Prediction of new vaccine targets in the core genome of Corynebacterium pseudotuberculosis through omics approaches and reverse vaccinology

Recent advances in the characterization of essential genes and development of a database of essential genes

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.

Discovery and development of COVID-19 vaccine from laboratory to clinic

The world has recently experienced one of the biggest and most severe public health disasters with severe acute respiratory syndrome coronavirus (SARS-CoV-2). SARS-CoV-2 is responsible for the coronavirus disease of 2019 (COVID-19) which is one of the most widespread and powerful infections affecting human lungs. Current figures show that the epidemic had reached 216 nations, where it had killed about 6,438,926 individuals and infected 590,405,710. WHO proclaimed the outbreak of the Ebola virus disease (EVD), in 2014 that killed hundreds of people in West Africa. The development of vaccines for SARS-CoV-2 becomes more difficult due to the viral mutation in its non-structural proteins (NSPs) especially NSP2 and NSP3, S protein, and RNA-dependent RNA polymerase (RdRp). Continuous monitoring of SARS-CoV-2, dynamics of the genomic sequence, and spike protein mutations are very important for the successful development of vaccines with good efficacy. Hence, the vaccine development for SARS-CoV-2 faces specific challenges starting from viral mutation. The requirement of long-term immunity development, safety, efficacy, stability, vaccine allocation, distribution, and finally, its cost is discussed in detail. Currently, 169 vaccines are in the clinical development stage, while 198 vaccines are in the preclinical development stage. The majority of these vaccines belong to the Ps-Protein subunit type which has 54, and the minor BacAg-SPV (Bacterial antigen-spore expression vector) type, at least 1 vaccination. The use of computational methods and models for vaccine development has revolutionized the traditional methods of vaccine development. Further, this updated review highlights the upcoming vaccine development strategies in response to the current pandemic and post-pandemic era, in the field of vaccine development.

Proteomics of Toxigenic Corynebacteria

Within the genus Corynebacterium, six species are potential carriers of the tox gene, which encodes the highly potent diphtheria exotoxin: Corynebacterium diphtheriae, Corynebacterium belfantii, Corynebacterium rouxii, Corynebacterium ulcerans, Corynebacterium pseudotuberculosis and Corynebacterium silvaticum. Based on their potential to infect different host species and cause either human infections, zoonotic diseases or infections of economically important animals, these bacteria are of high scientific and economic interest and different research groups have carried out proteome analyses. These showed that especially the combination of MS-based proteomics with bioinformatic tools helped significantly to elucidate the functional aspects of corynebacterial genomes and to handle the genome and proteome complexity. The combination of proteomic and bioinformatic approaches was also used to discover new vaccine and drug targets. In addition, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry has been established as a fast and precise tool for the identification of these bacteria.

Potential therapeutic targets for combating Mycoplasma genitalium

Mycoplasma genitalium (M. genitalium) has emerged as a sexually transmitted infection (STI) all over the world in the last three decades. It has been identified as a cause of male urethritis, and there is now evidence that it also causes cervicitis and pelvic inflammatory disease in women. However, the precise role of M. genitalium in diseases such as pelvic inflammatory disease, and infertility is unknown, and more research is required. It is a slow-growing organism, and with the advent of the nucleic acid amplification test (NAAT), more studies are being conducted and knowledge about the pathogenicity of this organism is being elucidated. The accumulation of data has improved our understanding of the pathogen and its role in disease transmission. Despite the widespread use of single-dose azithromycin in the sexual health field, M. genitalium is known to rapidly develop antibiotic resistance. As a result, the media frequently refer to this pathogen as the "new STI superbug." Despite their rarity, antibiotics available today have serious side effects. As the cure rates for first-line antimicrobials have decreased, it is now a challenge to determine the effective antimicrobial therapy. In this review, we summarise recent M. genitalium research and investigate potential therapeutic targets for combating this pathogen.

Multi-Omics of Corynebacterium Pseudotuberculosis 12CS0282 and an In Silico Reverse Vaccinology Approach Reveal Novel Vaccine and Drug Targets

Corynebacterium pseudotuberculosis is an important animal pathogen, which is also able to infect humans. An optimal treatment of infections with this pathogen is not available today and consequently, more research is necessary to understand the infection process. Here, we present a combined -omics and bioinformatics approach to characterize C. pseudotuberculosis 12CS0282. The genome sequence of strain 12CS0282 was determined, analyzed in comparison with the available 130 C. pseudotuberculosis sequences and used as a basis for proteome analyses. In a reverse vaccinology approach, putative vaccine and drug targets for 12CS0208 were identified. Mass spectrometry analyses revealed the presence of multiple virulence factors even without host contact. In macrophage interaction studies, C. pseudotuberculosis 12CS0282 was highly resistant against human phagocytes and even multiplied within human THP-1 cells. Taken together, the data indicate a high pathogenic potential of the strain.

In silico approach to identify microsatellite candidate biomarkers to differentiate the biovar of Corynebacterium pseudotuberculosis genomes

Corynebacterium pseudotuberculosis is the causative bacterial agent of the zoonotic disease known as caseous lymphadenitis, and it presents several mechanisms of response to host defenses, including the presence of virulence factors (VFs). The genomes of these bacteria have several polymorphic markers known as microsatellites, or simple sequence repeats (SSRs), that can be used to characterize the genome, to study possible polymorphisms existing among strains, and to verify the effects of such polymorphic markers in coding regions and regions associated with VFs. In this study, several SSRs were identified within coding regions throughout the 54 genomes of this species, revealing possible polymorphisms associated with coding regions that could be used as strain-specific or serotype-specific identifiers of C. pseudotuberculosis. The similarities associated with SSRs amongst the different serum variants of C. pseudotuberculosis, biovars equi and ovis, were also evaluated, and it was possible to identify SSRs located in coding regions responsible for a VF enrolled in pathogenesis known to mediate bacterial adherence (SpaH-type pili virulence factor). Phylogenetic analyses revealed that strains sharing SSR patterns, including the possible polymorphisms identified in the same position of gene-coding regions, were displayed by strains with a common ancestor, corroborating with the Genome Tree Report of the NCBI. Statistical analysis showed that the microsatellite groups belonging to equi and ovis biovars have a significance of 0.006 (p-value) in similarity, thus indicating them as good biomarker candidates for C. pseudotuberculosis.

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.

Vaccinomics to Design a Multi-Epitopes Vaccine for Acinetobacter baumannii

Antibiotic resistance (AR) is the result of microbes’ natural evolution to withstand the action of antibiotics used against them. AR is rising to a high level across the globe, and novel resistant strains are emerging and spreading very fast. Acinetobacter baumannii is a multidrug resistant Gram-negative bacteria, responsible for causing severe nosocomial infections that are treated with several broad spectrum antibiotics: carbapenems, β-lactam, aminoglycosides, tetracycline, gentamicin, impanel, piperacillin, and amikacin. The A. baumannii genome is superplastic to acquire new resistant mechanisms and, as there is no vaccine in the development process for this pathogen, the situation is more worrisome. This study was conducted to identify protective antigens from the core genome of the pathogen. Genomic data of fully sequenced strains of A. baumannii were retrieved from the national center for biotechnological information (NCBI) database and subjected to various genomics, immunoinformatics, proteomics, and biophysical analyses to identify potential vaccine antigens against A. baumannii. By doing so, four outer membrane proteins were prioritized: TonB-dependent siderphore receptor, OmpA family protein, type IV pilus biogenesis stability protein, and OprD family outer membrane porin. Immuoinformatics predicted B-cell and T-cell epitopes from all four proteins. The antigenic epitopes were linked to design a multi-epitopes vaccine construct using GPGPG linkers and adjuvant cholera toxin B subunit to boost the immune responses. A 3D model of the vaccine construct was built, loop refined, and considered for extensive error examination. Disulfide engineering was performed for the stability of the vaccine construct. Blind docking of the vaccine was conducted with host MHC-I, MHC-II, and toll-like receptors 4 (TLR-4) molecules. Molecular dynamic simulation was carried out to understand the vaccine-receptors dynamics and binding stability, as well as to evaluate the presentation of epitopes to the host immune system. Binding energies estimation was achieved to understand intermolecular interaction energies and validate docking and simulation studies. The results suggested that the designed vaccine construct has high potential to induce protective host immune responses and can be a good vaccine candidate for experimental in vivo and in vitro studies.

Vaxi-DL: A web-based deep learning server to identify potential vaccine candidates

The development of a new vaccine is a challenging exercise involving several steps including computational studies, experimental work, and animal studies followed by clinical studies. To accelerate the process, in silico screening is frequently used for antigen identification. Here, we present Vaxi-DL, web-based deep learning (DL) software that evaluates the potential of protein sequences to serve as vaccine target antigens. Four different DL pathogen models were trained to predict target antigens in bacteria, protozoa, fungi, and viruses that cause infectious diseases in humans. Datasets containing antigenic and non-antigenic sequences were derived from known vaccine candidates and the Protegen database. Biological and physicochemical properties were computed for the datasets using publicly available bioinformatics tools. For each of the four pathogen models, the datasets were divided into training, validation, and testing subsets and then scaled and normalised. The models were constructed using Fully Connected Layers (FCLs), hyper-tuned, and trained using the training subset. Accuracy, sensitivity, specificity, precision, recall, and AUC (Area under the Curve) were used as metrics to assess the performance of these models. The models were benchmarked using independent datasets of known target antigens against other prediction tools such as VaxiJen and Vaxign-ML. We also tested Vaxi-DL on 219 known potential vaccine candidates (PVC) from 37 different pathogens. Our tool predicted 175 PVCs correctly out of 219 sequences. We also tested Vaxi-DL on different datasets obtained from multiple resources. Our tool has demonstrated an average sensitivity of 93% and will thus be a useful tool for prioritising PVCs for preclinical studies.

An integrated in silico based subtractive genomics and reverse vaccinology approach for the identification of novel vaccine candidate and chimeric vaccine against XDR Salmonella typhi H58

Salmonella typhi is notorious for causing enteric fever which is also known as typhoid fever. It emerged as an extreme drug resistant strain that requires urgent attention to prevent its global spread. Statistically, about 11-17 million typhoid illnesses are reported worldwide annually. The only alternative approach for the control of this illness is proper vaccination. However, available typhoid vaccine has certain limitations such as poor long-term efficacy, and non-recommendation for below 6 years children, which opens the avenues for designing new vaccines to overcome such limitations. Computational-based reverse vaccinology along with subtractive genomics analysis is one of the robust approaches used for the prioritization of vaccine candidates through direct screening of genome sequence assemblies. In the current study, we have successfully designed a peptide-based novel antigen chimeric vaccine candidate against the XDR strain of S. typhi H58. The pipeline revealed four peptides from WP_001176621.1 i.e., peptidoglycan-associated lipoprotein Pal and two peptides from WP_000747548.1 i.e., OmpA family lipoprotein as promising target for the induction of immune response against S. typhi. The six epitopes from both proteins were found as immunogenic, antigenic, virulent, highly conserved, nontoxic, and non-allergenic among whole Salmonella H58 proteome. Furthermore, the binding interaction between a chimeric vaccine and human population alleles was unveiled through structure-based studies. So far, these proteins have never been characterized as vaccine targets against S. typhi. The current study proposed that construct V2 could be a significant vaccine candidate against S. typhi H58. However, to ascertain this, future experimental holistic studies are recommended as follow-up.

A journey through the Corynebacterium pseudotuberculosis proteome promotes insights into its functional genome

Background

Corynebacterium pseudotuberculosis is a Gram-positive facultative intracellular pathogen and the etiologic agent of illnesses like caseous lymphadenitis in small ruminants, mastitis in dairy cattle, ulcerative lymphangitis in equines, and oedematous skin disease in buffalos. With the growing advance in high-throughput technologies, genomic studies have been carried out to explore the molecular basis of its virulence and pathogenicity. However, data large-scale functional genomics studies are necessary to complement genomics data and better understating the molecular basis of a given organism. Here we summarize, MS-based proteomics techniques and bioinformatics tools incorporated in genomic functional studies of C. pseudotuberculosis to discover the different patterns of protein modulation under distinct environmental conditions, and antigenic and drugs targets.

Methodology

In this study we performed an extensive search in Web of Science of original and relevant articles related to methods, strategy, technology, approaches, and bioinformatics tools focused on the functional study of the genome of C. pseudotuberculosis at the protein level.

Results

Here, we highlight the use of proteomics for understating several aspects of the physiology and pathogenesis of C. pseudotuberculosis at the protein level. The implementation and use of protocols, strategies, and proteomics approach to characterize the different subcellular fractions of the proteome of this pathogen. In addition, we have discussed the immunoproteomics, immunoinformatics and genetic tools employed to identify targets for immunoassays, drugs, and vaccines against C. pseudotuberculosis infection.

Conclusion

In this review, we showed that the combination of proteomics and bioinformatics studies is a suitable strategy to elucidate the functional aspects of the C. pseudotuberculosis genome. Together, all information generated from these proteomics studies allowed expanding our knowledge about factors related to the pathophysiology of this pathogen.

Proteins from the core genome of Corynebacterium ulcerans respond for pathogenicity and reveal promising vaccine targets for diphtheria

Corynebacterium ulcerans is an emerging pathogen able to transmit the acute infection diphtheria to humans. Although there is a well-established vaccine based on the toxin produced by Corynebacterium diphtheriae, another species of this genus known to cause the disease, there is still no vaccine formulations described for C. ulcerans; this fact contributes to the increase in cases of infection that has been observed. In this study, we want to provide information at the genomic level of this bacterium in order to suggest proteins as possible vaccine targets. We carried out an in silico prospection of vaccine candidates through reverse vaccinology for targets that exhibit antigenic potential against diphtheria. We found important virulence factors, such as adhesion-related ones, that are responsible for pathogen-host interaction after infection, but we did not find the diphtheria toxin, which is the main component of the currently available vaccine. This study provides detailed information about the exoproteome and hypothetical proteins from the core genome of C. ulcerans, suggesting vaccine targets to be further tested in vitro for the development of a new vaccine against diphtheria.

Pore-forming alpha-hemolysin efficiently improves the immunogenicity and protective efficacy of protein antigens

Highly immunogenic exotoxins are used as carrier proteins because they efficiently improve the immunogenicity of polysaccharides. However, their efficiency with protein antigens remains unclear. In the current study, the candidate antigen PA0833 from Pseudomonas aeruginosa was fused to the α-hemolysin mutant HlaH35A from Staphylococcus aureus to form a HlaH35A-PA0833 fusion protein (HPF). Immunization with HPF resulted in increased PA0833-specific antibody titers, higher protective efficacy, and decreased bacterial burden and pro-inflammatory cytokine secretion compared with PA0833 immunization alone. Using fluorescently labeled antigens to track antigen uptake and delivery, we found that HlaH35A fusion significantly improved antigen uptake in injected muscles and antigen delivery to draining lymph nodes. Both in vivo and in vitro studies demonstrated that the increased antigen uptake after immunization with HPF was mainly due to monocyte- and macrophage-dependent macropinocytosis, which was probably the result of HPF binding to ADAM10, the Hla host receptor. Furthermore, a transcriptome analysis showed that several immune signaling pathways were activated by HPF, shedding light on the mechanism whereby HlaH35A fusion improves immunogenicity. Finally, the improvement in immunogenicity by HlaH35A fusion was also confirmed with two other antigens, GlnH from Klebsiella pneumoniae and the model antigen OVA, indicating that HlaH35A could serve as a universal carrier protein to improve the immunogenicity of protein antigens.

Chimeric vaccine designs against Acinetobacter baumannii using pan genome and reverse vaccinology approaches

Acinetobacter baumannii (A. baumannii), an opportunistic, gram-negative pathogen, has evoked the interest of the medical community throughout the world because of its ability to cause nosocomial infections, majorly infecting those in intensive care units. It has also drawn the attention of researchers due to its evolving immune evasion strategies and increased drug resistance. The emergence of multi-drug-resistant-strains has urged the need to explore novel therapeutic options as an alternative to antibiotics. Due to the upsurge in antibiotic resistance mechanisms exhibited by A. baumannii, the current therapeutic strategies are rendered less effective. The aim of this study is to explore novel therapeutic alternatives against A. baumannii to control the ailed infection. In this study, a computational framework is employed involving, pan genomics, subtractive proteomics and reverse vaccinology strategies to identify core promiscuous vaccine candidates. Two chimeric vaccine constructs having B-cell derived T-cell epitopes from prioritized vaccine candidates; APN, AdeK and AdeI have been designed and checked for their possible interactions with host BCR, TLRs and HLA Class I and II Superfamily alleles. These vaccine candidates can be experimentally validated and thus contribute to vaccine development against A. baumannii infections.

Computational identification of putative common genomic drug and vaccine targets in Mycoplasma genitalium

Mycoplasma genitalium is an obligate intracellular bacterium that is responsible for several sexually transmitted infections, including non-gonococcal urethritis in men and several inflammatory reproductive tract syndromes in women. Here, we applied subtractive genomics and reverse vaccinology approaches for in silico prediction of potential vaccine and drug targets against five strains of M. genitalium. We identified 403 genes shared by all five strains, from which 104 non-host homologous proteins were selected, comprising of 44 exposed/secreted/membrane proteins and 60 cytoplasmic proteins. Based on the essentiality, functionality, and structure-based binding affinity, we finally predicted 19 (14 novel) putative vaccine and 7 (2 novel) candidate drug targets. The docking analysis showed six molecules from the ZINC database as promising drug candidates against the identified targets. Altogether, both vaccine candidates and drug targets identified here may contribute to the future development of therapeutic strategies to control the spread of M. genitalium worldwide.

rSodC is a potential antigen to diagnose Corynebacterium pseudotuberculosis by enzyme-linked immunoassay

Caseous lymphadenitis (CL) is a chronic infectious disease that affects sheep and goats. Many serological tests have been developed to detect the disease; one of the most widely used is the enzyme-linked immunosorbent assay (ELISA), due to its advantages, which include acceptable cost-effectiveness, applicability, sensitivity and specificity. ELISA formulations using recombinant proteins can exhibit significant sensitivity and specificity when using a single purified antigen. DTxR, Trx, TrxR, LexA, SodC, SpaC, NanH, and PknG recombinant proteins can be considered target proteins for ELISA development due to its extracellular or on the cell surface location, which allows a better recognition by the immune system. Therefore, the objectives of this study were to evaluate the antigenic reactivity of Corynebacterium pseudotuberculosis recombinant proteins in goat and sheep serum. Of eight proteins evaluated, rSodC was selected for validation assays with small ruminant serum samples from the semiarid region of the state of Bahia, Brazil. Validation assays with goat serum samples showed that ELISA-rSodC presented sensitivity and specificity of 96% and 94%, respectively. Validation assays with sheep serum showed that ELISA-rSodC exhibited sensitivity and specificity of 95% and 98%, respectively. Analysis of 756 field serum samples showed that rSodC identified 95 positive samples (23%) in goats and 75 positive samples (21%) in sheep. The ELISA with recombinant SodC protein developed in this study discriminated positive and negative serum samples with high levels of sensitivity and specificity. This formulation is promising for epidemiological surveys and CL control programs.

Trial registration AEC No 4958051018. 12/18/2018, retrospectively registered

In silico functional prediction of hypothetical proteins from the core genome of Corynebacterium pseudotuberculosis biovar ovis

Corynebacterium pseudotuberculosis is a pathogen of veterinary relevance diseases, being divided into two biovars: equi and ovis; causing ulcerative lymphangitis and caseous lymphadenitis, respectively. The isolation and sequencing of C. pseudotuberculosis biovar ovis strains in the Northern and Northeastern regions of Brazil exhibited the emergence of this pathogen, which causes economic losses to small ruminant producers, and condemnation of carcasses and skins of animals. Through the pan-genomic approach, it is possible to determine and analyze genes that are shared by all strains of a species—the core genome. However, many of these genes do not have any predicted function, being characterized as hypothetical proteins (HP). In this study, we considered 32 C. pseudotuberculosis biovar ovis genomes for the pan-genomic analysis, where were identified 172 HP present in a core genome composed by 1255 genes. We are able to functionally annotate 80 sequences previously characterized as HP through the identification of structural features as conserved domains and families. Furthermore, we analyzed the physicochemical properties, subcellular localization and molecular function. Additionally, through RNA-seq data, we investigated the differential gene expression of the annotated HP. Genes inserted in pathogenicity islands had their virulence potential evaluated. Also, we have analyzed the existence of functional associations for their products based on protein–protein interaction networks, and perform the structural prediction of three targets. Due to the integration of different strategies, this study can underlie deeper in vitro researches in the characterization of these HP and the search for new solutions for combat this pathogen.

EpitoCore: Mining Conserved Epitope Vaccine Candidates in the Core Proteome of Multiple Bacteria Strains

In reverse vaccinology approaches, complete proteomes of bacteria are submitted to multiple computational prediction steps in order to filter proteins that are possible vaccine candidates. Most available tools perform such analysis only in a single strain, or a very limited number of strains. But the vast amount of genomic data had shown that most bacteria contain pangenomes, i.e., their genomic information contains core, conserved genes, and random accessory genes specific to each strain. Therefore, in reverse vaccinology methods it is of the utmost importance to define core proteins and core epitopes. EpitoCore is a decision-tree pipeline developed to fulfill that need. It provides surfaceome prediction of proteins from related strains, defines core proteins within those, calculate their immunogenicity, predicts epitopes for a given set of MHC alleles defined by the user, and then reports if epitopes are located extracellularly and if they are conserved among the core homologs. Pipeline performance is illustrated by mining peptide vaccine candidates in Mycobacterium avium hominissuis strains. From a total proteome of ~4,800 proteins per strain, EpitoCore predicted 103 highly immunogenic core homologs located at cell surface, many of those related to virulence and drug resistance. Conserved epitopes identified among these homologs allows the users to define sets of peptides with potential to immunize the largest coverage of tested HLA alleles using peptide-based vaccines. Therefore, EpitoCore is able to provide automated identification of conserved epitopes in bacterial pangenomic datasets.

Computational tools for modern vaccine development

Vaccines play an essential role in controlling the rates of fatality and morbidity. Vaccines not only arrest the beginning of different diseases but also assign a gateway for its elimination and reduce toxicity. This review gives an overview of the possible uses of computational tools for vaccine design. Moreover, we have described the initiatives of utilizing the diverse computational resources by exploring the immunological databases for developing epitope-based vaccines, peptide-based drugs, and other resources of immunotherapeutics. Finally, the applications of multi-graft and multivalent scaffolding, codon optimization and antibodyomics tools in identifying and designing in silico vaccine candidates are described.