on the Gag vaccine construct.
https://doi.org/10.1371/journal.pone.0306559.s006
(DOCX)
S7 Table. Population coverage results of selected CTL and HTL epitopes in the Gag vaccine construct.
https://doi.org/10.1371/journal.pone.0306559.s007
(DOCX)
S8 Table. Numbers of predicted CTL, HTL, and BCL epitopes.
https://doi.org/10.1371/journal.pone.0306559.s008
(DOCX)
S9 Table. Results of screening the Gag vaccine construct.
The sequence of the vaccine model was antigenic, nontoxic, nonallergenic, and nonhomologous to the human proteome and lacked any signal sequence.
https://doi.org/10.1371/journal.pone.0306559.s009
(DOCX)
S10 Table. The consensus sequences of the HIV-1 Gag gene and vaccine construct.
https://doi.org/10.1371/journal.pone.0306559.s010
(DOCX)
S11 Table. Various features of the secondary structures of the HIV Gag gene and vaccine construct.
https://doi.org/10.1371/journal.pone.0306559.s011
(DOCX)
S12 Table. The residue data and scores of the predicted discontinuous BCL epitopes on the Gag vaccine construct.
https://doi.org/10.1371/journal.pone.0306559.s012
(DOCX)
S13 Table. Molecular docking results of TLRs and the Gag vaccine construct generated using the Cluspro2.0 tool.
(The best models are marked with a green box).
https://doi.org/10.1371/journal.pone.0306559.s013
(DOCX)
S14 Table. The sequence of the optimized codon of the vaccine models in human and E. coli hosts.
https://doi.org/10.1371/journal.pone.0306559.s014
(DOCX)
S1 Fig. Worldwide report of submitted subtypes and CRF sequences of HIV-1.
The figure was adjusted from the LANL database.
https://doi.org/10.1371/journal.pone.0306559.s015
(TIF)
Acknowledgments
The authors would like to thank Dr. Behzad Dehghani and Dr. Azra Kenarkouhi for their invaluable consultation on the data analysis. This article abides by the (IR.SUMS.MED.REC.1403.370) code of ethics.
References
- 1. Dehghani B, Hasanshahi Z, Hashempour T, Kazerooni PA (2021) Subtype classification by polymerase and gag genes of HIV-1 Iranian sequences registered in the NCBI GenBank. Current Proteomics 18: 153–161.
- 2. Hashempour T, Bamdad T, Bergamini A, Lavergne JP, Haj-Sheykholeslami A, et al. (2015) F protein increases CD4+ CD25+ T cell population in patients with chronic hepatitis C. Pathogens and Disease 73: ftv022. pmid:25862675
- 3. Halaji M, Hashempour T, Moayedi J, Pouladfar GR, Khansarinejad B, et al. (2019) Viral etiology of acute respiratory infections in children in Southern Iran. Revista da Sociedade Brasileira de Medicina Tropical 52.
- 4. Hashempoor T, Alborzi AM, Moayedi J, Ajorloo M, Bamdad T, et al. (2018) A decline in anti-core+ 1 antibody titer occurs in successful treatment of patients infected with hepatitis C virus. Jundishapur Journal of Microbiology 11.
- 5. Fani M, Sadooni R, Nejad MH, Mirzavieh NN, Mobarak S, et al. (2023) Two Oncoviruses of HPV and EBV in Breast Cancer: An Iran-based Study. Journal of Liaquat University of Medical & Health Sciences 22: 169–173.
- 6. Fani M, Khodadad N, Ebrahimi S, Nahidsamiei R, Makvandi M, et al. (2020) Zinc sulfate in narrow range as an in vitro anti-HSV-1 assay. Biological Trace Element Research 193: 410–413. pmid:31028520
- 7. Tavakoli F, Moattari A, Shamsi Shahr Abadi M, Kadivar MR, Khodadad N, et al. (2015) Antigenic Variation of the Haemagglutinin Gene of the Influenza A (H1N1) pdm09 Virus Circulating in Shiraz, February-April 2013. Iranian Journal of Immunology 12: 198–208.
- 8. Musavi Z, Hashempour T, Moayedi J, Dehghani B, Ghassabi F, et al. (2020) Antibody development to HCV alternate reading frame protein in liver transplant candidate and its computational analysis. Current Proteomics 17: 154–170.
- 9. Kamali P, Zandi M, Ghasemzadeh-Moghaddam H, Fani M (2022) Comparison between various biosensor methods for human T-lymphotropic virus-1 (HTLV-1) detection. Molecular Biology Reports 49: 1513–1517. pmid:34797491
- 10. Zandi M, Soltani S, Fani M, Abbasi S, Ebrahimi S, et al. (2021) Severe acute respiratory syndrome coronavirus 2 and respiratory syncytial virus coinfection in children. Osong Public Health and Research Perspectives 12: 286. pmid:34719220
- 11. Khodadad N, Fani M, Jamehdor S, Nahidsamiei R, Makvandi M, et al. (2020) A knockdown of the herpes simplex virus type-1 gene in all-in-one CRISPR vectors. Folia Histochemica et Cytobiologica 58: 174–181. pmid:32937678
- 12. Khodadad N, Moattari A, Abadi MSS, Kadivar MR, Sarvari J, et al. (2015) Prevalence of influenza A (H1N1) pdm09 virus resistant to oseltamivir in Shiraz, Iran, during 2012–2013. Jundishapur Journal of Microbiology 8.
- 13. Khodadad N, Seyedian SS, Moattari A, Haghighi SB, Pirmoradi R, et al. (2020) In silico functional and structural characterization of hepatitis B virus PreS/S-gene in Iranian patients infected with chronic hepatitis B virus genotype D. Heliyon 6. pmid:32695898
- 14. Ebrahimi S, Khanbabaei H, Abbasi S, Fani M, Soltani S, et al. (2022) CRISPR-cas system: A promising diagnostic tool for covid-19. Avicenna journal of medical biotechnology 14: 3. pmid:35509363
- 15. Fani M, Moossavi M, Bakhshi H, Jahrodi AN, Khazdair MR, et al. (2023) Targeting host calcium channels and viroporins: a promising strategy for SARS-CoV-2 therapy. Future Virology 18: 797–807. pmid:37700758
- 16. Ghassabi F, Hashempour T, Moghadami M, Davarpanah M, Kalani M, et al. (2017) Bacterial etiology and antibiotic resistance pattern of septicemia in HIV and non-HIV patients admitted to tertiary care hospitals, Shiraz, South of Iran. Cellular and Molecular Biology 63: 115–121. pmid:28980931
- 17. Naveed M, Ali U, Aziz T, Rasool MJ, Ijaz A, et al. (2023) A reverse vaccinology approach to design an mRNA-based vaccine to provoke a robust immune response against HIV-1. Acta Biochimica Polonica 70: 407–418. pmid:37329562
- 18.
Zahroh H, Ma’rup A, Tambunan USF, Parikesit AA (2016) Immunoinformatics approach in designing epitope-based vaccine against meningitis-inducing bacteria (Streptococcus pneumoniae, Neisseria meningitidis, and Haemophilus influenzae type b). Drug target insights 10: DTI. S38458.
- 19. Shawan MMAK, Sharma AR, Halder SK, Arian TA, Shuvo MN, et al. (2023) Advances in Computational and Bioinformatics Tools and Databases for Designing and Developing a Multi-Epitope-Based Peptide Vaccine. International Journal of Peptide Research and Therapeutics 29: 1–20. pmid:37251529
- 20. García-Machorro J, Ramírez-Salinas GL, Martinez-Archundia M, Correa-Basurto J (2022) The advantage of using immunoinformatic tools on vaccine design and development for coronavirus. Vaccines 10: 1844. pmid:36366353
- 21. Chiarella P, Massi E, De Robertis M, Fazio VM, Signori E (2009) Recent advances in epitope design for immunotherapy of cancer. Recent patents on anti-cancer drug discovery 4: 227–240. pmid:19522699
- 22. Slingluff CL Jr (2011) The present and future of peptide vaccines for cancer: single or multiple, long or short, alone or in combination? Cancer journal (Sudbury, Mass) 17: 343. pmid:21952285
- 23. Tough DF (2003) Deciphering the relationship between central and effector memory CD8+ T cells. Trends in immunology 24: 404–407. pmid:12909449
- 24. Akbari E, Kardani K, Namvar A, Ajdary S, Ardakani EM, et al. (2021) In silico design and in vitro expression of novel multiepitope DNA constructs based on HIV-1 proteins and Hsp70 T-cell epitopes. Biotechnology Letters 43: 1513–1550. pmid:33987776
- 25. Gorse GJ, Baden LR, Wecker M, Newman MJ, Ferrari G, et al. (2008) Safety and immunogenicity of cytotoxic T-lymphocyte poly-epitope, DNA plasmid (EP HIV-1090) vaccine in healthy, human immunodeficiency virus type 1 (HIV-1)-uninfected adults. Vaccine 26: 215–223. pmid:18055072
- 26. Pavlakis GN, Felber BK (2018) A new step towards an HIV/AIDS vaccine. The Lancet 392: 192–194. pmid:30047374
- 27. Ahmed SF, Quadeer AA, Morales-Jimenez D, McKay MR (2019) Sub-dominant principal components inform new vaccine targets for HIV Gag. Bioinformatics 35: 3884–3889. pmid:31250884
- 28. Li JZ, Team ACTGAS, Brumme ZL, Team ACTGAS, Brumme CJ, et al. (2011) Factors associated with viral rebound in HIV-1-infected individuals enrolled in a therapeutic HIV-1 gag vaccine trial. The Journal of infectious diseases 203: 976–983. pmid:21402549
- 29. Valentin A, Bergamaschi C, Rosati M, Angel M, Burns R, et al. (2022) Comparative immunogenicity of an mRNA/LNP and a DNA vaccine targeting HIV gag conserved elements in macaques. Frontiers in Immunology 13: 945706. pmid:35935984
- 30.
Gasteiger E, Hoogland C, Gattiker A, Duvaud Se, Wilkins MR, et al. (2005) Protein identification and analysis tools on the ExPASy server: Springer.
- 31. Sher H, Sharif H, Zaheer T, Khan SA, Ali A, et al. (2023) Employing computational tools to design a multi-epitope vaccine targeting human immunodeficiency virus-1 (HIV-1). BMC genomics 24: 1–22.
- 32. Hashempour T, Dehghani B, Mousavi Z, Yahaghi M, Hasanshahi Z, et al. (2020) Evaluating drug resistant mutations to HCV NS3 protease inhibitors in iranian Naïve patients. International Journal of Peptide Research and Therapeutics 26: 1699–1710.
- 33.
Murphy K, Weaver C (2016) Janeway’s immunobiology: Garland science.
- 34.
Reineke U, Schutkowski M (2009) Epitope mapping protocols: Springer.
- 35. Sanchez-Trincado JL, Gomez-Perosanz M, Reche PA (2017) Fundamentals and methods for T-and B-cell epitope prediction. Journal of immunology research 2017. pmid:29445754
- 36. Ponomarenko JV, Van Regenmortel MH (2009) B cell epitope prediction. Structural bioinformatics 2: 849–879.
- 37. Backert L, Kohlbacher O (2015) Immunoinformatics and epitope prediction in the age of genomic medicine. Genome medicine 7: 1–12.
- 38. Lundegaard C, Lund O, Keşmir C, Brunak S, Nielsen M (2007) Modeling the adaptive immune system: predictions and simulations. Bioinformatics 23: 3265–3275. pmid:18045832
- 39. Greenbaum JA, Andersen PH, Blythe M, Bui HH, Cachau RE, et al. (2007) Towards a consensus on datasets and evaluation metrics for developing B‐cell epitope prediction tools. Journal of Molecular Recognition: An Interdisciplinary Journal 20: 75–82. pmid:17205610
- 40. Ghasabi F, Hashempour A, Khodadad N, Bemani S, Keshani P, et al. (2022) First report of computational protein—ligand docking to evaluate susceptibility to HIV integrase inhibitors in HIV-infected Iranian patients. Biochemistry and Biophysics Reports 30: 101254. pmid:35368742
- 41. Parker J, Guo D, Hodges R (1986) New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry 25: 5425–5432. pmid:2430611
- 42. Saha S, Raghava GPS (2006) Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network. Proteins: Structure, Function, and Bioinformatics 65: 40–48.
- 43. Dehghani B, Hasanshahi Z, Hashempour T (2020) HIV capsid and protease, new targets of melittin. International Journal of Peptide Research and Therapeutics 26: 2057–2065.
- 44. Pandey RK, Ojha R, Aathmanathan VS, Krishnan M, Prajapati VK (2018) Immunoinformatics approaches to design a novel multi-epitope subunit vaccine against HIV infection. Vaccine 36: 2262–2272. pmid:29571972
- 45. Hashempour T, Dehghani B, Musavi Z, Moayedi J, Hasanshahi Z, et al. (2020) Impact of IL28 genotypes and modeling the interactions of HCV core protein on treatment of hepatitis C. Interdisciplinary Sciences: Computational Life Sciences 12: 424–437. pmid:32656614
- 46. Jespersen MC, Peters B, Nielsen M, Marcatili P (2017) BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic acids research 45: W24–W29. pmid:28472356
- 47. Hasanshahi Z, Hashempour A, Moayedi J, Musavi Z, Rezaei B, et al. (2023) Naturally occurring mutations in HIV-1 protease gene among people living with HIV. Acta Medica Iranica: 145–149.
- 48. Garnier J, Osguthorpe DJ, Robson B (1978) Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. Journal of molecular biology 120: 97–120. pmid:642007
- 49. Chou PY, Fasman GD (1974) Prediction of protein conformation. Biochemistry 13: 222–245. pmid:4358940
- 50. Emini EA, Hughes JV, Perlow D, Boger J (1985) Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. Journal of virology 55: 836–839. pmid:2991600
- 51. Karplus P, Schulz G (1985) Prediction of chain flexibility in proteins: a tool for the selection of peptide antigens. Naturwissenschaften 72: 212–213.
- 52. Kumar Pandey R, Ojha R, Mishra A, Kumar Prajapati V (2018) Designing B‐and T‐cell multi‐epitope based subunit vaccine using immunoinformatics approach to control Zika virus infection. Journal of cellular biochemistry 119: 7631–7642. pmid:29900580
- 53. Savsani K, Jabbour G, Dakshanamurthy S (2021) A new epitope selection method: application to design a multi-valent epitope vaccine targeting hras oncogene in squamous cell carcinoma. Vaccines 10: 63. pmid:35062725
- 54. Hashempour T, Dehghani B, Mousavi Z, Akbari T, Hasanshahi Z, et al. (2021) Association of mutations in the NS5A-PKRBD region and IFNL4 genotypes with hepatitis C interferon responsiveness and its functional and structural analysis. Current Proteomics 18: 38–49.
- 55. Hasanshahi Z, Hashempour A, Ghasabi F, Moayedi J, Musavi Z, et al. (2021) First report on molecular docking analysis and drug resistance substitutions to approved HCV NS5A and NS5B inhibitors amongst Iranian patients. Bmc Gastroenterology 21: 1–14.
- 56. Morozov V, Rodrigues CH, Ascher DB (2023) CSM-toxin: A web-server for predicting protein toxicity. Pharmaceutics 15: 431. pmid:36839752
- 57. Pandey RK, Bhatt TK, Prajapati VK (2018) Novel immunoinformatics approaches to design multi-epitope subunit vaccine for malaria by investigating anopheles salivary protein. Scientific reports 8: 1125. pmid:29348555
- 58. Dimitrov I, Naneva L, Doytchinova I, Bangov I (2014) AllergenFP: allergenicity prediction by descriptor fingerprints. Bioinformatics 30: 846–851. pmid:24167156
- 59. Tan C, Zhu F, Pan P, Wu A, Li C (2023) Development of multi-epitope vaccines against the monkeypox virus based on envelope proteins using immunoinformatics approaches. Frontiers in Immunology 14: 1112816. pmid:36993967
- 60.
Hallgren J, Tsirigos K, Damgaard Pedersen M, Juan J, Armenteros A, et al. (2022) DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks. BioRxiv, 2022.04. 08.487609.
- 61. Dehghani B, Hashempour T, Hasanshahi Z (2020) Using immunoinformatics and structural approaches to design a novel HHV8 vaccine. International Journal of Peptide Research and Therapeutics 26: 321–331.
- 62. Bhattacharya M, Chatterjee S, Nag S, Dhama K, Chakraborty C (2022) Designing, characterization, and immune stimulation of a novel multi-epitopic peptide-based potential vaccine candidate against monkeypox virus through screening its whole genome encoded proteins: An immunoinformatics approach. Travel Medicine and Infectious Disease 50: 102481. pmid:36265732
- 63. Bui H-H, Sidney J, Dinh K, Southwood S, Newman MJ, et al. (2006) Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC bioinformatics 7: 1–5.
- 64. Waqas M, Haider A, Sufyan M, Siraj S, Sehgal SA (2020) Determine the potential epitope based peptide vaccine against novel SARS-CoV-2 targeting structural proteins using immunoinformatics approaches. Frontiers in molecular biosciences 7: 227. pmid:33195402
- 65. Daniel M, Liang B, Luo M (2021) Assessment of the population coverage of an HIV-1 vaccine targeting sequences surrounding the viral protease cleavage sites in Gag, Pol, or all 12 protease cleavage sites. Vaccine 39: 2676–2683. pmid:33863573
- 66. Rahmani A, Baee M, Rostamtabar M, Karkhah A, Alizadeh S, et al. (2019) Development of a conserved chimeric vaccine based on helper T-cell and CTL epitopes for induction of strong immune response against Schistosoma mansoni using immunoinformatics approaches. International journal of biological macromolecules 141: 125–136. pmid:31479669
- 67. Manalu RT, Setyaningsih EP (2023) Peptide Based Hepatitis C Vaccine Design From RNA-dependent RNA polymerase (RdRp) NS5B: Immunoinformatics Approach. Journal of Research in Pharmaceutical Science 9: 31–39.
- 68. Ahmed N, Rabaan AA, Alwashmi AS, Albayat H, Mashraqi MM, et al. (2023) Immunoinformatic Execution and Design of an Anti-Epstein—Barr Virus Vaccine with Multiple Epitopes Triggering Innate and Adaptive Immune Responses. Microorganisms 11: 2448. pmid:37894106
- 69. Chen X, Zaro JL, Shen W-C (2013) Fusion protein linkers: property, design and functionality. Advanced drug delivery reviews 65: 1357–1369. pmid:23026637
- 70. Pandey RK, Sundar S, Prajapati VK (2016) Differential expression of miRNA regulates T cell differentiation and plasticity during visceral leishmaniasis infection. Frontiers in microbiology 7: 206. pmid:26941729
- 71. Mahmoudvand S, Esmaeili Gouvarchin Ghaleh H, Jalilian FA, Farzanehpour M, Dorostkar R (2023) Design of a multi‐epitope‐based vaccine consisted of immunodominant epitopes of structural proteins of SARS‐CoV‐2 using immunoinformatics approach. Biotechnology and Applied Biochemistry 70: 1189–1205. pmid:36577011
- 72.
Walker JM (2005) The proteomics protocols handbook: Springer.
- 73. Zuker M, Stiegler P (1981) Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic acids research 9: 133–148. pmid:6163133
- 74. McCaskill JS (1990) The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers: Original Research on Biomolecules 29: 1105–1119. pmid:1695107
- 75. Mathews DH, Sabina J, Zuker M, Turner DH (1999) Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. Journal of molecular biology 288: 911–940. pmid:10329189
- 76. Bruccoleri RE, Heinrich G (1988) An improved algorithm for nucleic acid secondary structure display. Bioinformatics 4: 167–173. pmid:2454712
- 77. Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein science 2: 1511–1519. pmid:8401235
- 78. Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic acids research 35: W407–W410. pmid:17517781
- 79. Lovell SC, Davis IW, Arendall WB III, De Bakker PI, Word JM, et al. (2003) Structure validation by Cα geometry: ϕ, ψ and Cβ deviation. Proteins: Structure, Function, and Bioinformatics 50: 437–450.
- 80. Ponomarenko J, Bui H-H, Li W, Fusseder N, Bourne PE, et al. (2008) ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC bioinformatics 9: 1–8.
- 81. Jones MS, Carter JM (2014) Prediction of B-cell epitopes in listeriolysin O, a cholesterol dependent cytolysin secreted by listeria monocytogenes. Advances in Bioinformatics 2014. pmid:24523732
- 82. Dehghani B, Hasanshahi Z, Hashempour T, Motamedifar M (2020) The possible regions to design Human Papilloma Viruses vaccine in Iranian L1 protein. Biologia 75: 749–759. pmid:32435064
- 83. Amereh F, Arabestani MR, Hosseini SM, Shokoohizadeh L (2023) Association of qnr Genes and OqxAB Efflux Pump in Fluoroquinolone-Resistant Klebsiella pneumoniae Strains. International Journal of Microbiology 2023. pmid:36865677
- 84. Pommier Y, Johnson AA, Marchand C (2005) Integrase inhibitors to treat HIV/AIDS. Nature reviews Drug discovery 4: 236–248. pmid:15729361
- 85. Abdulla F, Adhikari UK, Uddin MK (2019) Exploring T & B-cell epitopes and designing multi-epitope subunit vaccine targeting integration step of HIV-1 lifecycle using immunoinformatics approach. Microbial pathogenesis 137: 103791.
- 86. Weatherhead JE, Miller VE, Garcia MN, Hasbun R, Salazar L, et al. (2015) Long-term neurological outcomes in West Nile virus—infected patients: an observational study. The American journal of tropical medicine and hygiene 92: 1006. pmid:25802426
- 87. Sana M, Javed A, Jamal SB, Junaid M, Faheem M (2022) Development of multivalent vaccine targeting M segment of Crimean Congo Hemorrhagic Fever Virus (CCHFV) using immunoinformatic approaches. Saudi journal of biological sciences 29: 2372–2388. pmid:35531180
- 88. Elalouf A, Kedarya T, Elalouf H, Rosenfeld A (2023) Computational design and evaluation of mRNA-and protein-based conjugate vaccines for influenza A and SARS-CoV-2 viruses. Journal of Genetic Engineering and Biotechnology 21: 120. pmid:37966525
- 89. Puigbo P, Romeu A, Garcia-Vallve S (2007) HEG-DB: a database of predicted highly expressed genes in prokaryotic complete genomes under translational selection. Nucleic acids research 36: D524–D527. pmid:17933767
- 90. Kenarkoohi A, Soleimani M, Bamdad T, Soleimanjahi H, Estiri H, et al. (2014) Efficient lentiviral transduction of adipose tissue-derived mouse mesenchymal stem cells and assessment of their penetration in female mice cervical tumor model. Iranian journal of cancer prevention 7: 225. pmid:25628843
- 91.
Hashempour Ava, Khodadad Nastaran, Ziaei Reza, Rezaei Behzad, Ghasabi Farzaneh, Falahi Shahab, et al. (2023) Predictors of Antiretroviral Treatment Failure to the First Line Therapy: A Cross-sectional Study Among Iranian HIV-positive Adults.
- 92. Dehghani B, Hashempour T, Musavi Z, Hasanshahi Z, Moayedi J, et al. (2021) Assessment of new E2 protein domain interaction with PKR protein to control IFN signaling. Current Proteomics 18: 536–548.
- 93. Sharafi M, Mirahmadizadeh A, Hassanzadeh J, Seif M, Heiran A (2024) Factors associated with late initiation of antiretroviral therapy in Iran’s HIV/AIDS surveillance data. Scientific Reports 14: 199. pmid:38167855
- 94. Khan MT, Islam MJ, Parihar A, Islam R, Jerin TJ, et al. (2021) Immunoinformatics and molecular modeling approach to design universal multi-epitope vaccine for SARS-CoV-2. Informatics in medicine unlocked 24: 100578. pmid:33898733
- 95. Kazi A, Chuah C, Majeed ABA, Leow CH, Lim BH, et al. (2018) Current progress of immunoinformatics approach harnessed for cellular-and antibody-dependent vaccine design. Pathogens and global health 112: 123–131. pmid:29528265
- 96. Cherryholmes GA, Stanton SE, Disis ML (2015) Current methods of epitope identification for cancer vaccine design. Vaccine 33: 7408–7414. pmid:26238725
- 97. Liu F, Wang X, Zheng M, Xiong F, Liu X, et al. (2020) Immunization with DNA prime-subunit protein boost strategy based on influenza H9N2 virus conserved matrix protein M1 and its epitope screening. Scientific reports 10: 4144. pmid:32139720
- 98. Pumchan A, Krobthong S, Roytrakul S, Sawatdichaikul O, Kondo H, et al. (2020) Novel chimeric multiepitope vaccine for streptococcosis disease in Nile tilapia (Oreochromis niloticus Linn.). Scientific reports 10: 603. pmid:31953479
- 99. Khan MT, Islam R, Jerin TJ, Mahmud A, Khatun S, et al. (2021) Immunoinformatics and molecular dynamics approaches: Next generation vaccine design against West Nile virus. Plos one 16: e0253393. pmid:34138958
- 100. Samad A, Meghla NS, Nain Z, Karpiński TM, Rahman MS (2022) Immune epitopes identification and designing of a multi-epitope vaccine against bovine leukemia virus: a molecular dynamics and immune simulation approaches. Cancer Immunology, Immunotherapy 71: 2535–2548. pmid:35294591
- 101. Khatoon N, Pandey RK, Prajapati VK (2017) Exploring Leishmania secretory proteins to design B and T cell multi-epitope subunit vaccine using immunoinformatics approach. Scientific reports 7: 8285. pmid:28811600
- 102. Chauhan V, Rungta T, Goyal K, Singh MP (2019) Designing a multi-epitope based vaccine to combat Kaposi Sarcoma utilizing immunoinformatics approach. Scientific reports 9: 2517. pmid:30792446
- 103. Jyotisha , Singh S, Qureshi IA (2022) Multi-epitope vaccine against SARS-CoV-2 applying immunoinformatics and molecular dynamics simulation approaches. Journal of Biomolecular Structure and Dynamics 40: 2917–2933. pmid:33164664
- 104. Yang Z, Bogdan P, Nazarian S (2021) An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study. Scientific reports 11: 3238. pmid:33547334
- 105. Vartak A, Sucheck SJ (2016) Recent advances in subunit vaccine carriers. Vaccines 4: 12. pmid:27104575
- 106. Habib A, Liang Y, Xu X, Zhu N, Xie J (2024) Immunoinformatic Identification of Multiple Epitopes of gp120 Protein of HIV-1 to Enhance the Immune Response against HIV-1 Infection. International Journal of Molecular Sciences 25: 2432. pmid:38397105
- 107. Yang Y, Sun W, Guo J, Zhao G, Sun S, et al. (2015) In silico design of a DNA-based HIV-1 multi-epitope vaccine for Chinese populations. Human vaccines & immunotherapeutics 11: 795–805. pmid:25839222
- 108. McBurney SP, Ross TM (2008) Viral sequence diversity: challenges for AIDS vaccine designs. Expert review of vaccines 7: 1405–1417. pmid:18980542
- 109. Lin C, Mendoza-Espinosa P, Rouzina I, Guzmán O, Moreno-Razo JA, et al. (2019) Specific inter-domain interactions stabilize a compact HIV-1 Gag conformation. Plos one 14: e0221256. pmid:31437199
- 110.
Zhou AX-Z, Hammond JA, Sheng K, Millar DP, Williamson JR (2023) Early HIV-1 Gag Assembly on Lipid Membrane with vRNA. Research Square.
- 111. Marie V, Gordon ML (2022) The HIV-1 Gag Protein Displays Extensive Functional and Structural Roles in Virus Replication and Infectivity. International Journal of Molecular Sciences 23: 7569. pmid:35886917
- 112. Bhattacharya K, Shamkh IM, Khan MS, Lotfy MM, Nzeyimana JB, et al. (2022) Multi-epitope vaccine design against monkeypox virus via reverse vaccinology method exploiting immunoinformatic and bioinformatic approaches. Vaccines 10: 2010. pmid:36560421
- 113. Pantaleo G, Graziosi C, Fauci AS (1993) The role of lymphoid organs in the immunopathogenesis of HIV infection. Aids 7: S19–S24. pmid:8363784
- 114. Kakakhel S, Ahmad A, Mahdi WA, Alshehri S, Aiman S, et al. (2023) Annotation of Potential Vaccine Targets and Designing of mRNA-Based Multi-Epitope Vaccine against Lumpy Skin Disease Virus via Reverse Vaccinology and Agent-Based Modeling. Bioengineering 10: 430. pmid:37106617
- 115. Jacobs ES, Keating SM, Abdel-Mohsen M, Gibb SL, Heitman JW, et al. (2017) Cytokines elevated in HIV elite controllers reduce HIV replication in vitro and modulate HIV restriction factor expression. Journal of virology 91: pmid:28053103
- 116. Falahi S, Kenarkoohi A (2022) Host factors and vaccine efficacy: implications for COVID‐19 vaccines. Journal of Medical Virology 94: 1330–1335. pmid:34845730
- 117. Rayatdoost E, Rahmanian M, Sanie MS, Rahmanian J, Matin S, et al. (2022) Focus: Vaccines: Sufficient Sleep, Time of Vaccination, and Vaccine Efficacy: A Systematic Review of the Current Evidence and a Proposal for COVID-19 Vaccination. The Yale journal of biology and medicine 95: 221.
- 118. Falahi S, Mohamadi J, Sayyadi H, Pakzad I, Rashidi A, et al. (2022) COVID-19 vaccination, peltzman effect and possible increase in highrisk behaviors: a growing concern related to risk compensation and reduced compliance to public health protective measures after vaccines rollout. Infectious Disorders-Drug Targets (Formerly Current Drug Targets-Infectious Disorders) 22: 8–12. pmid:35440338
- 119. Ahmadi I, Estabraghnia Babaki H, Maleki M, Jarineshin H, Kaffashian MR, et al. (2022) Changes in Physiological Levels of Cortisol and Adrenocorticotropic Hormone upon Hospitalization Can Predict SARS-CoV-2 Mortality: A Cohort Study. International Journal of Endocrinology 2022. pmid:35251166
- 120. Mosmann T, Coffman R (1989) TH1 and TH2 cells: different patterns of lymphokine secretion lead to different functional properties. Annual review of immunology 7: 145–173. pmid:2523712
- 121.
Reinherz L, Schlossman F (1980) The differentiation and function of human T lymphocytes.
- 122. Venet A, Gomard E, Levy J-P (1993) Human T cell responses to HIV. Viruses and the Cellular Immune Response: 165–200.
- 123. Oyarzún P, Kobe B (2016) Recombinant and epitope-based vaccines on the road to the market and implications for vaccine design and production. Human vaccines & immunotherapeutics 12: 763–767. pmid:26430814
- 124. Bui H-H, Sidney J, Li W, Fusseder N, Sette A (2007) Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC bioinformatics 8: 1–6.
- 125. Zeba A, Sekar K, Ganjiwale A (2023) M Protein from Dengue virus oligomerizes to pentameric channel protein: in silico analysis study. Genomics & Informatics 21. pmid:37813637
- 126. Shukla P, Pandey P, Prasad B, Robinson T, Purohit R, et al. (2022) Immuno-informatics analysis predicts B and T cell consensus epitopes for designing peptide vaccine against SARS-CoV-2 with 99.82% global population coverage. Briefings in Bioinformatics 23: bbab496. pmid:34962259
- 127.
Lambert J (1967) Latin America: Univ of California Press.
- 128. Hemelaar J, Loganathan S, Elangovan R, Yun J, Dickson-Tetteh L, et al. (2020) Country level diversity of the HIV-1 pandemic between 1990 and 2015. Journal of virology 95: pmid:33087461
- 129. Paraskevis D, Hatzakis A (2019) Global molecular epidemiology of HIV-1: The chameleon challenge. The Lancet Infectious Diseases 19: 114–115. pmid:30509776
- 130. Subtype B (2013) A Single Early Introduction of HIV-1. J Virol 87: 7463.
- 131.
Ponce P, Martínez RM, Stival M (2017) Silencios, etnicidades y VIH: visibilizando algunas problemáticas en la incidencia y prevalencia de la epidemia en los pueblos originarios de Latinoamérica. Actualizaciones en Sida e Infectología.
- 132. Arai R, Ueda H, Kitayama A, Kamiya N, Nagamune T (2001) Design of the linkers which effectively separate domains of a bifunctional fusion protein. Protein engineering 14: 529–532. pmid:11579220
- 133. Ashgar SS, Faidah H, Bantun F, Jalal NA, Qusty NF, et al. (2023) Integrated immunoinformatics and subtractive proteomics approach for multi-epitope vaccine designing to combat S. pneumoniae TIGR4. Frontiers in Molecular Biosciences 10. pmid:37560463
- 134. van der Burg SH, Bijker MS, Welters MJ, Offringa R, Melief CJ (2006) Improved peptide vaccine strategies, creating synthetic artificial infections to maximize immune efficacy. Advanced drug delivery reviews 58: 916–930. pmid:16979788
- 135. Cuspoca AF, Díaz LL, Acosta AF, Peñaloza MK, Méndez YR, et al. (2021) An immunoinformatics approach for sars-cov-2 in latam populations and multi-epitope vaccine candidate directed towards the world’s population. Vaccines 9: 581. pmid:34205992
- 136. Mohan T, Sharma C, Bhat AA, Rao D (2013) Modulation of HIV peptide antigen specific cellular immune response by synthetic α-and β-defensin peptides. Vaccine 31: 1707–1716.
- 137. Wu C-Y, Monie A, Pang X, Hung C-F, Wu T (2010) Improving therapeutic HPV peptide-based vaccine potency by enhancing CD4+ T help and dendritic cell activation. Journal of biomedical science 17: 1–10.
- 138. Wang H, Su X, Zhang P, Liang J, Wei H, et al. (2011) Recombinant heat shock protein 65 carrying PADRE and HBV epitopes activates dendritic cells and elicits HBV-specific CTL responses. Vaccine 29: 2328–2335. pmid:21251902
- 139. Ólafsdóttir G, Svansson V, Ingvarsson S, Marti E, Torsteinsdóttir S (2008) In vitro analysis of expression vectors for DNA vaccination of horses: the effect of a Kozak sequence. Acta Veterinaria Scandinavica 50: 1–7.
- 140. Martinsen JT, Gunst JD, Højen JF, Tolstrup M, Søgaard OS (2020) The use of toll-like receptor agonists in HIV-1 cure strategies. Frontiers in immunology 11: 1112. pmid:32595636
- 141. Buitendijk M, Eszterhas SK, Howell AL (2013) Gardiquimod: a Toll-like receptor-7 agonist that inhibits HIV type 1 infection of human macrophages and activated T cells. AIDS research and human retroviruses 29: 907–918. pmid:23316755
- 142. Schlaepfer E, Audigé A, Joller H, Speck RF (2006) TLR7/8 triggering exerts opposing effects in acute versus latent HIV infection. The Journal of Immunology 176: 2888–2895. pmid:16493046
- 143. Tatematsu M, Nishikawa F, Seya T, Matsumoto M (2013) Toll-like receptor 3 recognizes incomplete stem structures in single-stranded viral RNA. Nature communications 4: 1833. pmid:23673618
- 144. Dela Justina V, Giachini FR, Priviero F, Webb RC (2020) Double-stranded RNA and Toll-like receptor activation: a novel mechanism for blood pressure regulation. Clinical Science 134: 303–313. pmid:31998948
- 145. Rafi MO, Al-Khafaji K, Sarker MT, Taskin-Tok T, Rana AS, et al. (2022) Design of a multi-epitope vaccine against SARS-CoV-2: immunoinformatic and computational methods. RSC advances 12: 4288–4310. pmid:35425433
- 146. Sartorius R, Trovato M, Manco R, D’Apice L, De Berardinis P (2021) Exploiting viral sensing mediated by Toll-like receptors to design innovative vaccines. npj Vaccines 6: 127. pmid:34711839
- 147. Sameer AS, Nissar S (2021) Toll-like receptors (TLRs): structure, functions, signaling, and role of their polymorphisms in colorectal cancer susceptibility. BioMed Research International 2021. pmid:34552981
- 148. Farias MVN, Lendez PA, Marin M, Quintana S, Martínez-Cuesta L, et al. (2016) Toll-like receptors, IFN-γ and IL-12 expression in bovine leukemia virus-infected animals with low or high proviral load. Research in veterinary science 107: 190–195.
- 149. Fu H, Liang Y, Zhong X, Pan Z, Huang L, et al. (2020) Codon optimization with deep learning to enhance protein expression. Sci Rep 10: 17617. pmid:33077783
- 150. Makrides SC (1996) Strategies for achieving high-level expression of genes in Escherichia coli. Microbiological reviews 60: 512–538. pmid:8840785
- 151. Manalu RT, Rahmasari M, Hamida F (2023) Design of HIV-1 Peptide-Based Vaccine from Matrix Protein p17: Immunoinformatics Approach. Asian Journal of Healthcare Analytics 2: 269–286.
- 152. Jemmy Christy H, Alex Anand D (2014) Immunoinformatics approach for the study of CD4+ epitopes of HIV-1 Gag protein restricted to HLA-DRB1* 07 Allele. Journal of Chemical and Pharmaceutical Research 6: 762–770.
- 153. Benlahrech A, Meiser A, Herath S, Papagatsias T, Athanasopoulos T, et al. (2012) Fragmentation of SIV-gag vaccine induces broader T cell responses. PloS one 7: e48038. pmid:23118924
- 154. Bosinger SE, Johnson ZP, Folkner KA, Patel N, Hashempour T, et al. (2013) Intact type I Interferon production and IRF7 function in sooty mangabeys. PLoS pathogens 9: e1003597. pmid:24009514
- 155. Tarrés-Freixas F, Aguilar-Gurrieri C, Rodríguez de la Concepción ML, Urrea V, Trinité B, et al. (2023) An engineered HIV-1 Gag-based VLP displaying high antigen density induces strong antibody-dependent functional immune responses. npj Vaccines 8: 51. pmid:37024469
- 156. Farzan M, Farzan M, Mirzaei Y, Aiman S, Azadegan-Dehkordi F, et al. (2023) Immunoinformatics-based multi-epitope vaccine design for the re-emerging monkeypox virus. International Immunopharmacology 123: 110725. pmid:37556996
- 157. He Y, Rappuoli R, De Groot AS, Chen RT (2010) Emerging vaccine informatics. BioMed Research International 2010. pmid:21772787
- 158. Liu W, Li H, Liu B, Lv T, Yang C, et al. (2023) A new vaccination regimen using adenovirus-vectored vaccine confers effective protection against African swine fever virus in swine. Emerging Microbes & Infections 12: 2233643. pmid:37401832