Skip to main content
NCBI home page
Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2022 Aug 18;101:107754. doi: 10.1016/j.compbiolchem.2022.107754

Exploiting reverse vaccinology approach for the design of a multiepitope subunit vaccine against the major SARS-CoV-2 variants

Daniel Melo de Oliveira Campos a, Maria Karolaynne da Silva a, Emmanuel Duarte Barbosa a, Chiuan Yee Leow b, Umberto Laino Fulco a, Jonas Ivan Nobre Oliveira a,⁎
PMCID: PMC9385604  PMID: 36037724

Abstract

The current COVID-19 pandemic, an infectious disease caused by the novel coronavirus (SARS-CoV-2), poses a threat to global health because of its high rate of spread and death. Currently, vaccination is the most effective method to prevent the spread of this disease. In the present study, we developed a novel multiepitope vaccine against SARS-CoV-2 containing Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (BA.1) variants. To this end, we performed a robust immunoinformatics approach based on multiple epitopes of the four structural proteins of SARS-CoV-2 (S, M, N, and E) from 475 SARS-CoV-2 genomes sequenced from the regions with the highest number of registered cases, namely the United States, India, Brazil, France, Germany, and the United Kingdom. To investigate the best immunogenic epitopes for linear B cells, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL), we evaluated antigenicity, allergenicity, conservation, immunogenicity, toxicity, human population coverage, IFN-inducing, post-translational modifications, and physicochemical properties. The tertiary structure of a vaccine prototype was predicted, refined, and validated. Through docking experiments, we evaluated its molecular coupling to the key immune receptor Toll-Like Receptor 3 (TLR3). To improve the quality of docking calculations, quantum mechanics/molecular mechanics calculations (QM/MM) were used, with the QM part of the simulations performed using the density functional theory formalism (DFT). Cloning and codon optimization were performed for the successful expression of the vaccine in E. coli. Finally, we investigated the immunogenic properties and immune response of our SARS-CoV-2 multiepitope vaccine. The results of the simulations show that administering our prototype three times significantly increases the antibody response and decreases the amount of antigens. The proposed vaccine candidate should therefore be tested in clinical trials for its efficacy in neutralizing SARS-CoV-2.

Keywords: Immunoinformatics, Quantum mechanics/molecular mechanics calculations, Multi-epitope vaccine, SARS-CoV-2, Variants

Graphical Abstract

ga1

Open in a new tab

1. Introduction

The SARS-CoV-2 pandemic is a global crisis that has yet to be resolved. Currently, the cumulative number of cases reported worldwide exceeds 396 million and the death toll is approximately 5.7 million Organization (2022). The mainstay of COVID-19 treatment is supportive care, but there is a high mortality rate, especially among the elderly and those with comorbidities. Intensive research is underway to find appropriate and effective therapies for the treatment and prevention of coronavirus infection, including safe and effective vaccines de Oliveira Campos et al., 2020, Campos et al., 2020.

Vaccination is an effective means of improving public health by building adaptive immunity to a target pathogen Ehreth (2003). However, screening vaccine targets for clinical validation and production of a vaccine takes a long time. Advances in bioinformatics and next-generation sequencing technology, immunoinformatics, and reverse vaccinology can minimize the time required to screen antigens from protein sequences of pathogens and offer advantages in finding potential new vaccine targets Scarselli et al. (2005).

In recent months, a dizzying amount of information has emerged from numerous laboratories. Several vaccine candidates are currently in various stages of development, and a small number of vaccine candidates have reached clinical phases. As of April 26, 2022, the COVID − 19 vaccine candidate landscape reported 142 vaccines in clinical trials (31 in phase 3 and 10 in phase 4) and another 195 in preclinical phase WHO (2022).

The major vaccines being studied in clinical trials appear to be safe and effective means of preventing severe COVID-19, hospitalizations, and deaths, but the quality of evidence varies widely by vaccine. Questions remain regarding booster vaccination and waning immunity, duration of immunity, and heterologous vaccination Fiolet et al. (2021). In addition, the emergence of SARS-CoV-2 variants could jeopardize the global impact of mass vaccination campaigns, because it is already known that the natural tendency toward high mutation rates is the reason for the failure of most vaccines against viruses Zuin et al., 2021, de Oliveira Campos et al., 2022. After Alpha, Beta, Gamma, and Delta variants, the most recent variant of concern (VOC) is the Omicron (B.1.1.529), which has evolved due to the accumulation of a high number of mutations, particularly in the spike protein, raising concerns that it is capable of evading pre-existing immunity acquired through vaccination or natural infection. In fact, it is highly transmissible and has low susceptibility to neutralization by antibodies Doria-Rose et al., 2021, England, 2021. Even after two doses of SARS-CoV-2 vaccine, neutralization of Omicron is far less than that of Delta or the parent viruses of SARS-CoV-2 Garcia-Beltran et al., 2021, Barda et al., 2021.

Conventional (biochemical, immunological, and microbiological) methods of vaccinology are inappropriate for this pandemic because antigen identification is time-consuming, culturing pathogens in laboratories is laborious, and costs are high María et al. (2017). Despite their success in many situations, these methods are unproductive for pathogens that cannot be cultured in vitro or for pathogens with highly variable antigen sequences such as HIV and influenza Stobart and Moore (2014). Alternatively, the methods of bioinformatics, vaccinogenomics, immunoinformatics, structural biology, and molecular simulations can be used for faster, more accurate, and less expensive vaccine development. They were first used to develop a vaccine against serogroup B meningococci and later against Streptococcus pneumoniae, Staphylococcus aureus, and Chlamydia Rappuoli, 2001, Cui, 2005. In recent years, these technologies have been successfully used in all phases of vaccine research, including preclinical, clinical, and postvaccine phases Soria-Guerra et al., 2015, Cui, 2005, Soleymani et al., 2022, Elliott et al., 2008, Lennerz et al., 2014.

Here, we used consistent immunoinformatics and computational methods to [i] identify structural SARS-CoV-2 proteins from 475 genomes sequenced from regions with the most registered cases to cover all SARS-CoV-2 variants of concern. We also [ii] screened the best linear B-cell epitopes, CTL epitopes, and HTL epitopes from a robust research protocol with antigenicity, immunogenicity, allergenicity, toxicity, IFN-γ inducing, population coverage, and conservation filters. Furthermore, [iii] we designed, refined and validated the multi-epitope subunit vaccine; [iv] then we optimized the codon sequence and inserted it into a plasmid to ensure cloning and expression efficiency; [v] finally, we evaluated the synthesis of immunoglobulins, immune complexes, cytokines, and interleukin and thus the consistency of the immune response elicited by the prototype vaccine.

2. Methodology

The flow chart of the methodology used in this study is shown graphically in Fig. 1. Recently, our research group validated similar immunoinformatics and molecular modeling approaches in the construction of a multiepitope vaccine against Mayaro virus Silva et al., 2021, daSilva et al., 2022.

Fig. 1.

Fig. 1

Open in a new tab

Flowchart showing the stepwise methodology of predicting epitopes from structural proteins of the SARS-CoV-2.

2.1. Acquisition of protein sequences

Initially, the primary sequences of SARS-CoV-2 structural proteins (S, M, N and E) of the Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (BA.1) variants were extracted using the filters from the Virus Pathogen Resource (ViPR) database: severe acute respiratory syndrome coronavirus 2; human (host); complete genome only. The viruses have several strains worldwide due to their natural tendency to show high mutational rates, which is could reason for fail of most of the vaccines Garcia-Boronat et al. (2008). For this reason, based on the multiple sequence alignment of 475 strains sequenced in one of the five countries with the highest number of cases (United States, India, Brazil, France, Germany, and United Kingdom), the sequential variability sites and the conserved fragments of four structural proteins of SARS-CoV-2 were mapped by the Protein Variability Server (PVS) (http://imed.med.ucm.es/PVS/) and MUSCLE algorithm.

2.2. Prediction of T cell epitopes

NetMHC 4.0 server (http://www.cbs.dtu.dk/services/NetMHC/) was used to predict the MHC I binding promiscuous epitopes in the consensus sequences, including 47 HLA class I alleles. Binding parameters using artificial neural networks (ANNs) Nielsen et al. (2003). Then, the NetCTL server (http://www.cbs.dtu.dk/services/NetCTL/) was used to predict cytotoxic T lymphocytes and values related to c-terminal cleavage, TAP transporter affinity, and HLA I binding affinity Peters et al. (2003). Models with high predictive accuracy were proposed for neural networks trained on 9-mer data, such that epitopes of this length are identified with higher sensitivity and specificity Lundegaard et al., 2008, Lundegaard et al., 2008. Finally, the data obtained from both platforms were stored in spreadsheets and compared to analyze only the epitopes that were present on both servers.

For consensus prediction of MHC II-restricted peptide epitopes, the NetMHCII tool (http://www.cbs.dtu.dk/services/NetMHCII/) was used. This tool has an allele-specific method that includes an individual predictor for each MHC molecule in the dataset. Thus, affinities for MHC molecules can be predicted, with classification into binders and non-binders Jensen et al. (2018). The NetMHCIIpan (http://www.cbs.dtu.DK/services/NetMHCIIpan/) has also been used as a guide for T cell peptide identification. It is based on a comprehensive dataset of > 100,000 quantitative peptide binding measurements from IEDB, including 36 HLA-DR, 27 HLA-DQ, 9 HLA-DP, and 8 mouse molecules MHC-II Andreatta et al. (2015). In addition to accurate identification of the binding core, this pan-specific method allows: quantification of the probability of multiple binding colors within a single antigenic peptide and assignment of reliability scores for each binding core prediction Andreatta and Nielsen (2018).

Predicted binders were selected using a percentile ranking method comparing predicted affinity to a collection of 400,000 random endogenous peptides and MHC binding affinity. IC50 values of < 50 nM and a percentile rank of < 0.5 were classified as high affinity (or strong binding), 50–500 nM as medium affinity, and 500–5000 nM as low affinity Nielsen et al. (2007), (2009)). According to Vita et al. (2015), no T-cell epitope has reached an IC50 value of > 5000 nM. Because regression at this scale (nM) is more challenging, it was linearized using the equation log50k=1−log(IC50)∕log(50000). To avoid false positives, all values in this log-transformed binding affinity (1 − log50k) identify peptides greater than or equal to 0.7 Fleri et al. (2017).

2.3. Antigenicity prediction

The antigenic properties of the selected theoretical epitopes were analyzed using VaxiJen 2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html), an alignment-independent method developed to overcome the limitations of alignment-dependent sequence similarity methods and based on automatic cross-covariance transformation (ACC) of protein sequences into uniform vectors of key amino acid properties. Vaxijen predicts antigens based on protein physicochemical properties with 70–89 % accuracy Doytchinova and Flower (2007b).

2.4. Allergenicity prediction

Allergen identification is a crucial factor in the development of the vaccine. Therefore, AllerTOP v.2.0 server (http://www.ddg-pharmfac.net/AllerTOP/) measures the allergenic properties of epitopes based on an alignment-independent method that has been optimized, cross-validated, and implemented. It uses an updated set of 2427 known allergens and 2427 non-allergenic proteins from widely used foods and non-immunogenic human proteins. Data processing detects 87 % of allergens and 91 % of non-allergens in the external test set Dimitrov et al. (2014).

2.5. Immunogenicity prediction

To predict epitopes capable of eliciting both humoral and cellular immune responses, we used the IEDB immunogenicity prediction tool (http://tools.iedb.org/immunogenicity/) using the following parameters: cutoff equal to zero and standard mask Vita et al. (2015). We assessed the immunogenicity of the antigens by summing the immunogenicity scores of all epitopes predicted to bind the MHC-I reference set of alleles in each antigenic region of the proteins. A high score indicates a greater likelihood of eliciting an immune response Calis et al. (2013).

2.6. Toxicity test

TonxinPred (http://crdd.osdd.net/raghava/toxinpred/) is based on an SVM model for classifying toxicity and non-toxicity based on various physicochemical properties of peptides. The database used in this method consists of 1805 toxic peptides (≤35 residues) and 3593 non-toxic peptides Gupta et al. (2013).

2.7. Conservation analysis

The IEDB Conservancy tool (http://tools.iedb.org/conservancy) was used to assess the degree of conservation of epitopes within the protein sequences of all available genotypes of SARS-CoV-2 obtained with varying degrees of sequence identity Bui et al. (2007). The degree of conservation can be defined as the proportion of protein sequences in which the epitope is present at a given degree of identity. In this way, it would be possible to verify which of the epitopes is most conserved and thus could become a vaccine candidate.

2.8. Population coverage study

The major histocompatibility complex (MHC) in humans refers to a genetic region containing hundreds of genes, including human leukocyte antigen (HLA) genes. T cells recognize a complex between a specific major histocompatibility complex (MHC) molecule and a specific pathogen-derived epitope, so the binding of peptides to MHC molecules is allele-specific. The selection of multiple peptides from SARS-CoV-2 with different HLA binding specificities will allow for greater coverage of the patient population targeted by peptide-based vaccines or diagnostics. Knowing that the small set of alleles covers most of the population, the IEDB Population Coverage tool (http://tools.iedb.org/population/) calculates the proportion of individuals from a given area (or worldwide) who are geographically predicted to respond to a given set of epitopes with known MHC restrictions based on their human leukocyte antigen (HLA) alleles Bui et al. (2006).

2.9. IFN-γ inducing epitope prediction

T helper cells can be identified by the production of various cytokines such as interferon-γ (IFN-γ), which subsequently play a role in the activation of various immune cells such as macrophages and natural killer cells Luckheeram et al. (2012). IFN-γ inducibility assessment of predicted HTL epitopes can be performed using the hybrid prediction approach and the Support Vector Machine (SVM) of IFNepitope (http://crdd.osdd.net/raghava/ifnepitope/).

2.10. Linear B-cell epitope prediction

B-cell epitopes are antigenic determinants and have the ability to elicit humoral immunity recognized by B-cell receptors or secreted antibodies of the immune system and represent the specific part of the antigen to which B lymphocytes bind. The linear B-cell epitopes of all proteins were predicted using BepiPred 2.0 from IEDB (http://tools.iedb.org/bcell/). The algorithm is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures Ponomarenko and Bourne (2007).

2.11. Construction of multi-epitope vaccine sequence

The CTL and HTL epitopes identified by the above immunoinformatic methods were used to carefully construct a vaccine sequence. These CTL and HTL epitopes were linked using AAY and GPGPG linkers Kadam et al., 2020, Mittal et al., 2020. In addition, β-defensin was introduced into the N-terminal end of the vaccine construct due to its adjuvant activity against viral infections Ling et al. (2017).

2.12. Homology modelling and tertiary structure refinement

The 3D building models were performed using Swissmodel server (https://swissmodel.expasy.org/), which allows homology modeling and the creation of 5 final models. At the same time, we also submit the primary sequence to the Robetta server. The Robetta server (https://robetta.bakerlab.org/) generates structural models using either comparative modeling or de novo structure prediction methods. Then the best models for each server were selected for the refinement step.

The 3D structures were refined using the GalaxyRefine2 tool (http://galaxy.seoklab.org/). This server is based on a refinement method that performs short molecular dynamics relaxations (MD) after repeated side-chain repack perturbations, allowing larger motions. Experimentalists have used it in functional protein modeling studies to improve the quality of model structures obtained with other prediction methods. A recent benchmark test of CASP (Critical Assessment of techniques for protein Structure Prediction) refinement targets showed that GalaxyRefine2 was successful in performing blind predictions Lee et al. (2019).

2.13. Tertiary structure validation

Validation of the 3D model of the vaccine construct was performed using ProSA-web, ERRAT, and Verify3D. ProSA-web (https://prosa.services.came.sbg.ac.at/prosa.php) is a freely available web server that is commonly used to validate the input 3D model. It assigns a quality score to the input structure if the score falls outside a range typical of native proteins and the structure is most likely to have errors. The ERRAT (http://services.mbi.ucla.edu/ERRAT/) and Verify3D (https://servicesn.mbi.ucla.edu/Verify3D/) servers were used to determine the non-bonded interactions within the structure and to determine the compatibility of an atomic model with its amino acid sequence by assigning the class of the structure based on its environment and comparing the results with suitable structures.

The validation analysis was also performed using the Molprobity server (http://molprobity.biochem.duke.edu/), which provides a broad, robust analysis of model quality at global and local levels for protein structures. It relies on the performance and sensitivity provided by optimized hydrogen placement and analysis of contacts between all atoms, complemented by covalent geometry and torsion angle criteria Williams et al. (2018). The central statistic of protein structure quality is represented by the "molprobity score", a combination of clash score, rotamer, and Ramachandran assessments in a single score normalized to the same scale as X-ray resolution Chen et al. (2010).

2.14. Physiochemical properties of the vaccine

ProtParam (http://web.expasy.org/protparam/) was used to predict the physicochemical properties of the vaccine candidate, including molecular weight, theoretical pI, instability index, amino acid composition, grand average of hydropathicity (GRAVY), atomic composition, estimated in vitro and in vivo half-life, aliphatic index, and extinction coefficient. A protein is considered stable if its value is below the threshold of 40, while the hydropathy index evaluates the probability that a region is hydrophobic (positive values) or hydrophilic (negative values) Gasteiger et al. (2005).

2.15. Prediction of post-translational modifications

For the prototype vaccine, post-translational modification analysis was performed using network prediction tools. NetOGlyc 4.0 (http://www.cbs.dtu.dk/services/NetOGlyc/) and NetNGlyc 1.0 (http://www.cbs.dtu.dk/services/NetNGlyc/) were used for glycosylation, which is one of the significant posttranslational modifications Steentoft et al. (2013). In addition, this analysis included the prediction of phosphorylation using the NetPhos 3.1 server (http://www.cbs.dtu.dk/services/NetPhos/) Blom et al. (2004). Finally, the CSS-Palm server (http://csspalm.biocuckoo.org/online.php) predicts palmitoylation of proteins, an essential posttranslational lipid modification of proteins Ren et al. (2008).

2.16. Codon optimization of final vaccine constructs

The Java Codon Adaptation Tool (JCat) https://www.jcat.de/was used for codon optimization and back translation and evaluated with the Codon Adaptation Tool to predict the appropriate expression in vector translation and cloning efficiency Grote et al. (2005) and to generate the vaccine cDNA sequence required for efficient expression in the Escherichia coli K-12 strain Grote et al. (2005). In addition, the optimized multiepitope vaccine sequence was inserted into the pET-28a (+) vector using the SnapGene tool.

2.17. Immune simulation of the vaccine

The C-ImmSim server https://kraken.iac.rm.cnr.it/C-IMMSIM/was used to characterize the actual immunogenic profiles and immune response of the vaccine prototype. C-ImmSim is an online server that uses agent-based modeling to characterize the immune response profile and immunogenicity of the chimeric peptides. The model is based on position-specific scoring matrices (PSSM) for peptide prediction derived from machine learning techniques for predicting immune interactions Rapin et al. (2010). Two injections of the target prophylactic SARS-CoV-2 vaccine profile were administered at different 4-week intervals. Our simulation lasted 1050 time steps (one time step of approximately 8 h), or nearly 12 months. All other simulation parameters were kept as default values Castiglione et al. (2012).

2.18. Molecular docking, QM/MM study and binding profile

The vaccine prototype must bind with target immune cell receptors, such as Toll-Like Receptor 3 (TLR3) to elicit an efficient immune response Perales-Linares and Navas-Martin (2013). Using PROPKA 3.1 software (https://github.com/jensengroup/propka/), the molecular structures of the ligand (vaccine prototype) and receptor (TLR3 - PDB ID: 1ziw) were first modified by adding charges (protonation or deprotonation) to the atoms and correcting the bonds. Since SARS-CoV-2 is present in the bloodstream, the physiological value (7.2–7.4) was used as the pH parameter. Atomic optimization of the hydrogen geometry was performed using CHARMm (Chemistry at Harvard Molecular Mechanics), a force field parameterized from molecular dynamics simulation specifically for organic molecules to increase the accuracy of the calculations Vianna et al., 2019, Campos et al., 2020, Bezerra et al., 2020.

To evaluate the interaction of the vaccine prototype with the molecule TLR, we performed a structure-based docking analysis on the PatchDock server (http://bioinfo3d.cs.tau.ac.il/PatchDock/), a molecular docking algorithm based on the principles of shape complementarity, i.e., molecular shape representation, surface patch matching, and partitioning and scoring Schneidman-Duhovny et al. (2005). First, the surfaces of the receptor and ligand molecules were partitioned into patches that corresponded to the shape of the surface. These patches then fit into specific patterns that allowed visual discrimination of the puzzle pieces. Once the patches were identified, shape matching methods were used to determine the superpositions of these patches.

Subsequently, the vaccine TLR3 candidates were refined using FireDock server (http://bioinfo3d.cs.tau.ac.il/FireDock/) by the constrained restructuring of the side chains at the interface and Monte Carlo minimization of the binding scoring function. The refined candidates were ranked based on a binding score parameterized using atomic contact energy, attenuated van der Waals interactions, partial electrostatics, and additional binding free energy estimates Andrusier et al. (2007).

We used the combined quantum mechanics/molecular mechanics technique (QM/MM) to select the most relevant vaccine-TLR3 (ligand-receptor) complex from the docking calculations. The QM/MM methods have now established themselves as the most advanced computational methods for biomolecular systems. The rapidly growing number of publications using QM/MM techniques is impressive evidence that they have come of age since their pioneering beginnings some thirty years ago Lindorff-Larsen et al., 2010, Senn and Thiel, 2009. The QM/MM formalism allows the partitioning of the total energy and, in particular, the interaction energy into different components, thus offering the possibility of analyzing the effects of the protein environment (down to individual wastes), especially in the presence of many electrostatic interactions Chung et al. (2015).

The QM/MM optimization was performed within the framework of the ONIOM multilayer method (our own Integrated Molecular Orbital and N-layer Molecular Mechanics), which is available in Gaussian code. It is a robust method that allows accurate ab initio calculations of the total energy of large complexes, such as biochemical systems, when these systems are divided into two or three layers. In this case, the TLR3 receptor was assigned to the MM layer, while the major amino acid residues of the vaccine were assigned to the QM layer. The popular B3LYP hybrid functional (Becke, three parameters, Lee-Yang-Parr) of exchange-correlation and basis set 6–311 G (d, p) were used to expand the electronic orbitals for the QM layer, and all amino acid residues within a radius of 6.0 Å from the centroid of the ligand were allowed to move during geometry optimization.

Finally, binding poses and parameters were examined using Discovery Studio Visualizer (https://discover.3ds.com/discovery-studio-visualizer-download/), LigPlot+ (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/), PoseView (https://proteins.Plus/), and: intermolecular hydrogen bonds (carbon, conventional, and pi-donor H-bonds), electrostatic (salt bridge, attractive charges, pi-cation, pi-anion), hydrophobic (pi-pi stacked, pi-pi stacked, alkyl, pi-sigma, pi-alkyl), halogens (Cl, fluorine, Br, and I), miscellaneous (charge repulsion, steric unevenness, acceptor-acceptor collision.

3. Results and discussion

Vaccination is a successful and cost-effective public health prevention strategy to combat deadly infectious diseases worldwide Chabot et al. (2004). The peptide-based vaccines or multiepitope vaccines have been shown to be the better choice for safe vaccination because they use short, nonallergenic peptide fragments to stimulate extremely targeted immunoprotective responses Li et al. (2014). Several previous in silico studies demonstrate the efficacy and effects of prototype multiepitope vaccines Yazdani et al., 2020, Rakib et al., 2020, AlSaba et al., 2021, Silva et al., 2021. Moreover, vaccine development in this manner has already gained momentum, with immunogenic potential demonstrated through in vivo experiments Lei et al., 2019, Zhao et al., 2021 and clinical trials Elliott et al., 2008, Lennerz et al., 2014.

In a critical global scenario triggered by the SARS-CoV-2 virus in recent years, the vaccines in the III and IV phases are relatively effective in controlling the disease, at least in exacerbating severe cases. However, the virus are known to have high genomic mutation rates that provide protection for them, and this is one of the main causes of vaccine failure Laughlin et al. (2015). Having traced the identified mutations of the SARS-CoV-2 virus to the present day (April 2022), it is clear that the accumulation of spike protein mutations is responsible for the increased infectivity and severity of SARS-CoV-2 and potentially hinders vaccine efficacy Harvey et al. (2021). Indeed, the emergence of variants of SARS-CoV-2 could compromise the global impact of mass vaccination campaigns Fiolet et al. (2021).

Recently, Zang et al. (2022) reported the development of an mRNA vaccine candidate specific to the receptor binding domain (RBD) of the Omicron variant. Two doses efficiently induce neutralizing antibodies in mice. However, the antisera are effective only in the Omicron variant and not in the wild-type and Delta strains. Lee et al. (2022) developed the "hybrid" vaccine containing the RBD with all 16 point mutations shown in Omicron and Delta RBD, and a bivalent vaccine consisting of Omicron and Delta RBD-L at half dose. Taken together, their data indicate that the Omicron-specific mRNA vaccine can elicit a strong neutralizing antibody response against Omicron, but that the inclusion of epitopes from other variants may be required to elicit cross-protection.

Therefore, we used an extensive immunoinformatics and molecular modeling protocol to develop a prototype vaccine against all of the above variants, in this case consisting of unique mutant epitopes of each variant.

3.1. Sequence retrieval

Here, the amino acids sequences for four structural proteins (Spike glycoprotein - S, membrane glycoprotein - M, envelope protein - E and nucleoprotein - N) of SARS-CoV-2 variants (B.1.1.7 - Alpha, B.1.351 - Beta, P.1 - Gamma, B.1.617.2 - Delta, and BA.1 - Omicron) were retrieved from the Vipr database Waterhouse et al. (2009) and used to predict the B and T cell epitopes. Thus, we encompassed 475 proteomes of the major circulating SARS-CoV-2 variants, including 38 (16) reported non-synonymous mutations found in the protein spike (nucleocapsid phosphoprotein).

3.2. Cytotoxic T Lymphocytes (CTL)

The major histocompatibility complex class I (MHC-I) processing pathway involves the degradation of protein antigens by a constitutive proteasome or immunoproteasome, resulting in the production of antigenic peptides. The Antigen Processing Protein-Associated (TAP) transporter transports peptides from the cytosol to the endoplasmic reticulum (ER), binds peptides to human leukocyte antigen (HLA) class I, and transports the peptide-MHC complex through the Golgi complexes to the cell surface, where it is presented to CD8 + T cells. Therefore, MHC I-peptide complexes are critical for infection surveillance by T cells Gruhler and Früh, 2000, Abbas et al., 2014.

When CTL peptides were identified using NetMHCI and NetCTL 1.2 server, a total of 220 CTL (9-mer) ligands were predicted for the four polyproteins from ancestral viral sequences based on their high combinatorial scores. In addition, a total of 416 epitopes of SARS-CoV-2 variants were predicted based on their high score and submetabolized for other parameters. In particular, for the epitopes with the highest number of alleles (S 257−265, S 1059−1067, S 326−334, S 506−314, and S 611−629), the binding of 14, 8, 7, 5, and 5 alleles, respectively, was predicted. Epitopes E 53−67 and E 54−68 were predicted to bind the highest number of alleles, 12 and 10, respectively. Similarly, M 72−86 and M 71−85 were predicted to bind 7 and 6 alleles of the HLA class, II, respectively. Finally, N 49−64, N 310−324, and N 311−325 were predicted to bind 12, 12, and 11 alleles, respectively. The other epitopes are characterized by an affinity strength between 1 and 4 alleles Table 1.

Table 1.

List of predicted CTL epitopes of the SARS-CoV-2 polyprotein S with all variants Alpha, Beta, Gamma, Delta, and Omicron, each peptide sequence and their number of alleles, antigenicity prediction score, allergenicity, immunogenicity score, toxicity and conservancy.

NetMHCI NetCTL VaxiJen Allertop IEDB ToxinPred IEDB
Protein S Sequence Alleles Supertypes Antigenicity Allergenicity Immunogenicity Toxicity Conservation
Original Strain
857 - GLTVLPPLL 1 1 0,6621 NON-ALLERGEN 0,01706 Non-Toxin 96.30 %(339/352)
413 - GQTGKIADY 1 2 1,4019 NON-ALLERGEN 0,00796 Non-Toxin 94.03 %(331/352)
699 - LGAENSVAY 1 1 0,4173 NON-ALLERGEN 0,00912 Non-Toxin 97.44 %(343/352)
628 - QLTPTWRVY 1 2 1,2119 NON-ALLERGEN 0,31555 Non-Toxin 98.30 %(346/352)
680 - SPRRARSVA 1 2 0,7729 NON-ALLERGEN 0,04020 Non-Toxin 96.30 %(339/352)
512 - VLSFELLHA 1 1 1,0776 NON-ALLERGEN 0,16070 Non-Toxin 97.73 %(344/352)
886 - WTFGAGAAL 1 2 0,4918 NON-ALLERGEN 0,19798 Non-Toxin 99.43 %(350/352)
88 - GVYFASTEK 4 1 0,7112 NON-ALLERGEN 0,09023 Non-Toxin 95.30 % (345/362)
504 - YQPYRVVVL 2 5 0,5964 NON-ALLERGEN 0,1409 Non-Toxin 98.89 % (358/362)
68 - HVSGTNGTK 4 1 1,0956 NON-ALLERGEN 0,06339 Non-Toxin 96.13 % (348/362)
326 - VRFPNITNL 7 2 1,1141 NON-ALLERGEN 0,1748 Non-Toxin 95.52 % (347/362)
61 - VTWFHAIHV 4 1 0,5426 NON-ALLERGEN 0,38925 Non-Toxin 95.58 % (346/362)
Original Strain/Alpha/Beta/Delta/Gamma/Omicron
341 - FNATRFASV 7 1 0,5609 NON-ALLERGEN 0,14872 Non-Toxin 98.35 % (357/363)
717 - FTISVTTEI 4 3 0,8535 NON-ALLERGEN 0,04473 Non-Toxin 97.80 % (355/363)
891 - AALQIPFAM 4 2 0,7747 NON-ALLERGEN 0,12066 Non-Toxin 98.90 % (359/363)
711 - IAIPTNFTI 3 3 0,7052 NON-ALLERGEN 0,18523 Non-Toxin 97.52 % (354/363)
257 - WTAGAAAYY 14 4 0,6306 NON-ALLERGEN 0,15259 Non-Toxin 91.18 % (331/363)
713 - IPTNFTISV 4 1 0,8820 NON-ALLERGEN 0,17229 Non-Toxin 97.52 % (354/363)
506 - PYRVVVLSF 5 1 1,0281 NON-ALLERGEN 0,03138 Non-Toxin 98.90 % (359/363)
826 - TLADAGFIK 4 1 0,5781 NON-ALLERGEN 0,28158 Non-Toxin 98.90 % (359/363)
83 - LPFNDGVYF 1 1 0,5593 NON-ALLERGEN 0,11767 Non-Toxin 96.42 % (350/363)
Alpha/Beta/Delta/Gamma/Omicron
1015 - AEIRASANL 4 1 0,7082 NON-ALLERGEN 0,00689 Non-Toxin 100 % (10/10)
611 - YQGVNCTEV 5 1 1,3957 NON-ALLERGEN 0,08675 Non-Toxin 100 % (10/10)
1100 - HWFVTQRNF 4 1 0746 NON-ALLERGEN 0,0482 Non-Toxin 100 % (10/10)
1 - FVFLVLLPL 4 4 0,8601 NON-ALLERGEN 0,04076 Non-Toxin 100 % (10/10)
1224 - IAIVMVTIM 3 1 1,1339 NON-ALLERGEN 0,06312 Non-Toxin 100 % (10/10)
665 - IGAGICASY 1 2 0,6368 NON-ALLERGEN 0,06201 Non-Toxin 100 % (10/10)
1208 - YIKWPWYIW 3 1 0,9673 NON-ALLERGEN 0,42524 Non-Toxin 100 % (10/10)
881 - ITSGWTFGA 2 1 0,4577 NON-ALLERGEN 0,35124 Non-Toxin 100 % (10/10)
82 - VLPFNDGVY 1 2 0,4642 NON-ALLERGEN 0,1815 Non-Toxin 100 % (10/10)
1059 - VVFLHVTYV 8 1 1,5122 NON-ALLERGEN 0,1278 Non-Toxin 100 % (10/10)
Alpha/Beta/Gamma
407 - RQIAPGQTG 2 1 1,7890 NON-ALLERGEN 0,02859 Non-Toxin 100 % (6/6)
442 - SKVGGNYNY 1 1 0,9111 NON-ALLERGEN 0,06751 Non-Toxin 100 % (6/6)
203 - YSKHTPINL 3 1 1,0547 NON-ALLERGEN 0,09845 Non-Toxin 100 % (6/6)
1213 - WYIWLGFIA 1 1 1,0356 NON-ALLERGEN 0,46375 Non-Toxin 100 % (6/6)
1180 - KEIDRLNEV 2 1 0,5300 NON-ALLERGEN 0,15852 Non-Toxin 100 % (6/6)
629 - TPTWRVYST 3 1 0,4605 NON-ALLERGEN 0,22497 Non-Toxin 100 % (6/6)
763 - NRALTGIAV 3 1 0,5302 NON-ALLERGEN 0,20642 Non-Toxin 100 % (6/6)
1058 - GVVFLHVTY 3 1 1,4104 NON-ALLERGEN 0,20837 Non-Toxin 100 % (6/6)
260 - GAAAYYVGY 1 3 0,6604 NON-ALLERGEN 0,09963 Non-Toxin 100 % (6/6)
261 - AAAYYVGYL 2 1 0,4605 NON-ALLERGEN 0,07068 Non-Toxin 100 % (6/6)
Open in a new tab

Speed and efficiency are the advantages of in silico screening of genomic information for vaccine development in the post-genomic era Doytchinova and Flower (2007a). However, this approach is dependent on the accuracy of antigen prediction. Thus, the antigenicity of a sequence may be encoded in a subtle and arcane manner that cannot be identified by direct sequence alignment Doytchinova and Flower (2007b).

Epitope analysis with Vaxijen found only 118 epitopes of proteins from precursor viruses to be antigenic. For sequences of S protein variants, 116 epitopes with high antigenicity scores were selected. Of these, only 9 epitopes were common to all variants, representing one of the lowest concordance rates ever reported Abdelmageed et al., 2020, ul Qamar et al., 2020. This suggests that mutations occurred in regions of high antigenic potential between the different strains. The highest values of antigenicity of the amino acid sequences of protein S (S 407−415, S 1059−1067, S 1058−1066, S 611−619, S 1224−1232, S 326−334, S 68−76, S 203−211, S 1213−1221, S 506−514, S 1208−116, S 442−450, S 713−721, S 1−9, S 717−725, S 891−899, S 1100−1108, S 88−96, S 1015−1023, S 711−719, S 260−268, S 665−673, S 257−265, S 504−512, S 826−834, S 341−349, S 83−91, S 61−69, S 763−771, S 1180−1188, S 82−90, S 629−637, S 261−269 and S 881−889) were 1.7890, 1.5122, 1.4104, 1.3957, 1.1339, 1.1141, 1.0956, 1.0547, 1.0356, 1.0281, 0,9673, 0.9111, 0.8820, 0,8601, 0,8535, 0,7747, 0.7460, 0.7112, 0.7082, 0.7052, 0.6604, 0.6368, 0.6306, 0.5964, 0.5781, 0.5609, 0.5593, 0.5426, 0.5302, 0.5300, 0.4642, 0.4605, 0.4605, and 0.4577 (Table 1).

For the protein E (M), the scores of E 61−69, E 31−39, E 55−63, E 49−57, E 30−38, E 57−65, E 29−37, E 20−28, E 51−59 and E 50−58 (M 57−65, M 23−31, M 138−146, M 84−92, M 188−196, M 172−180, M 171−179, M 170−178, M 37−45, M 136−144, M 108−116, M 65−73, M 50−58, M 46−54, M 94−102, M 54−62 and M 44−52) were 0.8998, 0.8872, 0.8251, 0.7476, 0.7223, 0.7020, 0.6140, 0.5308, 0.4213 and 0.4140 (1.1590, 1.1465, 1.1027, 0.9457, 0.9095, 0.7889, 0.7785, 0.7633, 0.7197, 0.6409, 0.6108, 0.5136, 0.4968, 0.4865, 0.4821, 0.4811 and 0.4257), respectively - see details in Table 2 and Table 3.

Table 2.

List of predicted CTL epitopes of the SARS-CoV-2 polyprotein E with each peptide sequence and their number ofalleles, antigenicity prediction score, allergenicity, immunogenicity score, toxicity and conservancy.

NetMHCI NetCTL VaxiJen Allertop IEDB ToxinPred IEDB
Protein Sequence Alleles Supertypes Antigenicity Allergenicity Immunogenicity Toxicity Conservation
E 30 - TLAILTALR 2 1 0,7223 NON-ALLERGEN 0.19890 Non-Toxin 87.50 % (14/16)
29 - VTLAILTAL 2 1 0,6140 NON-ALLERGEN 0.21055 Non-Toxin 87.50 % (14/16)
20 - FLAFVVFLL 1 4 0,5308 NON-ALLERGEN 0.30188 Non-Toxin 87.50 % (14/16)
31 - LAILTALRL 1 1 0,8872 NON-ALLERGEN 0.12755 Non-Toxin 87.50 % (14/16)
Open in a new tab

Table 3.

List of predicted CTL epitopes of the SARS-CoV-2 polyprotein M with each peptide sequence and their number ofalleles, antigenicity prediction score, allergenicity, immunogenicity score, toxicity and conservancy.

NetMHCI NetCTL VaxiJen Allertop IEDB ToxinPred IEDB
Protein Sequence Alleles Supertypes Antigenicity Allergenicity Immunogenicity Toxicity Conservation
M 57 - LWPVTLACF 2 1 1,1590 NON-ALLERGEN 0,06682 Non-Toxin 98,21 % (55/56)
136 - SELVIGAVI 1 1 0,6409 NON-ALLERGEN 0,25658 Non-Toxin 96,43 % (54/56)
188 - AGDSGFAAY 1 1 0,9095 NON-ALLERGEN 0,03981 Non-Toxin 96,43 % (54/56)
138 - LVIGAVILR 3 1 1,1027 NON-ALLERGEN 0,2601 Non-Toxin 94,64 % (53/56)
23 - VIGFLFLTW 1 1 1,1465 NON-ALLERGEN 0,24152 Non-Toxin 92,86 % (52/56)
65 - FVLAAVYRI 2 1 0,5136 NON-ALLERGEN 0,13985 Non-Toxin 92,86 % (52/56)
94 - SYFIASFRL 2 2 0,4821 NON-ALLERGEN 0,18333 Non-Toxin 91,07 % (51/56)
50 - KLIFLWLLW 1 1 0,4968 NON-ALLERGEN 0,34287 Non-Toxin 89,29 % (50/56)
46 - LYIIKLIFL 2 1 0,4865 NON-ALLERGEN 0,1374 Non-Toxin 87,50 % (49/56)
44 - RFLYIIKLI 1 1 0,4257 NON-ALLERGEN 0,05908 Non-Toxin 87,50 % (49/56)
Open in a new tab

Finally the amino acids sequences of protein N N 32−41, N 103−111, N 100−108, N 104−112, N 183−191, N 345−353, N 187−195, N 386−394, N 53−61, N 249−257, N 66−74, N 361−369, N 322−330, N 305−313, N 379−387, N 105−113, N 240−248, N 316−324, N 266−274 N 306−314, N 78−86, N 79−87 and N 323−331 were 1.7874, 1.7645, 1.7462, 1.2832, 1.2286, 1.1677, 1.1218, 0.9285, 0.8510, 0.7679, 0.7585, 0.7571, 0.7550, 0.7468, 0.7432, 0.7340, 0.6709, 0.6287, 0.5669, 0.5495, 0.5260, 0.4863 and 0.4548, respectivel ( Table 4).

Table 4.

List of predicted CTL epitopes of the SARS-CoV-2 polyprotein N with each peptide sequence and their number ofalleles, antigenicity prediction score, allergenicity, immunogenicity score, toxicity and conservancy.

NetMHCI NetCTL VaxiJen Allertop IEDB ToxinPred IEDB
Protein Sequence Alleles Supertypes Antigenicity Allergenicity Immunogenicity Toxicity Conservation
N 104 - LSPRWYFYY 1 5 1,2832 NON-ALLERGEN 0,35734 Non-Toxin 97.92 % (141/144)
105 - SPRWYFYYL 2 2 0,7340 NON-ALLERGEN 0,34101 Non-Toxin 97.92 % (141/144)
103 - DLSPRWYFY 2 3 1,7645 NON-ALLERGEN 0,25933 Non-Toxin 97.92 % (141/144)
316 - GMSRIGMEV 2 1 0,6287 NON-ALLERGEN 0,07018 Non-Toxin 97.22 % (140/144)
323 - EVTPSGTWL 2 1 0,4548 NON-ALLERGEN 0,03442 Non-Toxin 97.22 % (140/144)
78 - SSPDDQIGY 2 3 0,5260 NON-ALLERGEN 0,0634 Non-Toxin 95.14 % (137/144)
79 - SPDDQIGYY 3 1 0,4863 NON-ALLERGEN 0,06844 Non-Toxin 94.44 % (136/144)
361 - KTFPPTEPK 5 1 0,7571 NON-ALLERGEN 0,1306 Non-Toxin 93.06 % (134/144)
Open in a new tab

The final peptides were selected and submitted for further analysis. Because of the high current incidence, allergenicity was checked to ensure that the vaccine candidate would not induce allergic reactions once introduced into uniform vectors of equal length using an alignment-independent protein presentation method based on the key physiochemical properties of protein sequences Dimitrov et al. (2013). Of 118 antigenic epitopes, 67 were found to be nonallergenic (Tables 1, 2,3, and 4).

One of the main concerns in subunit vaccine development is the specificity of the immunogenic epitopes. In case, the immunogenicity step is important to determine whether a peptide-MHC complex (pMHC) can be an epitope Calis et al. (2013). Here, only 56 nonallergenic epitopes were selected based on high immunogenicity values. The highest immunogenicity values for the S, E, M, and N proteins of SARS-CoV-2 were 0.46375, 0.30188, 0.34287, and 0.34101, respectively. Subsequently, these epitopes were also found to be non-toxic. Thus, the probability of our epitope-based vaccines causing immunogenic and toxic reactions is very low.

A look at the S protein sequences shows that 21 epitopes with high immunogenicity values were present only in the original strain of SARS-CoV-2, 9 were common to all subvariants and 19 in the Omicron variant, namely S 341−349, S 717−725, S 891−899, S 711−719, S 257−765, S 713−721, S 506−514, S 826−834, S 83−91, S 1015−1023, S 611−619, S 1100−1108, S 1−8, S 1224−1232, S 665−673, S 1208−1216, S 881−889, S 82−90 and S 1059−1067.

Of the 63 epitopes with the best antigenicity, allergenicity, immunogenicity, and toxicity values, 56 obtained conservation index above 90 %. These results suggest that most epitopes in SARS-CoV-2 are highly conserved and are good targets for vaccine development, as these epitopes are regions that evolve slowly and can be expected to be present independently of a particular pathogen strain Bui et al. (2007).

In order to predict whether virus-specific T cells recognize variants, we determined the epitopes with had strong binding affinity with MHC-I. Those with the highest conservation rates more conserved in all variants would be in the construction of the vaccine.

3.3. Helper T Lymphocyte (HTL)

The MHC Class I and Class II epitopes can be recognized by T cell, due to its antigenic nature and acknowledged by the T cell receptors (TCR). MHC class II molecules contain exogenous antigens or pathogen’s surface proteins, processed through endocytic pathways to assist the T lymphocytes or CD4 +T cells Reynisson et al. (2020). Thus, with powerful tools for analysis of prediction of T cell epitopes a total of 931 HTL epitopes were predicted from the four SARS-CoV-2 virus proteins using NetMHCII and NetMHCIIpan 4.0 server. In addition, a total of 558 epitopes of all variants with the highest number of alleles were predicted and submetabolized for other parameters. Specifically, for proteins S and E, the epitopes S 1014−1028, S 795−809, S 690−704, S 310−324, E 53−67, and E 54−68 were predicted to bind 12, 10, 9, 8 12, and 10 alleles, respectively. Similarly, M 72−86 and M 71−85 (N 49−64, N 310−324, and N 311−325) were predicted to bind 7 and 6 (12, 12, and 11) alleles of the HLA class II (see details in Table 5, Table 6, Table 7, and Table 8).

Table 5.

List of predicted HTL epitopes of the SARS-CoV-2 polyprotein S with all variants Alpha, Beta, Gamma, Delta, and Omicron, each peptide sequence and their number of alleles, antigenicity prediction score, allergenicity, Epitope IFN-γ, toxicity and conservancy.

NetMHCII/NetMHCIIpan VaxiJen Allertop IFNepitope ToxinPred IEDB
Protein S Peptide sequence Alleles Antigenicity Allergenicity Epitope IFN-γ Toxicity Conservation
Original Strain
139 - PFLGVYYHKNNKSWM 2 0,6641 NON-ALLERGEN 0.36453817 Non-Toxin 89.23 % (315/353)
142 - GVYYHKNNKSWMESE 2 0,4684 NON-ALLERGEN 0.36198865 Non-Toxin 89.23 % (315/353)
141 - LGVYYHKNNKSWMES 2 0,4937 NON-ALLERGEN 0.40530432 Non-Toxin 90.08 % (318/353
257 - GWTAGAAAYYVGYLQ 4 0,5669 NON-ALLERGEN 0.71724775 Non-Toxin 90.93 % (321/353)
168 - FEYVSQPFLMDLEGK 2 0,8278 NON-ALLERGEN 0.44798419 Non-Toxin 93.76 % (331/353)
Original Strain/Alpha/Beta/Delta/Gamma
763 - LNRALTGIAVEQDKN 2 0,4710 NON-ALLERGEN 0,58194087 Non-Toxin 98.61 % (357/362)
761 - TQLNRALTGIAVEQD 2 0,4153 NON-ALLERGEN 0,43169637 Non-Toxin 98.61 % (357/362)
59 - FSNVTWFHAIHVSGT 3 0,7533 NON-ALLERGEN 0,094599621 Non-Toxin 94.47 % (342/362)
431 - GCVIAWNSNNLDSKV 3 0,4585 NON-ALLERGEN 0,022799683 Non-Toxin 95.58 % (346/362)
Original Strain/Alpha/Beta/Delta/Gamma/Omicron
895 - QIPFAMQMAYRFNGI 3 0,9573 NON-ALLERGEN -0,17853133 Non-Toxin 98.62 % (358/363)
894 - LQIPFAMQMAYRFNG 2 0,7205 NON-ALLERGEN -0,21947589 Non-Toxin 98.62 % (358/363)
885 - GWTFGAGAALQIPFA 5 0,4665 NON-ALLERGEN -0,24676874 Non-Toxin 98.62 % (358/363)
797 - FGGFNFSQILPDPSK 2 0,4404 NON-ALLERGEN -0,4760424 Non-Toxin 96.42 % (350/363)
716 - TNFTISVTTEILPVS 4 1,1691 NON-ALLERGEN -0,1175069 Non-Toxin 97.25 % (353/363)
715 - PTNFTISVTTEILPV 5 1,1349 NON-ALLERGEN 0,006286322 Non-Toxin 97.25 % (353/363)
713 - AIPTNFTISVTTEIL 3 0,6806 NON-ALLERGEN -0,20095993 Non-Toxin 97.52 % (354/363)
712 - IAIPTNFTISVTTEI 3 0,7719 NON-ALLERGEN -0,019108711 Non-Toxin 97.52 % (354/363)
663- DIPIGAGICASYQTQ 2 1,1088 NON-ALLERGEN -0,12625968 Non-Toxin 96.42 % (350/363)
634 - RVYSTGSNVFQTRAG 4 0,4544 NON-ALLERGEN 0,10536516 Non-Toxin 97.25 % (353/363)
632 - TWRVYSTGSNVFQTR 3 0,4253 NON-ALLERGEN 0,10894371 Non-Toxin 97.52 % (354/363)
346 - RFASVYAWNRKRISN 5 0,4243 NON-ALLERGEN 0,61013335 Non-Toxin 97.80 % (355/363)
324 - ESIVRFPNITNLCPF 2 0,6125 NON-ALLERGEN -0,43992195 Non-Toxin 95.32 % (346/363)
255 - SSGWTAGAAAYYVGY 2 0,4136 NON-ALLERGEN 0,87705602 Non-Toxin 89.53 % (325/363)
233 - INITRFQTLLALHRS 2 0,4118 NON-ALLERGEN -0,007153463 Non-Toxin 91.46 % (332/363)
232 - GINITRFQTLLALHR 6 0,5582 NON-ALLERGEN 0,10772613 Non-Toxin 91.18 % (331/363)
231 - IGINITRFQTLLALH 5 0,8391 NON-ALLERGEN -0,38029989 Non-Toxin 91.46 % (332/363)
230 - PIGINITRFQTLLAL 2 0,8877 NON-ALLERGEN -0,042411043 Non-Toxin 91.18 % (331/363)
166 - CTFEYVSQPFLMDLE 4 0.5700 NON-ALLERGEN 0,043175621 Non-Toxin 92.01 % (334/363)
165 - NCTFEYVSQPFLMDL 2 0,5206 NON-ALLERGEN -0,35483268 Non-Toxin 91.74 % (333/363)
Original Strain/Alpha/Beta/Omicron
718 - FTISVTTEILPVSMT 3 1,2603 NON-ALLERGEN -0.40760154 Non-Toxin 99.15 % (353/356)
1210 - IKWPWYIWLGFIAGL 4 0,9153 NON-ALLERGEN 0.61171913 Non-Toxin 99.43 % (354/356)
509 - RVVVLSFELLHAPAT 6 0,7485 NON-ALLERGEN 0.5092653 Non-Toxin 98.87 % (352/356)
2 - FVFLVLLPLVSSQCV 6 0,7185 NON-ALLERGEN 0.092039768 Non-Toxin 97.47 % (347/356)
238 - FQTLLALHRSYLTPG 2 0,5789 NON-ALLERGEN 0.26071055 Non-Toxin 93.53 % (333/356)
1 - MFVFLVLLPLVSSQC 7 0,5741 NON-ALLERGEN 0.084372674 Non-Toxin 97.47 % (347/356)
237 - RFQTLLALHRSYLTP 4 0,5470 NON-ALLERGEN -0.022265746 Non-Toxin 94.10 % (335/356)
1103 - FVTQRNFYEPQIITT 2 0,5314 NON-ALLERGEN -0.4029137 Non-Toxin 100.00 % (356/356)
52 - QDLFLPFFSNVTWFH 3 0,4159 NON-ALLERGEN -0.051186129 Non-Toxin 95,78 % (341/356)
1060 - VVFLHVTYVPAQEKN 2 1,1720 NON-ALLERGEN 0.035308408 Non-Toxin 100.00 % (356/356)
1059 - GVVFLHVTYVPAQEK 5 1,1043 NON-ALLERGEN 0.25556948 Non-Toxin 100.00 % (356/356)
Alpha/Beta/Gamma/Omicron
1061 - VFLHVTYVPAQEKNF 4 1,0339 NON-ALLERGEN -0.070782391 Non-Toxin 100.00 % (7/7)
511 - VVLSFELLHAPATVC 5 0,8618 NON-ALLERGEN 0.47729732 Non-Toxin 100.00 % (7/7)
508 - YRVVVLSFELLHAPA 2 0,7072 NON-ALLERGEN 0.77626818 Non-Toxin 100.00 % (7/7)
886 - WTFGAGAALQIPFAM 2 0,6670 NON-ALLERGEN -0.38373216 Non-Toxin 100.00 % (7/7)
1214 - WYIWLGFIAGLIAIV 4 0,5770 NON-ALLERGEN 10.656.151 Non-Toxin 100.00 % (7/7)
512 - VLSFELLHAPATVCG 2 0,4784 NON-ALLERGEN 0.096827098 Non-Toxin 100.00 % (7/7)
Alpha/Beta/Gamma
259 - TAGAAAYYVGYLQPR 2 1,0413 NON-ALLERGEN 0.55255681 Non-Toxin 100.00 % (4/4)
754 - LQYGSFCTQLNRALT 2 1027 NON-ALLERGEN 0.1500564 Non-Toxin 100.00 % (4/4)
628 - QLTPTWRVYSTGSNV 2 0,9276 NON-ALLERGEN -0.24711649 Non-Toxin 100.00 % (4/4)
260 - AGAAAYYVGYLQPRT 2 0,9134 NON-ALLERGEN 0.59506751 Non-Toxin 100.00 % (4/4)
1218 - LGFIAGLIAIVMVTI 2 0,8933 NON-ALLERGEN 0.32119174 Non-Toxin 100.00 % (4/4)
751 - NLLLQYGSFCTQLNR 2 0,8668 NON-ALLERGEN 0.1651956 Non-Toxin 100.00 % (4/4)
750 - SNLLLQYGSFCTQLN 2 0,8305 NON-ALLERGEN 0.27039446 Non-Toxin 100.00 % (4/4)
890 - AGAALQIPFAMQMAY 2 0,8216 NON-ALLERGEN -0.40373248 Non-Toxin 100.00 % (4/4)
325 - SIVRFPNITNLCPFG 2 0,7899 NON-ALLERGEN -0.23626945 Non-Toxin 100.00 % (4/4)
312 - IYQTSNFRVQPTESI 2 0,7459 NON-ALLERGEN 0.30406292 Non-Toxin 100.00 % (4/4)
760 - CTQLNRALTGIAVEQ 3 0,7454 NON-ALLERGEN 0.16758267 Non-Toxin 100.00 % (4/4)
1212 - WPWYIWLGFIAGLIA 2 0,7293 NON-ALLERGEN 11.863.034 Non-Toxin 100.00 % (4/4)
591 - SFGGVSVITPGTNTS 2 0,6553 NON-ALLERGEN -0.21404582 Non-Toxin 100.00 % (4/4)
218 - QGFSALEPLVDLPIG 2 0,6177 NON-ALLERGEN -0.044834443 Non-Toxin 100.00 % (4/4)
1216 - IWLGFIAGLIAIVMV 6 0615 NON-ALLERGEN 0.86284921 Non-Toxin 100.00 % (4/4)
1215 - YIWLGFIAGLIAIVM 5 0609 NON-ALLERGEN 0.89392418 Non-Toxin 100.00 % (4/4)
50 - STQDLFLPFFSNVTW 2 0,6034 NON-ALLERGEN 0.043916948 Non-Toxin 100.00 % (4/4)
1219 - GFIAGLIAIVMVTIM 2 0,5098 NON-ALLERGEN 0.077612198 Non-Toxin 100.00 % (4/4)
55 - FLPFFSNVTWFHAIH 2 0,4883 NON-ALLERGEN 0.17628387 Non-Toxin 100.00 % (4/4)
140 - FLGVYYHKNNKSWME 4 0,4793 NON-ALLERGEN 0.53199532 Non-Toxin 100.00 % (4/4)
368 - LYNSASFSTFKCYGV 2 0,4171 NON-ALLERGEN 0.25053962 Non-Toxin 100.00 % (4/4)
1259 - DDSEPVLKGVKLHYT 2 1,1849 NON-ALLERGEN -0.70684166 Non-Toxin 100.00 % (4/4)
892 - AALQIPFAMQMAYRF 5 0,9108 NON-ALLERGEN -0.71321984 Non-Toxin 100.00 % (4/4)
749 - CSNLLLQYGSFCTQL 3 0,6336 NON-ALLERGEN 0.2869462 Non-Toxin 100.00 % (4/4)
264 - AYYVGYLQPRTFLLK 6 0,4269 NON-ALLERGEN 0.31280017 Non-Toxin 100.00 % (4/4)
Open in a new tab

Table 6.

List of predicted HTL epitopes of the SARS-CoV-2 polyprotein M each peptide sequence and their number of alleles, antigenicity prediction score, allergenicity, Epitope IFN-γ, toxicity and conservancy.

NetMHCII/NetMHCIIpan VaxiJen Allertop IFNepitope ToxinPred IEDB
Protein Sequence Alleles Antigenicity Allergenicity Epitope IFN-γ Toxicity Conservation
M 136 - SELVIGAVILRGHLR 2 0,6768 NON-ALLERGEN 0.67529222 Non-Toxin 94.64 % (53/56)
72 - RINWITGGIAIAMAC 7 1,1629 NON-ALLERGEN 0.097493902 Non-Toxin 89.29 % (50/56)
71 - YRINWITGGIAIAMA 6 1,1274 NON-ALLERGEN -0.032197757 Non-Toxin 89.29 % (50/56)
34 - LLQFAYANRNRFLYI 4 0,7387 NON-ALLERGEN -0.59982611 Non-Toxin 85.71 % (48/56)
Open in a new tab

Table 7.

List of predicted HTL epitopes of the SARS-CoV-2 polyprotein E each peptide sequence and their number of alleles, antigenicity prediction score, allergenicity, Epitope IFN-γ, toxicity and conservancy.

NetMHCII/NetMHCIIpan VaxiJen Allertop IFNepitope ToxinPred IEDB
Protein Sequence Alleles Antigenicity Allergenicity Epitope IFN-γ Toxicity Conservation
E 53 - KPSFYVYSRVKNLNS 12 0,8229 NON-ALLERGEN -0.52339177 Non-Toxin 68.75 % (11/16)
52 - VKPSFYVYSRVKNLN 9 1,2319 NON-ALLERGEN -0.46468316 Non-Toxin 68.75 % (11/16)
51 - LVKPSFYVYSRVKNL 6 0,7311 NON-ALLERGEN -0.39721306 Non-Toxin 68.75 % (11/16)
56 - FYVYSRVKNLNSSRV 5 0,6103 NON-ALLERGEN -0.1032629 Non-Toxin 62.50 % (10/16)
54 - PSFYVYSRVKNLNSS 10 0,7986 NON-ALLERGEN -0.35688916 Non-Toxin 56.25 % (9/16)
55 - SFYVYSRVKNLNSSR 8 0,6291 NON-ALLERGEN -0.4195908 Non-Toxin 56.25 % (9/16)
57 - YVYSRVKNLNSSRVP 3 0,4492 NON-ALLERGEN -0.22463037 Non-Toxin 50.00 % (8/16)
Open in a new tab

Table 8.

List of predicted HTL epitopes of the SARS-CoV-2 polyprotein N each peptide sequence and their number of alleles, antigenicity prediction score, allergenicity, Epitope IFN-γ, toxicity and conservancy.

NetMHCII/NetMHCIIpan VaxiJen Allertop IFNepitope ToxinPred IEDB
Protein Peptide sequence Alleles Antigenicity Allergenicity Epitope IFN-γ Toxicity Conservation
N 108 - PSASAFFGMSRIGME 4 0,6408 NON-ALLERGEN 0,94891009 Non-Toxin 97.22 % (140/144)
83 - QIAQFAPSASAFFGM 6 0,4032 NON-ALLERGEN 0,83655244 Non-Toxin 98.61 % (142/144)
259 - SASAFFGMSRIGMEV 3 0,6584 NON-ALLERGEN 0,79707565 Non-Toxin 97.22 % (140/144)
310 - QIGYYRRATRRIRGG 12 0,4614 NON-ALLERGEN 0,70344772 Non-Toxin 95.83 % (138/144)
311 - IGYYRRATRRIRGGD 11 0,6649 NON-ALLERGEN 0,6416295 Non-Toxin 95.83 % (138/144)
386 - QRQKKQQTVTLLPAA 1 0,6824 NON-ALLERGEN 0,13245676 Non-Toxin 95.14 % (137/144)
264 - ASAFFGMSRIGMEVT 3 0,8620 NON-ALLERGEN 0,085634049 Non-Toxin 97.22 % (140/144)
330 - QTVTLLPAADLDDFS 4 0,5213 NON-ALLERGEN 0,072849059 Non-Toxin 95.83 % (138/144)
49 - TASWFTALTQHGKED 12 0,4491 NON-ALLERGEN -0,079044179 Non-Toxin 98.61 % (142/144)
84 - RQKRTATKAYNVTQA 3 0,6318 NON-ALLERGEN -0,095407182 Non-Toxin 97.92 % (141/144)
389 - RQKKQQTVTLLPAAD 2 0,6555 NON-ALLERGEN -0,098541454 Non-Toxin 94.44 % (136/144)
266 - GTWLTYTGAIKLDDK 6 0,9934 NON-ALLERGEN -0,13745422 Non-Toxin 96.53 % (139/144)
390 - QKKQQTVTLLPAADL 4 0,7662 NON-ALLERGEN -0,14422474 Non-Toxin 94.44 % (136/144)
303 - TWLTYTGAIKLDDKD 2 1,2416 NON-ALLERGEN -0,21623699 Non-Toxin 96.53 % (139/144)
329 - QQTVTLLPAADLDDF 4 0,4614 NON-ALLERGEN -0,24539346 Non-Toxin 95.83 % (138/144)
384 - RWYFYYLGTGPEAGL 2 0,7505 NON-ALLERGEN -0,28690274 Non-Toxin 95.14 % (137/144)
328 - SGTWLTYTGAIKLDD 4 0,6215 NON-ALLERGEN -0,3048811 Non-Toxin 95.83 % (138/144)
105 - ATKAYNVTQAFGRRG 4 0,7146 NON-ALLERGEN -0,41990668 Non-Toxin 97.92 % (141/144)
327 - PRWYFYYLGTGPEAG 2 0,8083 NON-ALLERGEN -0,46153318 Non-Toxin 95.83 % (138/144)
309 - WLTYTGAIKLDDKDP 1 1,2787 NON-ALLERGEN -0,5483585 Non-Toxin 96.53 % (139/144)
265 - SPRWYFYYLGTGPEA 1 0,8767 NON-ALLERGEN -0,61601606 Non-Toxin 96.53 % (139/144)
107 - KAYNVTQAFGRRGPE 1 0,6104 NON-ALLERGEN -0,76630992 Non-Toxin 97.92 % (141/144)
385 - WYFYYLGTGPEAGLP 3 0,7188 NON-ALLERGEN -0,88957872 Non-Toxin 95.14 % (137/144)
106 - TKAYNVTQAFGRRGP 5 0,5975 NON-ALLERGEN -0,89365915 Non-Toxin 97.92 % (141/144)
Open in a new tab

Because antigenicity is the ability to recognize a specific antigen accompanied by an immune response, antigenic epitopes that can be recognized by host immune cells and elicit both a humoral and a cellular immune response to the viral antigen could be used as potential vaccine targets Doytchinova and Flower (2007b). To this end, 169 epitopes were found to be antigens when analyzed with Vaxijen. For the sequence of all variants of the S protein, 320 epitopes with a high antigenicity score were selected. In contrast, 95 epitopes were identical to the CTL epitopes of the original strain. The highest antigenicity scores for the amino acid sequences of protein S (S 1060−1074, S 716−730, S 715−729, S 663−677, S 1059−1074, and S 1061−1075) were 1.1720, 1.1691, 1.1349, 1.1088, 1.1043, and 1.0339. For protein E (E 53−67, E 53−67, and E 54−68), the values were 1.2319, 0.8229, and 0.7986, respectively. Similarly, the values for protein M (M 72−86 and M 71−85) were 1.1629 and 1.1274, respectively. In addition, the values for protein N (N 309−325 and N 303−317) were 1.2787 and 1.2416, respectively.

These epitopes have been subjected to allergenicity and toxicity analysis because they represent an important hurdle in vaccine development. Therefore, Allertop and Toxinpred aim to predict allergens and non-allergens with high sensitivity and specificity Dimitrov et al. (2013). The servers identified 130 HTL epitopes (15-mer) as likely non-allergens and non-toxic. This type of analysis saves time, resources, and money for the pharmaceutical and vaccine industries Gupta et al. (2013). For the S protein, 160 epitopes were selected as nonallergenic and nontoxic, of which 50 epitopes are present in the wild-type strain. Therefore, the likelihood of our epitope-based vaccines causing allergic and toxic reactions is very low. Finally, we identified the 7 mutations with strong binding affinity, high antigenicity, allergenicity, immunogenicity, and nontoxicity present in protein S of all SARS-CoV-2 variants that we could readily use in the construction of our prototype vaccine.

A total of 75 epitopes of the original strain have a conservation of more than 90 %, but only 20 epitopes are present in all variants. These values suggest that the final epitopes are regions that are good targets for vaccine development because the sequences are likely to be conserved regardless of disease stage or a particular pathogen strain, making them effective vaccines. Bui et al. (2007).

3.4. IFN-γ inducing epitope prediction

A total of 106 potential IFN-γ inducing epitopes (15-mer) were predicted by the IFNepitope server. Of these, only 55 proved to be IFN-γ positive epitopes. These epitopes were selected on the basis of the high percentile for further analysis of overlap with B-cell epitopes. This validation is important because HTL epitopes that release cytokines such as interferon-gamma (IFN-γ) have been shown to be an important mediator of protection against SARS-CoV-2 Lagunas-Rangel and Chávez-Valencia (2020). After immunization, there is production of IFN-γ and a consistent increase in the Th (helper) cell population with memory development Seder et al. (2008). Therefore, IFN-∕gamma plays an important role in the clearance of viral infection Chesler and Reiss (2002).

3.5. Population coverage

The frequency of HLA genotype changes in different populations of the world and the nature of HLA polymorphism affects the binding of a peptide identified as an "epitope" during vaccine development, in part because the binding of peptides to MHC molecules is allele-specific Bui et al. (2006). Because we know that selecting multiple peptides of SARS-CoV-2 with different HLA binding specificities will provide greater coverage of the patient population targeted by our SARS-CoV-2 multiepitope vaccine, the IEDB Population Coverage server was used to calculate the population coverage value of each peptide in different geographic regions based on their MHC binding alleles Bui et al. (2006).

In this study, the MHC-I-binding alleles of 56 epitopes were identified, mainly alleles common in populations from North and South American regions ( Table 9). The highest and lowest population coverage in North America were 63.33 % and 0.07 % for S 1059−1073 and S 1213−1227, respectively. The highest (lowest) value of population coverage in South America was 48.29 % (0.00 %) for S 326−345 (S 442−456, S 1213−1227, and M 61−75). Similarly, the population coverage method was used to detect 129 MHC-II -binding allele epitopes. Despite reasonable antigenicity and immunogenicity values, 45 achieved the highest population coverage of more than 90 % ( Table 10). Of note, S 795−809 and S 165−209 were predicted to be 100 % (99.53 % and 99.45 %, respectively) in North America (South America).

Table 9.

Population coverage by MHC-I (CD8+ T cell) epitopes.

IEDB
 IEDB
Protein Sequence North America South America Average Protein Sequence North America South America Average
Original Strain Original Strain/Alpha/Beta/Delta/Gamma
E FLAFVVFLL 42.82 % 21.41 % 32.12 % GVYFASTEK 8.94 % 3.10 % 6.02 %
TLAILTALR 9.22 % 14.30 % 11.76 % YQPYRVVVL 11.77 % 8.94 % 10.36 %
LAILTALRL 8.37 % 3.96 % 6.17 % HVSGTNGTK 23.66 % 11.42 % 17.54 %
M VTLAILTAL 5.33 % 1.84 % 3.59 % VTWFHAIHV 0.19 % 0.00 % 0.10 %
LVIGAVILR 15.97 % 29.57 % 22.77 % Original Strain/Alpha/Beta/Delta/Gamma/Omicron
LYIIKLIFL 30.06 % 27.61 % 28.84 % IGAGICASY 5.11 % 3.14 % 4.13 %
LWPVTLACF 30.06 % 27.61 % 28.84 % AEIRASANL 26.88 % 18.96 % 22.92 %
FVLAAVYRI 44.14 % 21.85 % 33.00 % IAIVMVTIM 20.11 % 5.15 % 12.63 %
SYFIASFRL 30.06 % 27.61 % 28.84 % VLPFNDGVY 5.11 % 3.14 % 4.13 %
VIGFLFLTW 3.63 % 1.35 % 2.49 % VRFPNITNL 53.93 % 48.29 % 51.11 %
RFLYIIKLI 8.21 % 4.42 % 6.32 % VVFLHVTYV 63.33 % 42.17 % 52.75 %
KLIFLWLLW 3.63 % 1.35 % 2.49 % ITSGWTFGA 5.13 % 7.74 % 6.44 %
SELVIGAVI 9.61 % 2.81 % 6.21 % YIKWPWYIW 13.76 % 8.27 % 11.02 %
N AGDSGFAAY 12.72 % 6.03 % 9.38 % YQGVNCTEV 1.74 % 23.97 % 12.86 %
KTFPPTEPK 17.71 % 7.92 % 12.82 % AALQIPFAM 13.41 % 3.78 % 8.60 %
SPDDQIGYY 30.10 % 11.07 % 20.59 % FNATRFASV 27.37 % 9.21 % 18.29 %
SSPDDQIGY 17.71 % 7.92 % 12.82 % FTISVTTEI 6.75 % 12.84 % 9.80 %
DLSPRWYFY 12.72 % 6.03 % 9.38 % FVFLVLLPL 42.67 % 46.56 % 44.62 %
SPRWYFYYL 21.04 % 7.77 % 14.41 % IAIPTNFTI 6.45 % 2.24 % 4.35 %
GMSRIGMEV 42.97 % 21.41 % 32.19 % IPTNFTISV 6.42 % 7.06 % 6.74 %
EVTPSGTWL 9.95 % 8.47 % 9.21 % LPFNDGVYF 17.71 % 7.92 % 12.82 %
LSPRWYFYY 40.44 % 39.43 % 39.94 % PYRVVVLSF 13.41 % 3.78 % 8.60 %
S AAAYYVGYL 6.48 % 5.08 % 5.78 % TLADAGFIK 12.70 % 4.75 % 8.73 %
TPTWRVYST 4.93 % 2.01 % 3.47 % HWFVTQRNF 29.33 % 33.71 % 31.52 %
NRALTGIAV 7.85 % 4.90 % 6.38 % S WTAGAAAYY 6.45 % 2.24 % 4.35 %
KEIDRLNEV 6.85 % 12.30 % 9.58 %
SKVGGNYNY 1.65 % 0.00 % 0.83 %
WYIWLGFIA 0.07 % 0.00 % 0.04 %
YSKHTPINL 17.49 % 14.45 % 15.97 %
RQIAPGQTG 7.87 % 6.16 % 7.02 %
GVVFLHVTY 6.02 % 5.78 % 5.90 %
GAAAYYVGY 5.48 % 4.17 % 4.83 %
Open in a new tab

Table 10.

Population coverage by MHC-II (CD4+ T cell) epitopes.

IEDB
 IEDB
Protein Sequence North America South America Average Protein Sequence North America South America Average
Original Strain Original Strain/Alpha/Beta
E PSFYVYSRVKNLNSS 99.98 % 99.44 % 99.71 % VVFLHVTYVPAQEKN 35.60 % 57.57 % 46.59 %
KPSFYVYSRVKNLNS 99.98 % 99.24 % 99.61 % GVVFLHVTYVPAQEK 67.03 % 59.26 % 63.15 %
VKPSFYVYSRVKNLN 79.78 % 92.60 % 86.19 % VFLHVTYVPAQEKNF 99.99 % 99.82 % 99.91 %
SFYVYSRVKNLNSSR 48.03 % 64.00 % 56.02 % TAGAAAYYVGYLQPR 99.26 % 68.99 % 84.13 %
LVKPSFYVYSRVKNL 51.70 % 42.86 % 47.28 % LQYGSFCTQLNRALT 15.47 % 2.28 % 8.88 %
FYVYSRVKNLNSSRV 21.11 % 23.71 % 22.41 % QLTPTWRVYSTGSNV 2.23 % 20.17 % 11.20 %
YVYSRVKNLNSSRVP 21.46 % 11.15 % 16.31 % AGAAAYYVGYLQPRT 13.04 % 15.13 % 14.09 %
M RINWITGGIAIAMAC 97.28 % 98.63 % 97.96 % LGFIAGLIAIVMVTI 0.0 25.70 % 12.85 %
YRINWITGGIAIAMA 94.50 % 97.43 % 95.97 % NLLLQYGSFCTQLNR 1.24 % 1.25 % 1.25 %
SELVIGAVILRGHLR 84.66 % 77.13 % 80.90 % SNLLLQYGSFCTQLN 99.26 % 68.99 % 84.13 %
LLQFAYANRNRFLYI 32.68 % 13.63 % 23.16 % AGAALQIPFAMQMAY 13.32 % 12.73 % 13.03 %
N QIGYYRRATRRIRGG 99.98 % 97.25 % 98.66 % SIVRFPNITNLCPFG 15.47 % 2.28 % 8.88 %
RQKRTATKAYNVTQA 99.91 % 92.19 % 96.06 % IYQTSNFRVQPTESI 19.38 % 9.31 % 14.35 %
IGYYRRATRRIRGGD 99.87 % 92.91 % 96.40 % CTQLNRALTGIAVEQ 22.34 % 37.89 % 30.12 %
TASWFTALTQHGKED 98.56 % 99.78 % 99.17 % WPWYIWLGFIAGLIA 99.26 % 68.99 % 84.13 %
QKKQQTVTLLPAADL 88.07 % 85.16 % 86.62 % SFGGVSVITPGTNTS 59.47 % 46.88 % 53.18 %
QIAQFAPSASAFFGM 85.01 % 87.02 % 86.02 % QGFSALEPLVDLPIG 13.04 % 15.13 % 14.09 %
WYFYYLGTGPEAGLP 76.52 % 70.01 % 73.27 % IWLGFIAGLIAIVMV 79.97 % 60.77 % 70.37 %
GTWLTYTGAIKLDDK 75.35 % 91.49 % 83.42 % YIWLGFIAGLIAIVM 99.82 % 90.68 % 95.25 %
QTVTLLPAADLDDFS 74.96 % 72.42 % 73.69 % STQDLFLPFFSNVTW 13.04 % 15.13 % 14.09 %
QQTVTLLPAADLDDF 74.96 % 72.42 % 73.69 % GFIAGLIAIVMVTIM 5.33 % 7.77 % 6.55 %
PSASAFFGMSRIGME 67.63 % 77.19 % 72.41 % FLPFFSNVTWFHAIH 0.0 0.0 0.00 %
TKAYNVTQAFGRRGP 42.56 % 52.26 % 47.41 % FLGVYYHKNNKSWME 25.44 % 7.30 % 16.37 %
SGTWLTYTGAIKLDD 41.80 % 44.97 % 43.39 % LYNSASFSTFKCYGV 19.38 % 9.31 % 14.35 %
RQKKQQTVTLLPAAD 36.68 % 28.10 % 32.39 % FTISVTTEILPVSMT 19.38 % 32.61 % 26.00 %
ATKAYNVTQAFGRRG 36.54 % 50.20 % 43.37 % DDSEPVLKGVKLHYT 3.96 % 1.70 % 2.83 %
RWYFYYLGTGPEAGL 3.96 % 3.16 % 3.56 % IKWPWYIWLGFIAGL 99.88 % 95.00 % 97.44 %
PRWYFYYLGTGPEAG 3.96 % 3.16 % 3.56 % AALQIPFAMQMAYRF 99.45 % 71.11 % 85.28 %
QRQKKQQTVTLLPAA 27.70 % 19.95 % 23,83 % VVLSFELLHAPATVC 99.32 % 84.10 % 91.71 %
TWLTYTGAIKLDDKD 24.15 % 16.70 % 20,43 % RVVVLSFELLHAPAT 99.91 % 87.89 % 93.90 %
WLTYTGAIKLDDKDP 19.38 % 9.31 % 14.35 % FVFLVLLPLVSSQCV 99.97 % 96.08 % 98.03 %
ASAFFGMSRIGMEVT 16.68 % 7.75 % 12.26 % YRVVVLSFELLHAPA 13.04 % 15.13 % 14.09 %
SASAFFGMSRIGMEV 16.68 % 7.75 % 12.26 % WTFGAGAALQIPFAM 10.17 % 35.26 % 22.72 %
SPRWYFYYLGTGPEA 1.24 % 1.25 % 1.25 % CSNLLLQYGSFCTQL 99.43 % 70.60 % 85.02 %
KAYNVTQAFGRRGPE 0.0 % 0.0 % 0.0 % FQTLLALHRSYLTPG 19.38 % 9.31 % 14.35 %
S CDIPIGAGICASYQT 70.98 % 63.08 % 70.98 % WYIWLGFIAGLIAIV 26.04 % 49.32 % 37.68 %
FEYVSQPFLMDLEGK 91.41 % 85.45 % 91.41 % MFVFLVLLPLVSSQC 99.97 % 96.08 % 98.03 %
FKIYSKHTPINLVRD 99.97 % 98.58 % 99.97 % RFQTLLALHRSYLTP 23.87 % 30.46 % 27.17 %
GIVNNTVYDPLQPEL 98.81 % 99.88 % 98.81 % FVTQRNFYEPQIITT 13.04 % 15.13 % 14.09 %
GIYQTSNFRVQPTES 16.32 % 40.21 % 16.32 % VLSFELLHAPATVCG 22.79 % 5.20 % 14.00 %
GVYYHKNNKSWMESE 75.37 % 66.91 % 75.37 % AYYVGYLQPRTFLLK 99.90 % 95.21 % 97.56 %
GWTAGAAAYYVGYLQ 54.63 % 61.17 % 54.63 % QDLFLPFFSNVTWFH 30.64 % 38.63 % 34.64 %
HWFVTQRNFYEPQII 99.95 % 96.22 % 99.95 % Original Strain/Alpha/Beta/Delta/Gama
IPTNFTISVTTEILP 95.33 % 97.35 % 95.33 % LNRALTGIAVEQDKN 14.11 % 16.18 % 15.15 %
ITSGWTFGAGAALQI 49.82 % 54.58 % 49.82 % TQLNRALTGIAVEQD 29.93 % 33.06 % 31.50 %
KDFGGFNFSQILPDP 100.00 % 99.53 % 100 % FSNVTWFHAIHVSGT 29.56 % 24.14 % 26.85 %
KGIYQTSNFRVQPTE 1.24 % 1.25 % 1.24 % GCVIAWNSNNLDSKV 99.83 % 88.95 % 94.39 %
KHTPINLVRDLPQGF 99.99 % 98.93 % 99.99 % Original Strain/Alpha/Beta/Delta/Gama/Omicron
LGVYYHKNNKSWMES 8.30 % 3.78 % 8.3 % QIPFAMQMAYRFNGI 88.37 % 91.57 % 89.97 %
NGVGYQPYRVVVLSF 31.09 % 10.07 % 31.09 % LQIPFAMQMAYRFNG 88.37 % 91.57 % 89.97 %
NRALTGIAVEQDKNT 41.80 % 44.97 % 41.8 % GWTFGAGAALQIPFA 46.92 % 41.86 % 44.39 %
NTSNQVAVLYQGVNC 99.96 % 98.03 % 99.96 % FGGFNFSQILPDPSK 45.94 % 42.30 % 44.12 %
PFLGVYYHKNNKSWM 99.99 % 99.96 % 99.99 % TNFTISVTTEILPVS 62.96 % 82.19 % 72.58 %
QSIIAYTMSLGAENS 49.82 % 54.58 % 49.82 % PTNFTISVTTEILPV 91.63 % 94.17 % 92.90 %
RAAEIRASANLAATK 0.0 % 0.0 % 0 % AIPTNFTISVTTEIL 94.87 % 97.54 % 96.21 %
TESIVRFPNITNLCP 99.97 % 98.38 % 99.97 % IAIPTNFTISVTTEI 98.85 % 99.84 % 99.35 %
TESNKKFLPFQQFGR 99.98 % 99.00 % 99.98 % DIPIGAGICASYQTQ 70.98 % 63.08 % 67.03 %
TFEYVSQPFLMDLEG 99.99 % 99.21 % 99.99 % RVYSTGSNVFQTRAG 99.74 % 99.94 % 99.84 %
TPINLVRDLPQGFSA 99.97 % 98.58 % 99.97 % TWRVYSTGSNVFQTR 99.76 % 93.83 % 96.80 %
TPPIKDFGGFNFSQI 38.89 % 21.78 % 38.89 % RFASVYAWNRKRISN 99.94 % 98.24 % 99.09 %
VYSTGSNVFQTRAGC 98.01 % 98.62 % 98.01 % ESIVRFPNITNLCPF 99.97 % 98.50 % 99.24 %
WRVYSTGSNVFQTRA 99.64 % 76.09 % 99.64 % SSGWTAGAAAYYVGY 38.89 % 21.78 % 30.34 %
YQTSNFRVQPTESIV 8.30 % 3.78 % 8.3 % INITRFQTLLALHRS 27.70 % 19.95 % 23.83 %
GINITRFQTLLALHR 33.20 % 11.48 % 22.34 %
IGINITRFQTLLALH 39.86 % 33.92 % 36.89 %
PIGINITRFQTLLAL 23.28 % 10.60 % 16.94 %
CTFEYVSQPFLMDLE 99.99 % 99.18 % 99.59 %
S NCTFEYVSQPFLMDL 100.00 % 99.45 % 99.73 %
Open in a new tab

3.6. Identification of B-cell epitopes

B cells have the ability to recognize infectious pathogens or cancer cells and provide long-term protection by producing antibodies. These antibodies recognize antigen by binding highly selectively to an epitope. This recognition is used in subunit vaccines to provide long-term protection against the desired pathogens Jespersen et al. (2017).

The BepiPred 2.0 tool was used to predict B-cell epitopes of different lengths. A total of 34 sequential B-cell epitopes were predicted from the IEDB database in proteins of SARS-CoV-2. These B-cell epitopes were listed based on their position, sequence, length, and conservation in Table 11. It is worth noting that of the total 34 epitopes ranging in length from 1 to 52 mer, the best epitopes of protein S, namely S 748−748, S 602−606, S 555−562, and S 1107−1118, had a conservation of 100 %, 99.72 %, 99.17 %, and 99.17 %, respectively. For proteins M and N, 5 and 11 (1 and 4) linear B-cell epitopes ranging from 2 to 20 mer in length had a conservation index greater (less) than 90 %. Finally, of the 2 epitopes in protein E, E 6−9 was predicted to have a conservation index of 93.75 % and the other was discarded with a conservation index of 0 % ( Table 12).

Table 11.

B cells linear epitopes of SARS-CoV-2 structural proteins.

IEDB
Protein Start End Peptide Length Conservancy
Original Strain
E 6 9 SEET 4 93.75 %(15/16)
59 78 YSRVKNLNSSRVPDLLVLPP 20 00.0 %(0/16)
M 5 20 NGTITVEELKKLLEQW 16 89.29 % (50/56)
40 41 AN 2 98.21 % (55/56)
132 137 PLLESE 6 96.43 % (54/56)
161 163 IKD 3 94.64 % (53/56)
180 191 KLGASQRVAGDS 12 96.43 % (54/56)
199 218 YRIGNYKLNTDHSSSSDNIA 20 94.64 % (53/56)
N 4 15 NGPQNQRNAPRI 12 95.83 % (138/144)
17 48 FGGPSDSTGSNQNGERSGARSKQRRPQGLPNN 32 88.89 % (128/144)
59 105 HGKEDLKFPRGQGVPINTNSSPDDQIGYYRRATRRIRGGDGKMKDLS 47 90.28 % (130/144)
119 127 AGLPYGANK 9 96.53 % (139/144)
137 163 GALNTPKDHIGTRNPANNAAIVLQLPQ 27 93.75 % (135/144)
165 216 TTLPKGFYAEGSRGGSQASSRSSSRSRNSSRNSTPGSSRGTSPARMAGNGGD 52 61.81 % (89/144)
226 267 RLNQLESKMSGKGQQQQGQTVTKKSAAEASKKPRQKRTATKA 42 88.19 % (127/144)
276 299 RRGPEQTQGNFGDQELIRQGTDYK 24 97.92 % (141/144)
343 348 DPNFKD 6 99.31 % (143/144)
358 402 DAYKTFPPTEPKKDKKKKADETQALPQRQKKQQTVTLLPAADLDD 45 84.03 % (121/144)
404 416 SKQLQQSMSSADS 13 96.53 % (139/144)
Original Strain/Alpha/Beta/Delta/Gamma/Omicron
S 13 37 SQCVNLTTRTQLPPAYTNSFTRGVY 25 90.63 % (329/363)
177 189 MDLEGKQGNFKNL 13 87.33 % (317/363)
293 296 LDPL 4 95.59 % (347/363)
329 363 FPNITNLCPFGEVFNATRFASVYAWNRKRISNCVA 35 95.04 % (345/363)
369 393 YNSASFSTFKCYGVSPTKLNDLCFT 25 95.32 % (346/363)
555 562 SNKKFLPF 8 99.17 % (360/363)
602 606 TNTSN 5 99.72 % (362/363)
617 632 CTEVPVAIHADQLTPT 16 95.87 % (348/363)
748 748 E 1 100.00 % (363/363)
773 779 EQDKNTQ 7 98.35 % (357/363)
828 842 LADAGFIKQYGDCLG 15 98.07 % (356/363)
1107 1118 RNFYEPQIITTD 12 99.17 % (360/363)
1133 1172 VNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGI 40 95.87 % (348/363)
1203 1206 LGKY 4 98.35 % (357/363)
Open in a new tab

Table 12.

Predicted CTL and HTL epitopes among the SARS-CoV-2 virus structural proteins overlapping with B-cell epitopes of the same.

IEDB
Protein CTL epitope HTL epitope B-Cell epitope
M SELVIGAVILRGHLR PLLESE
N SSPDDQIGY QIGYYRRATRRIRGG
N SPDDQIGYY IGYYRRATRRIRGGD HGKEDLKFPRGQGVPINTNSSPDDQIGYYRRATRRIRGGDGKMKDLS
N KTFPPTEPK DAYKTFPPTEPKKDKKKKADETQALPQRQKKQQTVTLLPAADLDD
S FNATRFASV RFASVYAWNRKRISN FPNITNLCPFGEVFNATRFASVYAWNRKRISNCVA
S VRFPNITNL VRFPNITNLCPFGEVFNATRFASVYAWN
S HWFVTQRNF RNFYEPQIITTD
S YQGVNCTEV CTEVPVAIHADQLTPT
S HVSGTNGTK FSNVTWFHAIHVSGTNGTKRFDN
S WTAGAAAYY TPGDSSSGWTA
S TLADAGFIK LADAGFIKQYGDCLG
S CTFEYVSQPFLMDLE DLEGKQGNFKN
S TWRVYSTGSNVFQTR
S RVYSTGSNVFQTRAG TGSNVFQ
S GINITRFQTLLALHR NITRFQ
Open in a new tab

We found that 10 CTL epitopes of the original strain overlapped with B-cell epitopes and matched those found in the sequences of the Alpha, Beta, Delta, Gamma, and Omicron variants. We conclude that these epitopes in these variants are capable of activating humoral and cellular immune responses simultaneously and may play a role in vaccine construction. For vaccine construction, 6 HTL epitopes were selected that overlapped with B-cell epitopes and matched HTL epitopes of protein S. The overlap of peptides makes it possible to reduce the cost of producing a large number of peptides and should be used to evaluate the sensitivity of the study Lehtinen et al. (1995).

3.7. Multiepitope vaccine construction

Proper epitope selection is essential for vaccine construction using the in silico biological method Yin et al. (2016). In the current study, the epitopes (CTL and HTL) of the whole SARS-CoV-2 proteome were screened based on several immune filters. They should be [i] antigenic, immunogenic, nontoxic, and nonallergenic, [ii] have affinity for at least 2 HLA alleles, [iii] produce IFN-γ (HTL epitopes), [iv] contain overlapping HTL and CTL epitopes with B-cell epitopes, and [v] contain at least 90 % conserved regions. Furthermore, because this is a virus that increases its virulence potential after mutation events, we increased the potency of our vaccine by adding regions of high intervariant divergence. In this way, we selected a total of 18 epitopes, 10 CTL (N 78−86, N 79−87, N 361−369, S 341−349, S 326−334, S 1100−1108, S 611−619, S 68−76 and S 257−265) and 8 HTL (M 136−150, N 310−324, N 311−323, S 166−180, S 346−360, S 632−646, S 634−648, and S 232−246) that will make up our vaccine prototype ( Table 13.

Table 13.

CTL and HTL epitopes used to produce a multiepitope vaccine; their original strain and their conservation in other strain variants.

CTL Epitopes
Protein Sequence Initial sequence prediction Subtypes
N SSPDDQIGY Original Strain
SPDDQIGYY Original Strain
KTFPPTEPK Original Strain
S FNATRFASV Original Strain Alpha/Beta/Delta/Gamma/Omicron
VRFPNITNL Original Strain Alpha/Beta/Delta/Gamma
HWFVTQRNF Delta Alpha/Beta/Gamma/Omicron
YQGVNCTEV Delta Alpha/Beta/Gamma/Omicron
HVSGTNGTK Original Strain Alpha/Beta/Delta/Gamma
WTAGAAAYY Original Strain Alpha/Beta/Delta/Gamma/Omicron
TLADAGFIK Original Strain Alpha/Beta/Delta/Gamma/Omicron
HTL Epitopes
Protein Sequence Initial sequence prediction Subtypes
M SELVIGAVILRGHLR Original Strain
N QIGYYRRATRRIRGG Original Strain
IGYYRRATRRIRGGD Original Strain
S CTFEYVSQPFLMDLE Original Strain Alpha/Beta/Delta/Gamma/Omicron
RFASVYAWNRKRISN Original Strain Alpha/Beta/Delta/Gamma/Omicron
TWRVYSTGSNVFQTR Original Strain Alpha/Beta/Delta/Gamma/Omicron
RVYSTGSNVFQTRAG Original Strain Alpha/Beta/Delta/Gamma/Omicron
GINITRFQTLLALHR Original Strain Alpha/Beta/Delta/Gamma/Omicron
Open in a new tab

We linked the 10 CTL epitopes and 8 HTL epitopes that overlap with the B-cell epitopes using the AAY (GPGPG) linker for CTL (HTL) epitopes to form the final vaccine construct. The AAY linker is a type of proteasome cleavage site that was used to manipulate the protein in favor of greater stability, folding, and expression patterns Shamriz et al., 2016, Abdellrazeq et al., 2020, Folegatti et al., 2020. The GPGPG was added because it could enhance some biological activities of the protein by increasing solubility and facilitating immunological processing of the vaccine construct Kavoosi et al. (2007). After the addition of linkers and adjuvant, the final vaccine construct was 325 amino acids long.

In addition, the adjuvant β-defensin was added at the N-terminus of the multi-epitope with the EAAAK linker. It helps elicit a high level of cellular and immunogenic humoral responses to specific antigens and enhances the stability and longevity of the vaccine Bonam et al. (2017). Indeed, β-defensins are peptides with antimicrobial, antiviral, antibacterial, and antifungal activity and have the ability to recruit antigen-presenting immune cells with MHC-I and MHC-II Lehrer and Lu (2012).

The constructed vaccine had high population coverage for countries with high morbidity and infection. Most studies on SARS-CoV-2 vaccine construction had focused only on S in predicting B- and T-cell epitopes Chukwudozie et al., 2021, Khan et al., 2021, Kar et al., 2020. In the present study, we predicted T-cell epitopes from the conserved regions of all 4 structural proteins of SARS-CoV-2 and from sequences containing the variants in question (alpha, beta, gamma, delta, and omicron). This certainly justifies the robustness of our vaccine construct against the new variants.

It is not difficult to conclude that the vaccine we developed has several advantages over single-epitope and conventional vaccines due to the following particular features: (a) it comprises multiple MHC epitopes and therefore can be recognized by multiple T/B cell receptors, (b) it contains overlapping CTL and HTL epitopes and therefore can activate both innate and adaptive immunity, (c) it comprises polyepitopes of virulent target antigens, and (d) it has an immune stimulator (adjuvant) to improve a long-lasting immune response (Jiang et al., 2017, He et al., 2018) ( Table 14).

Table 14.

Post-translational modification results of prototype vaccine.

Number of N glycosylation region N glycosylation region located in exposed surface O glycosylation region Acetylation region Acetylation region score Phosphorylation region
6 115, 136, 163, 313 34, 199, 218, 264, 276, 294 1 0.467 5, 9, 22, 34, 35, 51, 76, 86, 110, 127, 134, 138, 142, 146, 152, 171, 199, 214, 218, 235, 237, 254, 256, 264, 271, 276, 294, 319
Open in a new tab

3.8. Construction, refinement and validation of tertiary structure model

During homology modeling, the primary structure for the final multiepitope subunit vaccine was submitted using Robetta and Swiss Model, resulting in 1 and 5 models. The best model in each case was refined using Galaxy Refine. Of the 20 models provided at this stage, the model with the best stereochemical and structural parameters was selected, namely GDT-HA (0.9053), RMSD (0.552), MolProbity (1.319), Clash score (4.0), Poor rotamers (1.3) and Ramachandran plot (97.8). The superimposed structure of the template and the vaccine construct along with the template scores is shown in Fig. 2. Comparison of the quality indicators shows that the original tertiary structure models improved after refinement.

Fig. 2.

Fig. 2

Open in a new tab

3D structural conformation of the multi-epitope subunit vaccine after homology modeling and refinement by SwissModel and GalaxyRefine servers.

Ramachandran plot, Z-score, ERRAT, and Verify3D analyzes were performed to verify the structural quality of the predicted model. After (before) refinement, 97.33 % (92.77 %) of the structure was in the preferred region of the plot, whereas 2.67 % (6.60 %) of the residuals were in the allowed region and 0.00 % (0.63 %) of the structure was in the outlier regions, demonstrating the overall quality of the vaccine construct ( Fig. 3).

Fig. 3.

Fig. 3

Open in a new tab

Validation of the final structural subunit vaccine model (A) Vaccine 3D Structure Validation by ProSA-web illustrating Z-score; (B) Quality factor and quality score by ERRAT (C) Verify3D tools, respectively.

The Z-score of − 0.88, which was in the range of scores of proteins of comparable size, indicates the reliability of the predicted model (3a) Wiederstein and Sippl (2007). The ERRAT score was 60.4651, significantly higher than the threshold of 50 Messaoudi et al. (2013) (3b). We also use the Structure Assessment server for Ramachandran plot analysis and Local Quality Estimation, which produces results consistent with GalaxyRefine results. After refinement, the model showed an overall QMEANDisco value of 0.35 + /- 0.05 Fig. 4.

Fig. 4.

Fig. 4

Open in a new tab

Ramachandran plot indicated a high proportion of residues.

3.9. Physiochemical properties and solubility prediction

Physiochemical property evaluation was required for vaccine formulation to demonstrate that the developed vaccine candidate was stable and met standards. It contained a total of 325 amino acids, and its molecular weight and instability index were 35,804.75 and 42.95, respectively. The hydropathy value of the final vaccine is predicted to be − 0.332, which means that our final vaccine is hydrophilic in nature. The half-life is predicted to be 30 h in vitro and > 20 h in vivo. The pI value of the final vaccine is calculated to be 10.04, which is an alkaline value of the strongly basic in nature.

3.10. Prediction of post-translational modifications

Post-translational modification analysis was performed for the prototype vaccine. The analysis revealed the presence of N-linked glycosylation positions, which is one of the major post translational modifications. It was predicted that most N-linked glycosylation positions are located on the exposed surface of the protein. This parameter is known to increase the accuracy of glycosylation Hamby and Hirst (2008). The presence of post-translational modifications in eukaryotic cells such as parasites, including T. gondii, is critical for selecting the correct expression system for recombinant protein production. Accordingly, the results of our post translational modifications indicated that the presence of N-linked glycosylation, phosphorylation, and acetylation should be considered in the recombinant production of a vaccine prototype and that eukaryotic expression systems such as yeast, insect, and mammalian are preferable to bacterial systems ( Fig. 5).

Fig. 5.

Fig. 5

Open in a new tab

Structure and refinement of the docking complex. (A) Refinement of the docking complex using the FireDock tool. (B) Docked vaccine TLR3 complex and binding affinities before and after QM/MM simulation.

3.11. Codon optimization and E. coli expression

In silico cloning was performed so that the vaccine candidate could be expressed into the E. coli and the expression system was optimized by codon adaptation, which can increase protein expression up to > 1000-fold Mauro (2018). Therefore, it was necessary to optimize the codon for the vaccine construct according to the use of the E. coli expression system to ensure efficient translation and increased protein production Rosano and Ceccarelli (2014). The results of codon optimization of the vaccine with a GC content of 50.73 %, which is in the optimal range (30–70 %), indicate good comprehensive stability of the mRNA of the synthetic gene Grote et al., 2005, Nouri et al., 2016. The CAI value was predicted to be 1.0, in the range (0.8–1.0) signifies the maximum codon affinity and indicates high expression of our designed vaccine constructs in E. coli K12 strain Chen (2012). The generated cDNA sequence was 1,263 nucleotides long after codon optimization. Finally, the recombinant plasmid was designed by computationally inserting the aligned codon sequences into the pET-28a (+) vector using SnapGene software Fig. 6. This research was conducted to develop a successful cloning approach that has a great chance of producing a high quality vaccine. Apart from all these analyzes and conclusions, further experimental validations are required to confirm the safety and efficacy of the designed vaccine.

Fig. 6.

Fig. 6

Open in a new tab

In silico restriction cloning of the final vaccine construct into pET28a (+) expression vector where red part representing the vaccine insert and black circle showing the vector.

3.12. Immune simulation of the vaccine

Immune simulations suggest that the vaccine elicits a consistent immune response. After each administration and repeated exposure, there was a significant increase in antibody response with a concomitant decrease in antigen levels. Administration of the vaccine by three injections was good at inducing different immunoglobulins. The humoral response was predominantly IgM > IgG, indicating some degree of seroconversion ( Fig. 7a). The primary response was reflected by an increase in IgM levels that led to a decrease in antigen concentration. The secondary and tertiary responses with high immunoglobulin activities (IgG1, IgG2, IgG + IgM) were higher than the primary response (IgM). This indicates the emergence of an immune memory and thus increased antigen excretion during subsequent exposures.

Fig. 7.

Fig. 7

Open in a new tab

C-ImmSim server prediction results of immune response after administering vaccine construct; (a) Ab titer increases with each successive injection as antigen number decreases; (b) B cell population (indicates increase in various B cell types and their potential to switch classes); (c) PLB (plasma B cell) population (indicates presence of memory B cells and active B cell proliferation); (d) TH cell population (indicates significant increase in TH memory cells); (E) TH cell population per state (indicates increase in TH cells in active state); (F) TC cell population (indicates fluctuation of TC cell population with time); (G) NK cell population (indicates the fluctuation of NK cell population with time); (H) macrophage population (indicates the fluctuation of macrophage population with time); I) concentration of cytokines and interleukins (indicates the increased IFN-γ and IL-2 production).

In addition, multiple long-lived B cell isotypes were found, suggesting that long-lived B cells have the ability to switch isotype and develop memory cells (Fig. 7b). This indicates a high number of TH cells and consequently efficient Ig production supporting a humoral response (7c), as the increased TH cells promote clonal growth of B cells and antibody production Smith et al. (2000).

In cell populations TH (helper cells) and TC (cytotoxic cells) with corresponding memory development, the results were much closer to memory development (7d). A similar enhanced response was observed, indicating the immunogenicity of the T cell epitopes included in the vaccine construct. TC cells increased to near the maximum of over 1160 cells per mm3 (Fig. 7e). NK cell numbers were also increased, averaging 350 cells per mm3 (Fig. 7f). This is observed in the preactivation of the TC cell response during vaccination.

The activity of NK cells (Fig. 7g) and dendritic cells was noted along with higher macrophage activity during exposure. The number of active macrophages increased with subsequent doses and then decreased to less than 25 cells per mm3 days after the third dose (7h). This suggests another good performance indicator of the vaccine construct and demonstrates its ability to stimulate the correct immunologic compartment for an effective response, considering that IFN-γ is released from NK and the presence of IFN-γ stimulates macrophage activation Tau and Rothman (1999).

High levels of IFN-γ and IL − 2 were also detected, consistent with the prediction of IFN-γ epitopes in the vaccine. Because it contains antiviral components, IFN-γ plays an important role in both innate and adaptive immunity. A significant increase in the levels of IFN-γ, IL − 10, IL − 23, and IL − 12 was also observed with subsequent exposure. The increased IFN-γ production justifies the selection of IFN-γ producing MHC class II epitopes. This implies that the vaccine elicits a robust immune response with a short exposure and that immunity also increases with subsequent, repeated exposure (Fig. 7i).

Cytokine and interleukin production is also indicative of a successful immune response, as IFN-γ and IL − 2 production remained constant after the first injection, consistent with the prediction of IFN-γ epitopes in the vaccine. Significant increases in IFN-γ levels IL − 10, IL − 23, and IL − 12, which are important for co-stimulatory signaling of T-cell activation, were also observed during subsequent exposure. This suggests that the vaccine elicits a robust immune response during a brief exposure and that immunity also increases during subsequent, repeated exposures.

In addition, after the first dose, there was an initial increase in IFN-g responses (associated with both CD8 + T cells and CD4 + Th 1 responses) and IL − 10 & TGF-b cytokines, which are associated with the T-reg phenotype. These immune responses are also associated with a broader base in peak antigen quantification. However, after the first booster dose, IFNg also peaked. At the second booster dose on day 56, the IgG response was faster and higher than the IgM response (suggesting complete seroconversion and a B-cell memory response), faster antigen clearance as evidenced by a narrower base of the antigen spike compared with the previous doses, and lower levels of T-reg-associated cytokines (TGFb & IL −10) compared with the response to the first booster dose. Overall, the results showed a concomitant increase in immune response with each vaccination regimen.

Further studies of immunogenicity, efficacy, and possible adverse effects should be conducted both in vitro and in vivo, including expression of this vaccine candidate in a bacterial system to verify immunoreactivity by serologic analysis.

3.13. Molecular docking, QM/MM study and binding profile

Interaction between the prototype vaccine and an appropriate immune receptor molecule is required for adequate elucidation of the immune response. Antiviral immunity is mainly activated by the family of cytosolic receptors for pathogen recognition, including TLR3. Activation of TLR3 impairs replication of several viruses, including SARS-CoV Gralinski et al. (2017), MERS-CoV Mubarak et al. (2019), dengue virus (DENV), human immunodeficiency virus (HIV), influenza virus, and respiratory syncytial virus (RSV), herpes simplex virus (HSV), and Marek’s disease virus (MDV) Perales-Linares and Navas-Martin, 2013, Prathyusha et al., 2020.

Here, the X-ray data of the extracellular domain of Human Toll-like Receptor 3 with a resolution of 2.10 Å (PDB ID: 1ZIW) were used as a template for the docking experiments with our vaccine prototype. Vaccine-TLR3 docking studies were performed and refined by PatchDock and FireDock simulations, respectively. The Firedock score of − 12.22 for global binding energy indicates good binding affinity, and a negative score indicates better docking, in addition to adequate values for attractive (−21.57) and repulsive (8.66) van der Walls forces, desolvation (2.23), and HB (−6.91) energies (Fig. 5a).

It is known that due to the low accuracy of the scoring functions, more robust approaches should be used to find the best pose among the docking results Cho et al. (2009). Another important issue related to ligand-protein docking is to consider the flexibility of the protein structure to make the results more realistic Brooijmans and Kuntz, 2003, Burger et al., 2011. Therefore, quantum mechanical/molecular mechanical (QM /MM) calculations were subsequently performed to optimize the molecular geometries within the framework of the density functional theory formalism (DFT) (Fig. 5b) to allow for some degree of flexibility in the binding pocket of the receptor, which is essential for its function and to correct for vaccine matching but is not present in the crude docking procedure Sousa et al., 2006, de Medeiros et al., 2016, Harmalkar and Gray, 2021. The efficacy of this procedure has already been validated in the development of improved schizophrenia drugs Zanatta et al. (2014) and multiepitope peptide vaccines against Mayaro virus Silva et al., 2021, daSilva et al., 2022.

The improved vaccine-TLR3 structure had a binding energy (ΔG) and dissociation constant (Kd) of − 16.5 kcal/mol and 6.7E-07, respectively, at 25 ∘C, whereas the first structure ΔG was equal to − 8.4 and Kd of 2.6E-8. Moreover, there are 322 intermolecular contacts (ICs) at the interface within the threshold distance of 5.5 Å, and the percentage of charged and apolar noninteracting surface (NIS %) is 33.12 % and 41.28 %, respectively. The van der Waals energy (E vdw), electrostatic energy (E elec), and desolvation energy were − 31.1 kcal/mol, − 23.3 kcal/mol, and − 1.1 kcal/mol, respectively. RMSD analysis revealed a deviation of 3.9 for the simulated QM:MM vaccine-TLR3 complex from the original structure.

Finally, the QM/MM simulation shows important binding contacts, namely GLN283-GLN167, ARG285-PHE121, ALA120-ARG285, LYS145-GLY286, LYS200-ARG316, LYS102-GLN318, VAL144-PRO287, ARG285-LYS145, and PHE121-PRO249 in the vaccine-TLR3 system ( Fig. 8). GLN283-GLN167 is characterized by non-classical hydrogen bonding, indicating that the donor is a polarized carbon atom. The cationic part of the guanidinium of ARG285 and the anionic carboxylate group of PHE121 have electrostatic attraction. ALA120-ARG285, LYS145-GLY286, LYS200-ARG316, and LYS102-GLN318 also exert electrostatic attraction. VAL144-PRO287 and ARG285-LYS145 belong to the hydrophobic alkyl type and have a surface area equal to or greater than the area of a methyl group multiplied by the surface area scaling factor (default value 0.65), which defines them as non-polarized, non-P. In contrast, PHE121-PRO249 are characterized as a hydrophobic Pi/alkyl mixture, defined as weak interactions between a hydrogen and a Pi ring system.

Fig. 8.

Fig. 8

Open in a new tab

Docking complex exhibiting intermolecular interactions between the vaccine component and TLR − 3, where in the receptor surface is characterized by (A) H-bond donor-acceptor, (B) interpolated charge, and (C) hydrophobicity.

Considering that TLR-3 has been shown to have a recognition function in both SARS-CoV and MERS-CoV Gralinski et al., 2017, Mubarak et al., 2019 and SARS-CoV-2 has a similar genome organization to these, it is hypothesized that this immune receptor is involved in the response against the current pandemic virus. The pattern of molecular interactions of the vaccine with TLR-3 and our in silico simulations indicate that this receptor is capable of acting as a sensor for recognizing molecular patterns of the pathogen and eliciting both innate and adaptive immune responses.

When developing a multiepitope vaccine, it is important to consider the viral subtypes so that this vaccine has a higher specificity. Here, our multiepitope vaccine was tested against the BA.1 and BA.2 subtypes of Omicron (B.1.1.529) using in silico techniques, but we believe that it is also effective against other Omicron sublineages such as B.A.3 and B.A.4/5. We compared the mutational profiles of the epitopes and found that 44.5 % of the epitopes of the vaccine prototype are unique to the Omicron subvariants, i.e., they represent regions of the viral proteins that mutated during the emergence of the subvariants BA.1, BA.2, BA.3 and BA.4/5. More importantly, 55.5 % have completely identical peptide sequences conserved in all strains reported up toJuly 2022, namely: 5 CTL (S 341−349, S 1100−1108, S 611−619, S 257−265, and S 826−834) and 5 HTL (S 166−180, S 346−360, S 632−646, S 634−648, and S 232−246).

Immunoinformatics is one of the areas accelerating the progress of immunological research towards the development of effective vaccines. Consistent with the scientific literature, peptide epitope-based vaccines have potential outcomes against several highly infectious diseases such as SARS-COV − 2 Bhattacharya et al. (2020), MERS-COV UlQamar et al. (2019), Dengue Brinton (2002), Chikungunya Narula et al. (2018), Japanese Encephalitis Chakraborty et al. (2020), HIV Abdulla et al., 2019, Jardine et al., 2013, and Tuberculosis Ong et al. (2020). Thus, the epitopes described in this study and the prototype vaccine could be tested as diagnostic reagents and for their potential immunizing capacity against SARS-CoV-2, respectively.

4. Conclusion

Here, we developed a multiple epitope-based subunit vaccine against the circulating SARS-CoV-2 variants, namely alpha, beta, gamma, delta, and omicron. Initially, we predicted B-cell and T-cell epitopes of four SARS-CoV-2 viral proteins (S, M, N, and E) obtained from 475 genomes sequenced from the regions with the highest number of registered cases using antigenicity, immunogenicity, allergenicity, toxicity, IFN-γ inducing, population coverage, and conservation filters. A total of 18 effective epitopes were combined by using appropriate linkers and adjuvant to enhance their immunogenicity, resulted in a multi-epitope vaccine with length of 325aa. The specificity of binding of this vaccine candidate to the TLR-3 immune cell receptor was evaluated by molecular docking, followed by the application of a QM/MM energy minimization strategy to improve the quality of the result. Finally, we found that GLN283-GLN167, ARG285-PHE121, ALA120-ARG285, LYS145-GLY286, LYS200-ARG316,LYS102-GLN318, VAL144-PRO287, ARG285-LYS145 and PHE121-PRO249 formed the binding pocket of the complex.

This is the first study that has considered such a large amount of protein data, specifically 475 proteomes of the major circulating SARS-CoV-2 variants, including 38 (16) reported non-synonymous mutations found in the protein spike (nucleocapsid phosphoprotein). It is also the only multi-epitope vaccine prototype consisting of B-cell and T-cell epitopes of all structural polyproteins of this virus, developed using hybrid quantum mechanical/molecular mechanical approaches. Further in vitro and in vivo studies are required to confirm the efficacy of our vaccine prototype against the major SARS-CoV-2 variants.

CRediT authorship contribution statement

Daniel M. de O. Campos: Data curation, Investigation, Formal analysis, Visualization, Writing – original draft. Maria K. Silva: Formal analysis, Validation, Visualization. Emmanuel D. Barbosa: Formal analysis, Writing – review & editing. Chiuan Y. Leow: Formal analysis, Writing – review & editing. Umberto L. Fulco: Supervision, Formal analysis, Writing – review & editing. Jonas I. N. Oliveira: Conceptualization, Methodology, Funding acquisition, Project administration, Writing – review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work has received financial support from the Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq, and the Coordenaçs̃o de Aperfeiçoamento de Pessoal de Nível Superior - CAPES. We would like to thank the Núcleo de Processamento de Alto Desempenho of the Universidade Federal do Rio Grande do Norte - NPAD/UFRN to allow us to access their computer facilities.

References

  1. Abbas A.K., Lichtman A.H., Pillai S. Elsevier Health Sciences; 2014. Cellular and molecular immunology E-book. [Google Scholar]
  2. Abdellrazeq G.S., Fry L.M., Elnaggar M.M., Bannantine J.P., Schneider D.A., Chamberlin W.M., Mahmoud A.H., Park K.-T., Hulubei V., Davis W.C. Simultaneous cognate epitope recognition by bovine cd4 and cd8 t cells is essential for primary expansion of antigen-specific cytotoxic t-cells following ex vivo stimulation with a candidate mycobacterium avium subsp. paratuberculosis peptide vaccine. Vaccine. 2020;38(8):2016–2025. doi: 10.1016/j.vaccine.2019.12.052. [DOI] [PubMed] [Google Scholar]
  3. Abdelmageed M.I., Abdelmoneim A.H., Mustafa M.I., Elfadol N.M., Murshed N.S., Shantier S.W., Makhawi A.M. Design of a multiepitope-based peptide vaccine against the e protein of human covid-19: an immunoinformatics approach. BioMed. Res. Int. 2020 doi: 10.1155/2020/2683286. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Abdulla F., Adhikari U.K., Uddin M.K. Exploring t & b-cell epitopes and designing multi-epitope subunit vaccine targeting integration step of hiv-1 lifecycle using immunoinformatics approach. Microb. Pathog. 2019;137 doi: 10.1016/j.micpath.2019.103791. [DOI] [PubMed] [Google Scholar]
  5. AlSaba A., Adiba M., Saha P., Hosen M.I., Chakraborty S., Nabi A.N. An in-depth in silico and immunoinformatics approach for designing a potential multi-epitope construct for the effective development of vaccine to combat against sars-cov-2 encompassing variants of concern and interest. Comput. Biol. Med. 2021;136 doi: 10.1016/j.compbiomed.2021.104703. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Andreatta M., Nielsen M. Epitope Mapping Protocols. Springer; 2018. Bioinformatics tools for the prediction of t-cell epitopes; pp. 269–281. [DOI] [PubMed] [Google Scholar]
  7. Andreatta M., Karosiene E., Rasmussen M., Stryhn A., Buus S., Nielsen M. Accurate pan-specific prediction of peptide-mhc class ii binding affinity with improved binding core identification. Immunogenetics. 2015;67(11–12):641–650. doi: 10.1007/s00251-015-0873-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Andrusier N., Nussinov R., Wolfson H.J. Firedock: fast interaction refinement in molecular docking. Proteins: Struct. Funct. Bioinformatics. 2007;69(1):139–159. doi: 10.1002/prot.21495. [DOI] [PubMed] [Google Scholar]
  9. Barda N., Dagan N., Cohen C., Hernán M.A., Lipsitch M., Kohane I.S., Reis B.Y., Balicer R.D. Effectiveness of a third dose of the bnt162b2 mrna covid-19 vaccine for preventing severe outcomes in israel: an observational study. Lancet. 2021;398(10316):2093–2100. doi: 10.1016/S0140-6736(21)02249-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bezerra K., Vianna J., Neto J.L., Oliveira J.I.N., Albuquerque E., Fulco U. Interaction energies between two antiandrogenic and one androgenic agonist receptor in the presence of a t877a mutation in prostate cancer: a quantum chemistry analysis. N. J. Chem. 2020;44(15):5903–5912. [Google Scholar]
  11. Bhattacharya M., Sharma A.R., Patra P., Ghosh P., Sharma G., Patra B.C., Lee S.-S., Chakraborty C. Development of epitope-based peptide vaccine against novel coronavirus 2019 (sars-cov-2): Immunoinformatics approach. J. Med. Virol. 2020;92(6):618–631. doi: 10.1002/jmv.25736. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Blom N., Sicheritz-Pontén T., Gupta R., Gammeltoft S., Brunak S. Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics. 2004;4(6):1633–1649. doi: 10.1002/pmic.200300771. [DOI] [PubMed] [Google Scholar]
  13. Bonam S.R., Partidos C.D., Halmuthur S.K.M., Muller S. An overview of novel adjuvants designed for improving vaccine efficacy. Trends Pharmacol. Sci. 2017;38(9):771–793. doi: 10.1016/j.tips.2017.06.002. [DOI] [PubMed] [Google Scholar]
  14. Brinton M.A. The molecular biology of west nile virus: a new invader of the western hemisphere. Annu. Rev. Microbiol. 2002;56(1):371–402. doi: 10.1146/annurev.micro.56.012302.160654. [DOI] [PubMed] [Google Scholar]
  15. Brooijmans N., Kuntz I.D. Molecular recognition and docking algorithms. Annu. Rev. Biophys. Biomol. Struct. 2003;32(1):335–373. doi: 10.1146/annurev.biophys.32.110601.142532. [DOI] [PubMed] [Google Scholar]
  16. Bui H.-H., Sidney J., Dinh K., Southwood S., Newman M.J., Sette A. Predicting population coverage of t-cell epitope-based diagnostics and vaccines. BMC Bioinformatics. 2006;7(1):153. doi: 10.1186/1471-2105-7-153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Bui H.-H., Sidney J., Li W., Fusseder N., Sette A. Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinformatics. 2007;8(1):361. doi: 10.1186/1471-2105-8-361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Burger S.K., Thompson D.C., Ayers P.W. Quantum mechanics/molecular mechanics strategies for docking pose refinement: distinguishing between binders and decoys in cytochrome c peroxidase. J. Chem. Inf. Model. 2011;51(1):93–101. doi: 10.1021/ci100329z. [DOI] [PubMed] [Google Scholar]
  19. Calis J.J., Maybeno M., Greenbaum J.A., Weiskopf D., De Silva A.D., Sette A., Keşmir C., Peters B. Properties of mhc class i presented peptides that enhance immunogenicity. PLoS Comput. Biol. 2013;9(10) doi: 10.1371/journal.pcbi.1003266. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Campos D., Oliveira C., Andrade J., Oliveira J. Fighting covid-19. Braz. J. Biol. 2020;80:698–701. doi: 10.1590/1519-6984.238155. [DOI] [PubMed] [Google Scholar]
  21. Campos D.M., Bezerra K.S., Esmaile S.C., Fulco U.L., Albuquerque E.L., Oliveira J.I. Intermolecular interactions of cn-716 and acyl-kr-aldehyde dipeptide inhibitors against zika virus. Phys. Chem. Chem. Phys. 2020;22(27):15683–15695. doi: 10.1039/d0cp02254c. [DOI] [PubMed] [Google Scholar]
  22. Castiglione F., Mantile F., De Berardinis P., Prisco A. How the interval between prime and boost injection affects the immune response in a computational model of the immune system. Comput. Math. Methods Med. 2012 doi: 10.1155/2012/842329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Chabot I., Goetghebeur M.M., Grégoire J.-P. The societal value of universal childhood vaccination. Vaccine. 2004;22(15–16):1992–2005. doi: 10.1016/j.vaccine.2003.10.027. [DOI] [PubMed] [Google Scholar]
  24. Chakraborty S., Barman A., Deb B. Japanese encephalitis virus: a multi-epitope loaded peptide vaccine formulation using reverse vaccinology approach. Infect. Genet. Evol. 2020;78 doi: 10.1016/j.meegid.2019.104106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Chen R. Bacterial expression systems for recombinant protein production: E. coli and beyond. Biotechnol. Adv. 2012;30(5):1102–1107. doi: 10.1016/j.biotechadv.2011.09.013. [DOI] [PubMed] [Google Scholar]
  26. Chen V.B., Arendall W.B., Headd J.J., Keedy D.A., Immormino R.M., Kapral G.J., Murray L.W., Richardson J.S., Richardson D.C. Molprobity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. Sect. D: Biol. Crystallogr. 2010;66(1):12–21. doi: 10.1107/S0907444909042073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Chesler D.A., Reiss C.S. The role of ifn-γ in immune responses to viral infections of the central nervous system. Cytokine Growth Factor Rev. 2002;13(6):441–454. doi: 10.1016/s1359-6101(02)00044-8. [DOI] [PubMed] [Google Scholar]
  28. Cho A.E., Chung J.Y., Kim M., Park K. Quantum mechanical scoring for protein docking. J. Chem. Phys. 2009;131(13) doi: 10.1063/1.3239504. [DOI] [PubMed] [Google Scholar]
  29. Chukwudozie O.S., Gray C.M., Fagbayi T.A., Chukwuanukwu R.C., Oyebanji V.O., Bankole T.T., Adewole R.A., Daniel E.M. Immuno-informatics design of a multimeric epitope peptide based vaccine targeting sars-cov-2 spike glycoprotein. PLoS One. 2021;16(3) doi: 10.1371/journal.pone.0248061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Chung L.W., Sameera W., Ramozzi R., Page A.J., Hatanaka M., Petrova G.P., Harris T.V., Li X., Ke Z., Liu F., et al. The oniom method and its applications. Chem. Rev. 2015;115(12):5678–5796. doi: 10.1021/cr5004419. [DOI] [PubMed] [Google Scholar]
  31. Cui Z. Dna vaccine. Adv. Genet. 2005;54:257–289. doi: 10.1016/S0065-2660(05)54011-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. daSilva M.K., Azevedo A.A.C., Campos D.M. d.O., de Souto J.T., Fulco U.L., Oliveira J.I.N. Computational vaccinology guided design of multi-epitope subunit vaccine against a neglected arbovirus of the americas. J. Biomol. Struct. Dyn. 2022:1–18. doi: 10.1080/07391102.2022.2050301. [DOI] [PubMed] [Google Scholar]
  33. Dimitrov I., Flower D.R., Doytchinova I. Allertop-a server for in silico prediction of allergens. BMC Bioinformatics. 2013 doi: 10.1186/1471-2105-14-S6-S4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Dimitrov I., Bangov I., Flower D.R., Doytchinova I. Allertop v. 2–a server for in silico prediction of allergens. J. Mol. Model. 2014;20(6):2278. doi: 10.1007/s00894-014-2278-5. [DOI] [PubMed] [Google Scholar]
  35. Doria-Rose N.A., Shen X., Schmidt S.D., O’Dell S., McDanal C., Feng W., Tong J., Eaton A., Maglinao M., Tang H., et al. Booster of mRNA-1273 vaccine reduces sars-cov-2 omicron escape from neutralizing antibodies. medRxiv. 2021 [Google Scholar]
  36. Doytchinova I.A., Flower D.R. Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties. Vaccine. 2007;25(5):856–866. doi: 10.1016/j.vaccine.2006.09.032. [DOI] [PubMed] [Google Scholar]
  37. Doytchinova I.A., Flower D.R. Vaxijen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007;8(1):1–7. doi: 10.1186/1471-2105-8-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Ehreth J. The global value of vaccination. Vaccine. 2003;21(7–8):596–600. doi: 10.1016/s0264-410x(02)00623-0. [DOI] [PubMed] [Google Scholar]
  39. Elliott S.L., Suhrbier A., Miles J.J., Lawrence G., Pye S.J., Le T.T., Rosenstengel A., Nguyen T., Allworth A., Burrows S.R., et al. Phase i trial of a cd8. t-cell peptide epitope-based vaccine for infectious mononucleosis. J. Virol. 2008;82(3):1448–1457. doi: 10.1128/JVI.01409-07. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. England P.H. 2021. Sars-cov-2 variants of concern and variants under investigation in england, technical briefing 12 (2021).
  41. Fiolet T., Kherabi Y., MacDonald C.-J., Ghosn J., Peiffer-Smadja N. Comparing covid-19 vaccines for their characteristics, efficacy and effectiveness against sars-cov-2 and variants of concern: a narrative review. Clin. Microbiol. Infect. 2021 doi: 10.1016/j.cmi.2021.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Fleri W., Paul S., Dhanda S.K., Mahajan S., Xu X., Peters B., Sette A. The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design. Front. Immunol. 2017;8:278. doi: 10.3389/fimmu.2017.00278. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Folegatti P.M., Ewer K.J., Aley P.K., Angus B., Becker S., Belij-Rammerstorfer S., Bellamy D., Bibi S., Bittaye M., Clutterbuck E.A., et al. Safety and immunogenicity of the chadox1 ncov-19 vaccine against sars-cov-2: a preliminary report of a phase 1/2, single-blind, randomised controlled trial. Lancet. 2020;396(10249):467–478. doi: 10.1016/S0140-6736(20)31604-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Garcia-Beltran W., Denis K., St, Hoelzemer A., Lam E., Nitido A., Sheehan M., Berrios C., Ofoman O., Chang C., Hauser B., et al. mRNA-based covid-19 vaccine boosters induce neutralizing immunity against sars-cov-2 omicron variant. medrxiv. 2021 doi: 10.1016/j.cell.2021.12.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Garcia-Boronat M., Diez-Rivero C.M., Reinherz E.L., Reche P.A. Pvs: a web server for protein sequence variability analysis tuned to facilitate conserved epitope discovery. Nucleic Acids Res. 2008;36(suppl_2):W35–W41. doi: 10.1093/nar/gkn211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Gasteiger E., Hoogland C., Gattiker A., Wilkins M.R., Appel R.D., Bairoch A., et al. The Proteomics Protocols Handbook. Springer; 2005. Protein identification and analysis tools on the expasy server; pp. 571–607. [Google Scholar]
  47. Gralinski L.E., Menachery V.D., Morgan A.P., Totura A.L., Beall A., Kocher J., Plante J., Harrison-Shostak D.C., Schäfer A., Pardo-Manuel de Villena F., et al. Allelic variation in the toll-like receptor adaptor protein ticam2 contributes to sars-coronavirus pathogenesis in mice. G3: Genes Genomes Genet. 2017;7(6):1653–1663. doi: 10.1534/g3.117.041434. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Grote A., Hiller K., Scheer M., Münch R., Nörtemann B., Hempel D.C., Jahn D. Jcat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res. 2005;33(suppl_2):W526–W531. doi: 10.1093/nar/gki376. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Gruhler A., Früh K. Control of mhc class i traffic from the endoplasmic reticulum by cellular chaperones and viral anti-chaperones. Traffic. 2000;1(4):306–311. doi: 10.1034/j.1600-0854.2000.010403.x. [DOI] [PubMed] [Google Scholar]
  50. Gupta S., Kapoor P., Chaudhary K., Gautam A., Kumar R., Raghava G.P., Consortium O.S.D.D., et al. In silico approach for predicting toxicity of peptides and proteins. PloS One. 2013;8(9) doi: 10.1371/journal.pone.0073957. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Hamby S.E., Hirst J.D. Prediction of glycosylation sites using random forests. BMC Bioinformatics. 2008;9(1):1–13. doi: 10.1186/1471-2105-9-500. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Harmalkar A., Gray J.J. Advances to tackle backbone flexibility in protein docking. Curr. Opin. Struct. Biol. 2021;67:178–186. doi: 10.1016/j.sbi.2020.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Harvey W.T., Carabelli A.M., Jackson B., Gupta R.K., Thomson E.C., Harrison E.M., Ludden C., Reeve R., Rambaut A., Peacock S.J., et al. Sars-cov-2 variants, spike mutations and immune escape. Nat. Rev. Microbiol. 2021;19(7):409–424. doi: 10.1038/s41579-021-00573-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. He R., Yang X., Liu C., Chen X., Wang L., Xiao M., Ye J., Wu Y., Ye L. Efficient control of chronic lcmv infection by a cd4 t cell epitope-based heterologous prime-boost vaccination in a murine model. Cell. Mol. Immunol. 2018;15(9):815–826. doi: 10.1038/cmi.2017.3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Jardine J., Julien J.-P., Menis S., Ota T., Kalyuzhniy O., McGuire A., Sok D., Huang P.-S., MacPherson S., Jones M., et al. Rational hiv immunogen design to target specific germline b cell receptors. Science. 2013;340(6133):711–716. doi: 10.1126/science.1234150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Jensen K.K., Andreatta M., Marcatili P., Buus S., Greenbaum J.A., Yan Z., Sette A., Peters B., Nielsen M. Improved methods for predicting peptide binding affinity to mhc class ii molecules. Immunology. 2018;154(3):394–406. doi: 10.1111/imm.12889. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Jespersen M.C., Peters B., Nielsen M., Marcatili P. Bepipred-2.0: improving sequence-based b-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017;45(W1):W24–W29. doi: 10.1093/nar/gkx346. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Jiang P., Cai Y., Chen J., Ye X., Mao S., Zhu S., Xue X., Chen S., Zhang L. Evaluation of tandem chlamydia trachomatis momp multi-epitopes vaccine in balb/c mice model. Vaccine. 2017;35(23):3096–3103. doi: 10.1016/j.vaccine.2017.04.031. [DOI] [PubMed] [Google Scholar]
  59. Kadam A., Sasidharan S., Saudagar P. Computational design of a potential multi-epitope subunit vaccine using immunoinformatics to fight ebola virus. Infect., Genet. Evol. 2020;85 doi: 10.1016/j.meegid.2020.104464. [DOI] [PubMed] [Google Scholar]
  60. Kar T., Narsaria U., Basak S., Deb D., Castiglione F., Mueller D.M., Srivastava A.P. A candidate multi-epitope vaccine against sars-cov-2. Sci. Rep. 2020;10(1):1–24. doi: 10.1038/s41598-020-67749-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Kavoosi M., Creagh A.L., Kilburn D.G., Haynes C.A. Strategy for selecting and characterizing linker peptides for cbm9-tagged fusion proteins expressed in escherichia coli. Biotechnol. Bioeng. 2007;98(3):599–610. doi: 10.1002/bit.21396. [DOI] [PubMed] [Google Scholar]
  62. Khan A., Khan S., Saleem S., Nizam-Uddin N., Mohammad A., Khan T., Ahmad S., Arshad M., Ali S.S., Suleman M., et al. Immunogenomics guided design of immunomodulatory multi-epitope subunit vaccine against the sars-cov-2 new variants, and its validation through in silico cloning and immune simulation. Comput. Biol. Med. 2021;133 doi: 10.1016/j.compbiomed.2021.104420. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Lagunas-Rangel F.A., Chávez-Valencia V. High il-6/ifn-γ ratio could be associated with severe disease in covid-19 patients. J. Med. Virol. 2020 doi: 10.1002/jmv.25900. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Laughlin C., Schleif A., Heilman C.A. Addressing viral resistance through vaccines. Future Virol. 2015;10(8):1011–1022. doi: 10.2217/fvl.15.53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Lee G.R., Won J., Heo L., Seok C. Galaxyrefine2: simultaneous refinement of inaccurate local regions and overall protein structure. Nucleic Acids Res. 2019;47(W1):W451–W455. doi: 10.1093/nar/gkz288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Lee I.-J., Sun C.-P., Wu P.-Y., Lan Y.-H., Wang I.-H., Liu W.-C., Tseng S.-C., Tsung S.-I., Chou Y.-C., Kumari M., et al. BioRxiv; 2022. Omicron-Specific mRNA Vaccine Induced Potent Neutralizing Antibody Against Omicron But Not Other Sars-cov-2 Variants. [Google Scholar]
  67. Lehrer R.I., Lu W. α -defensins in human innate immunity. Immunol. Rev. 2012;245(1):84–112. doi: 10.1111/j.1600-065X.2011.01082.x. [DOI] [PubMed] [Google Scholar]
  68. Lehtinen M., Hibma M.H., Stellato G., Kuoppala T., Paavonen J. Human t helper cell epitopes overlap b cell and putative cytotoxic t cell epitopes in the e2 protein of human papillomavirus type 16. Biochem. Biophys. Res. Commun. 1995;209(2):541–546. doi: 10.1006/bbrc.1995.1535. [DOI] [PubMed] [Google Scholar]
  69. Lei Y., Shao J., Zhao F., Li Y., Lei C., Ma F., Chang H., Zhang Y. Artificially designed hepatitis b virus core particles composed of multiple epitopes of type a and o foot-and-mouth disease virus as a bivalent vaccine candidate. J. Med. Virol. 2019;91(12):2142–2152. doi: 10.1002/jmv.25554. [DOI] [PubMed] [Google Scholar]
  70. Lennerz V., Gross S., Gallerani E., Sessa C., Mach N., Boehm S., Hess D., VonBoehmer L., Knuth A., Ochsenbein A.F., et al. Immunologic response to the survivin-derived multi-epitope vaccine emd640744 in patients with advanced solid tumors. Cancer Immunol. Immunother. 2014;63(4):381–394. doi: 10.1007/s00262-013-1516-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Li W., Joshi M.D., Singhania S., Ramsey K.H., Murthy A.K. Peptide vaccine: progress and challenges. Vaccines. 2014;2(3):515–536. doi: 10.3390/vaccines2030515. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Lindorff-Larsen K., Piana S., Palmo K., Maragakis P., Klepeis J.L., Dror R.O., Shaw D.E. Improved side-chain torsion potentials for the amber ff99sb protein force field. Proteins: Struct. Funct. Bioinformatics. 2010;78(8):1950–1958. doi: 10.1002/prot.22711. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Ling Y.-M., Chen J.-Y., Guo L., Wang C.-Y., Tan W.-T., Wen Q., Zhang S.-D., Deng G.-H., Lin Y., Kwok H.F. , β -defensin 1 expression in hcv infected liver/liver cancer: an important role in protecting hcv progression and liver cancer development. Sci. Rep. 2017;7(1):1–14. doi: 10.1038/s41598-017-13332-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Luckheeram R.V., Zhou R., Verma A.D., Xia B. Cd4. t cells: differentiation and functions. Clin. Dev. Immunol. 2012 doi: 10.1155/2012/925135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Lundegaard C., Lamberth K., Harndahl M., Buus S., Lund O., Nielsen M. Netmhc-3.0: accurate web accessible predictions of human, mouse and monkey mhc class i affinities for peptides of length 8-11. Nucleic Acids Res. 2008;36(suppl_2):W509–W512. doi: 10.1093/nar/gkn202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Lundegaard C., Lund O., Nielsen M. Accurate approximation method for prediction of class i mhc affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Bioinformatics. 2008;24(11):1397–1398. doi: 10.1093/bioinformatics/btn128. [DOI] [PubMed] [Google Scholar]
  77. María R., Arturo C., Alicia J.-A., Paulina M., Gerardo A.-O. The impact of bioinformatics on vaccine design and development. Vaccines. 2017;2:3–6. [Google Scholar]
  78. Mauro V.P. Codon optimization in the production of recombinant biotherapeutics: potential risks and considerations. BioDrugs. 2018;32(1):69–81. doi: 10.1007/s40259-018-0261-x. [DOI] [PubMed] [Google Scholar]
  79. de Medeiros A.S., Zoppi A., Barbosa E.G., Oliveira J.I., Fernandes-Pedrosa M.F., Longhi M.R., daSilva-Júnior A.A. Supramolecular aggregates of oligosaccharides with co-solvents in ternary systems for the solubilizing approach of triamcinolone. Carbohydr. Polym. 2016;151:1040–1051. doi: 10.1016/j.carbpol.2016.06.044. [DOI] [PubMed] [Google Scholar]
  80. Messaoudi A., Belguith H., Hamida J.B. Homology modeling and virtual screening approaches to identify potent inhibitors of veb-1 β -lactamase. Theor. Biol. Med. Model. 2013;10(1):22. doi: 10.1186/1742-4682-10-22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Mittal A., Sasidharan S., Raj S., Balaji S., Saudagar P. Exploring the zika genome to design a potential multiepitope vaccine using an immunoinformatics approach. Int. J. Pept. Res. Ther. 2020:1–10. [Google Scholar]
  82. Mubarak A., Alturaiki W., Hemida M.G. Middle east respiratory syndrome coronavirus (mers-cov): infection, immunological response, and vaccine development. J. Immunol. Res. 2019 doi: 10.1155/2019/6491738. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Narula A., Pandey R.K., Khatoon N., Mishra A., Prajapati V.K. Excavating chikungunya genome to design b and t cell multi-epitope subunit vaccine using comprehensive immunoinformatics approach to control chikungunya infection. Infect., Genet. Evol. 2018;61:4–15. doi: 10.1016/j.meegid.2018.03.007. [DOI] [PubMed] [Google Scholar]
  84. Nielsen M., Lundegaard C., Worning P., Lauemøller S.L., Lamberth K., Buus S., Brunak S., Lund O. Reliable prediction of t-cell epitopes using neural networks with novel sequence representations. Protein Sci. 2003;12(5):1007–1017. doi: 10.1110/ps.0239403. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Nielsen M., Lundegaard C., Blicher T., Lamberth K., Harndahl M., Justesen S., Røder G., Peters B., Sette A., Lund O., et al. Netmhcpan, a method for quantitative predictions of peptide binding to any hla-a and-b locus protein of known sequence. PloS One. 2007;2(8) doi: 10.1371/journal.pone.0000796. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Nielsen P.H., Kragelund C., Seviour R.J., Nielsen J.L. Identity and ecophysiology of filamentous bacteria in activated sludge. FEMS Microbiol. Rev. 2009;33(6):969–998. doi: 10.1111/j.1574-6976.2009.00186.x. [DOI] [PubMed] [Google Scholar]
  87. Nouri H.R., Karkhah A., Varasteh A., Sankian M. Expression of a chimeric allergen with high rare codons content in codon bias-adjusted Escherichia coli: escherichia coli bl21 (de3)-codon plus ril as an efficient host. Curr. Microbiol. 2016;73(1):91–98. doi: 10.1007/s00284-016-1027-7. [DOI] [PubMed] [Google Scholar]
  88. de Oliveira Campos D.M., Fulco U.L., de Oliveira C.B.S., Oliveira J.I.N. Sars-cov-2 virus infection: targets and antiviral pharmacological strategies. J. Evid. Based Med. 2020 doi: 10.1111/jebm.12414. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. de Oliveira Campos D.M., daSilva M.K., Silva de Oliveira C.B., Fulco U.L., Nobre Oliveira J.I. Effectiveness of covid-19 vaccines against omicron variant. Immunotherapy. 2022;0 doi: 10.2217/imt-2022-0077. [DOI] [PubMed] [Google Scholar]
  90. Ong E., He Y., Yang Z. Epitope promiscuity and population coverage of mycobacterium tuberculosis protein antigens in current subunit vaccines under development. Infect. Genet. Evol. 2020 doi: 10.1016/j.meegid.2020.104186. [DOI] [PubMed] [Google Scholar]
  91. W.H. Organization, et al., Covid-19 weekly epidemiological update, edition 88, published 20 april 2022 (2022).
  92. Perales-Linares R., Navas-Martin S. Toll-like receptor 3 in viral pathogenesis: friend or foe? Immunology. 2013;140(2):153–167. doi: 10.1111/imm.12143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Peters B., Bulik S., Tampe R., Van Endert P.M., Holzhütter H.-G. Identifying mhc class i epitopes by predicting the tap transport efficiency of epitope precursors. J. Immunol. 2003;171(4):1741–1749. doi: 10.4049/jimmunol.171.4.1741. [DOI] [PubMed] [Google Scholar]
  94. Ponomarenko J.V., Bourne P.E. Antibody-protein interactions: benchmark datasets and prediction tools evaluation. BMC Struct. Biol. 2007;7(1):1–19. doi: 10.1186/1472-6807-7-64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Prathyusha A., Bhukya P.L., Bramhachari P.V. Dynamics of Immune Activation in Viral Diseases. Springer; 2020. Potentiality of toll-like receptors (tlrs) in viral infections; pp. 149–159. [Google Scholar]
  96. Rakib A., Sami S.A., Mimi N.J., Chowdhury M.M., Eva T.A., Nainu F., Paul A., Shahriar A., Tareq A.M., Emon N.U., et al. Immunoinformatics-guided design of an epitope-based vaccine against severe acute respiratory syndrome coronavirus 2 spike glycoprotein. Comput. Biol. Med. 2020;124 doi: 10.1016/j.compbiomed.2020.103967. [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Rapin N., Lund O., Bernaschi M., Castiglione F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PloS One. 2010;5(4) doi: 10.1371/journal.pone.0009862. [DOI] [PMC free article] [PubMed] [Google Scholar]
  98. Rappuoli R. Reverse vaccinology, a genome-based approach to vaccine development. Vaccine. 2001;19(17–19):2688–2691. doi: 10.1016/s0264-410x(00)00554-5. [DOI] [PubMed] [Google Scholar]
  99. Ren J., Wen L., Gao X., Jin C., Xue Y., Yao X. Css-palm 2.0: an updated software for palmitoylation sites prediction. Protein Eng. Des. Sel. 2008;21(11):639–644. doi: 10.1093/protein/gzn039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Reynisson B., Barra C., Kaabinejadian S., Hildebrand W.H., Peters B., Nielsen M. Improved prediction of mhc ii antigen presentation through integration and motif deconvolution of mass spectrometry mhc eluted ligand data. J. Proteome Res. 2020;19(6):2304–2315. doi: 10.1021/acs.jproteome.9b00874. [DOI] [PubMed] [Google Scholar]
  101. Rosano G.L., Ceccarelli E.A. Recombinant protein expression in Escherichia coli: advances and challenges. Front. Microbiol. 2014;5:172. doi: 10.3389/fmicb.2014.00172. [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Scarselli M., Giuliani M.M., Adu-Bobie J., Pizza M., Rappuoli R. The impact of genomics on vaccine design. Trends Biotechnol. 2005;23(2):84–91. doi: 10.1016/j.tibtech.2004.12.008. [DOI] [PubMed] [Google Scholar]
  103. Schneidman-Duhovny D., Inbar Y., Nussinov R., Wolfson H.J. Patchdock and symmdock: servers for rigid and symmetric docking. Nucleic Acids Res. 2005;33(suppl_2):W363–W367. doi: 10.1093/nar/gki481. [DOI] [PMC free article] [PubMed] [Google Scholar]
  104. Seder R.A., Darrah P.A., Roederer M. T-cell quality in memory and protection: implications for vaccine design. Nat. Rev. Immunol. 2008;8(4):247–258. doi: 10.1038/nri2274. [DOI] [PubMed] [Google Scholar]
  105. Senn H.M., Thiel W. Qm/mm methods for biomolecular systems. Angew. Chem. Int. Ed. 2009;48(7):1198–1229. doi: 10.1002/anie.200802019. [DOI] [PubMed] [Google Scholar]
  106. Shamriz S., Ofoghi H., Moazami N. Effect of linker length and residues on the structure and stability of a fusion protein with malaria vaccine application. Comput. Biol. Med. 2016;76:24–29. doi: 10.1016/j.compbiomed.2016.06.015. [DOI] [PubMed] [Google Scholar]
  107. Silva M.K., Gomes H.S., Silva O.L., Campanelli S.E., Campos D.M., Araújo J.M., Fernandes J.V., Fulco U.L., Oliveira J.I. Identification of promiscuous t cell epitopes on mayaro virus structural proteins using immunoinformatics, molecular modeling, and qm: Mm approaches. Infect., Genet. Evol. 2021;91 doi: 10.1016/j.meegid.2021.104826. [DOI] [PubMed] [Google Scholar]
  108. Smith K.M., Pottage L., Thomas E.R., Leishman A.J., Doig T.N., Xu D., Liew F.Y., Garside P. Th1 and th2 cd4. t cells provide help for b cell clonal expansion and antibody synthesis in a similar manner in vivo. J. Immunol. 2000;165(6):3136–3144. doi: 10.4049/jimmunol.165.6.3136. [DOI] [PubMed] [Google Scholar]
  109. Soleymani S., Tavassoli A., Housaindokht M.R. An overview of progress from empirical to rational design in modern vaccine development, with an emphasis on computational tools and immunoinformatics approaches. Comput. Biol. Med. 2022;140 doi: 10.1016/j.compbiomed.2021.105057. [DOI] [PubMed] [Google Scholar]
  110. Soria-Guerra R.E., Nieto-Gomez R., Govea-Alonso D.O., Rosales-Mendoza S. An overview of bioinformatics tools for epitope prediction: implications on vaccine development. J. Biomed. Inform. 2015;53:405–414. doi: 10.1016/j.jbi.2014.11.003. [DOI] [PubMed] [Google Scholar]
  111. Sousa S.F., Fernandes P.A., Ramos M.J. Protein-ligand docking: current status and future challenges. Proteins: Struct. Funct. Bioinformatics. 2006;65(1):15–26. doi: 10.1002/prot.21082. [DOI] [PubMed] [Google Scholar]
  112. Steentoft C., Vakhrushev S.Y., Joshi H.J., Kong Y., Vester-Christensen M.B., Schjoldager K.T.-B., Lavrsen K., Dabelsteen S., Pedersen N.B., Marcos-Silva L., et al. Precision mapping of the human o-galnac glycoproteome through simplecell technology. EMBO J. 2013;32(10):1478–1488. doi: 10.1038/emboj.2013.79. [DOI] [PMC free article] [PubMed] [Google Scholar]
  113. Stobart C.C., Moore M.L. Rna virus reverse genetics and vaccine design. Viruses. 2014;6(7):2531–2550. doi: 10.3390/v6072531. [DOI] [PMC free article] [PubMed] [Google Scholar]
  114. Tau G., Rothman P. Biologic functions of the ifn-γ receptors. Allergy. 1999;54(12):1233. doi: 10.1034/j.1398-9995.1999.00099.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  115. ul Qamar M.T., Alqahtani S.M., Alamri M.A., Chen L.-L. Structural basis of sars-cov-2 3clpro and anti-covid-19 drug discovery from medicinal plants. J. Pharm. Anal. 2020;10(4):313–319. doi: 10.1016/j.jpha.2020.03.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  116. UlQamar M.T., Saleem S., Ashfaq U.A., Bari A., Anwar F., Alqahtani S. Epitope-based peptide vaccine design and target site depiction against middle east respiratory syndrome coronavirus: an immune-informatics study. J. Transl. Med. 2019;17(1):362. doi: 10.1186/s12967-019-2116-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  117. Vianna J.F., Bezerra K.S., Oliveira J.I., Albuquerque E.L., Fulco U.L. Binding energies of the drugs capreomycin and streptomycin in complex with tuberculosis bacterial ribosome subunits. Phys. Chem. Chem. Phys. 2019;21(35):19192–19200. doi: 10.1039/c9cp03631h. [DOI] [PubMed] [Google Scholar]
  118. Vita R., Overton J.A., Greenbaum J.A., Ponomarenko J., Clark J.D., Cantrell J.R., Wheeler D.K., Gabbard J.L., Hix D., Sette A., et al. The immune epitope database (iedb) 3.0. Nucleic Acids Res. 2015;43(D1):D405–D412. doi: 10.1093/nar/gku938. [DOI] [PMC free article] [PubMed] [Google Scholar]
  119. Waterhouse A.M., Procter J.B., Martin D.M., Clamp M., Barton G.J. Jalview version 2–a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009;25(9):1189–1191. doi: 10.1093/bioinformatics/btp033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  120. WHO, Covid-19 vaccine tracker and landscape, Available at: 〈https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines〉.Accessed on 08 February 2022 (2022).
  121. Wiederstein M., Sippl M.J. Prosa-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007;35(suppl_2):W407–W410. doi: 10.1093/nar/gkm290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  122. Williams C.J., Headd J.J., Moriarty N.W., Prisant M.G., Videau L.L., Deis L.N., Verma V., Keedy D.A., Hintze B.J., Chen V.B., et al. Molprobity: more and better reference data for improved all-atom structure validation. Protein Sci. 2018;27(1):293–315. doi: 10.1002/pro.3330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  123. Yazdani Z., Rafiei A., Valadan R., Ashrafi H., Pasandi M., Kardan M. Designing a potent l1 protein-based hpv peptide vaccine: a bioinformatics approach. Comput. Biol. Chem. 2020;85 doi: 10.1016/j.compbiolchem.2020.107209. [DOI] [PubMed] [Google Scholar]
  124. Yin D., Li L., Song X., Li H., Wang J., Ju W., Qu X., Song D., Liu Y., Meng X., et al. A novel multi-epitope recombined protein for diagnosis of human brucellosis. BMC Infect. Dis. 2016;16(1):1–8. doi: 10.1186/s12879-016-1552-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  125. Zanatta G., Nunes G., Bezerra E.M., daCosta R.F., Martins A., Caetano E.W., Freire V.N., Gottfried C. Antipsychotic haloperidol binding to the human dopamine d3 receptor: beyond docking through qm/mm refinement toward the design of improved schizophrenia medicines. ACS Chem. Neurosci. 2014;5(10):1041–1054. doi: 10.1021/cn500111e. [DOI] [PubMed] [Google Scholar]
  126. Zang J., Zhang C., Yin Y., Xu S., Qiao W., Lavillette D., Wang H., Huang Z. BioRxiv; 2022. An mRNA Vaccine Candidate For The Sars-cov-2 Omicron Variant. [Google Scholar]
  127. Zhao Z., Ma X., Zhang R., Hu F., Zhang T., Liu Y., Han M.H., You F., Yang Y., Zheng W. A novel liposome-polymer hybrid nanoparticles delivering a multi-epitope self-replication dna vaccine and its preliminary immune evaluation in experimental animals. Nanomed.: Nanotechnol. Biol. Med. 2021;35 doi: 10.1016/j.nano.2020.102338. [DOI] [PMC free article] [PubMed] [Google Scholar]
  128. Zuin M., Zuliani G., Roncon L. High covid-19 transmission rates jeopardize global mass vaccination campaigns. Pathog. Glob. Health. 2021;115(4):213–214. doi: 10.1080/20477724.2021.1894042. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Computational Biology and Chemistry are provided here courtesy of Elsevier

RESOURCES