Identification Of Vaccine Targets Research Articles

Genomic annotation for vaccine target identification and immunoinformatics-guided multi-epitope-based vaccine design against Songling virus through screening its whole genome encoded proteins.

Songling virus (SGLV), a newly discovered tick-borne orthonairovirus, was recently identified in human spleen tissue. It exhibits cytopathic effects in human hepatoma cells and is associated with clinical symptoms including headache, fever, depression, fatigue, and dizziness, but no treatments or vaccines exist for this pathogenic virus. In the current study, immunoinformatics techniques were employed to identify potential vaccine targets within SGLV by comprehensively analyzing SGLV proteins. Four proteins were chosen based on specific thresholds to identify B-cell and T-cell epitopes, validated through IFN-γ epitopes. Six overlap MHC-I, MHC-II, and B cell epitopes were chosen to design a comprehensive vaccine candidate, ensuring 100% global coverage. These structures were paired with different adjuvants for broader protection against international strains. Vaccine constructions' 3D models were high-quality and validated by structural analysis. After molecular docking, SGLV-V4 was selected for further research due to its lowest binding energy (-66.26 kcal/mol) and its suitable immunological and physiochemical properties. The vaccine gene is expressed significantly in E. coli bacteria through in silico cloning. Immunological research and MD simulations supported its molecular stability and robust immune response within the host cell. These findings can potentially be used in designing safer and more effective experimental SGLV-V4 vaccines.

Mining gene expression data for rational identification of novel drug targets and vaccine candidates against the cattle tick, Rhipicephalus microplus

Control of complex parasites via vaccination remains challenging, with the current combination of vaccines and small drugs remaining the choice for an integrated control strategy. Studies conducted to date, are providing evidence that multicomponent vaccines will be needed for the development of protective vaccines against endo- and ectoparasites, though multicomponent vaccines require an in-depth understanding of parasite biology which remains insufficient for ticks. With the rapid development and spread of acaricide resistance in ticks, new targets for acaricide development also remains to be identified, along with novel targets that can be exploited for the design of lead compounds. In this study, we analysed the differential gene expression of Rhipicephalus microplus ticks that were fed on cattle vaccinated with a multi-component vaccine (Bm86 and 3 putative Bm86-binding proteins). The data was scrutinised for the identification of vaccine targets, small drug targets and novel pathways that can be evaluated in future studies. Limitations associated with targeting novel proteins for vaccine and/or drug design is also discussed and placed into the context of challenges arising when targeting large protein families and intracellular localised proteins. Lastly, this study provide insight into how Bm86-based vaccines may reduce successful uptake and digestion of the bloodmeal and overall tick fecundity.

3pHLA-score improves structure-based peptide-HLA binding affinity prediction

Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those computational methods rely exclusively on sequence-based data, which leads to inherent limitations. Recent studies have shown that structure-based data can address some of these limitations. In this work we propose a novel machine learning (ML) structure-based protocol to predict binding affinity of peptides to HLA receptors. For that, we engineer the input features for ML models by decoupling energy contributions at different residue positions in peptides, which leads to our novel per-peptide-position protocol. Using Rosetta’s ref2015 scoring function as a baseline we use this protocol to develop 3pHLA-score. Our per-peptide-position protocol outperforms the standard training protocol and leads to an increase from 0.82 to 0.99 of the area under the precision-recall curve. 3pHLA-score outperforms widely used scoring functions (AutoDock4, Vina, Dope, Vinardo, FoldX, GradDock) in a structural virtual screening task. Overall, this work brings structure-based methods one step closer to epitope discovery pipelines and could help advance the development of cancer and viral vaccines.

Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches.

An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September 2021. This study is focused on finding potential vaccine candidates and designing an in-silico subunit multi-epitope vaccine candidates using a unique computational pipeline, integrating reverse vaccinology, molecular docking and simulation methods. A protein named spike protein of SARS-CoV-2 with the GenBank ID QHD43416.1 was shortlisted as a potential vaccine candidate and was examined for presence of B-cell and T-cell epitopes. We also investigated antigenicity and interaction with distinct polymorphic alleles of the epitopes. High ranking epitopes such as DLCFTNVY (B cell epitope), KIADYNKL (MHC Class-I) and VKNKCVNFN (MHC class-II) were shortlisted for subsequent analysis. Digestion analysis verified the safety and stability of the shortlisted peptides. Docking study reported a strong binding of proposed peptides with HLA-A*02 and HLA-B7 alleles. We used standard methods to construct vaccine model and this construct was evaluated further for its antigenicity, physicochemical properties, 2D and 3D structure prediction and validation. Further, molecular docking followed by molecular dynamics simulation was performed to evaluate the binding affinity and stability of TLR-4 and vaccine complex. Finally, the vaccine construct was reverse transcribed and adapted for E. coli strain K 12 prior to the insertion within the pET-28-a (+) vector for determining translational and microbial expression followed by conservancy analysis. Also, six multi-epitope subunit vaccines were constructed using different strategies containing immunogenic epitopes, appropriate adjuvants and linker sequences. We propose that our vaccine constructs can be used for downstream investigations using in-vitro and in-vivo studies to design effective and safe vaccine against different strains of COVID-19.

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens.

T cell recognition of a cognate peptide–major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T cell receptors would greatly facilitate identification of vaccine targets for both pathogenic diseases and personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the center of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as severe acute respiratory syndrome coronavirus 2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in the training data of the models seems to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen versus cancer peptides. Overall, we demonstrate that accurate and reliable predictions of immunogenic CD8+ T cell targets remain unsolved; thus, we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.

Identification of vaccine targets in pathogens and design of a vaccine using computational approaches

Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.

Sexually Transmitted Neisseria gonorrhoeae Infections-Update on Drug Treatment and Vaccine Development.

Background: Sexually transmitted gonorrhea, caused by the Gram-negative diplococcus Neisseria gonorrhoeae, continues to be a serious global health challenge despite efforts to eradicate it. Multidrug resistance among clinical N. gonorrhoeae isolates has limited treatment options, and attempts to develop vaccines have not been successful. Methods: A search of published literature was conducted, and information extracted to provide an update on the status of therapeutics and vaccine development for gonorrheal infection. Results: Recommended pharmacological treatment for gonorrhea has changed multiple times due to increasing acquisition of resistance to existing antibiotics by N. gonorrhoeae. Only broad-spectrum cephalosporin-based combination therapies are currently recommended for treatment of uncomplicated urogenital and anorectal gonococcal infections. With the reported emergence of ceftriaxone resistance, successful strategies addressing the global burden of gonorrhea must include vaccination. Century-old efforts at developing an effective vaccine against gonorrhea, leading to only four clinical trials, have not yielded any successful vaccine. Conclusions: While it is important to continue to explore new drugs for the treatment of gonorrhea, the historical trend of resistance acquisition suggests that any long-term strategy should include vaccine development. Advanced technologies in proteomics and in silico approaches to vaccine target identification may provide templates for future success.

Immunogenicity and protection induced by recombinant Toxocara canis proteins in a murine model of toxocariasis

Toxocariasis, a natural helminth infection of dogs and cats caused by Toxocara canis and T. cati, respectively, that are transmitted to mammals, including humans. Infection control is based currently on periodic antihelmintic treatment and there is a need for the development of vaccines to prevent this infection. Materials and Methods: Eight potential vaccine candidate T. canis recombinant proteins were identified by in silico (rTcGPRs, rTcCad, rTcVcan, rTcCyst) and larval proteomics (rTES26, rTES32, rMUC-3 and rCTL-4) analyses. Immunogenicity and protection against infectious challenge for seven of these antigens were determined in a murine model of toxocariasis. C57BL/6 female mice were immunized with each of or combinations of recombinant antigens prior to challenge with 500 T. canis embryonated eggs. Levels of specific antibodies (IgG, IgG1, IgG2a and IgE) in sera and cytokines (IL-5, INF-ɣ and IL-10) produced by antigens-stimulated splenocytes, were measured. Presence of specific antibodies to the molecules was measured in sera of T. canis-seropositive dogs and humans. Results: All seven molecules were immunogenic in immunized mice; all stimulated significantly elevated levels of specific IgG, IgG1 or IgG2a and six were associated with elevated levels of specific IgE; all induced elevated production of IFN- ɣ and IL-10 by splenocytes, but only the in silico-identified membrane-associated recombinants (rTcCad, rTcVcan, and rTcCyst) induced significantly increased IL-5 production. Vaccination with two of the latter (rTcCad and rTcVcan) reduced larval loads in the T. canis challenged mice by 54.3% and 53.9% (P < 0.0001), respectively, compared to unimmunized controls. All seven recombinants were recognized by T. canis-seropositive dog and human sera. Conclusion: The identification of vaccine targets by in silico analysis was an effective strategy to identify immunogenic T. canis proteins capable of reducing larval burdens following challenge with the parasite. Two recombinant proteins, rTcCad and rTcVcan, were identified as promising vaccine candidates for canine toxocariasis.

In silico identification of vaccine targets for 2019-nCoV.

Background: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China. Methods: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0. Results: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential. Conclusions: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.

In silico identification of vaccine targets for 2019-nCoV

Background: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China. Methods: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0. Results: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential. Conclusions: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.

In silico identification of vaccine targets for 2019-nCoV

Background: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China. Methods: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0. Results: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential. Conclusions: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.

Mapping HLA-A2, -A3 and -B7 supertype-restricted T-cell epitopes in the ebolavirus proteome

BackgroundEbolavirus (EBOV) is responsible for one of the most fatal diseases encountered by mankind. Cellular T-cell responses have been implicated to be important in providing protection against the virus. Antigenic variation can result in viral escape from immune recognition. Mapping targets of immune responses among the sequence of viral proteins is, thus, an important first step towards understanding the immune responses to viral variants and can aid in the identification of vaccine targets. Herein, we performed a large-scale, proteome-wide mapping and diversity analyses of putative HLA supertype-restricted T-cell epitopes of Zaire ebolavirus (ZEBOV), the most pathogenic species among the EBOV family.MethodsAll publicly available ZEBOV sequences (14,098) for each of the nine viral proteins were retrieved, removed of irrelevant and duplicate sequences, and aligned. The overall proteome diversity of the non-redundant sequences was studied by use of Shannon’s entropy. The sequences were predicted, by use of the NetCTLpan server, for HLA-A2, -A3, and -B7 supertype-restricted epitopes, which are relevant to African and other ethnicities and provide for large (~86%) population coverage. The predicted epitopes were mapped to the alignment of each protein for analyses of antigenic sequence diversity and relevance to structure and function. The putative epitopes were validated by comparison with experimentally confirmed epitopes.Results & discussionZEBOV proteome was generally conserved, with an average entropy of 0.16. The 185 HLA supertype-restricted T-cell epitopes predicted (82 (A2), 37 (A3) and 66 (B7)) mapped to 125 alignment positions and covered ~24% of the proteome length. Many of the epitopes showed a propensity to co-localize at select positions of the alignment. Thirty (30) of the mapped positions were completely conserved and may be attractive for vaccine design. The remaining (95) positions had one or more epitopes, with or without non-epitope variants. A significant number (24) of the putative epitopes matched reported experimentally validated HLA ligands/T-cell epitopes of A2, A3 and/or B7 supertype representative allele restrictions. The epitopes generally corresponded to functional motifs/domains and there was no correlation to localization on the protein 3D structure. These data and the epitope map provide important insights into the interaction between EBOV and the host immune system.

Analysis of viral diversity for vaccine target discovery

BackgroundViral vaccine target discovery requires understanding the diversity of both the virus and the human immune system. The readily available and rapidly growing pool of viral sequence data in the public domain enable the identification and characterization of immune targets relevant to adaptive immunity. A systematic bioinformatics approach is necessary to facilitate the analysis of such large datasets for selection of potential candidate vaccine targets.ResultsThis work describes a computational methodology to achieve this analysis, with data of dengue, West Nile, hepatitis A, HIV-1, and influenza A viruses as examples. Our methodology has been implemented as an analytical pipeline that brings significant advancement to the field of reverse vaccinology, enabling systematic screening of known sequence data in nature for identification of vaccine targets. This includes key steps (i) comprehensive and extensive collection of sequence data of viral proteomes (the virome), (ii) data cleaning, (iii) large-scale sequence alignments, (iv) peptide entropy analysis, (v) intra- and inter-species variation analysis of conserved sequences, including human homology analysis, and (vi) functional and immunological relevance analysis.ConclusionThese steps are combined into the pipeline ensuring that a more refined process, as compared to a simple evolutionary conservation analysis, will facilitate a better selection of vaccine targets and their prioritization for subsequent experimental validation.

An In Silico Identification of Common Putative Vaccine Candidates against Treponema pallidum: A Reverse Vaccinology and Subtractive Genomics Based Approach.

Sexually transmitted infections (STIs) are caused by a wide variety of bacteria, viruses, and parasites that are transmitted from one person to another primarily by vaginal, anal, or oral sexual contact. Syphilis is a serious disease caused by a sexually transmitted infection. Syphilis is caused by the bacterium Treponema pallidum subspecies pallidum. Treponema pallidum (T. pallidum) is a motile, gram-negative spirochete, which can be transmitted both sexually and from mother to child, and can invade virtually any organ or structure in the human body. The current worldwide prevalence of syphilis emphasizes the need for continued preventive measures and strategies. Unfortunately, effective measures are limited. In this study, we focus on the identification of vaccine targets and putative drugs against syphilis disease using reverse vaccinology and subtractive genomics. We compared 13 strains of T. pallidum using T. pallidum Nichols as the reference genome. Using an in silicoapproach, four pathogenic islands were detected in the genome of T. pallidum Nichols. We identified 15 putative antigenic proteins and sixdrug targets through reverse vaccinology and subtractive genomics, respectively, which can be used as candidate therapeutic targets in the future.

Putative Drug and Vaccine Target Identification in Leishmania donovani Membrane Proteins Using Naïve Bayes Probabilistic Classifier.

Predicting the role of protein is one of the most challenging problems. There are few approaches available for the prediction of role of unknown protein in terms of drug target or vaccine candidate. We propose here Naïve Bayes probabilistic classifier, a promising method for reliable predictions. This method is tested on the proteins identified in our mass spectrometry based membrane protemics study of Leishmania donovani parasite that causes a fatal disease (Visceral Leishmaniasis) in humans all around the world. Most of the vaccine/drug targets belonging to membrane proteins are represented as key players in the pathogenesis of Leishmania infection. Analyses of our previous results, using Naïve Bayes probabilistic classifier, indicate that this method predicts the role of unknown/hypothetical protein (as drug target/vaccine candidate) significantly with higher precision. We have employed this method in order to provide probabilistic predictions of unknown/hypothetical proteins as targets. This study reports the unknown/hypothetical proteins of Leishmania membrane fraction as a potential drug targets and vaccine candidate which is vital information for this parasite. Future molecular studies and characterization of these potent targets may produce a recombinant therapeutic/prophylactic tool against Visceral Leishmaniasis. These unknown/hypothetical proteins may open a vast research field to be exploited for novel treatment strategies.

Methods and Protocols for Developing Prion Vaccines.

Prion diseases denote a distinct form of infectivity that is based in the misfolding of a self-protein (PrP(C)) into a pathological, infectious conformation (PrP(Sc)). Efforts to develop vaccines for prion diseases have been complicated by the potential dangers that are associated with induction of immune responses against a self-protein. As a consequence, there is considerable appeal for vaccines that specifically target the misfolded prion conformation. Such conformation-specific immunotherapy is made possible through the identification of vaccine targets (epitopes) that are exclusively presented as a consequence of misfolding. An immune response directed against these targets, termed disease-specific epitopes (DSEs), has the potential to spare the function of the native form of the protein while clearing, or neutralizing, the infectious isomer. Although identification of DSEs represents a critical first step in the induction of conformation-specific immune responses, substantial efforts are required to translate these targets into functional vaccines. Due to the poor immunogenicity that is inherent to self-proteins, and that is often associated with short peptides, substantial efforts are required to overcome tolerance-to-self and maximize the resultant immune response following DSE-based immunization. This often includes optimization of target sequences in terms of immunogenicity and development of effective formulation and delivery strategies for the associated peptides. Further, these vaccines must satisfy additional criteria from perspectives of specificity (PrP(C) vs. PrP(Sc)) and safety (antibody-induced template-driven misfolding of PrP(C)). The emphasis of this report is on the steps required to translate DSEs into prion vaccines and subsequent evaluation of the resulting immune responses.

A computational method for identification of vaccine targets from protein regions of conserved human leukocyte antigen binding.

BackgroundComputational methods for T cell-based vaccine target discovery focus on selection of highly conserved peptides identified across pathogen variants, followed by prediction of their binding of human leukocyte antigen molecules. However, experimental studies have shown that T cells often target diverse regions in highly variable viral pathogens and this diversity may need to be addressed through redefinition of suitable peptide targets.MethodsWe have developed a method for antigen assessment and target selection for polyvalent vaccines, with which we identified immune epitopes from variable regions, where all variants bind HLA. These regions, although variable, can thus be considered stable in terms of HLA binding and represent valuable vaccine targets.ResultsWe applied this method to predict CD8+ T-cell targets in influenza A H7N9 hemagglutinin and significantly increased the number of potential vaccine targets compared to the number of targets discovered using the traditional approach where low-frequency peptides are excluded.ConclusionsWe developed a webserver with an intuitive visualization scheme for summarizing the T cell-based antigenic potential of any given protein or proteome using human leukocyte antigen binding predictions and made a web-accessible software implementation freely available at http://met-hilab.cbs.dtu.dk/blockcons/.

Omics‐Based Systems Vaccinology for Vaccine Target Identification

Preclinical ResearchCopyright 2011 Wiley-Liss, Inc., A Wiley CompanyThis article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency. Omics technologies include genomics, transcriptomics, proteomics, metabolomics, and immunomics. These technologies have been used in vaccine research, which can be summarized using the term “vaccinomics.” These omics technologies combined with advanced bioinformatics analysis form the core of “systems vaccinology.” Omics technologies provide powerful methods in vaccine target identification. The genomics‐based reverse vaccinology starts with predicting vaccine protein candidates through in silico bioinformatics analysis of genome sequences. The VIOLIN Vaxign vaccine design program (http://www.violinet.org/vaxign) is the first web‐based vaccine target prediction software based on the reverse vaccinology strategy. Systematic transcriptomics and proteomics analyses facilitate rational vaccine target identification by detesting genome‐wide gene expression profiles. Immunomics is the study of the set of antigens recognized by host immune systems and has also been used for efficient vaccine target prediction. With the large amount of omics data available, it is necessary to integrate various vaccine data using ontologies, including the Gene Ontology (GO) and Vaccine Ontology (VO), for more efficient vaccine target prediction and assessment. All these omics technologies combined with advanced bioinformatics analysis methods for a systems biology‐based vaccine target prediction strategy. This article reviews the various omics technologies and how they can be used in vaccine target identification.

Identification of potential apicoplast associated therapeutic targets in human and animal pathogen Toxoplasma gondii ME49.

Toxoplasma gondii ME49 is an obligatory intracellular apicomplexa parasite that causes toxoplasmosis in humans, domesticated and wild animals. Waterborne outbreaks of acute toxoplasmosis worldwide reinforce the transmission of Toxoplasma gondii ME49 to humans through contaminated water and may have a greater epidemiological impact than previously believed. In the quest for drug and vaccine target identification subtractive genomics involving subtraction between the host and pathogen genome has been implemented for enlisting essential pathogen specific proteins. Using this approach, our analysis on both human and Toxoplasma gondii ME49 reveals that out of 7987 protein coding sequences of the pathogen, 950 represent essential non human-homologous proteins. Subcellular localization prediction & comparative-biochemical pathway analysis of these essential proteins gives a list of apicoplast-associated proteins having unique pathogen-specific metabolic pathway. These apicoplast-associated enzymes involved in fatty acid biosynthesis pathway of Toxoplasma gondii ME49, may be used as potential drug targets, as the pathway is vital for the protozoan's survival. Structure prediction of drug target proteins was done using fold based recognition method. Screening of the functional inhibitors against these novel targets may result in discovery of novel therapeutic compounds that can be effective against Toxoplasma gondii ME49. DEG - Database of Essential Gene, KEGG - Kyoto Encyclopaedia of Genes and Genomes, KAAS - KEGG Automated Annotation Server, PFP - Protein Function Prediction, COG - Cluster of Orthologous Genes.

Bacterial surface proteins and vaccines

Surface-associated proteins play a key role in bacterial physiology and pathogenesis and are the major targets for vaccine development. Recent advances in defining the proteins associated with, and protruding out of, bacterial cells to a high level of accuracy are substantially contributing to accelerating the process of vaccine target identification and development.