Lessons from Reverse Vaccinology for viral vaccine design

A Comprehensive Review of Our Understanding and Challenges of Viral Vaccines against Swine Pathogens

Pig farming has become a strategically significant and economically important industry across the globe. It is also a potentially vulnerable sector due to challenges posed by transboundary diseases in which viral infections are at the forefront. Among the porcine viral diseases, African swine fever, classical swine fever, foot and mouth disease, porcine reproductive and respiratory syndrome, pseudorabies, swine influenza, and transmissible gastroenteritis are some of the diseases that cause substantial economic losses in the pig industry. It is a well-established fact that vaccination is undoubtedly the most effective strategy to control viral infections in animals. From the period of Jenner and Pasteur to the recent new-generation technology era, the development of vaccines has contributed significantly to reducing the burden of viral infections on animals and humans. Inactivated and modified live viral vaccines provide partial protection against key pathogens. However, there is a need to improve these vaccines to address emerging infections more comprehensively and ensure their safety. The recent reports on new-generation vaccines against swine viruses like DNA, viral-vector-based replicon, chimeric, peptide, plant-made, virus-like particle, and nanoparticle-based vaccines are very encouraging. The current review gathers comprehensive information on the available vaccines and the future perspectives on porcine viral vaccines.

Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum

Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens, many of which were identified without utilizing systematic genome-level approaches. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.

Multidimensional futuristic approaches to address the pandemics beyond COVID-19

Globally, the impact of the coronavirus disease 2019 (COVID-19) pandemic has been enormous and unrelenting with ∼6.9 million deaths and ∼765 million infections. This review mainly focuses on the recent advances and potentially novel molecular tools for viral diagnostics and therapeutics with far-reaching implications in managing the future pandemics. In addition to briefly highlighting the existing and recent methods of viral diagnostics, we propose a couple of potentially novel non-PCR-based methods for rapid, cost-effective, and single-step detection of nucleic acids of viruses using RNA mimics of green fluorescent protein (GFP) and nuclease-based approaches. We also highlight key innovations in miniaturized Lab-on-Chip (LoC) devices, which in combination with cyber-physical systems, could serve as ideal futuristic platforms for viral diagnosis and disease management. We also discuss underexplored and underutilized antiviral strategies, including ribozyme-mediated RNA-cleaving tools for targeting viral RNA, and recent advances in plant-based platforms for rapid, low-cost, and large-scale production and oral delivery of antiviral agents/vaccines. Lastly, we propose repurposing of the existing vaccines for newer applications with a major emphasis on Bacillus Calmette-Guérin (BCG)-based vaccine engineering.

Pan-Genome Analysis of Oral Bacterial Pathogens to Predict a Potential Novel Multi-Epitopes Vaccine Candidate

Porphyromonas gingivalis is a Gram-negative anaerobic bacterium, mainly present in the oral cavity and causes periodontal infections. Currently, no licensed vaccine is available against P. gingivalis and other oral bacterial pathogens. To develop a vaccine against P. gingivalis, herein, we applied a bacterial pan-genome analysis (BPGA) on the bacterial genomes that retrieved a total number of 4908 core proteins, which were further utilized for the identification of good vaccine candidates. After several vaccine candidacy analyses, three proteins, namely lytic transglycosylase domain-containing protein, FKBP-type peptidyl-propyl cis-trans isomerase and superoxide dismutase, were shortlisted for epitopes prediction. In the epitopes prediction phase, different types of B and T-cell epitopes were predicted and only those with an antigenic, immunogenic, non-allergenic, and non-toxic profile were selected. Moreover, all the predicted epitopes were joined with each other to make a multi-epitopes vaccine construct, which was linked further to the cholera toxin B-subunit to enhance the antigenicity of the vaccine. For downward analysis, a three dimensional structure of the designed vaccine was modeled. The modeled structure was checked for binding potency with major histocompatibility complex I (MHC-I), major histocompatibility complex II (MHC-II), and Toll-like receptor 4 (TLR-4) immune cell receptors which revealed that the designed vaccine performed proper binding with respect to immune cell receptors. Additionally, the binding efficacy of the vaccine was validated through a molecular dynamic simulation that interpreted strong intermolecular vaccine-receptor binding and confirmed the exposed situation of vaccine epitopes to the host immune system. In conclusion, the study suggested that the model vaccine construct has the potency to generate protective host immune responses and that it might be a good vaccine candidate for experimental in vivo and in vitro studies.

Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.

SARS-CoV-2-Specific Vaccine Candidates; the Contribution of Structural Vaccinology

SARS-CoV-2 vaccine production has taken us by storm. We aim to fill in the history of concepts and the work of pioneers and provide a framework of strategies employing structural vaccinology. Cryo-electron microscopy became crucial in providing three-dimensional (3D) structures and creating candidates eliciting T and B cell-mediated immunity. It also determined structural changes in the emerging mutants in order to design new constructs that can be easily, quickly and safely added to the vaccines. The full-length spike (S) protein, the S1 subunit and its receptor binding domain (RBD) of the virus are the best candidates. The vaccine development to cease this COVID-19 pandemic sets a milestone for the pan-coronavirus vaccine's designing and manufacturing. By employing structural vaccinology, we propose that the mRNA and the protein sequences of the currently approved vaccines should be modified rapidly to keep up with the more infectious new variants.

Proteome-Wide Mapping and Reverse Vaccinology Approaches to Design a Multi-Epitope Vaccine against Clostridium perfringens

C. perfringens is a highly versatile bacteria of livestock and humans, causing enteritis (a common food-borne illness in humans), enterotoxaemia (in which toxins are formed in the intestine which damage and destroy organs, i.e., the brain), and gangrene (wound infection). There is no particular cure for the toxins of C. perfringens. Supportive care (medical control of pain, intravenous fluids) is the standard treatment. Therefore, a multiple-epitope vaccine (MEV) should be designed to battle against C. perfringens infection. Furthermore, the main objective of this in silico investigation is to design an MEV that targets C. perfringens. For this purpose, we selected the top three proteins that were highly antigenic using immuno-informatics approaches, including molecular docking. B-cells, IFN-gamma, and T cells for target proteins were predicted and the most conserved epitopes were selected for further investigation. For the development of the final MEV, epitopes of LBL5, CTL17, and HTL13 were linked to GPGPG, AAY, and KK linkers. The vaccine N-end was joined to an adjuvant through an EAAK linker to improve immunogenicity. After the attachment of linkers and adjuvants, the final construct was 415 amino acids. B-cell and IFN-gamma epitopes demonstrate that the model structure is enhanced for humoral and cellular immune responses. To validate the immunogenicity and safety of the final construct, various physicochemical properties, and other properties such as antigenicity and non-allergens, were evaluated. Furthermore, molecular docking was carried out for verification of vaccine compatibility with the receptor, evaluated in silico. Also, in silico cloning was employed for the verification of the proper expression and credibility of the construct.

Development of a Novel Multi-Epitope Vaccine Against Crimean-Congo Hemorrhagic Fever Virus: An Integrated Reverse Vaccinology, Vaccine Informatics and Biophysics Approach

Crimean-Congo hemorrhagic fever (CCHF) is a highly severe and virulent viral disease of zoonotic origin, caused by a tick-born CCHF virus (CCHFV). The virus is endemic in many countries and has a mortality rate between 10% and 40%. As there is no licensed vaccine or therapeutic options available to treat CCHF, the present study was designed to focus on application of modern computational approaches to propose a multi-epitope vaccine (MEV) expressing antigenic determinants prioritized from the CCHFV genome. Integrated computational analyses revealed the presence of 9 immunodominant epitopes from Nucleoprotein (N), RNA dependent RNA polymerase (RdRp), Glycoprotein N (Gn/G2), and Glycoprotein C (Gc/G1). Together these epitopes were observed to cover 99.74% of the world populations. The epitopes demonstrated excellent binding affinity for the B- and T-cell reference set of alleles, the high antigenic potential, non-allergenic nature, excellent solubility, zero percent toxicity and interferon-gamma induction potential. The epitopes were engineered into an MEV through suitable linkers and adjuvating with an appropriate adjuvant molecule. The recombinant vaccine sequence revealed all favorable physicochemical properties allowing the ease of experimental analysis in vivo and in vitro. The vaccine 3D structure was established ab initio. Furthermore, the vaccine displayed excellent binding affinity for critical innate immune receptors: TLR2 (-14.33 kcal/mol) and TLR3 (-6.95 kcal/mol). Vaccine binding with these receptors was dynamically analyzed in terms of complex stability and interaction energetics. Finally, we speculate the vaccine sequence reported here has excellent potential to evoke protective and specific immune responses subject to evaluation of downstream experimental analysis.

Identification of SARS-CoV-2 CTL epitopes for development of a multivalent subunit vaccine for COVID-19

An immunoinformatics-based approach was used to identify potential multivalent subunit CTL vaccine candidates for SARS-CoV-2. Criteria for computational screening included antigen processing, antigenicity, allergenicity, and toxicity. A total of 2604 epitopes were found to be strong binders to MHC class I molecules when analyzed using IEDB tools. Further testing for antigen processing yielded 826 peptides of which 451 were 9-mers that were analyzed for potential antigenicity. Antigenic properties were predicted for 102 of the 451 peptides. Further assessment for potential allergenicity and toxicity narrowed the number of candidate CTL epitopes to 50 peptide sequences, 45 of which were present in all strains of SARS-CoV-2 that were tested. The predicted CTL epitopes were then tested to eliminate those with MHC class II binding potential, a property that could induce hyperinflammatory responses mediated by T_H2 cells in immunized hosts. Eighteen of the 50 epitopes did not show class II binding potential. To our knowledge this is the first comprehensive analysis on the proteome of SARS-CoV-2 for prediction of CTL epitopes lacking binding properties that could stimulate unwanted T_H2 responses. Future studies will be needed to assess these epitopes as multivalent subunit vaccine candidates which stimulate protective CTL responses against SARS-COV-2.

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies.

Identification of relevant regions on structural and nonstructural proteins of Zika virus for vaccine and diagnostic test development: an in silico approach

Zika virus (ZIKV) is an arbovirus belonging to the Flaviviridae family and the genus Flavivirus. Infection with ZIKV causes a mild, self-limiting febrile illness called Zika fever. However, ZIKV infection has been recently associated with microcephaly and Guillain-Barré syndrome. Vaccines for the disease are a high priority of World Health Organization. Several studies are currently being conducted to develop a vaccine against ZIKV, but until now there is no licensed ZIKV vaccine. This study used a novel immunoinformatics approach to identify potential T-cell immunogenic epitopes present in the structural and nonstructural proteins of ZIKV. Fourteen T-cell candidate epitopes were identified on ZIKV structural and nonstructural proteins: pr^36-50; C^61-75; C^103-117; E^374-382; E^477-491; NS2a^90-104; NS2a^174-188; NS2a^179-193; NS2a^190-204; NS2a^195-209; NS2a^200-214; NS3^175-189; and NS4a^82-96; NS4a^99-113. Among these epitopes, only E^374-382 is a human leukocyte antigen (HLA) type I restricted epitope. All identified epitopes showed a low similarity with other important flaviviruses but had a high conservation rate among the ZIKV strains and a high population coverage rate. Therefore, these predicted T-cell epitopes are potential candidates targets for development of vaccines to prevent ZIKV infection.

Advanced In Silico Tools for Designing of Antigenic Epitope as Potential Vaccine Candidates Against Coronavirus

Vaccines are the most economical and potent substitute of available medicines to cure various bacterial and viral diseases. Earlier, killed or attenuated pathogens were employed for vaccine development. But in present era, the peptide vaccines are in much trend and are favoured over whole vaccines because of their superiority over conventional vaccines. These vaccines are either based on single proteins or on synthetic peptides including several B-cell and T-cell epitopes. However, the overall mechanism of action remains the same and works by prompting the immune system to activate the specific B-cell- and T-cell-mediated responses against the pathogen. Rino Rappuoli and others have contributed in this field by plotting the design of the most potent and fully computational approach for discovery of potential vaccine candidates which is popular as reverse vaccinology. This is quite an unambiguous advance for vaccine evolution where one begins with the genome information of the pathogen and ends up with the list of certain epitopes after application of multiple bioinformatics tools. This book chapter is an effort to bring this approach of reverse vaccinology into notice of readers using example of coronavirus.

An Overview of Current Approaches Toward the Treatment and Prevention of West Nile Virus Infection

The persistence of West Nile virus (WNV) infections throughout the USA since its inception in 1999 and its continuous spread throughout the globe calls for an urgent need of effective treatments and prevention measures. Although the licensing of several WNV vaccines for veterinary use provides a proof of concept, similar efforts on the development of an effective vaccine for humans remain still unsuccessful. Increased understanding of biology and pathogenesis of WNV together with recent technological advancements have raised hope that an effective WNV vaccine may be available in the near future. In addition, rapid progress in the structural and functional characterization of WNV and other flaviviral proteins have provided a solid base for the design and development of several classes of inhibitors as potential WNV therapeutics. Moreover, the therapeutic monoclonal antibodies demonstrate an excellent efficacy against WNV in animal models and represent a promising class of WNV therapeutics. However, there are some challenges as to the design and development of a safe and efficient WNV vaccine or therapeutic. In this chapter, we discuss the current approaches, progress, and challenges toward the development of WNV vaccines, therapeutic antibodies, and antiviral drugs.

Vaccines against Botulism

Botulinum neurotoxins (BoNT) cause the flaccid paralysis of botulism by inhibiting the release of acetylcholine from motor neurons. There are seven serotypes of BoNT (A-G), with limited therapies, and no FDA approved vaccine for botulism. An investigational formalin-inactivated penta-serotype-BoNT/A-E toxoid vaccine was used to vaccinate people who are at high risk of contracting botulism. However, this formalin-inactivated penta-serotype-BoNT/A-E toxoid vaccine was losing potency and was discontinued. This article reviews the different vaccines being developed to replace the discontinued toxoid vaccine. These vaccines include DNA-based, viral vector-based, and recombinant protein-based vaccines. DNA-based vaccines include plasmids or viral vectors containing the gene encoding one of the BoNT heavy chain receptor binding domains (HC). Viral vectors reviewed are adenovirus, influenza virus, rabies virus, Semliki Forest virus, and Venezuelan Equine Encephalitis virus. Among the potential recombinant protein vaccines reviewed are HC, light chain-heavy chain translocation domain, and chemically or genetically inactivated holotoxin.

New approaches and omics tools for mining of vaccine candidates against vector-borne diseases

Vector-borne diseases (VBDs) present a major threat to human and animal health, as well as place a substantial burden on livestock production. As a way of sustainable VBD control, focus is set on vaccine development. Advances in genomics and other "omics" over the past two decades have given rise to a "third generation" of vaccines based on technologies such as reverse vaccinology, functional genomics, immunomics, structural vaccinology and the systems biology approach. The application of omics approaches is shortening the time required to develop the vaccines and increasing the probability of discovery of potential vaccine candidates. Herein, we review the development of new generation vaccines for VBDs, and discuss technological advancement and overall challenges in the vaccine development pipeline. Special emphasis is placed on the development of anti-tick vaccines that can quell both vectors and pathogens.

Towards developing a vaccine for rheumatic heart disease

Rheumatic heart disease (RHD) is the most serious manifestations of rheumatic fever, which is caused by group A Streptococcus (GAS or Streptococcus pyogenes) infection. RHD is an auto immune sequelae of GAS pharyngitis, rather than the direct bacterial infection of the heart, which leads to chronic heart valve damage. Although antibiotics like penicillin are effective against GAS infection, improper medical care such as poor patient compliance, overcrowding, poverty, and repeated exposure to GAS, leads to acute rheumatic fever and RHD. Thus, efforts have been put forth towards developing a vaccine. However, a potential global vaccine is yet to be identified due to the widespread diversity of S. pyogenes strains and cross reactivity of streptococcal proteins with host tissues. In this review, we discuss the available vaccine targets of S. pyogenes and the significance of in silico approaches in designing a vaccine for RHD.

Omics Approaches for the Study of Adaptive Immunity to Staphylococcus aureus and the Selection of Vaccine Candidates

Staphylococcus aureus is a dangerous pathogen both in hospitals and in the community. Due to the crisis of antibiotic resistance, there is an urgent need for new strategies to combat S. aureus infections, such as vaccination. Increasing our knowledge about the mechanisms of protection will be key for the successful prevention or treatment of S. aureus invasion. Omics technologies generate a comprehensive picture of the physiological and pathophysiological processes within cells, tissues, organs, organisms and even populations. This review provides an overview of the contribution of genomics, transcriptomics, proteomics, metabolomics and immunoproteomics to the current understanding of S. aureus‑host interaction, with a focus on the adaptive immune response to the microorganism. While antibody responses during colonization and infection have been analyzed in detail using immunoproteomics, the full potential of omics technologies has not been tapped yet in terms of T-cells. Omics technologies promise to speed up vaccine development by enabling reverse vaccinology approaches. In consequence, omics technologies are powerful tools for deepening our understanding of the “superbug” S. aureus and for improving its control.

A modern approach for epitope prediction: identification of foot-and-mouth disease virus peptides binding bovine leukocyte antigen (BoLA) class I molecules

Major histocompatibility complex (MHC) class Imolecules regulate adaptive immune responses through the presentation of antigenic peptides to CD8+ T cells. Polymorphisms in the peptide binding region of class I molecules determine peptide binding affinity and stability during antigen presentation, and different antigen peptide motifs are associated with specific genetic sequences of class I molecules. Understanding bovine leukocyte antigen (BoLA), peptide-MHC class I binding specificities may facilitate development of vaccines or reagents for quantifying the adaptive immune response to intracellular pathogens, such as foot-and-mouth disease virus (FMDV). Six synthetic BoLA class I (BoLA-I) molecules were produced, and the peptide binding motif was generated for five of the six molecules using a combined approach of positional scanning combinatorial peptide libraries (PSCPLs) and neural network-based predictions (NetMHCpan). The updated NetMHCpan server was used to predict BoLA-I binding peptides within the P1 structural polyprotein sequence of FMDV (strain A24 Cruzeiro) for Bo-LA-1*01901, BoLA-2*00801, BoLA-2*01201, and BoLA-4*02401. Peptide binding affinity and stability were determined for these BoLA-I molecules using the luminescent oxygen channeling immunoassay (LOCI) and scintillation proximity assay (SPA). The functional diversity of known BoLA alleles was predicted using theMHCcluster tool, and functional predictions for peptide motifs were compared to observed data from this and prior studies. The results of these analyses showed that BoLA alleles cluster into three distinct groups with the potential to define BBoLA supertypes.^ This streamlined approach identifies potential T cell epitopes from pathogens, such as FMDV, and provides insight into T cell immunity following infection or vaccination.