Immunoinformatics design of B and T-cell epitope-based SARS-CoV-2 peptide vaccination.

SARS-COV-2 is a virulent respiratory virus, first identified in China (Wuhan) at the end of 2019. Scientists and researchers are trying to find any possible solution to this deadly viral disease. Different drug source agents have been identified, including western medicine, natural products, and traditional Chinese medicine. They have the potential to counteract COVID-19. This virus immediately affects the liver and causes a decrease in oxygen levels. In this study, multiple vacciome approaches were employed for designing a multi-epitope subunit vaccine for battling against SARS-COV-2. Vaccine designing, immunogenicity, allergenic, and physico-chemical assessment were performed by using the vacciome approach. The vaccine design is likely to be antigenic and produce potent interactions with ACE2 and NSP3 receptors. The developed vaccine has also been given to in-silico cloning models and immune response predictions. A total number of 12 CTL and 12 HTL antigenic epitopes were predicted from three selected covid-19 virulent proteins (spike protein, nucleocapsid protein, and membrane proteins, respectively) based on C-terminal cleavage and MHC binding scores. These predicted epitopes were amalgamated by AYY and GPGPG linkers, and a ß-defensins adjuvant was inserted into the N-terminus of this vaccine. This analysis shows that the recommended vaccine can produce immune responses against SARS-COV-2. Designing and developing of the mentioned vaccine will require further experimental validation.

Critical review of conformational B-cell epitope prediction methods.

Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.

Developing a multiepitope vaccine for the prevention of SARS-CoV-2 and monkeypox virus co-infection: A reverse vaccinology analysis.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and monkeypox virus (MPXV) severely threaten human health; however, currently, no vaccine can prevent a co-infection with both viruses. METHODS: Five antigens were selected to predict dominant T and B cell epitopes screened for immunogenicity, antigenicity, toxicity, and sensitization. After screening, all antigens joined in the construction of a novel multiepitope vaccine. The physicochemical and immunological characteristics, and secondary and tertiary structures of the vaccine were predicted and analyzed using bio- and immunoinformatics. Finally, codon optimization and cloning in-silico were performed. RESULTS: A new multiepitope vaccine, named S7M8, was constructed based on four helper T lymphocyte (HTL) epitopes, six cytotoxic T lymphocyte (CTL) epitopes, five B cell epitopes, as well as Toll-like receptor (TLR) agonists. The antigenicity and immunogenicity scores of the S7M8 vaccine were 0.907374 and 0.6552, respectively. The S7M8 vaccine was comprised of 26.96% α-helices, the optimized Z-value of the tertiary structure was -5.92, and the favored area after majorization in the Ramachandran plot was 84.54%. Molecular docking showed that the S7M8 vaccine could tightly bind to TLR2 (-1100.6 kcal/mol) and TLR4 (-950.3 kcal/mol). In addition, the immune stimulation prediction indicated that the S7M8 vaccine could activate T and B lymphocytes to produce high levels of Th1 cytokines and antibodies. CONCLUSION: S7M8 is a promising biomarker with good antigenicity, immunogenicity, non-toxicity, and non-sensitization. The S7M8 vaccine can trigger significantly high levels of Th1 cytokines and antibodies and may be a potentially powerful tool in preventing SARS-CoV-2 and MPXV.

Identification of a novel linear B-cell epitope in porcine deltacoronavirus nucleocapsid protein.

Porcine deltacoronavirus (PDCoV) is an emerging swine enteropathogenic coronavirus that caused diarrhea and/or vomiting in neonatal piglets worldwide. Coronaviruses nucleocapsid (N) protein is the most conserved structural protein for viral replication and possesses good antigenicity. In this study, three monoclonal antibodies (mAbs), 3B4, 4D3, and 4E3 identified as subclass IgG2aκ were prepared using the lymphocytic hybridoma technology against PDCoV N protein. Furthermore, the B-cell epitope recognized by mAb 4D3 was mapped by dozens of overlapping truncated recombinant proteins based on the western blotting. The polypeptide 28QFRGNGVPLNSAIKPVE44 (EP-4D3) in the N-terminal of PDCoV N protein was identified as the minimal linear epitope for binding mAb 4D3. And the EP-4D3 epitope's amino acid sequence homology study revealed that PDCoV strains are substantially conserved, with the exception of the Alanine43 substitution Valine43 in the China lineage, the Early China lineage, and the Thailand, Vietnam, and Laos lineage. The epitope sequences shared high similarity (94.1%) with porcine coronavirus HKU15-155 (PorCoV HKU15), Asian leopard cats coronavirus (ALCCoV), sparrow coronavirus HKU17 (SpCoV HKU17), and sparrow deltacoronavirus. In contrast, the epitope sequences shared a very low homology (11.8 to 29.4%) with other porcine CoVs (PEDV, TGEV, PRCV, SADS-CoV, PHEV). Overall, the study will enrich the biological function of PDCoV N protein and provide foundational data for further development of diagnostic applications. KEY POINTS: • Three monoclonal antibodies against PDCoV N protein were prepared. • Discovery of a novel B-cell liner epitope (28QFRGNGVPLNSAIKPVE44) of PDCoV N protein. • The epitope EP-4D3 was conserved among PDCoV strains.

SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning.

One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1v, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the SARS-CoV-2 RBD domain. SEMA is available at https://github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.

Bioinformatics approach for the construction of multiple epitope vaccine against omicron variant of SARS-CoV-2.

The World Health Organization categorized SARS-CoV-2 as a variant of concern, having numerous mutations in spike protein, which have been found to evade the effect of antibodies stimulated by the COVID-19 vaccine. The susceptibility to omicron variant by immunization-induced antibodies are direly required for risk evaluation. To avoid the risk of arising viral illness, the construction of a specific vaccine that triggers the production of targeted antibodies to combat infection remains highly imperative. The aim of the present study is to develop a particular vaccine exploiting bioinformatics approaches which can target B- and T-cells epitopes. Through this approach, novel epitopes of the S protein-SARS-CoV-2 were predicted for the development of a multiple epitope vaccine. Multiple epitopes were selected on the basis of toxicity, immunogenicity and antigenicity, and vaccine subunit was constructed having potential immunogenic properties. The epitopes were linked with 3 types of linker EAAAK, AAY and GPGPG for vaccine construction. Subsequently, vaccine structure was docked with the receptor and cloned in a pET-28a (+) vector. The constructed vaccine was ligated in pET-28a (+) vector in E. coli using the SnapGene tool for the expression study and a good immune response was observed. Several computational tools were used to predict and analyze the vaccine constructed by using spike protein sequence of omicrons. The current study identified a Multi-Epitope Vaccine (MEV) as a significant vaccine candidate that could potentially help the global world to combat SARS-CoV-2 infections.

Bioinformatics-based SARS-CoV-2 epitopes design and the impact of spike protein mutants on epitope humoral immunities.

BACKGROUND: Epitope selection is the key to peptide vaccines development. Bioinformatics tools can efficiently improve the screening of antigenic epitopes and help to choose the right ones. OBJECTIVE: To predict, synthesize and testify peptide epitopes at spike protein, assess the effect of mutations on epitope humoral immunity, thus provide clues for the design and development of epitope peptide vaccines against SARS-CoV-2. METHODS: Bioinformatics servers and immunological tools were used to identify the helper T lymphocyte, cytotoxic T lymphocyte, and linear B lymphocyte epitopes on the S protein of SARS-CoV-2. Physicochemical properties of candidate epitopes were analyzed using IEDB, VaxiJen, and AllerTOP online software. Three candidate epitopes were synthesized and their antigenic responses were evaluated by binding antibody detection. RESULTS: A total of 20 antigenic, non-toxic and non-allergenic candidate epitopes were identified from 1502 epitopes, including 6 helper T-cell epitopes, 13 cytotoxic T-cell epitopes, and 1 linear B cell epitope. After immunization with antigen containing candidate epitopes S206-221, S403-425, and S1157-1170 in rabbits, the binding titers of serum antibody to the corresponding peptide, S protein, receptor-binding domain protein were (415044, 2582, 209.3), (852819, 45238, 457767) and (357897, 10528, 13.79), respectively. The binding titers to Omicron S protein were 642, 12,878 and 7750, respectively, showing that N211L, DEL212 and K417N mutations cause the reduction of the antibody binding activity. CONCLUSIONS: Bioinformatic methods are effective in peptide epitopes design. Certain mutations of the Omicron would lead to the loss of antibody affinity to Omicron S protein.

Identification of virus-specific B-cell epitopes by convalescent plasma from COVID-19 patients.

Identification of immunologic epitopes against SARS-CoV-2 is crucial for the discovery of diagnostic, therapeutic, and preventive targets. In this study, we used a pan-coronavirus peptide microarray to screen for potential B-cell epitopes and validated the results with peptide-based ELISA. Specifically, we identified three linear B-cell epitopes on the SARS-CoV-2 proteome, which were recognized by convalescent plasma from COVID-19 patients. Interestingly, two epitopes (S 809-823 and R1ab 909-923) strongly reacted to convalescent plasma collected at the early phase (< 90 days) of COVID-19 symptom onset, whereas one epitope (M 5-19) reacted to convalescent plasma collected > 90 days after COVID-19 symptom onset. Neutralization assays using antibody depletion with the identified spike (S) peptides revealed that three S epitopes (S 557-571, S 789-803, and S 809-823) elicited neutralizing antibodies in COVID-19 patients. However, the levels of virus-specific antibody targeting S 789-803 only positively correlated with the neutralizing rates at the early phase (<60 days) after disease onset, and the antibody titers diminished quickly with no correlation to the neutralizing activity beyond two months after recovery from COVID-19. Importantly, stimulation of peripheral blood mononuclear cells from COVID-19-recovered patients with these SARS-CoV-2 S peptides resulted in poor virus-specific B cell activation, proliferation, differentiation into memory B cells, and production of immunoglobulin G (IgG) antibodies, despite the B-cells being functionally competent as demonstrated by their response to non-specific stimulation. Taken together, these findings indicate that these newly identified SARS-CoV-2-specific B-cell epitopes can elicit neutralizing antibodies, with titers and/or neutralizing activities declining significantly within 2-3 months in the convalescent plasma of COVID-19 patients.

Bioinformatics pipeline unveils genetic variability to synthetic vaccine design for Indian SARS-CoV-2 genomes.

In the worrisome scenarios of various waves of SARS-CoV-2 pandemic, a comprehensive bioinformatics pipeline is essential to analyse the virus genomes in order to understand its evolution, thereby identifying mutations as signature SNPs, conserved regions and subsequently to design epitope based synthetic vaccine. We have thus performed multiple sequence alignment of 4996 Indian SARS-CoV-2 genomes as a case study using MAFFT followed by phylogenetic analysis using Nextstrain to identify virus clades. Furthermore, based on the entropy of each genomic coordinate of the aligned sequences, conserved regions are identified. After refinement of the conserved regions, based on its length, one conserved region is identified for which the primers and probes are reported for virus detection. The refined conserved regions are also used to identify T-cell and B-cell epitopes along with their immunogenic and antigenic scores. Such scores are used for selecting the most immunogenic and antigenic epitopes. By executing this pipeline, 40 unique signature SNPs are identified resulting in 23 non-synonymous signature SNPs which provide 28 amino acid changes in protein. On the other hand, 12 conserved regions are selected based on refinement criteria out of which one is selected as the potential target for virus detection. Additionally, 22 MHC-I and 21 MHC-II restricted T-cell epitopes with 10 unique HLA alleles each and 17 B-cell epitopes are obtained for 12 conserved regions. All the results are validated both quantitatively and qualitatively which show that from genetic variability to synthetic vaccine design, the proposed pipeline can be used effectively to combat SARS-CoV-2.

A novel vaccine based on SARS-CoV-2 CD4+ and CD8+ T cell conserved epitopes from variants Alpha to Omicron.

COVID-19 caused, as of September, 1rst, 2022, 599,825,400 confirmed cases, including 6,469,458 deaths. Currently used vaccines reduced severity and mortality but not virus transmission or reinfection by different strains. They are based on the Spike protein of the Wuhan reference virus, which although highly antigenic suffered many mutations in SARS-CoV-2 variants, escaping vaccine-generated immune responses. Multiepitope vaccines based on 100% conserved epitopes of multiple proteins of all SARS-CoV-2 variants, rather than a single highly mutating antigen, could offer more long-lasting protection. In this study, a multiepitope multivariant vaccine was designed using immunoinformatics and in silico approaches. It is composed of highly promiscuous and strong HLA binding CD4+ and CD8+ T cell epitopes of the S, M, N, E, ORF1ab, ORF 6 and ORF8 proteins. Based on the analysis of one genome per WHO clade, the epitopes were 100% conserved among the Wuhan-Hu1, Alpha, Beta, Gamma, Delta, Omicron, Mµ, Zeta, Lambda and R1 variants. An extended epitope-conservancy analysis performed using GISAID metadata of 3,630,666 SARS-CoV-2 genomes of these variants and the additional genomes of the Epsilon, Lota, Theta, Eta, Kappa and GH490 R clades, confirmed the high conservancy of the epitopes. All but one of the CD4 peptides showed a level of conservation greater than 97% among all genomes. All but one of the CD8 epitopes showed a level of conservation greater than 96% among all genomes, with the vast majority greater than 99%. A multiepitope and multivariant recombinant vaccine was designed and it was stable, mildly hydrophobic and non-toxic. The vaccine has good molecular docking with TLR4 and promoted, without adjuvant, strong B and Th1 memory immune responses and secretion of high levels of IL-2, IFN-γ, lower levels of IL-12, TGF-ß and IL-10, and no IL-6. Experimental in vivo studies should validate the vaccine's further use as preventive tool with cross-protective properties.

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

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.

Novel multi epitope-based vaccine against monkeypox virus: vaccinomic approach.

While mankind is still dealing with the COVID-19 pandemic, a case of monkeypox virus (MPXV) has been reported to the WHO on May 7, 2022. Monkeypox is a viral zoonotic disease that has been a public health threat, particularly in Africa. However, it has recently expanded to other parts of the world, so it may soon become a global issue. Thus, the current work was planned and then designed a multi-epitope vaccine against MPXV utilizing the cell surface-binding protein as a target in order to develop a novel and safe vaccine that can evoke the desirable immunological response. The proposed MHC-I, MHC-II, and B-cell epitopes were selected to design multi-epitope vaccine constructs linked with suitable linkers in combination with different adjuvants to enhance the immune responses for the vaccine constructs. The proposed vaccine was composed of 275 amino acids and was shown to be antigenic in Vaxijen server (0.5311) and non-allergenic in AllerTop server. The 3D structure of the designed vaccine was predicted, refined and validated by various in silico tools to assess the stability of the vaccine. Moreover, the solubility of the vaccine construct was found greater than the average solubility provided by protein-Sol server which indicating the solubility of the vaccine construct. Additionally, the most promising epitopes bound to MHC I and MHC II alleles were found having good binding affinities with low energies ranging between - 7.0 and - 8.6 kcal/mol. According to the immunological simulation research, the vaccine was found to elicit a particular immune reaction against the monkeypox virus. Finally, the molecular dynamic study shows that the designed vaccine is stable with minimum RMSF against MHC I allele. We conclude from our research that the cell surface-binding protein is one of the primary proteins involved in MPXV pathogenesis. As a result, our study will aid in the development of appropriate therapeutics and prompt the development of future vaccines against MPXV.

Modular nanoarray vaccine for SARS-CoV-2.

The current vaccine development strategies for the COVID-19 pandemic utilize whole inactive or attenuated viruses, virus-like particles, recombinant proteins, and antigen-coding DNA and mRNA with various delivery strategies. While highly effective, these vaccine development strategies are time-consuming and often do not provide reliable protection for immunocompromised individuals, young children, and pregnant women. Here, we propose a novel modular vaccine platform to address these shortcomings using chemically synthesized peptides identified based on the validated bioinformatic data about the target. The vaccine is based on the rational design of an immunogen containing two defined B-cell epitopes from the spike glycoprotein of SARS-CoV-2 and the universal T-helper epitope PADRE. The epitopes were conjugated to short DNA probes and combined with a complementary scaffold strand, resulting in sequence-specific self-assembly. The immunogens were then formulated by conjugation to gold nanoparticles by three methods or by co-crystallization with epsilon inulin. BALB/C mice were immunized with each formulation, and the IgG immune responses and virus neutralizing titers were compared. The results demonstrate that this assembly is immunogenic and generates neutralizing antibodies against wildtype SARS-CoV-2 and the Delta variant.

Using Self-Assembling ADDomer Platform to Display B and T Epitopes of Type O Foot-and-Mouth Disease Virus.

Foot-and-mouth disease virus (FMDV) is a highly contagious and devastating virus that infects cloven-hoofed livestock and various wildlife species. Vaccination is the best measure to prevent FMD. ADDomer, as a kind of non-infectious adenovirus-inspired nanoparticle, has the advantage of high thermal stability. In this study, two dominant B-cell antigen epitopes (residues 129~160 and 200~213) and a dominant T-cell antigen epitope (residues 16~44) of type O FMDV were inserted into the ADDomer variable loop (VL) and arginine-glycine-aspartic acid (RGD) loop. The 3D structure of the recombinant protein (ADDomer-RBT) was simulated by homology modeling. First, the recombinant proteins were expressed by the baculovirus expression system and detected by western blot and Q Exactive mass spectrometry. Then the formation of VLPs was observed under a transmission electron micrograph (TEM). Finally, we evaluated the immunogenicity of chimeric VLPs with a murine model. Bioinformatic software analysis preliminarily corroborated that the chosen epitopes were successfully exposed on the surface of ADDomer VLPs. The TEM assay demonstrated the structural integrity of the VLPs. After immunizing, it was found that FMDV-specific antibodies can be produced in mice to induce humoral and cellular immune responses. To sum up, the ADDomer platform can be used as an effective antigen carrier to deliver antigen epitopes. This study presents one of the candidate vaccines to prevent and control FMDV.

In silico designing of a novel epitope-based candidate vaccine against Streptococcus pneumoniae with introduction of a new domain of PepO as adjuvant.

BACKGROUND: Streptococcus pneumoniae is the leading reason for invasive diseases including pneumonia and meningitis, and also secondary infections following viral respiratory diseases such as flu and COVID-19. Currently, serotype-dependent vaccines, which have several insufficiency and limitations, are the only way to prevent pneumococcal infections. Hence, it is plain to need an alternative effective strategy for prevention of this organism. Protein-based vaccine involving conserved pneumococcal protein antigens with different roles in virulence could provide an eligible alternative to existing vaccines. METHODS: In this study, PspC, PhtD and PsaA antigens from pneumococcus were taken to account to predict B-cell and helper T-cell epitopes, and epitope-rich regions were chosen to build the construct. To enhance the immunogenicity of the epitope-based vaccine, a truncated N-terminal fragment of pneumococcal endopeptidase O (PepO) was used as a potential TLR2/4 agonist which was identified by molecular docking studies. The ultimate construct was consisted of the chosen epitope-rich regions, along with the adjuvant role (truncated N-PepO) and suitable linkers. RESULTS: The epitope-based vaccine was assessed as regards physicochemical properties, allergenicity, antigenicity, and toxicity. The 3D structure of the engineered construct was modeled, refined, and validated. Molecular docking and simulation of molecular dynamics (MD) indicated the proper and stable interactions between the vaccine and TLR2/4 throughout the simulation periods. CONCLUSIONS: For the first time this work presents a novel vaccine consisting of epitopes of PspC, PhtD, and PsaA antigens which is adjuvanted with a new truncated domain of PepO. The computational outcomes revealed that the suggested vaccine could be deemed an efficient therapeutic vaccine for S. pneumoniae; nevertheless, in vitro and in vivo examinations should be performed to prove the potency of the candidate vaccine.

Molecular modelling on multiepitope-based vaccine against SARS-CoV-2 using immunoinformatics, molecular docking, and molecular dynamics simulation.

The pandemic of COVID-19 caused by SARS-CoV-2 has made a worldwide health emergency. Despite the fact that current vaccines are readily available, several SARSCoV-2 variants affecting the existing vaccine are to be less effective due to the mutations in the structural proteins. Furthermore, the appearance of the new variants cannot be easily predicted in the future. Therefore, the attempts to construct new vaccines or to modify the current vaccines are still pivotal works for preventing the spread of the virus. In the present investigation, the computational analysis through immunoinformatics, molecular docking, and molecular dynamics (MD) simulation is employed to construct an effective vaccine against SARS-CoV2. The structural proteins of SARS-CoV2 are utilized to create a multiepitope-based vaccine (MEV). According to our findings presented by systematic procedures in the current investigation, the MEV construct may be able to trigger a strong immunological response against the virus. Therefore, the designed MEV could be a potential vaccine candidate against SARS-CoV-2, and also it is expected to be effective for other variants.

Predicting Epitope Candidates for SARS-CoV-2.

Epitopes are short amino acid sequences that define the antigen signature to which an antibody or T cell receptor binds. In light of the current pandemic, epitope analysis and prediction are paramount to improving serological testing and developing vaccines. In this paper, known epitope sequences from SARS-CoV, SARS-CoV-2, and other Coronaviridae were leveraged to identify additional antigen regions in 62K SARS-CoV-2 genomes. Additionally, we present epitope distribution across SARS-CoV-2 genomes, locate the most commonly found epitopes, and discuss where epitopes are located on proteins and how epitopes can be grouped into classes. The mutation density of different protein regions is presented using a big data approach. It was observed that there are 112 B cell and 279 T cell conserved epitopes between SARS-CoV-2 and SARS-CoV, with more diverse sequences found in Nucleoprotein and Spike glycoprotein.

Insight into the first multi-epitope-based peptide subunit vaccine against avian influenza A virus (H5N6): An immunoinformatics approach.

The rampant spread of highly pathogenic avian influenza A (H5N6) virus has drawn additional concerns along with ongoing Covid-19 pandemic. Due to its migration-related diffusion, the situation is deteriorating. Without an existing effective therapy and vaccines, it will be baffling to take control measures. In this regard, we propose a revers vaccinology approach for prediction and design of a multi-epitope peptide based vaccine. The induction of humoral and cell-mediated immunity seems to be the paramount concern for a peptide vaccine candidate; thus, antigenic B and T cell epitopes were screened from the surface, membrane and envelope proteins of the avian influenza A (H5N6) virus, and passed through several immunological filters to determine the best possible one. Following that, the selected antigenic with immunogenic epitopes and adjuvant were linked to finalize the multi-epitope-based peptide vaccine by appropriate linkers. For the prediction of an effective binding, molecular docking was carried out between the vaccine and immunological receptors (TLR8). Strong binding affinity and good docking scores clarified the stringency of the vaccines. Furthermore, molecular dynamics simulation was performed within the highest binding affinity complex to observe the stability, and minimize the designed vaccine's high mobility region to order to increase its stability. Then, Codon optimization and other physicochemical properties were performed to reveal that the vaccine would be suitable for a higher expression at cloning level and satisfactory thermostability condition. In conclusion, predicting the overall in silico assessment, we anticipated that our designed vaccine would be a plausible prevention against avian influenza A (H5N6) virus.

Immunoinformatic approach for the construction of multi-epitopes vaccine against omicron COVID-19 variant.

The newly discovered SARS-CoV-2 Omicron variant B.1.1.529 is a Variant of Concern (VOC) announced by the World Health Organization (WHO). It's becoming increasingly difficult to keep these variants from spreading over the planet. The fifth wave has begun in several countries because of Omicron variant, and it is posing a threat to human civilization. As a result, we need effective vaccination that can tackle Omicron SARS-CoV-2 variants that are bound to emerge. Therefore, the current study is an initiative to design a peptide-based chimeric vaccine that may potentially battle SARS-CoV-2 Omicron variant. As a result, the most relevant epitopes present in the mutagenic areas of Omicron spike protein were identified using a set of computational tools and immunoinformatic techniques to uncover common MHC-1, MHC-II, and B cell epitopes that may have the ability to influence the host immune mechanism. A final of three epitopes from CD8+ T-cell, CD4+ T-cell epitopes, and B-cell were shortlisted from spike protein, and that are highly antigenic, IFN-γ inducer, as well as overlapping for the construction of twelve vaccine models. As a result, the antigenic epitopes were coupled with a flexible and stable peptide linker, and the adjuvant was added at the N-terminal end to create a unique vaccine candidate. The structure of a 3D vaccine candidate was refined, and its quality was assessed by using web servers. However, the applied immunoinformatic study along with the molecular docking and simulation of 12 modeled vaccines constructs against six distinct HLAs, and TLRs (TLR2, and TLR4) complexes revealed that the V1 construct was non-allergenic, non-toxic, highly immunogenic, antigenic, and most stable. The vaccine candidate's stability was confirmed by molecular dynamics investigations. Finally, we studied the expression of the suggested vaccination using codon optimization and in-silico cloning. The current study proposed V1 Multi-Epitope Vaccine (MEV) as a significant vaccine candidate that may help the scientific community to treat SARS-CoV-2 infections.

Considering epitopes conservity in targeting SARS-CoV-2 mutations in variants: a novel immunoinformatics approach to vaccine design.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has gained mutations at an alarming rate in the past years. Developing mutations can increase the virus's pathogenicity and virulence; reduce the efficacy of vaccines, antibodies neutralization, and even challenge adaptive immunity. So, it is essential to identify conserved epitopes (with fewer mutations) in different variants with appropriate antigenicity to target the variants by an appropriate vaccine design. Yet as, 3369 SARS-CoV-2 genomes were collected from global initiative on sharing avian flu data. Then, mutations in the immunodominant regions (IDRs), immune epitope database (IEDB) epitopes, and also predicted epitopes were calculated. In the following, epitopes conservity score against the total number of events (mutations) and the number of mutated sites in each epitope was weighted by Shannon entropy and then calculated by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Based on the TOPSIS conservity score and antigenicity score, the epitopes were plotted. The result demonstrates that almost all epitopes and IDRs with various lengths have gained different numbers of mutations in dissimilar sites. Herein, our two-step calculation for conservity recommends only 8 IDRs, 14 IEDB epitopes, and 10 predicted epitopes among all epitopes. The selected ones have higher conservity and higher immunogenicity. This method is an open-source multi-criteria decision-making platform, which provides a scientific approach to selecting epitopes with appropriate conservity and immunogenicity; against ever-changing viruses.