Epitope based vaccine prediction for SARS-COV-2 by deploying immuno-informatics approach

Novel SARS-COV2 poly epitope phage-based candidate vaccine and its immunogenicity

Background and purpose

The global emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has prompted widespread concern. Bacteriophages have recently gained attention as a cost-effective and stable alternative for vaccine development due to their adjuvant properties. This study aimed to design and validate a poly epitope composed of viral proteins.

Experimental approach

SARS-CoV-2 proteins (spike, nucleocapsid, membrane, envelope, papain-like protease, and RNA-dependent RNA polymerase) were selected for analysis. Immunoinformatic methods were employed to predict B and T cell epitopes, assessing their antigenicity, allergenicity, and toxicity. Epitopes meeting criteria for high antigenicity, non-allergenicity, and non-toxicity were linked to form poly epitopes. These sequences were synthesized and cloned into pHEN4 plasmids to generate Poly1 and Poly2 phagemid vectors. Recombinant Poly1 and Poly2 phages were produced by transforming M13ΔIII plasmids and phagemid vectors into E. coli TG1. Female Balb/c mice were immunized with a cocktail of Poly1 and Poly2 phages, and their serum was collected for ELISA testing. Interferon-gamma (IFN-γ) testing was performed on spleen-derived lymphocytes to evaluate immune system activation.

Findings/Results

Recombinant Poly1 and Poly2 phages were produced, and their titer was determined as 1013 PFU/mL. Efficient humoral immune responses and cellular immunity activation in mice were achieved following phage administration.

Conclusion and implication

Poly epitopes displayed on phages exhibit adjuvant properties, enhancing humoral and cellular immunity in mice. This suggests that phages could serve as adjuvants to bolster immunity against SARS-Cov-2. Recombinant phages could be applied as effective candidates for injectable and oral vaccine development strategies.

Insights into structural vaccinology harnessed for universal coronavirus vaccine development

Structural vaccinology is pivotal in expediting vaccine design through high-throughput screening of immunogenic antigens. Leveraging the structural and functional characteristics of antigens and immune cell receptors, this approach employs protein structural comparison to identify conserved patterns in key pathogenic components. Molecular modeling techniques, including homology modeling and molecular docking, analyze specific three-dimensional (3D) structures and protein interactions and offer valuable insights into the 3D interactions and binding affinity between vaccine candidates and target proteins. In this review, we delve into the utilization of various immunoinformatics and molecular modeling tools to streamline the development of broad-protective vaccines against coronavirus disease 2019 variants. Structural vaccinology significantly enhances our understanding of molecular interactions between hosts and pathogens. By accelerating the pace of developing effective and targeted vaccines, particularly against the rapidly mutating severe acute respiratory syndrome coronavirus 2 and other prevalent infectious diseases, this approach stands at the forefront of advancing immunization strategies. The combination of computational techniques and structural insights not only facilitates the identification of potential vaccine candidates but also contributes to the rational design of vaccines, fostering a more efficient and targeted approach to combatting infectious diseases.

T cell epitope based vaccine design while targeting outer capsid proteins of rotavirus strains infecting neonates: an immunoinformatics approach

Gastrointestinal diarrhea is majorly caused by the rotavirus (RV) in the children who generally are under the age group of 5 years. WHO estimates that ∼95% of the children contract RV infection, by this age. The disease is highly contagious; notably in many cases, it is proven fatal with high mortality rates especially in the developing countries. In India alone, an estimated 145,000 yearly deaths occurs due to RV related gastrointestinal diarrhea. WHO pre-qualified vaccines that are available for RV are all live attenuated vaccines with modest efficacy range between 40 and 60%. Further, the risk of intussusceptions has been reported in some children on RV vaccination. Thus, in a quest to develop alternative candidate to overcome challenges associated with these oral vaccines, we chose immunoinformatics approach to design a multi-epitope vaccine (MEV) while targeting the outer capsid viral proteinsVP4 and VP7 of the neonatal strains of rotavirus. Interestingly, ten epitopes, that is, six CD8+T-cells and four CD4+T-cell epitopes were identified which were predicted to be antigenic, non-allergic, non-toxic and stable. These epitopes were then linked to adjuvants, linkers, and PADRE sequences to create a multi-epitope vaccine for RV. The in silico designed RV-MEV and human TLR5 complex displayed stable interactions during molecular dynamics simulations. Further, the immune simulation studies of RV-MEV corroborated that the vaccine candidate emerges as a promising immunogen. Future investigations while performing in vitro and in vivo analyses with designed RV-MEV construct are highly desirable to warrant the potential of this vaccine candidate in protective immunity against different strains of RVs infecting neonates.Communicated by Ramaswamy H. Sarma.

Plausible Influence of HLA Class I and Class II Diversity on SARS-CoV-2 Vulnerability

Severe acute respiratory syndrome CoV-2 (SARS-CoV-2) caused the global coronavirus disease 2019 (COVID-19) pandemic, which adversely affected almost all aspects of human life and resulted in the loss of millions of lives, while affecting nearly 0.67 billion people worldwide. SARS-CoV-2 still poses a challenge to the healthcare system as there are more than 200,000 active cases of COVID-19 around the globe. Epidemiological data suggests that the magnitude of morbidity and mortality due to COVID-19 was low in a few geographical regions and was unpredictably higher in a few regions. The genetic diversity of different geographical regions might explain the sporadic prevalence of the disease. In this context, human leukocyte antigens (HLA) represent the most polymorphic gene-dense region of the human genome and serve as an excellent mini-genome model for evaluating population genetic diversity in the context of susceptibility and progression of various diseases. In this review, we highlight the plausible influence of HLA in susceptibility, severity, immune response, and designing of epitope-based vaccines for COVID-19. Further, there is a need for extensive investigations for illustration and clarification of the functional impact of HLA class I and II alleles in the pathogenesis and progression of SARS-CoV-2.

Immunodominant conserved moieties on spike protein of SARS-CoV-2 renders virulence factor for the design of epitope-based peptide vaccines

The outbreak of novel SARS-CoV-2 virion has wreaked havoc with a high prevalence of respiratory illness and high transmission due to a vague understanding of the viral antigenicity, augmenting the dire challenge to public health globally. This viral member necessitates the expansion of diagnostic and therapeutic tools to track its transmission and confront it through vaccine development. Therefore, prophylactic strategies are mandatory. Virulent spike proteins can be the most desirable candidate for the computational design of vaccines targeting SARS-CoV-2, followed by the meteoric development of immune epitopes. Spike protein was characterized using existing bioinformatics tools with a unique roadmap related to the immunological profile of SARS-CoV-2 to predict immunogenic virulence epitopes based on antigenicity, allergenicity, toxicity, immunogenicity, and population coverage. Applying in silico approaches, a set of twenty-four B lymphocyte-based epitopes and forty-six T lymphocyte-based epitopes were selected. The predicted epitopes were evaluated for their intrinsic properties. The physico-chemical characterization of epitopes qualifies them for further in vitro and in vivo analysis and pre-requisite vaccine development. This study presents a set of screened epitopes that bind to HLA-specific allelic proteins and can be employed for designing a peptide vaccine construct against SARS-CoV-2 that will confer vaccine-induced protective immunity due to its structural stability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-023-00852-9.

Immunoinformatics study to explore dengue (DENV-1) proteome to design multi-epitope vaccine construct by using CD4+ epitopes

BACKGROUND

Immunoinformatics is an emerging interdisciplinary field which integrates immunology, bioinformatics, and computational biology to study the immune system. In this study, we apply immunoinformatics approaches to explore the dengue proteome in order to design a multi-epitope vaccine construct.

METHODS

We used existing databases and algorithms to predict potential epitopes on dengue proteins and used a bioinformatics approach to identify the most promising epitopes. We then used molecular modelling to develop a multi-epitope construct which could be used as a potential vaccine. The results of this study demonstrate that immunoinformatics is a powerful tool for exploring and designing potential vaccines for infectious diseases like dengue.

RESULTS

Here, we found four CD4+ epitopes NLKYSVIVTVHTGDQ, ANPIVTDKEKPVNIE, LDPVVYDAKFEKQL, and VGAIALDFKPGTSGS that were used to design vaccine construct. The vaccine construct docked with TLR5. RMSD values suggest that docked complex of TLR5 and vaccine construct have putative stable interaction to induce immunogenic effects on host.

CONCLUSIONS

Furthermore, our study provides a proof of concept for the use of immunoinformatics approaches in DENV vaccine design. This vaccine can be effective in treating patients infected with DENV virus.

MERS virus spike protein HTL-epitopes selection and multi-epitope vaccine design using computational biology

MERS-CoV, a zoonotic virus, poses a serious threat to public health globally. Thus, it is imperative to develop an effective vaccination strategy for protection against MERS-CoV. Immunoinformatics and computational biology tools provide a faster and more cost-effective strategy to design potential vaccine candidates. In this work, the spike proteins from different strains of MERS-CoV were selected to predict HTL-epitopes that show affinity for T-helper MHC-class II HTL allelic determinant (HLA-DRB1:0101). The antigenicity and conservation of these epitopes among the selected spike protein variants in different MERS-CoV strains were analyzed. The analysis identified five epitopes with high antigenicity: QSIFYRLNGVGITQQ, DTIKYYSIIPHSIRS, PEPITSLNTKYVAPQ, INGRLTTLNAFVAQQ and GDMYVYSAGHATGTT. Then, a multi-epitope vaccine candidate was designed using linkers and adjuvant molecules. Finally, the vaccine construct was subjected to molecular docking with TLR5 (Toll-like receptor-5). The proposed vaccine construct had strong binding energy of -32.3 kcal/mol when interacting with TLR5.Molecular dynamics simulation analysis showed that the complex of the vaccine construct and TLR5 is stable. Analysis using in silico immune simulation also showed that the prospective multi-epitope vaccine design had the potential to elicit a response within 70 days, with the immune system producing cytokines and immunoglobulins. Finally, codon adaptation and in silico cloning analysis showed that the candidate vaccine could be expressed in the Escherichia coli K12 strain. Here we also designed support vaccine construct MEV-2 by using B-cell and CD8+ CTL epitopes to generate the complete immunogenic effect. This study opens new avenues for the extension of research on MERS vaccine development.Communicated by Ramaswamy H. Sarma.

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

HLA-I and HLA-II Peptidomes of SARS-CoV-2: A Review

The adaptive (T-cell-mediated) immune response is a key player in determining the clinical outcome, in addition to neutralizing antibodies, after SARS-CoV-2 infection, as well as supporting the efficacy of vaccines. T cells recognize viral-derived peptides bound to major histocompatibility complexes (MHCs) so that they initiate cell-mediated immunity against SARS-CoV-2 infection or can support developing a high-affinity antibody response. SARS-CoV-2-derived peptides bound to MHCs are characterized via bioinformatics or mass spectrometry on the whole proteome scale, named immunopeptidomics. They can identify potential vaccine targets or therapeutic approaches for SARS-CoV-2 or else may reveal the heterogeneity of clinical outcomes. SARS-CoV-2 epitopes that are naturally processed and presented on the human leukocyte antigen class I (HLA-I) and class II (HLA-II) were identified for immunopeptidomics. Most of the identified SARS-CoV-2 epitopes were canonical and out-of-frame peptides derived from spike and nucleocapsid proteins, followed by membrane proteins, whereby many of which are not caught by existing vaccines and could elicit effective responses of T cells in vivo. This review addresses the detection of SARS-CoV-2 viral epitopes on HLA-I and HLA-II using bioinformatics prediction and mass spectrometry (HLA peptidomics). Profiling the HLA-I and HLA-II peptidomes of SARS-CoV-2 is also detailed.

Differential haplotype expression in class I MHC genes during SARS-CoV-2 infection of human lung cell lines

Introduction

Cell entry of SARS-CoV-2 causes genome-wide disruption of the transcriptional profiles of genes and biological pathways involved in the pathogenesis of COVID-19. Expression allelic imbalance is characterized by a deviation from the Mendelian expected 1:1 expression ratio and is an important source of allele-specific heterogeneity. Expression allelic imbalance can be measured by allele-specific expression analysis (ASE) across heterozygous informative expressed single nucleotide variants (eSNVs). ASE reflects many regulatory biological phenomena that can be assessed by combining genome and transcriptome information. ASE contributes to the interindividual variability associated with the disease. We aim to estimate the transcriptome-wide impact of SARS-CoV-2 infection by analyzing eSNVs.

Methods

We compared ASE profiles in the human lung cell lines Calu-3, A459, and H522 before and after infection with SARS-CoV-2 using RNA-Seq experiments.

Results

We identified 34 differential ASE (DASE) sites in 13 genes (HLA-A, HLA-B, HLA-C, BRD2, EHD2, GFM2, GSPT1, HAVCR1, MAT2A, NQO2, SUPT6H, TNFRSF11A, UMPS), all of which are enriched in protein binding functions and play a role in COVID-19. Most DASE sites were assigned to the MHC class I locus and were predominantly upregulated upon infection. DASE sites in the MHC class I locus also occur in iPSC-derived airway epithelium basal cells infected with SARS-CoV-2. Using an RNA-Seq haplotype reconstruction approach, we found DASE sites and adjacent eSNVs in phase (i.e., predicted on the same DNA strand), demonstrating differential haplotype expression upon infection. We found a bias towards the expression of the HLA alleles with a higher binding affinity to SARS-CoV-2 epitopes.

Discussion

Independent of gene expression compensation, SARS-CoV-2 infection of human lung cell lines induces transcriptional allelic switching at the MHC loci. This suggests a response mechanism to SARS-CoV-2 infection that swaps HLA alleles with poor epitope binding affinity, an expectation supported by publicly available proteome data.

Recombinant Protein Vaccines against Human Betacoronaviruses: Strategies, Approaches and Progress

Betacoronaviruses have already troubled humanity more than once. In 2002-2003 and 2012, the SARS-CoV and MERS-CoV, respectively, caused outbreaks of respiratory syndromes with a fatal outcome. The spread of the SARS-CoV-2 coronavirus has become a pandemic. These three coronaviruses belong to the genus Betacoronavirus and have a zoonotic origin. The emergence of new coronavirus infections in the future cannot be ruled out, and vaccination is the main way to prevent the spread of the infection. Previous experience in the development of vaccines against SARS and MERS has helped to develop a number of vaccines against SARS-CoV-2 in a fairly short time. Among them, there are quite a few recombinant protein vaccines, which seem to be very promising in terms of safety, minimization of side effects, storage and transportation conditions. The problem of developing a universal betacoronavirus vaccine is also still relevant. Here, we summarize the information on the designing of vaccines based on recombinant proteins against highly pathogenic human betacoronaviruses SARS-CoV, MERS-CoV and SARS-CoV-2.

Mapping Potential Vaccine Candidates Predicted by VaxiJen for Different Viral Pathogens between 2017-2021-A Scoping Review

Reverse vaccinology (RV) is a promising alternative to traditional vaccinology. RV focuses on in silico methods to identify antigens or potential vaccine candidates (PVCs) from a pathogen's proteome. Researchers use VaxiJen, the most well-known RV tool, to predict PVCs for various pathogens. The purpose of this scoping review is to provide an overview of PVCs predicted by VaxiJen for different viruses between 2017 and 2021 using Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR) guidelines. We used the term 'vaxijen' to search PubMed, Scopus, Web of Science, EBSCOhost, and ProQuest One Academic. The protocol was registered at the Open Science Framework (OSF). We identified articles on this topic, charted them, and discussed the key findings. The database searches yielded 1033 articles, of which 275 were eligible. Most studies focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), published between 2020 and 2021. Only a few articles (8/275; 2.9%) conducted experimental validations to confirm the predictions as vaccine candidates, with 2.2% (6/275) articles mentioning recombinant protein expression. Researchers commonly targeted parts of the SARS-CoV-2 spike (S) protein, with the frequently predicted epitopes as PVCs being major histocompatibility complex (MHC) class I T cell epitopes WTAGAAAYY, RQIAPGQTG, IAIVMVTIM, and B cell epitope IAPGQTGKIADY, among others. The findings of this review are promising for the development of novel vaccines. We recommend that vaccinologists use these findings as a guide to performing experimental validation for various viruses, with SARS-CoV-2 as a priority, because better vaccines are needed, especially to stay ahead of the emergence of new variants. If successful, these vaccines could provide broader protection than traditional vaccines.

Immunology to Immunotherapeutics of SARS-CoV-2: Identification of Immunogenic Epitopes for Vaccine Development

The emergence of COVID19 pandemic caused by SARS-CoV-2 virus has created a global public health and socio-economic crisis. Immunoinformatics-based approaches to investigate the potential antigens is the fastest way to move towards a multiepitope-based vaccine development. This review encompasses the underlying mechanisms of pathogenesis, innate and adaptive immune signaling along with evasion pathways of SARS-CoV-2. Furthermore, it compiles the promiscuous peptides from in silico studies which are subjected to prediction of cytokine milieu using web-based servers. Out of the 434 peptides retrieved from all studies, we have identified 33 most promising T cell vaccine candidates. This review presents a list of the most potential epitopes from several proteins of the virus based on their immunogenicity, homology, conservancy and population coverage studies. These epitopes can form a basis of second generation of vaccine development as the first generation vaccines in various stages of trials mostly focus only on Spike protein. We therefore, propose them as most potential candidates which can be taken up immediately for confirmation by experimental studies.

Reverse vaccinology-based prediction of a multi-epitope SARS-CoV-2 vaccine and its tailoring to new coronavirus variants

The genome feature of SARS-CoV-2 leads the virus to mutate and creates new variants of concern. Tackling viral mutations is also an important challenge for the development of a new vaccine. Accordingly, in the present study, we undertook to identify B- and T-cell epitopes with immunogenic potential for eliciting responses to SARS-CoV-2, using computational approaches and its tailoring to coronavirus variants. A total of 47 novel epitopes were identified as immunogenic triggering immune responses and no toxic after investigation with in silico tools. Furthermore, we found these peptide vaccine candidates showed a significant binding affinity for MHC I and MHC II alleles in molecular docking investigations. We consider them to be promising targets for developing peptide-based vaccines against SARS-CoV-2. Subsequently, we designed two efficient multi-epitopes vaccines against the SARS-CoV-2, the first one based on potent MHC class I and class II T-cell epitopes of S (FPNITNLCPF-NYNYLYRLFR-MFVFLVLLPLVSSQC), M (MWLSYFIASF-GLMWLSYFIASFRLF), E (LTALRLCAY-LLFLAFVVFLLVTLA), and N (SPRWYFYYL-AQFAPSASAFFGMSR). The second candidate is the result of the tailoring of the first designed vaccine according to three classes of SARS-CoV-2 variants. Molecular docking showed that the protein-protein binding interactions between the vaccines construct and TLR2-TLR4 immune receptors are stable complexes. These findings confirmed that the final multi-epitope vaccine could be easily adapted to new viral variants. Our study offers a shortlist of promising epitopes that can accelerate the development of an effective and safe vaccine against the virus and its adaptation to new variants.Communicated by Ramaswamy H. Sarma.

Drummondin E and Flinderole B are potential inhibitors of RNA-dependent RNA polymerase of SARS-CoV-2: an in silico study

Coronavirus disease 2019 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected 235.6 million people worldwide. In the present study, RNA-dependent RNA polymerase (RdRp) (PDB Id: 6M71) of SARS-CoV-2, an essential enzyme needed for subgenomic replication and amplification of RNA, was selected. Similar to other RdRps, it is a conserved protein and a popular target for antiviral drug therapy. Based on a computational approach, potential RdRp inhibitors were identified. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of selected molecules were determined using computation tools. The potential inhibitors were docked to the RdRp and later confirmed by Molecular Dynamics (MD) using the "Flare" module of Cresset software. Drummondin E and Flinderole B had higher drug similarity scores among the compounds selected in this study. Both these compounds are noncarcinogenic, nonirritant, nontumorigenic, and nonmutagenic. Molecular docking studies showed that both compounds can bind to RdRp. The best ligand interaction patterns were validated by MD using the "Flare" module. MD was performed for the period of 100 ns with the time step of 1 fs. The simulation results suggest that Thr-680, Arg-624, Lys-676, and Val-557 are key interacting partners in the Drummondin E-RdRp complex, while Asp-618, Asp-760, Asp-623, Arg-624, and Asp-761 are the interacting partners in the Flinderole B-RdRp complex. Based on the in silico drug-likeness score; ADMET properties; and molecular simulation result, we surmise that Flinderole B and Drummondin E could impede SARS-CoV-2 genome replication and transcription by targeting the RdRp protein.

Immunogenic Epitope-Based Vaccine Prediction from Surface Glycoprotein of MERS-CoV by Deploying Immunoinformatics Approach

Middle East respiratory syndrome coronavirus (MERS-CoV) has caused a high mortality rate since its emergence in 2012 in the Middle East. Currently, no effective drug or vaccine is available for MERS-CoV. Supportive care and prevention are the only ways to manage infection. In this study, we identified an epitope-based vaccine that could be an optimal solution for the prevention of MERS-CoV infection. By deploying an immunoinformatics approach, we predicted a subunit vaccine based on the surface glycoprotein (S protein) of MERS-CoV. For this purpose, the proteome of the MERS-CoV spike protein was obtained from the NCBI GenBank database. Then, it was subjected to a check for allergenicity using the Allergen FP v.1.0 tool. The Vaxijen v.2.0 tool was used to conduct antigenicity tests for binding with major histocompatibility complex class I and II molecules. The solidity of the predicted epitope-allele docked complex was evaluated by a molecular dynamics simulation. After docking a total of eight epitopes from the MERS-CoV S protein, further analyses predicted their non-toxicity and therapeutic immunogenic properties. These epitopes have potential utility as vaccine candidates against MERS-CoV, to be validated by wet-lab testing.

Codon usage studies and epitope-based peptide vaccine prediction against Tropheryma whipplei

Background

The Tropheryma whipplei causes acute gastroenteritis to neuronal damages in Homo sapiens. Genomics and codon adaptation studies would be helpful advancements of disease evolution prediction, prevention, and treatment of disease. The codon usage data and codon usage measurement tools were deployed to detect the rare, very rare codons, and also synonymous codons usage. The higher effective number of codon usage values indicates the low codon usage bias in T. whipplei and also in the 23S and 16S ribosomal RNA genes.

Results

In T. whipplei, it was found to hold low codon biasness in genomic sets. The synonymous codons possess the base content in 3rd position that was calculated as A3S% (24.47 and 22.88), C3S% (20.99 and 22.88), T3S% (21.47 and 19.53), and G3S% (33.08 and 34.71) for 23s and 16s rRNA, respectively.

Conclusion

Amino acids like valine, aspartate, leucine, and phenylalanine hold high codon usage frequency and also found to be present in epitopes KPSYLSALSAHLNDK and FKSFNYNVAIGVRQP that were screened from proteins excinuclease ABC subunit UvrC and 3-oxoacyl-ACP reductase FabG, respectively. This method opens novel ways to determine epitope-based peptide vaccines against different pathogenic organisms.

Self-assembling ferritin nanoparticles coupled with linear sequences from canine distemper virus haemagglutinin protein elicit robust immune responses

BACKGROUND

Canine distemper virus (CDV), which is highly infectious, has caused outbreaks of varying scales in domestic and wild animals worldwide, so the development of a high-efficiency vaccine has broad application prospects. Currently, the commercial vaccine of CDV is an attenuated vaccine, which has the disadvantages of a complex preparation process, high cost and safety risk. It is necessary to develop a safe and effective CDV vaccine that is easy to produce on a large scale. In this study, sequences of CDV haemagglutinin (HA) from the Yanaka strain were aligned, and three potential linear sequences, termed YaH₃, YaH₄, and YaH₅, were collected. To increase the immunogenicity of the epitopes, ferritin was employed as a self-assembling nanoparticle element. The ferritin-coupled forms were termed YaH₃F, YaH₄F, and YaH₅F, respectively. A full-length HA sequence coupled with ferritin was also constructed as a DNA vaccine to compare the immunogenicity of nanoparticles in prokaryotic expression.

RESULT

The self-assembly morphology of the proteins from prokaryotic expression was verified by transmission electron microscopy. All the proteins self-assembled into nanoparticles. The expression of the DNA vaccine YaHF in HEK-293T cells was also confirmed in vitro. After subcutaneous injection of epitope nanoparticles or intramuscular injection of DNA YaHF, all vaccines induced strong serum titres, and long-term potency of antibodies in serum could be detected after 84 days. Strong anti-CDV neutralizing activities were observed in both the YaH₄F group and YaHF group. According to antibody typing and cytokine detection, YaH₄F can induce both Th1 and Th2 immune responses. The results of flow cytometry detection indicated that compared with the control group, all the immunogens elicited an increase in CD3. Simultaneously, the serum antibodies induced by YaH₄F and YaHF could significantly enhance the ADCC effect compared with the control group, indicating that the antibodies in the serum effectively recognized the antigens on the cell surface and induced NK cells to kill infected cells directly.

CONCLUSIONS

YaH₄F self-assembling nanoparticle obtained by prokaryotic expression has no less of an immune effect than YaHF, and H₄ has great potential to become a key target for the easy and rapid preparation of epitope vaccines.

Immunoinformatics and reverse vaccinomic approaches for effective design

The emergence of mutagenic strains of severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) worst hit the world which already suffered from the Coronavirus disease-2019 (COVID-19) pandemic for 2 years. Due to recent advances in vaccinomics, many vaccine candidates are available but their efficacy against a mutant version of SARS-CoV-2 has remained uncertain. The immune-informatics-based reverse vaccinomic approaches have shown promising investigations recently for the development of cost-effective vaccinomics candidates in a very short period of time. The strategic vaccine development of selected epitopes using artificial intelligence for both B- and T-cells is a very crucial step in this process. This approach provides a highly effective and immunogenic vaccine that offers immunological safety against autoimmunity and other adverse effects over ethnicities, pregnant women, and vulnerable age groups. Several researchers have developed effective vaccine candidates using computational vaccinology and the immune-informatics approach. In this process, a unique peptide sequence of viral proteins such as Nucleocapsid, spike, envelope protein was identified by various in silico tools which are acting as immunological epitopes against TLRs, T-cells, and B-cells. While the conventional immunological vaccine studies take years for vaccine candidature, the immunoinformatics approach is a time-efficient way for the next generation research to study host-pathogen interactions and vaccine development. It is also cost-effective and leads to a better understanding of disease pathogenesis, diagnosis, and immunological response. Owing to the advantage of immunoinformatics-based vaccine approaches the present chapter aimed to discuss vaccine development using immunoinformatics approaches. Besides, the current challenges and future aspects have also been discussed herewith.

T-cell epitope-based vaccine designing against Orthohantavirus: a causative agent of deadly cardio-pulmonary disease

Orthohantavirus, a zoonotic virus responsible for causing human cardio-pulmonary disease, is proven to be a fatal disease. Due to the paucity of regimens to cure the disease and efficient management to eradicate this deadly virus, there is a constant need to expand in-silico approaches belonging to immunology domain to formulate best feasible peptide-based vaccine against it. In lieu of that, we have predicted and validated an epitope of nine-residue-long sequence “MIGLLSSRI”. The predicted epitope has shown best interactions with HLA alleles of MHC Class II proteins, namely HLA DRB1_0101, DRB1_0401, DRB1_0405, DRB1_0701, DRB1_0901, DRB1_1302, and DRB1_1501. The structure of the epitope was modeled by deploying PEPFOLD 3.5 and verified by Ramachandran plot analysis. Molecular docking and simulation studies reveal that this epitope has satisfactory binding scores, ACE value and global energies for docked complexes along with selectable range of RMSD and RMSF values. Also, the predicted epitope “MIGLLSSRI” exhibits population coverage of more than 62% in world population and maximum of 70% in the United States of America. In this intensive study, we have used many tools like AllergenFP, NETMHCII 3.2, VaxiJen, ToxinPred, PEPFOLD 3.5, DINC, IEDB-Population coverage, MHCPred and JCat server. Most of these tools are based on modern innovative statistical algorithms like HMM, ANN, ML, etc. that help in better predictions of putative candidates for vaccine crafting. This innovative methodology is facile, cost-effective and time-efficient, which could facilitate designing of a vaccine against this virus.

Prediction of suitable T and B cell epitopes for eliciting immunogenic response against SARS-CoV-2 and its mutant

Spike glycoprotein of SARS-CoV-2 is mainly responsible for the recognition and membrane fusion within the host and this protein has an ability to mutate. Hence, T cell and B cell epitopes were derived from the spike glycoprotein sequence of wild SARS-CoV-2. The proposed T cell and B cell epitopes were found to be antigenic and conserved in the sequence of SARS-CoV-2 mutant (B.1.1.7). Thus, the proposed epitopes are effective against SARS-CoV-2 and its B.1.1.7 mutant. MHC-I that best interacts with the proposed T cell epitopes were found, using immune epitope database. Molecular docking and molecular dynamic simulations were done for ensuring a good binding between the proposed MHC-I and T cell epitopes. The finally proposed T cell epitope was found to be antigenic, non-allergenic, non-toxic and stable. Further, the finally proposed B cell epitopes were also found to be antigenic. The population conservation analysis has ensured the presence of MHC-I molecule (respective to the finally proposed T cell) in human population of most affected countries with SARS-CoV-2. Thus the proposed T and B cell epitope could be effective in designing an epitope-based vaccine, which is effective on SARS-CoV-2 and its B.1.1.7mutant.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13721-021-00348-w.

Computer simulation in the development of vaccines against covid-19 based on the hla-system antigens

The genetic variability of population may explain different individual immune responses to the SARS-CoV-2 virus. The use of genome- and peptidome-based technologies makes it possible to develop vaccines by optimizing the target antigens. The computer modeling methodology provides the scientific community with a more complete list of immunogenic peptides, including a number of new and cross-reactive candidates. Studies conducted independently of each other with different approaches provide a high degree of confidence in the reproducibility of results. Most of the effort in developing vaccines and drugs against SARS-CoV-2 is directed towards the thorn glycoprotein (protein S), a major inducer of neutralizing antibodies. Several vaccines have been shown to be effective in the preclinical studies and have been tested in the clinical trials to combat the COVID-19 infection. This review presents the profile of in silico predicted immunogenic peptides of the SARS-CoV-2 virus for the subsequent functional validation and vaccine development, and highlights the current advances in the development of subunit vaccines to combat COVID-19, taking into account the experience that has been previously achieved with SARS-CoV and MERS-CoV. The immunoinformatics techniques reduce the time and cost of developing vaccines that together can stop this new viral infection.

An immunogenetic view of COVID-19

Meeting the challenges brought by the COVID-19 pandemic requires an interdisciplinary approach. In this context, integrating knowledge of immune function with an understanding of how genetic variation influences the nature of immunity is a key challenge. Immunogenetics can help explain the heterogeneity of susceptibility and protection to the viral infection and disease progression. Here, we review the knowledge developed so far, discussing fundamental genes for triggering the innate and adaptive immune responses associated with a viral infection, especially with the SARS-CoV-2 mechanisms. We emphasize the role of the HLA and KIR genes, discussing what has been uncovered about their role in COVID-19 and addressing methodological challenges of studying these genes. Finally, we comment on questions that arise when studying admixed populations, highlighting the case of Brazil. We argue that the interplay between immunology and an understanding of genetic associations can provide an important contribution to our knowledge of COVID-19.

An immunoinformatics study: designing multivalent T-cell epitope vaccine against canine circovirus

BACKGROUND

Canine circovirus is a deadly pathogen of dogs and causes vasculitis and hemorrhagic enteritis. It causes lethal gastroenteritis in pigs, fox, and dogs. Canine circovirus genome contains two main (and opposite) transcription units which encode two open reading frames (ORFs), a replicase-associated protein (Rep) and the capsid (Cap) protein. The replicase protein and capsid protein consist of 303 amino acids and 270 amino acids respectively. Several immuno-informatics methods such as epitope screening, molecular docking, and molecular-dynamics simulations were used to craft peptide-based vaccine construct against canine circovirus.

RESULTS

The vaccine construct was designed by joining the selected epitopes with adjuvants by suitable linker. The cloning and expression of the vaccine construct was also performed using in silico methods. Screening of epitopes was conducted by NetMHC server that uses ANN (Artificial neural networking) algorithm. These methods are fast and cost-effective for screening epitopes that can interact with dog leukocyte antigens (DLA) and initiate an immune response. Overall, 5 epitopes, YQHLPPFRF, YIRAKWINW, ALYRRLTLI, HLQGFVNLK, and GTMNFVARR, were selected and used to design a vaccine construct. The molecular docking and molecular dynamics simulation studies show that these epitopes can bind with DLA molecules with stability. The codon adaptation and in silico cloning studies show that the vaccine can be expressed by Escherichia coli K12 strain.

CONCLUSION

The results suggest that the vaccine construct can be useful in preventing the dogs from canine circovirus infections. However, the results need further validation by performing other in vitro and in vivo experiments.

Immunoinformatic approach to assess SARS-CoV-2 protein S epitopes recognised by the most frequent MHC-I alleles in the Brazilian population

AIMS

Brazil is nowadays one of the epicentres of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and new therapies are needed to face it. In the context of specific immune response against the virus, a correlation between Major Histocompatibility Complex Class I (MHC-I) and the severity of the disease in patients with COVID-19 has been suggested. Aiming at better understanding the biology of the infection and the immune response against the virus in the Brazilian population, we analysed SARS-CoV-2 protein S peptides in order to identify epitopes able to elicit an immune response mediated by the most frequent MHC-I alleles using in silico methods.

METHODS

Our analyses consisted in searching for the most frequent Human Leukocyte Antigen (HLA)-A, HLA-B and HLA-C alleles in the Brazilian population, excluding the genetic isolates; then, we performed: molecular modelling for unsolved structures, MHC-I binding affinity and antigenicity prediction, peptide docking and molecular dynamics of the best fitted MHC-I/protein S complexes.

RESULTS

We identified 24 immunogenic epitopes in the SARS-CoV-2 protein S that could interact with 17 different MHC-I alleles (namely, HLA-A*01:01; HLA-A*02:01; HLA-A*11:01; HLA-A*24:02; HLA-A*68:01; HLA-A*23:01; HLA-A*26:01; HLA-A*30:02; HLA-A*31:01; HLA-B*07:02; HLA-B*51:01; HLA-B*35:01; HLA-B*44:02; HLA-B*35:03; HLA-C*05:01; HLA-C*07:01 and HLA-C*15:02) in the Brazilian population.

CONCLUSIONS

Being aware of the intrinsic limitations of in silico analysis (mainly the differences between the real and the Protein Data Bank (PDB) structure; and accuracy of the methods for simulate proteasome cleavage), we identified 24 epitopes able to interact with 17 MHC-I more frequent alleles in the Brazilian population that could be useful for the development of strategic methods for vaccines against SARS-CoV-2.

Exploring peptide studies related to SARS-CoV to accelerate the development of novel therapeutic and prophylactic solutions against COVID-19

Recent advances in peptide research revolutionized therapeutic discoveries for various infectious diseases. In view of the ongoing threat of the COVID-19 pandemic, there is an urgent need to develop potential therapeutic options. Intense and accomplishing research is being carried out to develop broad-spectrum vaccines and treatment options for corona viruses, due to the risk of recurrent infection by the existing strains or pandemic outbreaks by new mutant strains. Developing a novel medicine is costly and time consuming, which increases the value of repurposing existing therapies. Since, SARS-CoV-2 shares significant genomic homology with SARS-CoV, we have summarized various peptides identified against SARS-CoV using in silico and molecular studies and also the peptides effective against SARS-CoV-2. Dissecting the molecular mechanisms underlying viral infection could yield fundamental insights in the discovery of new antiviral agents, targeting viral proteins or host factors. We postulate that these peptides can serve as effective components for therapeutic options against SARS-CoV-2, supporting clinical scientists globally in selectively identifying and testing the therapeutic and prophylactic agents for COVID-19 treatment. In addition, we also summarized the latest updates on peptide therapeutics against SARS-CoV-2.

Lessons Learned from Cutting-Edge Immunoinformatics on Next-Generation COVID-19 Vaccine Research

Presently, immunoinformatics and bioinformatics approaches are contributing actively to COVID-19 vaccine research. The first immunoinformatics-based vaccine construct against SARS-CoV-2 was published in February 2020. Following this, immunoinformatics and bioinformatics approaches have created a new direction in COVID-19 vaccine research. Several researchers have designed the next-generation COVID-19 vaccines using these approaches. Presently, immunoinformatics has accelerated immunology research immensely in the area of COVID-19. Hence, we have tried to depict the current scenario of immunoinformatics and bioinformatics in COVID-19 vaccine research.

Identification of presented SARS-CoV-2 HLA class I and HLA class II peptides using HLA peptidomics

The human leukocyte antigen (HLA)-bound viral antigens serve as an immunological signature that can be selectively recognized by T cells. As viruses evolve by acquiring mutations, it is essential to identify a range of presented viral antigens. Using HLA peptidomics, we are able to identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-derived peptides presented by highly prevalent HLA class I (HLA-I) molecules by using infected cells as well as overexpression of SARS-CoV-2 genes. We find 26 HLA-I peptides and 36 HLA class II (HLA-II) peptides. Among the identified peptides, some are shared between different cells and some are derived from out-of-frame open reading frames (ORFs). Seven of these peptides were previously shown to be immunogenic, and we identify two additional immunoreactive peptides by using HLA multimer staining. These results may aid the development of the next generation of SARS-CoV-2 vaccines based on presented viral-specific antigens that span several of the viral genes.

COVID-19 vaccines and nanomedicine

Background

The COVID‐19 virus‐induced pandemic has been the deadliest pandemic to have occurred in two generations, besides HIV/AIDS. Epidemiologists predicted that the SARS‐Cov 2 pandemic would not be able to be brought under control until a majority of the world’s population had been inoculated with safe and effective vaccines. A world‐wide effort to expedite vaccine development was successful. Previous research for vaccines to prevent SARS and MERS, also coronaviruses, was vital to this success. Nanotechnology was essential to this vaccine development. Key elements are presented here to better understand the relationship between nanomedicine and the COVID‐19 vaccine development.

Methods

NLM PubMed searches for COVID‐19 vaccines, nanotechnology and nanomedicine were done. There were 6911 articles screened, 235 of which were deemed appropriate to this subject and utilized here, together with two landmark nanomedicine texts used to expand understanding of the basic science of nanotechnology.

Results

SARS‐Cov 2, caused by the COVID‐19 virus, was first recognized in China in December of 2019 and was declared as a pandemic in March of 2020. The RNA sequence was identified in January of 2020. Within 4 months of the viral genome being released, over 259 vaccines had been in development. The World Health Organization (WHO) anticipated a vaccine with a 50‐80% efficacy to be developed within 1‐2 years. Ahead of schedule, the Food and Drug Administration (FDA) announced the emergency authorization approval for two mRNA vaccines within 11 month’s time. Nanotechnology was the key to the success of these rapidly developed, safe and effective vaccines. A brief review of pertinent basic science principles of nanomedicine are presented. The development of COVID vaccines is reviewed. Future considerations are discussed.

Conclusions

Control of the COVID‐19 SARS‐Cov2 pandemic benefitted from nanomedicine principles used to develop highly effective, yet very safe and relatively inexpensive vaccines. These nanovaccines can be much more easily altered to adjust for viral variants than traditional live or inactivated legacy‐type whole virus vaccines.

An Immunoinformatics Approach for SARS-CoV-2 in Latam Populations and Multi-Epitope Vaccine Candidate Directed towards the World's Population

The coronavirus pandemic is a major public health crisis affecting global health systems with dire socioeconomic consequences, especially in vulnerable regions such as Latin America (LATAM). There is an urgent need for a vaccine to help control contagion, reduce mortality and alleviate social costs. In this study, we propose a rational multi-epitope candidate vaccine against SARS-CoV-2. Using bioinformatics, we constructed a library of potential vaccine peptides, based on the affinity of the most common major human histocompatibility complex (HLA) I and II molecules in the LATAM population to predict immunological complexes among antigenic, non-toxic and non-allergenic peptides extracted from the conserved regions of 92 proteomes. Although HLA-C, had the greatest antigenic peptide capacity from SARS-CoV-2, HLA-B and HLA-A, could be more relevant based on COVID-19 risk of infection in LATAM countries. We also used three-dimensional structures of SARS-CoV-2 proteins to identify potential regions for antibody production. The best HLA-I and II predictions (with increased coverage in common alleles and regions evoking B lymphocyte responses) were grouped into an optimized final multi-epitope construct containing the adjuvants Beta defensin-3, TpD, and PADRE, which are recognized for invoking a safe and specific immune response. Finally, we used Molecular Dynamics to identify the multi-epitope construct which may be a stable target for TLR-4/MD-2. This would prove to be safe and provide the physicochemical requirements for conducting experimental tests around the world.

In-silico designing of epitope-based vaccine against the seven banded grouper nervous necrosis virus affecting fish species

Neural necrosis virus (NNV) of family Nodaviridae affect wide range of fish species with viral encephalopathy and retinopathy causing mass mortality up to 100%. Currently there is no effective treatment and depopulation is only suggested recommendation. New avenues and approach are required to control this harmful malady. In this study we developed an epitope-based vaccine (EBV), against NNV using computation approach. We have selected two conserved proteins RNA-dependent RNA polymerase (RdRP) and capsid proteins. Based on more than ~ 1000 epitopes we selected six antigenic epitopes. These were conjugated to adjuvant and linker peptides to generate a full-length vaccine candidate. Biochemical structural properties were analyzed by Phyre2 server. ProtParam, Molprobity. Ramachandran plot results indicate that 98.7% residues are in a favorable region and 93.4% residues in the favored region. The engineered EBV binds to toll like receptor-5 (TLR5) an important elicitor of immune response. Further molecular docking by PatchDock server reveals the atomic contact energy (i.e. − 267.08) for the best docked model of EBV and TLR5 receptor. The molecular simulation results suggest a stable interaction; the RMSD and RMSF values are 1–4 Ǻ and 1–12Ǻ, respectively. Further we have suggested the best possible codon optimized sequence for its cloning and subsequent purification of the protein. Overall, this is a first report to suggest an in-silico method for generation of an EBV candidate against NNV. We surmise that the method and approach suggested could be used as a promising cure for NNVs.

Bioinformatic HLA Studies in the Context of SARS-CoV-2 Pandemic and Review on Association of HLA Alleles with Preexisting Medical Conditions

After the announcement of a new coronavirus in China in December 2019, which was then called SARS-CoV-2, this virus changed to a global concern and it was then declared as a pandemic by WHO. Human leukocyte antigen (HLA) alleles, which are one of the most polymorphic genes, play a pivotal role in both resistance and vulnerability of the body against viruses and other infections as well as chronic diseases. The association between HLA alleles and preexisting medical conditions such as cardiovascular diseases and diabetes mellitus is reported in various studies. In this review, we focused on the bioinformatic HLA studies to summarize the HLA alleles which responded to SARS-CoV-2 peptides and have been used to design vaccines. We also reviewed HLA alleles that are associated with comorbidities and might be related to the high mortality rate among COVID-19 patients. Since both genes and patients' medical conditions play a key role in both severity of the disease and the mortality rate in COVID-19 patients, a better understanding of the connection between HLA alleles and SARS-CoV-2 can provide a wider perspective on the behavior of the virus. Such understanding can help scientists, especially in terms of protecting healthcare workers and designing effective vaccines.

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.

Design of an epitope-based peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach

The recurrent and recent global outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has turned into a global concern which has infected more than 42 million people all over the globe, and this number is increasing in hours. Unfortunately, no vaccine or specific treatment is available, which makes it more deadly. A vaccine-informatics approach has shown significant breakthrough in peptide-based epitope mapping and opens the new horizon in vaccine development. In this study, we have identified a total of 15 antigenic peptides [including thymus cells (T-cells) and bone marrow or bursa-derived cells] in the surface glycoprotein (SG) of SARS-CoV-2 which is nontoxic and nonallergenic in nature, nonallergenic, highly antigenic and non-mutated in other SARS-CoV-2 virus strains. The population coverage analysis has found that cluster of differentiation 4 (CD4+) T-cell peptides showed higher cumulative population coverage over cluster of differentiation 8 (CD8+) peptides in the 16 different geographical regions of the world. We identified 12 peptides ((LTDEMIAQY, WTAGAAAYY, WMESEFRVY, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF, FGAGAALQI, YGFQPTNGVGYQ, LPDPSKPSKR, QTQTNSPRRARS and VITPGTNTSN) that are $80\hbox{--} 90\%$ identical with experimentally determined epitopes of SARS-CoV, and this will likely be beneficial for a quick progression of the vaccine design. Moreover, docking analysis suggested that the identified peptides are tightly bound in the groove of human leukocyte antigen molecules which can induce the T-cell response. Overall, this study allows us to determine potent peptide antigen targets in the SG on intuitive grounds, which opens up a new horizon in the coronavirus disease (COVID-19) research. However, this study needs experimental validation by in vitro and in vivo.

The Relevance of Bioinformatics Applications in the Discovery of Vaccine Candidates and Potential Drugs for COVID-19 Treatment

The application of bioinformatics to vaccine research and drug discovery has never been so essential in the fight against infectious diseases. The greatest combat of the 21st century against a debilitating disease agent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus discovered in Wuhan, China, December 2019, has piqued an unprecedented usage of bioinformatics tools in deciphering the molecular characterizations of infectious pathogens. With the viral genome data of SARS-COV-2 been made available barely weeks after the reported outbreak, bioinformatics platforms have become an all-time critical tool to gain time in the fight against the disease pandemic. Before the outbreak, different platforms have been developed to explore antigenic epitopes, predict peptide-protein docking and antibody structures, and simulate antigen-antibody reactions and lots more. However, the advent of the pandemic witnessed an upsurge in the application of these pipelines with the development of newer ones such as the Coronavirus Explorer in the development of efficacious vaccines, drug repurposing, and/or discovery. In this review, we have explored the various pipelines available for use, their relevance, and limitations in the timely development of useful therapeutic candidates from genomic data knowledge to clinical therapy.

Bioinformatics Analysis of SARS-CoV-2 to Approach an Effective Vaccine Candidate Against COVID-19

The emerging Coronavirus Disease 2019 (COVID-19) pandemic has posed a serious threat to the public health worldwide, demanding urgent vaccine provide. According to the virus feature as an RNA virus, a high rate of mutations imposes some vaccine design difficulties. Bioinformatics tools have been widely used to make advantage of conserved regions as well as immunogenicity. In this study, we aimed at immunoinformatic evaluation of SARS-CoV-2 proteins conservancy and immunogenicity to design a preventive vaccine candidate. Spike, Membrane and Nucleocapsid amino acid sequences were obtained, and four possible fusion proteins were assessed and compared in terms of structural features and immunogenicity, and population coverage. MHC-I and MHC-II T-cell epitopes, the linear and conformational B-cell epitopes were evaluated. Among the predicted models, the truncated form of Spike in fusion with M and N protein applying AAY linker has high rate of MHC-I and MCH-II epitopes with high antigenicity and acceptable population coverage of 82.95% in Iran and 92.51% in Europe. The in silico study provided truncated Spike-M-N SARS-CoV-2 as a potential preventive vaccine candidate for further in vivo evaluation.

Immunoinformatics prediction of overlapping CD8+ T-cell, IFN-γ and IL-4 inducer CD4+ T-cell and linear B-cell epitopes based vaccines against COVID-19 (SARS-CoV-2)

At the beginning of the year 2020, the world was struck with a global pandemic virus referred to as SARS-CoV-2 (COVID-19) which has left hundreds of thousands of people dead. To control this virus, vaccine design becomes imperative. In this study, potential epitopes-based vaccine candidates were explored. Six hundred (600) genomes of SARS-CoV-2 were retrieved from the viPR database to generate CD8⁺ T-cell, CD4+ T-cell and linear B-cell epitopes which were screened for antigenicity, immunogenicity and non-allergenicity. The results of this study provide 19 promising candidate CD8⁺ T-cell epitopes that strongly overlap with 8 promising B-cells epitopes. Another 19 CD4⁺ T-cell epitopes were also identified that can induce IFN-γ and IL-4 cytokines. The most conserved MHC-I and MHC-II for both CD8⁺ and CD4⁺ T-cell epitopes are HLA-A*02:06 and HLA-DRB1*01:01 respectively. These epitopes also bound to Toll-like receptor 3 (TLR3). The population coverage of the conserved Major Histocompatibility Complex Human Leukocyte Antigen (HLA) for both CD8⁺ T-cell and CD4⁺ T-cell ranged from 65.6% to 100%. The detailed analysis of the potential epitope-based vaccine and their mapping to the complete COVID-19 genome reveals that they are predominantly found in the location of the surface (S) and membrane (M) glycoproteins suggesting the potential involvement of these structural proteins in the immunogenic response and antigenicity of the virus. Since the majority of the potential epitopes are located on M protein, the design of multi-epitope vaccine with the structural protein is highly promising though the whole M protein could also serve as a viable epitope for the development of an attenuated vaccine. Our findings provide a baseline for the experimental design of a suitable vaccine against SARS-CoV-2.

Immunoinformatics designed T cell multi epitope dengue peptide vaccine derived from non structural proteome

Dengue viral disease has been reported as an Aedes aegypti mosquito-borne human disease and causing a severe global public health concern. In this study, immunoinformatics methods was deployed for crafting CTL T-cell epitopes as dengue vaccine candidates. The NS1 protein sequence of dengue serotype 1 strain retrieved from the protein database and T-cell epitopes (n = 85) were predicted by the artificial neural network. The conserved epitopes (n = 10) were predicted and selected for intensive computational analysis. The machine learning technique and quantitative matrix-based toxicity analysis assured nontoxic peptide selection. Hidden Markov Model derived Structural Alphabet (SA) based algorithm predicted the 3D molecular structure and all-atom structure of peptide ligand validated by Ramachandran-plot. Three-tier molecular docking approaches were used to predictthe peptide - HLA docking complex. Molecular dynamics (MD) simulation study confirmed the docking complex was stable in the time frame of 100ns. Population coverage analysis predicted the interaction epitope interaction with a particular population of HLA. These results concluded that the computationally designed HTLWSNGVL and FTTNIWLKL epitope peptides could be used as putative agents for the multi CTL T cell epitope vaccine. The vaccine protein sequence expression and translation were analyzed in the prokaryotic vector adapted by codon usage. Such in silico formulated CTL T-cell-based prophylactic vaccines could encourage the commercial development of dengue vaccines.

Design of a multi-epitope-based vaccine targeting M-protein of SARS-CoV2: an immunoinformatics approach

In the present study, one of the targets present on the envelopes of coronaviruses, membrane glycoprotein (M) was chosen for the design of a multi-epitope vaccine by Immunoinformatics approach. The B-cell and T-cell epitopes used for the construction of vaccine were antigenic, nonallergic and nontoxic. An adjuvant, β-defensin and PADRE sequence were included at the N-terminal end of the vaccine. All the epitopes were joined by linkers for decreasing the junctional immunogenicity. Various physicochemical parameters of the vaccine were evaluated. Secondary and tertiary structures were predicted for the vaccine construct. The tertiary structure was further refined, and various parameters related to the refinement of the protein structure were validated by using different tools. Humoral immunity induced by B-cells relies upon the identification of antigenic determinants on the surface of the vaccine construct. In this regard, the vaccine construct was found to consist of several B-cell epitopes in its three-dimensional conformation. Molecular docking of the vaccine was carried out with TLR-3 receptor to study their binding and its strength. Further, protein-protein interactions in the docked complex were visualized using LigPlot+. Population coverage analysis had shown that the multi-epitope vaccine covers 94.06% of the global population. The vaccine construct was successfully cloned in silico into pET-28a (+). Immune simulation studies showed the induction of primary, secondary and tertiary immune responses marked by the increased levels of antibodies, INF-γ, IL-2, TGF-β, B- cells, CD4+ and CD8+ cells. Finally, the vaccine construct was able to elicit immune response as desired.Communicated by Ramaswamy H. Sarma.

Molecular docking and simulation investigation: effect of beta-sesquiphellandrene with ionic integration on SARS-CoV2 and SFTS viruses

Background

At present, viral diseases become major concern for the world. SARS-CoV2 and SFTS viruses are deadly in nature, and there is a need for developing best treatments for them. Modern in silico approaches were found to be very handy in determining putative drug molecules. In this study, we analyze interaction of beta-sesquiphellandrene (compound belongs to ginger) with spike protein (Sp) and membrane glycoprotein polyprotein (MPp).

Results

Our molecular docking and simulation study reveals the perfect binding pocket of Sp and MPp holding beta-sesquiphellandrene (bS). Binding energies for MPp-bS and Sp-bS were found to be − 9.5 kcal/mol and − 10.3 kcal/mol respectively. RMSD and RMSF values for docked complexes were found to be in selectable range, i.e., 1 to 3 Å and 1 to 8 Å respectively. Modern computational tools were used here to make this investigation fast and effective. Further, ADME analysis reveals the therapeutic validations for beta-sesquiphellandrene to act as a useful pharmacoactive compound. Beta-sesquiphellandrene provides not only inhibitory effect on spike protein of SARS-CoV2 but also similar inhibitory effects on membrane glycoprotein polyprotein complex of SFTS virus, which hampers the pathological initiation of the diseases caused by both the viruses, i.e., COVID-19 and severe fever with thrombocytopenia syndrome.

Conclusion

This method of computational analysis was found to be rapid and effective, and opens new doors in the domain of in silico drug discovery. Beta-sesquiphellandrene can be used as effective medicine to control these harmful pathogens after wet lab validations.

A multiple peptides vaccine against COVID-19 designed from the nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the immunoinformatics approach

Due to the current Coronavirus (COVID-19) pandemic, the rapid discovery of a safe and effective vaccine is an essential issue. Consequently, this study aims to predict a potential COVID-19 peptide-based vaccine utilizing the Nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the Immunoinformatics approach. To achieve this goal, several Immune Epitope Database (IEDB) tools, molecular docking, and safety prediction servers were used. According to the results, The Spike peptide SQCVNLTTRTQLPPAYTNSFTRGVY is predicted to have the highest binding affinity to the B-Cells. The Spike peptide FTISVTTEI has the highest binding affinity to the Major Histocompatibility Complex class 1 (MHC I) Human Leukocyte Allele HLA-B*1503 (according to the MDockPeP and HPEPDOCK servers, docking scores were −153.9 and −229.356, respectively). The Nucleocapsid peptides KTFPPTEPK and RWYFYYLGTGPEAGL have the highest binding affinity to the MHC I HLA-A0202 allele and the three the Major Histocompatibility Complex class 2 (MHC II) Human Leukocyte Allele HLA-DPA1*01:03/DPB1*02:01, HLA-DQA1*01:02/DQB1-*06:02, HLA-DRB1, respectively. Docking scores of peptide KTFPPTEPK were −153.9 and −220.876. In contrast, docking scores of peptide RWYFYYLGTGPEAGL were ranged from 218 to 318. Furthermore, those peptides were predicted as non-toxic and non-allergen. Therefore, the combination of those peptides is predicted to stimulate better immunological responses with respectable safety.

Immunoinformatic Analysis of SARS-CoV-2 Nucleocapsid Protein and Identification of COVID-19 Vaccine Targets

COVID-19 is a worldwide emergency; therefore, there is a critical need for foundational knowledge about B and T cell responses to SARS-CoV-2 essential for vaccine development. However, little information is available defining which determinants of SARS-CoV-2 other than the spike glycoprotein are recognized by the host immune system. In this study, we focus on the SARS-CoV-2 nucleocapsid protein as a suitable candidate target for vaccine formulations. Major B and T cell epitopes of the SARS-CoV-2 N protein are predicted and resulting sequences compared with the homolog immunological domains of other coronaviruses that infect human beings. The most dominant of B cell epitope is located between 176–206 amino acids in the SRGGSQASSRSSSRSRNSSRNSTPGSSRGTS sequence. Further, we identify sequences which are predicted to bind multiple common MHC I and MHC II alleles. Most notably there is a region of potential T cell cross-reactivity within the SARS-CoV-2 N protein position 102–110 amino acids that traverses multiple human alpha and betacoronaviruses. Vaccination strategies designed to target these conserved epitope regions could generate immune responses that are cross-reactive across human coronaviruses, with potential to protect or modulate disease. Finally, these predictions can facilitate effective vaccine design against this high priority virus.

A bioinformatic prediction of antigen presentation from SARS-CoV-2 spike protein revealed a theoretical correlation of HLA-DRB1*01 with COVID-19 fatality in Mexican population: An ecological approach

SARS‐CoV‐2 infection is causing a pandemic disease that is reflected in challenging public health problems worldwide. Human leukocyte antigen (HLA)‐based epitope prediction and its association with disease outcomes provide an important base for treatment design. A bioinformatic prediction of T cell epitopes and their restricted HLA Class I and II alleles was performed to obtain immunogenic epitopes and HLA alleles from the spike protein of the severe acute respiratory syndrome coronavirus 2 virus. Also, a correlation with the predicted fatality rate of hospitalized patients in 28 states of Mexico was done. Here, we describe a set of 10 highly immunogenic epitopes, together with different HLA alleles that can efficiently present these epitopes to T cells. Most of these epitopes are located within the S1 subunit of the spike protein, suggesting that this area is highly immunogenic. A statistical negative correlation was found between the frequency of HLA‐DRB1*01 and the fatality rate in hospitalized patients in Mexico.

First HLA association study for COVID‐19 in Mexico

An epitope prediction for HLA Class I and II provided a list of highly immunogenic epitopes from the S protein of SARS‐CoV2 with potential use for vaccine development.

A multi‐level approach revealed a correlation of HLA‐DRB1*01 frequency with fatality in Mexican hospitalized patients at ecological level.

Exploring the out of sight antigens of SARS-CoV-2 to design a candidate multi-epitope vaccine by utilizing immunoinformatics approaches

SARS-CoV-2 causes a severe respiratory disease called COVID-19. Currently, global health is facing its devastating outbreak. However, there is no vaccine available against this virus up to now. In this study, a novel multi-epitope vaccine against SARS-CoV-2 was designed to provoke both innate and adaptive immune responses. The immunodominant regions of six non-structural proteins (nsp7, nsp8, nsp9, nsp10, nsp12 and nsp14) of SARS-CoV-2 were selected by multiple immunoinformatic tools to provoke T cell immune response. Also, immunodominant fragment of the functional region of SARS-CoV-2 spike (400-510 residues) protein was selected for inducing neutralizing antibodies production. The selected regions' sequences were connected to each other by furin-sensitive linker (RVRR). Moreover, the functional region of β-defensin as a well-known agonist for the TLR-4/MD complex was added at the N-terminus of the vaccine using (EAAAK)3 linker. Also, a CD4 + T-helper epitope, PADRE, was used at the C-terminal of the vaccine by GPGPG and A(EAAAK)2A linkers to form the final vaccine construct. The physicochemical properties, allergenicity, antigenicity, functionality and population coverage of the final vaccine construct were analyzed. The final vaccine construct was an immunogenic, non-allergen and unfunctional protein which contained multiple CD8 + and CD4 + overlapping epitopes, IFN-γ inducing epitopes, linear and conformational B cell epitopes. It could form stable and significant interactions with TLR-4/MD according to molecular docking and dynamics simulations. Global population coverage of the vaccine for HLA-I and II were estimated 96.2% and 97.1%, respectively. At last, the final vaccine construct was reverse translated to design the DNA vaccine. Although the designed vaccine exhibited high efficacy in silico, further experimental validation is necessary.

A flow virometry process proposed for detection of SARS-CoV-2 and large-scale screening of COVID-19 cases

The viral pneumonia COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly over 210 countries and declared as pandemic by WHO. WHO has emphasized on the scale-up of testing capacity, followed by isolation of infected individuals, and contact tracing, as the ‘backbone’ of managing the pandemic. Globally, the detection of SARS-CoV-2 in patients is done by real-time PCR (RT-PCR) and blood antibody-based testing. Here, a flow cytometry-based high-throughput screening system is proposed for testing of COVID-19 cases where the virus particle binds to specific primary antibodies and the resultant virus–antibody complex then binds to fluorescent-tagged secondary antibodies. The fluorescence signal could be measured in a flow channel for qualitative detection of virus in the test sample.

An insight into the epitope-based peptide vaccine design strategy and studies against COVID-19

SARS-CoV-2 is a new member of the coronavirus family and caused the pandemic of coronavirus disease 2019 (COVID-19) in 2020. It is crucial to design and produce an effective vaccine for the prevention of rapid transmission and possible deaths wcaused by the disease. Although intensive work and research are being carried out all over the world to develop a vaccine, an effective and approved formulation that can prevent the infection and limit the outbreak has not been announced yet. Among all types of vaccines, epitope-based peptide vaccines outshine with their low-cost production, easy modification in the structure, and safety. In this review, vaccine studies against COVID-19 have been summarized and detailed information about the epitope-based peptide vaccines against COVID-19 has been provided. We have not only compared the peptide vaccine with other types of vaccines but also presented comprehensive literature information about development steps for an effective and protective formulation to give an insight into on-going peptide vaccine studies against SARS-CoV-2.