iVAX: An integrated toolkit for the selection and optimization of antigens and the design of epitope-driven vaccines

Inferring B-cell derived T-cell receptor induced multi-epitope-based vaccine candidate against enterovirus 71: a reverse vaccinology approach

Purpose

Enterovirus 71, a pathogen that causes hand-foot and mouth disease (HFMD) is currently regarded as an increasing neurotropic virus in Asia and can cause severe complications in pediatric patients with blister-like sores or rashes on the hand, feet, and mouth. Notwithstanding the significant burden of the disease, no authorized vaccine is available. Previously identified attenuated and inactivated vaccines are worthless over time owing to changes in the viral genome.

Materials and Methods

A novel vaccine construct using B-cell derived T-cell epitopes from the virulent polyprotein found the induction of possible immune response. In order to boost the immune system, a beta-defensin 1 preproprotein adjuvant with EAAAK linker was added at the N-terminal end of the vaccine sequence.

Results

The immunogenicity of the designed, refined, and verified prospective three-dimensional-structure of the multi-epitope vaccine was found to be quite high, exhibiting non-allergenic and antigenic properties. The vaccine candidates bound to toll-like receptor 3 in a molecular docking analysis, and the efficacy of the potential vaccine to generate a strong immune response was assessed through in silico immunological simulation.

Conclusion

Computational analysis has shown that the proposed multi-epitope vaccine is possibly safe for use in humans and can elicit an immune response.

Core-genome-mediated promising alternative drug and multi-epitope vaccine targets prioritization against infectious Clostridium difficile

Prevention of Clostridium difficile infection is challenging worldwide owing to its high morbidity and mortality rates. C. difficile is currently being classified as an urgent threat by the CDC. Devising a new therapeutic strategy become indispensable against C. difficile infection due to its high rates of reinfection and increasing antimicrobial resistance. The current study is based on core proteome data of C. difficile to identify promising vaccine and drug candidates. Immunoinformatics and vaccinomics approaches were employed to construct multi-epitope-based chimeric vaccine constructs from top-ranked T- and B-cell epitopes. The efficacy of the designed vaccine was assessed by immunological analysis, immune receptor binding potential and immune simulation analyses. Additionally, subtractive proteomics and druggability analyses prioritized several promising and alternative drug targets against C. difficile. These include FMN-dependent nitroreductase which was prioritized for pharmacophore-based virtual screening of druggable molecule databases to predict potent inhibitors. A MolPort-001-785-965 druggable molecule was found to exhibit significant binding affinity with the conserved residues of FMN-dependent nitroreductase. The experimental validation of the therapeutic targets prioritized in the current study may worthy to identify new strategies to combat the drug-resistant C. difficile infection.

Genome-based prediction of cross-protective, HLA-DR-presented epitopes as putative vaccine antigens for multiple Bordetella species

IMPORTANCE

Pertussis, caused by Bordetella pertussis, can cause debilitating respiratory symptoms, so whole-cell pertussis vaccines (wPVs) were introduced in the 1940s. However, reactogenicity of wPV necessitated the development of acellular pertussis vaccines (aPVs) that were introduced in the 1990s. Since then, until the COVID-19 pandemic began, reported pertussis incidence was increasing, suggesting that aPVs do not induce long-lasting immunity and may not effectively prevent transmission. Additionally, aPVs do not provide protection against other Bordetella species that are observed during outbreaks. The significance of this work is in determining potential new vaccine antigens for multiple Bordetella species that are predicted to elicit long-term immune responses. Genome-based approaches have aided the development of novel vaccines; here, these methods identified Bordetella vaccine candidates that may be cross-protective and predicted to induce strong memory responses. These targets can lead to an improved vaccine with a strong safety profile while also strengthening the longevity of the immune response.

Putative novel outer membrane antigens multi-epitope DNA vaccine candidates identified by Immunoinformatic approaches to control Acinetobacter baumannii

Multi-epitope polypeptide vaccines, a fusion protein, often have a string-of-beads system composed of various specific peptide epitopes, potential adjuvants, and linkers. When choosing the sequence of various segments and linkers, many alternatives are available. These variables can influence the vaccine's effectiveness through their effects on physicochemical properties and polypeptide tertiary structure.The most conserved antigens were discovered using BLASTn. To forecast the proteins' subcellular distribution, PSORTb 3.0.2 was used. Vaxign was used for the preliminary screening and antigenicity assessment. Protein solubility was also predicted using the ccSOL omics. Using PRED-TMBB, it was anticipated that the protein would localize across membranes. The IEDB and BepiPred-2.0 databases were used to predict the immunogenicity of B cell epitopes. A multi-epitope construct was developed and analyzed to evaluate. Twenty epitopes from A. baumannii's outer membrane protein (omp) were included in the vaccination. TLR4 agonist explosibility was investigated. The physicochemical characteristics, secondary and tertiary structures, and B-cell epitopes of vaccine constructs were assessed. Additionally, docking and MD experiments were used to examine the relationship between TLR4 and its agonist.Thirteen antigens were discovered, and eight of the 13 chosen proteins were predicted to be surface proteins. The 34 kDa outer membrane protein, Omp38, Omp W, CarO, putative porin, OmpA, were chosen as having the right antigenicity (≥0.5). FhuE and CdiA were eliminated from further study because of their low antigenicity. The vaccine design was developed by combining the most effective 10 B-cell and 10 MHC-I/MHCII combined coverage epitopes. The molecular formula of the vaccine was determined to be C1718H2615N507O630S17. The vaccine form has a molecular weight of 40,996.70 Da and 47 negatively charged residues (Asp + Glu), whereas 28 positively charged residues (Arg + Lys). The estimated half-life was 7.2 hours (mammalian reticulocytes, in vitro), > 20 hours (yeast, in vivo) and > 10 hours (Escherichia coli, in vivo) for the vaccine. The multi-epitope vaccine insertion is carried via the expression vector pcDNA3.1 (+).The multi-epitope vaccine may stimulate humoral and cellular immune responses, according to our findings, and it may be a candidate for an A. baumannii vaccine.

A mechanistically novel peptide agonist of the IL-7 receptor that addresses limitations of IL-7 cytokine therapy

Interleukin (IL)-7 is broadly active on T-cell populations, and modified versions have been clinically evaluated for a variety of therapeutic applications, including cancer, lymphopenia, and infectious diseases; and found to be relatively well-tolerated and biologically active. Here we describe novel IL-7R agonists that are unrelated in structure to IL-7, bind to the receptor subunits differently from IL-7, but closely emulate IL-7 biology. The small size, low structural complexity, and the natural amino acid composition of the pharmacologically active peptide MDK1472 allows facile incorporation into protein structures, such as the IgG2-Fc fusion MDK-703. This molecule possesses properties potentially better suited to therapeutic applications than native IL-7 or its derivatives. We compared these compounds with IL-7 for immune cell selectivity, induction of IL-7R signaling, receptor-mediated internalization, proliferation, and generation of immune cell phenotypes in human and non-human primate (NHP) peripheral blood cells in vitro; and found them to be similar in biological activity to IL-7. In cynomolgus macaques, MDK-703 exhibits a circulating half-life of 46 hr and produces sustained T-cell expansion characteristic of IL-7 treatment. In the huCD34+-engrafted NSG mouse model of the human immune system, MDK-703 induces an immune cell profile very similar to that generated by IL-7-derived compounds; including the pronounced expansion of memory T-cells, particularly the population of stem-like memory T-cells (Tscm) which may be important for anti-tumor activities reported with IL-7 treatment. Clinical administration of IL-7 and modified variants has been reported to induce anti-drug antibodies (ADAs), including IL-7 neutralizing antibodies. The novel peptide agonist reported here scores very low in predicted immunogenicity, and because the peptide lacks sequence similarity with IL-7, the problematic immunogenic neutralization of endogenous cytokine should not occur. The properties we report here implicate MDK-703 as a candidate for clinical evaluation in oncology, anti-viral and other infectious disease, vaccine enhancement, and treatment of lymphopenia.

Immunogenicity risk assessment of synthetic peptide drugs and their impurities

Peptide drugs play an important part in medicine owing to their many therapeutic applications. Of the 80 peptide drugs approved for use in humans, at least five are now off-patent and are consequently being developed as generic alternatives to the originator products. To accelerate access to generic products, the FDA has proposed new regulatory pathways that do not require direct comparisons of generics to originators in clinical trials. The 'Abbreviated New Drug Application' (ANDA) pathway recommends that sponsors provide information on any new impurities in the generic drug, compared with the originator product, because the impurities can have potential to elicit unwanted immune responses owing to the introduction of T-cell epitopes. This review describes how peptide drug impurities can elicit unexpected immunogenicity and describes a framework for performing immunogenicity risk assessment of all types of bioactive peptide products. Although this report primarily focuses on generic peptides and their impurities, the approach might also be of interest for developers of novel peptide drugs who are preparing their products for an initial regulatory review.

Immunoinformatic Risk Assessment of Host Cell Proteins During Process Development for Biologic Therapeutics

The identification and removal of host cell proteins (HCPs) from biologic products is a critical step in drug development. Despite recent improvements to purification processes, biologics such as monoclonal antibodies, enzyme replacement therapies, and vaccines that are manufactured in a range of cell lines and purified using diverse processes may contain HCP impurities, making it necessary for developers to identify and quantify impurities during process development for each drug product. HCPs that contain sequences that are less conserved with human homologs may be more immunogenic than those that are more conserved. We have developed a computational tool, ISPRI-HCP, that estimates the immunogenic potential of HCP sequences by evaluating and quantifying T cell epitope density and relative conservation with similar T cell epitopes in the human proteome. Here we describe several case studies that support the use of this method for classifying candidate HCP impurities according to their immunogenicity risk.

Identification of immunodominant T cell epitopes induced by natural Zika virus infection

Zika virus (ZIKV) is a flavivirus primarily transmitted by Aedes species mosquitoes, first discovered in Africa in 1947, that disseminated through Southeast Asia and the Pacific Islands in the 2000s. The first ZIKV infections in the Americas were identified in 2014, and infections exploded through populations in Brazil and other countries in 2015/16. ZIKV infection during pregnancy can cause severe brain and eye defects in offspring, and infection in adults has been associated with higher risks of Guillain-Barré syndrome. We initiated a study to describe the natural history of Zika (the disease) and the immune response to infection, for which some results have been reported. In this paper, we identify ZIKV-specific CD4+ and CD8+ T cell epitopes that induce responses during infection. Two screening approaches were utilized: an untargeted approach with overlapping peptide arrays spanning the entire viral genome, and a targeted approach utilizing peptides predicted to bind human MHC molecules. Immunoinformatic tools were used to identify conserved MHC class I supertype binders and promiscuous class II binding peptide clusters predicted to bind 9 common class II alleles. T cell responses were evaluated in overnight IFN-γ ELISPOT assays. We found that MHC supertype binding predictions outperformed the bulk overlapping peptide approach. Diverse CD4+ T cell responses were observed in most ZIKV-infected participants, while responses to CD8+ T cell epitopes were more limited. Most individuals developed a robust T cell response against epitopes restricted to a single MHC class I supertype and only a single or few CD8+ T cell epitopes overall, suggesting a strong immunodominance phenomenon. Noteworthy is that many epitopes were commonly immunodominant across persons expressing the same class I supertype. Nearly all of the identified epitopes are unique to ZIKV and are not present in Dengue viruses. Collectively, we identified 31 immunogenic peptides restricted by the 6 major class I supertypes and 27 promiscuous class II epitopes. These sequences are highly relevant for design of T cell-targeted ZIKV vaccines and monitoring T cell responses to Zika virus infection and vaccination.

Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics

Fungal infections represent a serious global health problem, causing damage to health and the economy on the scale of millions. Although vaccines are the most effective therapeutic approach used to combat infectious agents, at the moment, no fungal vaccine has been approved for use in humans. However, the scientific community has been working hard to overcome this challenge. In this sense, we aim to describe here an update on the development of fungal vaccines and the progress of methodological and experimental immunotherapies against fungal infections. In addition, advances in immunoinformatic tools are described as an important aid by which to overcome the difficulty of achieving success in fungal vaccine development. In silico approaches are great options for the most important and difficult questions regarding the attainment of an efficient fungal vaccine. Here, we suggest how bioinformatic tools could contribute, considering the main challenges, to an effective fungal vaccine.

CD171 Multi-epitope peptide design based on immuno-informatics approach as a cancer vaccine candidate for glioblastoma

Glioblastoma (GB) is a common primary malignancy of the central nervous system, and one of the highly lethal brain tumors. GB cells can promote therapeutic resistance and tumor angiogenesis. The CD171 is an adhesion molecule in neuronal cells that is expressed in glioma cells as a regulator of brain development during the embryonic period. CD171 is one of the immunoglobulin-like CAMs (cell adhesion molecules) families that can be associated with prognosis in a variety of human tumors. The multi-epitope peptide vaccines are based on synthetic peptides with a combination of both B-cell epitopes and T-cell epitopes, which can induce specific humoral or cellular immune responses. Moreover, Cholera toxin subunit B (CTB), a novel TLR agonist was utilized in the final construct to polarize CD4+ T cells toward T-helper 1 to induce strong cytotoxic T lymphocytes (CTL) responses. In the present study, several immune-informatics tools were used for analyzing the CD171 sequence and studying the important characteristics of a designed vaccine. The results included molecular docking, molecular dynamics simulation, immune response simulation, prediction and validation of the secondary and tertiary structure, physicochemical properties, solubility, conservancy, toxicity as well as antigenicity and allergenicity of the promising candidate for a vaccine against CD171. The immuno-informatic analyze suggested 12 predicted multi-epitope peptides, whose construction consists of 582 residues long. Therewith, cloning adaptation of the designed vaccine was performed, and eventually sequence was inserted into pET30a (+) vector for the application of the anti-glioblastoma vaccine development.Communicated by Ramaswamy H. Sarma.

Diversity and evolution of computationally predicted T cell epitopes against human respiratory syncytial virus

Human respiratory syncytial virus (RSV) is a major cause of lower respiratory infection. Despite more than 60 years of research, there is no licensed vaccine. While B cell response is a major focus for vaccine design, the T cell epitope profile of RSV is also important for vaccine development. Here, we computationally predicted putative T cell epitopes in the Fusion protein (F) and Glycoprotein (G) of RSV wild circulating strains by predicting Major Histocompatibility Complex (MHC) class I and class II binding affinity. We limited our inferences to conserved epitopes in both F and G proteins that have been experimentally validated. We applied multidimensional scaling (MDS) to construct T cell epitope landscapes to investigate the diversity and evolution of T cell profiles across different RSV strains. We find the RSV strains are clustered into three RSV-A groups and two RSV-B groups on this T epitope landscape. These clusters represent divergent RSV strains with potentially different immunogenic profiles. In addition, our results show a greater proportion of F protein T cell epitope content conservation among recent epidemic strains, whereas the G protein T cell epitope content was decreased. Importantly, our results suggest that RSV-A and RSV-B have different patterns of epitope drift and replacement and that RSV-B vaccines may need more frequent updates. Our study provides a novel framework to study RSV T cell epitope evolution. Understanding the patterns of T cell epitope conservation and change may be valuable for vaccine design and assessment.

MatchTope: A tool to predict the cross reactivity of peptides complexed with Major Histocompatibility Complex I

The therapeutic targeting of the immune system, for example in vaccinology and cancer treatment, is a challenging task and the subject of active research. Several in silico tools used for predicting immunogenicity are based on the analysis of peptide sequences binding to the Major Histocompatibility Complex (pMHC). However, few of these bioinformatics tools take into account the pMHC three-dimensional structure. Here, we describe a new bioinformatics tool, MatchTope, developed for predicting peptide similarity, which can trigger cross-reactivity events, by computing and analyzing the electrostatic potentials of pMHC complexes. We validated MatchTope by using previously published data from in vitro assays. We thereby demonstrate the strength of MatchTope for similarity prediction between targets derived from several pathogens as well as for indicating possible cross responses between self and tumor peptides. Our results suggest that MatchTope can enhance and speed up future studies in the fields of vaccinology and cancer immunotherapy.

H1N1 G4 swine influenza T cell epitope analysis in swine and human vaccines and circulating strains uncovers potential risk to swine and humans

BACKGROUND

Pandemic influenza viruses may emerge from animal reservoirs and spread among humans in the absence of cross-reactive antibodies in the human population. Immune response to highly conserved T cell epitopes in vaccines may still reduce morbidity and limit the spread of the new virus even when cross-protective antibody responses are lacking.

METHODS

We used an established epitope content prediction and comparison tool, Epitope Content Comparison (EpiCC), to assess the potential for emergent H1N1 G4 swine influenza A virus (G4) to impact swine and human populations. We identified and computed the total cross-conserved T cell epitope content in HA sequences of human seasonal and experimental influenza vaccines, swine influenza vaccines from Europe and the United States (US) against G4.

RESULTS

The overall T cell epitope content of US commercial swine vaccines was poorly conserved with G4, with an average T cell epitope coverage of 35.7%. EpiCC scores for the comparison between current human influenza vaccines and circulating human influenza strains were also very low. In contrast, the T cell epitope coverage of a recent European swine influenza vaccine (HL03) was 65.8% against G4.

CONCLUSIONS

Poor T cell epitope cross-conservation between emergent G4 and swine and human influenza vaccines in the US may enable G4 to spread in swine and spillover to human populations in the absence of protective antibody response. One European influenza vaccine, HL03, may protect against emergent G4. This study illustrates the use of the EpiCC tool for prospective assessment of existing vaccine strains against emergent viruses in swine and human populations.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning

Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.

A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding

T cells play a critical role in the adaptive immune response, recognizing peptide antigens presented on the cell surface by major histocompatibility complex (MHC) proteins. While assessing peptides for MHC binding is an important component of probing these interactions, traditional assays for testing peptides of interest for MHC binding are limited in throughput. Here, we present a yeast display-based platform for assessing the binding of tens of thousands of user-defined peptides in a high-throughput manner. We apply this approach to assess a tiled library covering the SARS-CoV-2 proteome and four dengue virus serotypes for binding to human class II MHCs, including HLA-DR401, -DR402, and -DR404. While the peptide datasets show broad agreement with previously described MHC-binding motifs, they additionally reveal experimentally validated computational false positives and false negatives. We therefore present this approach as able to complement current experimental datasets and computational predictions. Further, our yeast display approach underlines design considerations for epitope identification experiments and serves as a framework for examining relationships between viral conservation and MHC binding, which can be used to identify potentially high-interest peptide binders from viral proteins. These results demonstrate the utility of our approach to determine peptide-MHC binding interactions in a manner that can supplement and potentially enhance current algorithm-based approaches.

Evaluation of a Human T Cell-Targeted Multi-Epitope Vaccine for Q Fever in Animal Models of Coxiella burnetii Immunity

T cell-mediated immunity plays a central role in the control and clearance of intracellular Coxiella burnetii infection, which can cause Q fever. Therefore, we aimed to develop a novel T cell-targeted vaccine that induces pathogen-specific cell-mediated immunity to protect against Q fever in humans while avoiding the reactogenicity of the current inactivated whole cell vaccine. Human HLA class II T cell epitopes from C. burnetii were previously identified and selected by immunoinformatic predictions of HLA binding, conservation in multiple C. burnetii isolates, and low potential for cross-reactivity with the human proteome or microbiome. Epitopes were selected for vaccine inclusion based on long-lived human T cell recall responses to corresponding peptides in individuals that had been naturally exposed to the bacterium during a 2007-2010 Q fever outbreak in the Netherlands. Multiple viral vector-based candidate vaccines were generated that express concatemers of selected epitope sequences arranged to minimize potential junctional neo-epitopes. The vaccine candidates caused no antigen-specific reactogenicity in a sensitized guinea pig model. A subset of the vaccine epitope peptides elicited antigenic recall responses in splenocytes from C57BL/6 mice previously infected with C. burnetii. However, immunogenicity of the vaccine candidates in C57BL/6 mice was dominated by a single epitope and this was insufficient to confer protection against an infection challenge, highlighting the limitations of assessing human-targeted vaccine candidates in murine models. The viral vector-based vaccine candidates induced antigen-specific T cell responses to a broader array of epitopes in cynomolgus macaques, establishing a foundation for future vaccine efficacy studies in this large animal model of C. burnetii infection.

In silico design and analyses of a multi-epitope vaccine against Crimean-Congo hemorrhagic fever virus through reverse vaccinology and immunoinformatics approaches

Crimean Congo Hemorrhagic Fever virus (CCHFV) is a deadly human pathogen that causes an emerging zoonotic disease with a broad geographic spread, especially in Africa, Asia, and Europe, and the second most common viral hemorrhagic fever and widely transmitted tick-borne viral disease. Following infection, the patients are presented with a variety of clinical manifestations and a fatality rate of 40%. Despite the high fatality rate, there are unmet clinical interventions, as no antiviral drugs or vaccines for CCHF have been approved. Immunoinformatics pipeline and reverse vaccinology were used in this study to design a multi-epitope vaccine that may elicit a protective humoral and cellular immune response against Crimean-Congo hemorrhagic fever virus infection. Three essential virulent and antigenic proteins (S, M, and L) were used to predict seven CTL and 18 HTL epitopes that were non-allergenic, antigenic, IFN-γ inducing, and non-toxic. The epitopes were connected using linkers and 50S ribosomal protein L7/L12 was used as an adjuvant and raised a multi-epitope vaccine (MEV) that is 567 amino acids long. Molecular docking and simulation of the predicted 3D structure of the MEV with the toll-like (TLR2, TLR3, and TLR4) receptors and major histocompatibility complex (MCH-I and MCH-II) indicate high interactions and stability of the complexes, MM-GBSA free binding energy calculation revealed a favourable protein-protein complex. Maximum MEV expression was achieved with a CAI value of 0.98 through in silico cloning in the Drosophila melanogaster host. According to the immune simulation, IgG1, T-helper cells, T-cytotoxic cells, INF-γ, and IL-2 were predicted to be significantly elevated. These robust computational analyses demonstrated that the proposed MEV is effective in preventing CCHFV infections. However, it is still necessary to conduct both in vitro and in vivo experiments to validate the potential of the vaccine.

Challenges in Serologic Diagnostics of Neglected Human Systemic Mycoses: An Overview on Characterization of New Targets

Systemic mycoses have been viewed as neglected diseases and they are responsible for deaths and disabilities around the world. Rapid, low-cost, simple, highly-specific and sensitive diagnostic tests are critical components of patient care, disease control and active surveillance. However, the diagnosis of fungal infections represents a great challenge because of the decline in the expertise needed for identifying fungi, and a reduced number of instruments and assays specific to fungal identification. Unfortunately, time of diagnosis is one of the most important risk factors for mortality rates from many of the systemic mycoses. In addition, phenotypic and biochemical identification methods are often time-consuming, which has created an increasing demand for new methods of fungal identification. In this review, we discuss the current context of the diagnosis of the main systemic mycoses and propose alternative approaches for the identification of new targets for fungal pathogens, which can help in the development of new diagnostic tests.

In silico designed Staphylococcus aureus B-cell multi-epitope vaccine did not elicit antibodies against target antigens suggesting multi-domain approach

The vaccine development strategies have evolved from using an entire organism as an immunogen to a single antigen and further towards an epitope. Since an epitope is a relatively tiny and immunologically relevant part of an antigen, it has the potential to stimulate more robust and specific immune responses while causing minimal adverse effects. As a result, the recent focus of vaccine development has been to develop multi-epitope vaccines that can target multiple virulence mechanisms. Accordingly, we designed multi-epitope vaccine candidates B (multi-B-cell epitope immunogen) and CTB-B (an adjuvant - cholera toxin subunit B (CTB) - attached to immunogen B) against S. aureus by employing immunoinformatics approaches. The designed vaccines are composed of B-cell epitope segments (20-mer) of the eight well-characterized S. aureus virulence factors, namely ClfB, FnbpA, Hla, IsdA, IsdB, LukE, SdrD, and SdrE connected in series. The designed vaccines were expressed, purified, and administered to C57BL/6 mice with Freund adjuvant to evaluate the immunogenicity and protective efficacy. The results revealed that the immunized mice showed high IgG titer for the immunogen, and the antibody titer increased significantly following the second immunization. However, the generated antibodies did not protect mice from infection. The interaction of anti-B antibodies with source virulence factors showed that the generated antibodies have no binding affinity with any of the corresponding virulence factors. Our results demonstrate the limitation of in silico designed B-cell multi-epitope vaccine and suggest that a protein domain carrying both linear and conformational B-cell epitopes might be a better choice for developing an effective multi-epitope vaccine against S. aureus.

Neoantigen-based personalized cancer vaccines: the emergence of precision cancer immunotherapy

INTRODUCTION

The field of cancer therapy has undergone a major transformation in less than a decade due to the introduction of checkpoint inhibitors, the advent of next generation sequencing and the discovery of neoantigens. The key observation that the breadth of each patient's immune response to the unique mutations or neoantigens present in their tumor is directly related to their survival has led oncologists to focus on driving immune responses to neoantigens through vaccination. Oncology has entered the era of precision immunotherapy, and cancer vaccine development is undergoing a paradigm shift.

AREAS COVERED

Neoantigens are short peptide sequences found in tumors, but not noncancerous tissues, the vast majority of which are unique to each patient. In addition to providing a description of the distinguishing features of neoantigen discovery platforms, this review will address cross-cutting personalized cancer vaccine design themes and developmental stumbling blocks.

EXPERT OPINION

Immunoinformatic pipelines that can rapidly scan cancer genomes and identify 'the best' neoantigens are in high demand. Despite the need for such tools, immunoinformatic methods for identifying neoepitopes in cancer genomes are diverse and have not been well-validated. Validation of 'personalized vaccine design pipelines' will bring about a revolution in neoantigen-based vaccine design and delivery.

Identification and Immune Assessment of T Cell Epitopes in Five Plasmodium falciparum Blood Stage Antigens to Facilitate Vaccine Candidate Selection and Optimization

The hurdles to effective blood stage malaria vaccine design include immune evasion tactics used by the parasite such as redundant invasion pathways and antigen variation among circulating parasite strains. While blood stage malaria vaccine development primarily focuses on eliciting optimal humoral responses capable of blocking erythrocyte invasion, clinically-tested Plasmodium falciparum (Pf) vaccines have not elicited sterile protection, in part due to the dramatically high levels of antibody needed. Recent development efforts with non-redundant, conserved blood stage antigens suggest both high antibody titer and rapid antibody binding kinetics are important efficacy factors. Based on the central role of helper CD4 T cells in development of strong, protective immune responses, we systematically analyzed the class II epitope content in five leading Pf blood stage antigens (RH5, CyRPA, RIPR, AMA1 and EBA175) using in silico, in vitro, and ex vivo methodologies. We employed in silico T cell epitope analysis to enable identification of 67 HLA-restricted class II epitope clusters predicted to bind a panel of nine HLA-DRB1 alleles. We assessed a subset of these for HLA-DRB1 allele binding in vitro, to verify the in silico predictions. All clusters assessed (40 clusters represented by 46 peptides) bound at least two HLA-DR alleles in vitro. The overall epitope prediction to in vitro HLA-DRB1 allele binding accuracy was 71%. Utilizing the set of RH5 class II epitope clusters (10 clusters represented by 12 peptides), we assessed stimulation of T cells collected from HLA-matched RH5 vaccinees using an IFN-γ T cell recall assay. All clusters demonstrated positive recall responses, with the highest responses – by percentage of responders and response magnitude – associated with clusters located in the N-terminal region of RH5. Finally, a statistically significant correlation between in silico epitope predictions and ex vivo IFN-γ recall response was found when accounting for HLA-DR matches between the epitope predictions and donor HLA phenotypes. This is the first comprehensive analysis of class II epitope content in RH5, CyRPA, RIPR, AMA1 and EBA175 accompanied by in vitro HLA binding validation for all five proteins and ex vivo T cell response confirmation for RH5.

Bridging Computational Vaccinology and Vaccine Development Through Systematic Identification, Characterization, and Downselection of Conserved and Variable Circumsporozoite Protein CD4 T Cell Epitopes From Diverse Plasmodium falciparum Strains

An effective malaria vaccine must prevent disease in a range of populations living in regions with vastly different transmission rates and protect against genetically-diverse Plasmodium falciparum (Pf) strains. The protective efficacy afforded by the currently licensed malaria vaccine, Mosquirix™, promotes strong humoral responses to Pf circumsporozoite protein (CSP) 3D7 but protection is limited in duration and by strain variation. Helper CD4 T cells are central to development of protective immune responses, playing roles in B cell activation and maturation processes, cytokine production, and stimulation of effector T cells. Therefore, we took advantage of recent in silico modeling advances to predict and analyze human leukocyte antigen (HLA)-restricted class II epitopes from PfCSP – across the entire PfCSP 3D7 sequence as well as in 539 PfCSP sequence variants – with the goal of improving PfCSP-based malaria vaccines. Specifically, we developed a systematic workflow to identify peptide sequences capable of binding HLA-DR in a context relevant to achieving broad human population coverage utilizing cognate T cell help and with limited T regulatory cell activation triggers. Through this workflow, we identified seven predicted class II epitope clusters in the N- and C-terminal regions of PfCSP 3D7 and an additional eight clusters through comparative analysis of 539 PfCSP sequence variants. A subset of these predicted class II epitope clusters was synthesized as peptides and assessed for HLA-DR binding in vitro. Further, we characterized the functional capacity of these peptides to prime and activate human peripheral blood mononuclear cells (PBMCs), by monitoring cytokine response profiles using MIMIC^® technology (Modular IMmune In vitro Construct). Utilizing this decision framework, we found sufficient differential cellular activation and cytokine profiles among HLA-DR-matched PBMC donors to downselect class II epitope clusters for inclusion in a vaccine targeting PfCSP. Importantly, the downselected clusters are not highly conserved across PfCSP variants but rather, they overlap a hypervariable region (TH2R) in the C-terminus of the protein. We recommend assessing these class II epitope clusters within the context of a PfCSP vaccine, employing a test system capable of measuring immunogenicity across a broad set of HLA-DR alleles.

Identification, Selection and Immune Assessment of Liver Stage CD8 T Cell Epitopes From Plasmodium falciparum

Immunization with radiation-attenuated sporozoites (RAS) has been shown to protect against malaria infection, primarily through CD8 T cell responses, but protection is limited based on parasite strain. Therefore, while CD8 T cells are an ideal effector population target for liver stage malaria vaccine development strategies, such strategies must incorporate conserved epitopes that cover a large range of class I human leukocyte antigen (HLA) supertypes to elicit cross-strain immunity across the target population. This approach requires identifying and characterizing a wide range of CD8 T cell epitopes for incorporation into a vaccine such that coverage across a large range of class I HLA alleles is attained. Accordingly, we devised an experimental framework to identify CD8 T cell epitopes from novel and minimally characterized antigens found at the pre-erythrocytic stage of parasite development. Through in silico analysis we selected conserved P. falciparum proteins, using P. vivax orthologues to establish stringent conservation parameters, predicted to have a high number of T cell epitopes across a set of six class I HLA alleles representative of major supertypes. Using the decision framework, five proteins were selected based on the density and number of predicted epitopes. Selected epitopes were synthesized as peptides and evaluated for binding to the class I HLA alleles in vitro to verify in silico binding predictions, and subsequently for stimulation of human T cells using the Modular IMmune In-vitro Construct (MIMIC®) technology to verify immunogenicity. By combining the in silico tools with the ex vivo high throughput MIMIC platform, we identified 15 novel CD8 T cell epitopes capable of stimulating an immune response in alleles across the class I HLA panel. We recommend these epitopes should be evaluated in appropriate in vivo humanized immune system models to determine their protective efficacy for potential inclusion in future vaccines.

Quantifying the Persistence of Vaccine-Related T Cell Epitopes in Circulating Swine Influenza A Strains from 2013-2017

When swine flu vaccines and circulating influenza A virus (IAV) strains are poorly matched, vaccine-induced antibodies may not protect from infection. Highly conserved T cell epitopes may, however, have a disease-mitigating effect. The degree of T cell epitope conservation among circulating strains and vaccine strains can vary, which may also explain differences in vaccine efficacy. Here, we evaluate a previously developed conserved T cell epitope-based vaccine and determine the persistence of T cell epitope conservation over time. We used a pair-wise homology score to define the conservation between the vaccine’s swine leukocyte antigen (SLA) class I and II-restricted epitopes and T cell epitopes found in 1272 swine IAV strains sequenced between 2013 and 2017. Twenty-four of the 48 total T cell epitopes included in the epitope-based vaccine were highly conserved and found in >1000 circulating swine IAV strains over the 5-year period. In contrast, commercial swine IAV vaccines developed in 2013 exhibited a declining conservation with the circulating IAV strains over the same 5-year period. Conserved T cell epitope vaccines may be a useful adjunct for commercial swine flu vaccines and to improve protection against influenza when antibodies are not cross-reactive.

Vaccinomic approach for novel multi epitopes vaccine against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)

BACKGROUND

The spread of a novel coronavirus termed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in China and other countries is of great concern worldwide with no effective vaccine. This study aimed to design a novel vaccine construct against SARS-CoV-2 from the spike S protein and orf1ab polyprotein using immunoinformatics tools. The vaccine was designed from conserved epitopes interacted against B and T lymphocytes by the combination of highly immunogenic epitopes with suitable adjuvant and linkers.

RESULTS

The proposed vaccine composed of 526 amino acids and was shown to be antigenic in Vaxigen server (0.6194) and nonallergenic in Allertop server. The physiochemical properties of the vaccine showed isoelectric point of 10.19. The instability index (II) was 31.25 classifying the vaccine as stable. Aliphatic index was 84.39 and the grand average of hydropathicity (GRAVY) was - 0.049 classifying the vaccine as hydrophilic. Vaccine tertiary structure was predicted, refined and validated to assess the stability of the vaccine via Ramachandran plot and ProSA-web servers. Moreover, solubility of the vaccine construct was greater than the average solubility provided by protein sol and SOLpro servers indicating the solubility of the vaccine construct. Disulfide engineering was performed to reduce the high mobile regions in the vaccine to enhance stability. Docking of the vaccine construct with TLR4 demonstrated efficient binding energy with attractive binding energy of - 338.68 kcal/mol and - 346.89 kcal/mol for TLR4 chain A and chain B respectively. Immune simulation significantly provided high levels of immunoglobulins, T-helper cells, T-cytotoxic cells and INF-γ. Upon cloning, the vaccine protein was reverse transcribed into DNA sequence and cloned into pET28a(+) vector to ensure translational potency and microbial expression.

CONCLUSION

A unique vaccine construct from spike S protein and orf1ab polyprotein against B and T lymphocytes was generated with potential protection against the pandemic. The present study might assist in developing a suitable therapeutics protocol to combat SARSCoV-2 infection.

A review of COVID-19 biomarkers and drug targets: resources and tools

The stratification of patients at risk of progression of COVID-19 and their molecular characterization is of extreme importance to optimize treatment and to identify therapeutic options. The bioinformatics community has responded to the outbreak emergency with a set of tools and resource to identify biomarkers and drug targets that we review here. Starting from a consolidated corpus of 27 570 papers, we adopt latent Dirichlet analysis to extract relevant topics and select those associated with computational methods for biomarker identification and drug repurposing. The selected topics span from machine learning and artificial intelligence for disease characterization to vaccine development and to therapeutic target identification. Although the way to go for the ultimate defeat of the pandemic is still long, the amount of knowledge, data and tools generated so far constitutes an unprecedented example of global cooperation to this threat.

Computational Design and Preliminary Serological Analysis of a Novel Multi-Epitope Vaccine Candidate against Onchocerciasis and Related Filarial Diseases

: Onchocerciasis is a skin and eye disease that exerts a heavy socio-economic burden, particularly in sub-Saharan Africa, a region which harbours greater than 96% of either infected or at-risk populations. The elimination plan for the disease is currently challenged by many factors including amongst others; the potential emergence of resistance to the main chemotherapeutic agent, ivermectin (IVM). Novel tools, including preventative and therapeutic vaccines, could provide additional impetus to the disease elimination tool portfolio. Several observations in both humans and animals have provided evidence for the development of both natural and artificial acquired immunity. In this study, immuno-informatics tools were applied to design a filarial-conserved multi-epitope subunit vaccine candidate, (designated Ov-DKR-2) consisting of B-and T-lymphocyte epitopes of eight immunogenic antigens previously assessed in pre-clinical studies. The high-percentage conservation of the selected proteins and epitopes predicted in related nematode parasitic species hints that the generated chimera may be instrumental for cross-protection. Bioinformatics analyses were employed for the prediction, refinement, and validation of the 3D structure of the Ov-DKR-2 chimera. In-silico immune simulation projected significantly high levels of IgG1, T-helper, T-cytotoxic cells, INF-γ, and IL-2 responses. Preliminary immunological analyses revealed that the multi-epitope vaccine candidate reacted with antibodies in sera from both onchocerciasis-infected individuals, endemic normals as well as loiasis-infected persons but not with the control sera from European individuals. These results support the premise for further characterisation of the engineered protein as a vaccine candidate for onchocerciasis.

A Novel Multi-Epitopic Peptide Vaccine Candidate Against Helicobacter pylori: In-Silico Identification, Design, Cloning and Validation Through Molecular Dynamics

Helicobacter pylori is a highly potential pathogen to colonize in the human stomach. This bacterial strain is now alarming serious health concern all over the world. Combating through available drugs is a difficult task due to lack of appropriate common targets against genetically diverse strains. Therefore, the developments of effective targets vaccines require alternative strategies to eliminate the H. pylori infection. In this study, we developed a novel vaccine construct using B-cell derived T-cell epitopes from four target antigenic proteins (HpaA, FlaA, FlaB and Omp18), and found the induction of possible immune response using advanced immunoinformatics approaches. In order to boost immune system, we tagged adjuvant (50S ribosomal protein L7/L12) with a suitable linker at the N-terminus side of vaccine sequence. Protein–protein docking between human Toll like receptor 5 (TLR5) and vaccine construct help to predict the way of inductive signaling that leads to immune-response. The calculated negative score (− 151.4, + / − 8.7) of molecular docking complex signify the best binding interface. Molecular dynamics simulation studies confirmed the proper docking between TLR5 and vaccine candidate. Moreover, Normal mode analysis (NMA) calculates the molecular motion of the docking complex. The low eigenvalue (2.935e⁻⁰⁵) indicates the stable and flexible molecular motion in the binding interaction side. Finally, in-silico cloning of vaccine candidate was performed using expression vector pET28b (+) with the optimized restriction sites.

Future perspectives on swine viral vaccines: where are we headed?

Deliberate infection of humans with smallpox, also known as variolation, was a common practice in Asia and dates back to the fifteenth century. The world's first human vaccination was administered in 1796 by Edward Jenner, a British physician. One of the first pig vaccines, which targeted the bacterium Erysipelothrix rhusiopathiae, was introduced in 1883 in France by Louis Pasteur. Since then vaccination has become an essential part of pig production, and viral vaccines in particular are essential tools for pig producers and veterinarians to manage pig herd health. Traditionally, viral vaccines for pigs are either based on attenuated-live virus strains or inactivated viral antigens. With the advent of genomic sequencing and molecular engineering, novel vaccine strategies and tools, including subunit and nucleic acid vaccines, became available and are being increasingly used in pigs. This review aims to summarize recent trends and technologies available for the production and use of vaccines targeting pig viruses.

Immuno-informatics analysis and expression of a novel multi-domain antigen as a vaccine candidate against glioblastoma

Glioblastoma multiform is the most common of primary malignant brain tumors in adults. Currently, surgical resection of the tumor mass, followed by adjuvant radiotherapy and chemotherapy are standard treatments for glioblastoma multiform but so far are not effective treatments. Thus, the development of a vaccine, as a safe and efficient strategy for prophylactic or therapeutic purposes against glioblastoma multiform is very necessary. The present study aimed to design the multi-domain vaccine for glioblastoma multiform. An in silico approach was used to select the most potent domains of proteins to induce the host's B- and T-cell immune response against glioblastoma multiform. IL-13Rα-2 (amino acid positions 27-144), TNC (amino acid positions 1900-2100), and PTPRZ-1(amino acid positions 731-884) were found to have potent inducible immune responses. So, we considered them for fusing with a linker A(EAAAK)₃A to construct the multi-domain recombinant vaccine. The immuno-informatics analysis of the designed recombinant vaccine construct was performed to evaluate its efficacy. Although the designed recombinant vaccine construct did not show allergen property, its antigenicity was estimated at 0.78. The Physico-chemical properties of the recombinant vaccine construct were characterized and revealed the potency of the vaccine candidate. Then its secondary and tertiary structures, mRNA structure, molecular docking, and immune simulation were predicted using bioinformatics tools. Next, the designed recombinant vaccine construct was synthesized, and cloned into the pET28a vector and expressed in E. coli BL21. Besides, the circular dichroism spectroscopy was utilized for the investigation of the secondary structure changes of the recombinant vaccine construct. The results of the verification assessment of the recombinant vaccine construct expression indicated that in silico analysis was relatively accurate, and relatively change occurred on the protein secondary structure. In our future plan, the vaccine candidate that was confirmed by in silico tools should be validated by further in vitro and in vivo experimental studies.

Self-Replicating RNAs Drive Protective Anti-tumor T Cell Responses to Neoantigen Vaccine Targets in a Combinatorial Approach

Historically poor clinical results of tumor vaccines have been attributed to weakly immunogenic antigen targets, limited specificity, and vaccine platforms that fail to induce high-quality polyfunctional T cells, central to mediating cellular immunity. We show here that the combination of antigen selection, construct design, and a robust vaccine platform based on the Synthetically Modified Alpha Replicon RNA Technology (SMARRT), a self-replicating RNA, leads to control of tumor growth in mice. Therapeutic immunization with SMARRT replicon-based vaccines expressing tumor-specific neoantigens or tumor-associated antigen were able to generate polyfunctional CD4⁺ and CD8⁺ T cell responses in mice. Additionally, checkpoint inhibitors, or co-administration of cytokine also expressed from the SMARRT platform, synergized to enhance responses further. Lastly, SMARRT-based immunization of non-human primates was able to elicit high-quality T cell responses, demonstrating translatability and clinical feasibility of synthetic replicon technology for therapeutic oncology vaccines.

Bacterial Immunogenicity Prediction by Machine Learning Methods

The identification of protective immunogens is the most important and vigorous initial step in the long-lasting and expensive process of vaccine design and development. Machine learning (ML) methods are very effective in data mining and in the analysis of big data such as microbial proteomes. They are able to significantly reduce the experimental work for discovering novel vaccine candidates. Here, we applied six supervised ML methods (partial least squares-based discriminant analysis, k nearest neighbor (kNN), random forest (RF), support vector machine (SVM), random subspace method (RSM), and extreme gradient boosting) on a set of 317 known bacterial immunogens and 317 bacterial non-immunogens and derived models for immunogenicity prediction. The models were validated by internal cross-validation in 10 groups from the training set and by the external test set. All of them showed good predictive ability, but the xgboost model displays the most prominent ability to identify immunogens by recognizing 84% of the known immunogens in the test set. The combined RSM-kNN model was the best in the recognition of non-immunogens, identifying 92% of them in the test set. The three best performing ML models (xgboost, RSM-kNN, and RF) were implemented in the new version of the server VaxiJen, and the prediction of bacterial immunogens is now based on majority voting.

Designing of a novel multi-epitope peptide based vaccine against Brugia malayi: An in silico approach

In spite of the tremendous efforts of the World Health Organization, scientific and medical community to eradicate lymphatic filariasis (LF) within 2020, the disease is still taking a huge toll on mankind throughout the globe. The current therapeutic strategies and solution measures against this alarming condition are suffering from a number of limitations such as inadequate effectiveness of the drugs against the adult-stage parasites, low bioavailability, and emergence of resistance. Considering this situation, development of the new therapeutics are urgently needed to combat human LF, especially targeting the adult filarial nematodes. Brugia malayi, the causative parasite for the human brugian filariasis majorly found in the countries of the South-Asia. In this study, we have designed a vaccine candidate using B-cell and T-cell epitopes derived from the aspartic protease of B. malayi (BmASP-1) and found to display significant humoral and cell mediated immune responses using in-silico approaches. Protein-protein docking between the human Toll-like receptor 4 (TLR4) and the vaccine candidate helped us to predict the way of inductive signaling that leads to immune-response. Molecular dynamics (MD) simulation studies further confirmed the proper docking between the TLR4 and vaccine candidate. Moreover, in-silico cloning of the vaccine element within the expression vector was found useful to optimize the restriction sites as well as to determine the primer location. Taken together, the in-silico vaccine candidate depicted in this study promises could be a useful therapeutic option for treating LF and experimental validation of this study is expected to strengthen the candidature of the said vaccine in the future.

New Immunoinformatics Tools for Swine: Designing Epitope-Driven Vaccines, Predicting Vaccine Efficacy, and Making Vaccines on Demand

Novel computational tools for swine vaccine development can expand the range of immunization approaches available to prevent economically devastating swine diseases and spillover events between pigs and humans. PigMatrix and EpiCC are two new tools for swine T cell epitope identification and vaccine efficacy analysis that have been integrated into an existing computational vaccine design platform named iVAX. The iVAX platform is already in use for the development of human vaccines, thus integration of these tools into iVAX improves and expands the utility of the platform overall by making previously validated immunoinformatics tools, developed for humans, available for use in the design and analysis of swine vaccines. PigMatrix predicts T cell epitopes for a broad array of class I and class II swine leukocyte antigen (SLA) using matrices that enable the scoring of sequences for likelihood of binding to SLA. PigMatrix facilitates the prospective selection of T cell epitopes from the sequences of swine pathogens for vaccines and permits the comparison of those predicted epitopes with "self" (the swine proteome) and with sequences from other strains. Use of PigMatrix with additional tools in the iVAX toolkit also enables the computational design of vaccines in silico, for testing in vivo. EpiCC uses PigMatrix to analyze existing or proposed vaccines for their potential to protect, based on a comparison between T cell epitopes in the vaccine and circulating strains of the same pathogen. Performing an analysis of T cell epitope relatedness analysis using EpiCC may facilitate vaccine selection when a novel strain emerges in a herd and also permits analysis of evolutionary drift as a means of immune escape. This review of novel computational immunology tools for swine describes the application of PigMatrix and EpiCC in case studies, such as the design of cross-conserved T cell epitopes for swine influenza vaccine or for African Swine Fever. We also describe the application of EpiCC for determination of the best vaccine strains to use against circulating viral variants of swine influenza, swine rotavirus, and porcine circovirus type 2. The availability of these computational tools accelerates infectious disease research for swine and enable swine vaccine developers to strategically advance their vaccines to market.

Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations for SARS-CoV-2. Our approach optimizes the presentation likelihood of a diverse set of vaccine peptides conditioned on a target human-population HLA haplotype distribution and expected epitope drift. Our proposed SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA average hits per person (≥ 1 peptide: 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our proposed MHC class II vaccine formulations provide 97.21% predicted coverage with at least five vaccine peptide-HLA average hits per person with all peptides having an observed mutation probability of ≤ 0.001. We provide an open-source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts here: https://github.com/gifford-lab/optivax.

Immune-engineered H7N9 influenza hemagglutinin improves protection against viral influenza virus challenge

The influenza hemagglutinin (HA) isolated from avian H7N9 influenza virus strains elicit weak immune responses. This low immunogenicity may be due to a regulatory T cell (T_reg)-stimulating epitopes in HA from the H7N9 isolate A/Anhui/1/2013 (Anh/13). In this report, this T_reg stimulating sequence was removed from the wild-type (WT) H7 HA amino acid sequence and replaced with a conserved CD4 + T cell stimulating sequences from human seasonal H3N2 strains and designed OPT1 H7 HA. The effectiveness of this optimized H7 HA protein was determined using a humanized mouse (HLA-DR3) expressing the human leukocyte antigen (HLA) DR3 allele. HLA-DR3 mice were pre-immunized by infecting with H3N2 influenza virus, A/Hong Kong/4108/2014 and then vaccinated intramuscularly with either the WT H7 HA from Anh/13 or the OPT1 H7 HA antigen without adjuvant. The OPT1 H7 HA vaccination group elicited higher H7 HA-specific IgG titers that resulted in a lower mortality, weight loss, and lung viral titer following lethal challenge with the H7N9 Anh/13 influenza virus compared to WT-vaccinated mice. Overall, T-cell epitope-engineered vaccines can improve the immunogenicity of H7 HA antigens resulting in enhanced survival and lower morbidity against H7N9 influenza virus challenge.

An overview of in silico vaccine design against different pathogens and cancer

INTRODUCTION

Due to overcome the hardness of the vaccine design, computational vaccinology is emerging widely. Prediction of T cell and B cell epitopes, antigen processing analysis, antigenicity analysis, population coverage, conservancy analysis, allergenicity assessment, toxicity prediction, and protein-peptide docking are important steps in the process of designing and developing potent vaccines against various viruses and cancers. In order to perform all of the analyses, several bioinformatics tools and online web servers have been developed. Scientists must take the decision to apply more suitable and precise servers for each part based on their accuracy.

AREAS COVERED

In this review, a wide-range list of different bioinformatics tools and online web servers has been provided. Moreover, some studies were proposed to show the importance of various bioinformatics tools for predicting and developing efficient vaccines against different pathogens including viruses, bacteria, parasites, and fungi as well as cancer.

EXPERT OPINION

Immunoinformatics is the best way to find potential vaccine candidates against different pathogens. Thus, the selection of the most accurate tools is necessary to predict and develop potent preventive and therapeutic vaccines. To further evaluation of the computational and in silico vaccine design, in vitro/in vivo analyses are required to develop vaccine candidates.

The Long Road Toward COVID-19 Herd Immunity: Vaccine Platform Technologies and Mass Immunization Strategies

There is an urgent need for effective countermeasures against the current emergence and accelerating expansion of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Induction of herd immunity by mass vaccination has been a very successful strategy for preventing the spread of many infectious diseases, hence protecting the most vulnerable population groups unable to develop immunity, for example individuals with immunodeficiencies or a weakened immune system due to underlying medical or debilitating conditions. Therefore, vaccination represents one of the most promising counter-pandemic measures to COVID-19. However, to date, no licensed vaccine exists, neither for SARS-CoV-2 nor for the closely related SARS-CoV or Middle East respiratory syndrome-CoV. In addition, a few vaccine candidates have only recently entered human clinical trials, which hampers the progress in tackling COVID-19 infection. Here, we discuss potential prophylactic interventions for SARS-CoV-2 with a focus on the challenges existing for vaccine development, and we review pre-clinical progress and ongoing human clinical trials of COVID-19 vaccine candidates. Although COVID-19 vaccine development is currently accelerated via so-called fast-track programs, vaccines may not be timely available to have an impact on the first wave of the ongoing COVID-19 pandemic. Nevertheless, COVID-19 vaccines will be essential in the future for reducing morbidity and mortality and inducing herd immunity, if SARS-CoV-2 becomes established in the population like for example influenza virus.

Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations, and we find for SARS-CoV-2 that it provides superior predicted display of viral epitopes by MHC class I and MHC class II molecules over populations when compared to other candidate vaccines. Our method is robust to idiosyncratic errors in the prediction of MHC peptide display and considers target population HLA haplotype frequencies during optimization. To minimize clinical development time our methods validate vaccines with multiple peptide presentation algorithms to increase the probability that a vaccine will be effective. We optimize an objective function that is based on the presentation likelihood of a diverse set of vaccine peptides conditioned on a target population HLA haplotype distribution and expected epitope drift. We produce separate peptide formulations for MHC class I loci (HLA-A, HLA-B, and HLA-C) and class II loci (HLA-DP, HLA-DQ, and HLA-DR) to permit signal sequence based cell compartment targeting using nucleic acid based vaccine platforms. Our SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA hits on average in an individual (≥ 1 peptide 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our MHC class II vaccine formulations provide 90.17% predicted coverage with at least five vaccine peptide-HLA hits on average in an individual with all peptides having observed mutation probability ≤ 0.001. We evaluate 29 previously published peptide vaccine designs with our evaluation tool with the requirement of having at least five vaccine peptide-HLA hits per individual, and they have a predicted maximum of 58.51% MHC class I coverage and 71.65% MHC class II coverage given haplotype based analysis. We provide an open source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts.

Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens

MOTIVATION

Reverse vaccinology (RV) is a milestone in rational vaccine design, and machine learning (ML) has been applied to enhance the accuracy of RV prediction. However, ML-based RV still faces challenges in prediction accuracy and program accessibility.

RESULTS

This study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens (BPAgs). To identify the best ML method with optimized conditions, five ML methods were tested with biological and physiochemical features extracted from well-defined training data. Nested 5-fold cross-validation and leave-one-pathogen-out validation were used to ensure unbiased performance assessment and the capability to predict vaccine candidates against a new emerging pathogen. The best performing model (eXtreme Gradient Boosting) was compared to three publicly available programs (Vaxign, VaxiJen, and Antigenic), one SVM-based method, and one epitope-based method using a high-quality benchmark dataset. Vaxign-ML showed superior performance in predicting BPAgs. Vaxign-ML is hosted in a publicly accessible web server and a standalone version is also available.

AVAILABILITY AND IMPLEMENTATION

Vaxign-ML website at http://www.violinet.org/vaxign/vaxign-ml, Docker standalone Vaxign-ML available at https://hub.docker.com/r/e4ong1031/vaxign-ml and source code is available at https://github.com/VIOLINet/Vaxign-ML-docker.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Better Epitope Discovery, Precision Immune Engineering, and Accelerated Vaccine Design Using Immunoinformatics Tools

Computational vaccinology includes epitope mapping, antigen selection, and immunogen design using computational tools. Tools that facilitate the in silico prediction of immune response to biothreats, emerging infectious diseases, and cancers can accelerate the design of novel and next generation vaccines and their delivery to the clinic. Over the past 20 years, vaccinologists, bioinformatics experts, and advanced programmers based in Providence, Rhode Island, USA have advanced the development of an integrated toolkit for vaccine design called iVAX, that is secure and user-accessible by internet. This integrated set of immunoinformatic tools comprises algorithms for scoring and triaging candidate antigens, selecting immunogenic and conserved T cell epitopes, re-engineering or eliminating regulatory T cell epitopes, and re-designing antigens to induce immunogenicity and protection against disease for humans and livestock. Commercial and academic applications of iVAX have included identifying immunogenic T cell epitopes in the development of a T-cell based human multi-epitope Q fever vaccine, designing novel influenza vaccines, identifying cross-conserved T cell epitopes for a malaria vaccine, and analyzing immune responses in clinical vaccine studies. Animal vaccine applications to date have included viral infections of pigs such as swine influenza A, PCV2, and African Swine Fever. “Rapid-Fire” applications for biodefense have included a demonstration project for Lassa Fever and Q fever. As recent infectious disease outbreaks underscore the significance of vaccine-driven preparedness, the integrated set of tools available on the iVAX toolkit stand ready to help vaccine developers deliver genome-derived, epitope-driven vaccines.

Immunoinformatics and Vaccine Development: An Overview

The use of vaccines have resulted in a remarkable improvement in global health. It has saved several lives, reduced treatment costs and raised the quality of animal and human lives. Current traditional vaccines came empirically with either vague or completely no knowledge of how they modulate our immune system. Even at the face of potential vaccine design advance, immune-related concerns (as seen with specific vulnerable populations, cases of emerging/re-emerging infectious disease, pathogens with complex lifecycle and antigenic variability, need for personalized vaccinations, and concerns for vaccines' immunological safety -specifically vaccine likelihood to trigger non-antigen-specific responses that may cause autoimmunity and vaccine allergy) are being raised. And these concerns have driven immunologists toward research for a better approach to vaccine design that will consider these challenges. Currently, immunoinformatics has paved the way for a better understanding of some infectious disease pathogenesis, diagnosis, immune system response and computational vaccinology. The importance of this immunoinformatics in the study of infectious diseases is diverse in terms of computational approaches used, but is united by common qualities related to host–pathogen relationship. Bioinformatics methods are also used to assign functions to uncharacterized genes which can be targeted as a candidate in vaccine design and can be a better approach toward the inclusion of women that are pregnant into vaccine trials and programs. The essence of this review is to give insight into the need to focus on novel computational, experimental and computation-driven experimental approaches for studying of host–pathogen interactions and thus making a case for its use in vaccine development.

In silico identification and modification of T cell epitopes in pertussis antigens associated with tolerance

The resurgence of whooping cough since the introduction of acellular (protein) vaccines has led to a renewed interest in the development of improved pertussis vaccines; Outer Membrane Vesicles (OMVs) carrying pertussis antigens have emerged as viable candidates. An in silico immunogenicity screen was carried out on 49 well-known Bordetella pertussis proteins in order to better understand their potential role toward the efficacy of pertussis OMVs for vaccine design; seven proteins were identified as being good candidates for including in optimized cellular and acellular pertussis vaccines. We then screened these antigens for putative tolerance-inducing sequences, as proteins with reduced tolerogenicity have improved vaccine potency in preclinical models. We used specialized homology tools (JanusMatrix) to identify peptides in the proteins that were cross-reactive with human sequences. Four of the 19 identified cross-reactive peptides were detolerized in silico using a separate tool, OptiMatrix, which disrupted the potential of these peptides to bind to human HLA and murine MHC. Four selected cross-reactive peptides and their detolerized variants were synthesized and their binding to a set of eight common HLA class II alleles was assessed in vitro. Reduced binding affinity to HLA class II was observed for the detolerized variants compared to the wild-type peptides, highlighting the potential of this approach for designing more efficacious pertussis vaccines.

Designing a multi-epitopic vaccine against the enterotoxigenic Bacteroides fragilis based on immunoinformatics approach

Enterotoxigenic Bacteroides fragilis is an enteric pathogen which is described as a causative agent of various intestinal infections and inflammatory diseases. Moreover, various research studies have reported it to be a leading factor in the development of colorectal cancer. As a part of the normal human microbiome, its treatment has become quite a challenge due to the alarming resistance against the available antibiotics. Although, this particular strain of B. fragilis shows susceptibility to few antibiotics, it is pertinent to devise an effective vaccine strategy for its elimination. There is no vaccine available against this pathogen up to date; therefore, we systematically ventured the outer membrane toxin producing proteins found exclusively in the toxigenic B. fragilis through the in-silico approaches to predict a multi-epitopic chimeric vaccine construct. The designed protein constitutes of epitopes which are predicted for linear B cells, Helper and T cells of outer membrane proteins expected to be putative vaccine candidates. The finalized proteins are only expressed in the enterotoxigenic B. fragilis, thus proving them to be exclusive. The 3D structure of the protein was first predicted followed by its refinement and validation via utilizing the bioinformatic approaches. Docking of the designed protein with the TLR2 receptor forecasted apt binding. Upon immune simulation, notable levels were observed in the expression of the immune cells.

Differential functional patterns of memory CD4+ and CD8+ T-cells from volunteers immunized with Ty21a typhoid vaccine observed using a recombinant Escherichia coli system expressing S. Typhi proteins

It is widely accepted that CD4⁺ and CD8⁺ T-cells play a significant role in protection against Salmonella enterica serovar Typhi (S. Typhi), the causative agent of the typhoid fever. However, the antigen specificity of these T-cells remains largely unknown. Previously, we demonstrated the feasibility of using a recombinant Escherichia coli (E. coli) expression system to uncover the antigen specificity of CD4⁺ and CD8⁺ T cells. Here, we expanded these studies to include the evaluation of 12 additional S. Typhi proteins: 4 outer membrane proteins (OmpH, OmpL, OmpR, OmpX), 3 Vi-polysaccharide biosynthesis proteins (TviA, TviB, TviE), 3 cold shock proteins (CspA, CspB, CspC), and 2 conserved hypothetical proteins (Chp 1 and Chp2), all selected based on the bioinformatic analyses of the content of putative T-cell epitopes. CD4⁺ and CD8⁺ T cells from 15 adult volunteers, obtained before and 42 days after immunization with oral live attenuated Ty21a vaccine, were assessed for their functionality (i.e., production of cytokines and cytotoxic expression markers in response to stimulation with selected antigens) as measured by flow cytometry. Although volunteers differed on their T-cell antigen specificity, we observed T-cell immune responses against all S. Typhi proteins evaluated. These responses included 9 proteins, OmpH, OmpR, TviA, TviE, CspA, CspB, CspC, Chp 1 and Chp 2, which have not been previously reported to elicit T-cell responses. Interestingly, we also observed that, regardless of the protein, the functional patterns of the memory T-cells were different between CD4⁺ and CD8⁺ T cells. In sum, these studies demonstrated the feasibility of using bioinformatic analysis and the E. coli expressing system described here to uncover novel immunogenic T-cell proteins that could serve as potential targets for the production of protein-based vaccines.

Coxiella burnetii Epitope-Specific T-Cell Responses in Patients with Chronic Q Fever

Infection with Coxiella burnetii, the causative agent of Q fever, can result in life-threatening persistent infection. Reactogenicity hinders worldwide implementation of the only licensed human Q fever vaccine. We previously demonstrated long-lived immunoreactivity in individuals with past symptomatic and asymptomatic Coxiella infection (convalescents) to promiscuous HLA class II C. burnetii epitopes, providing the basis for a novel T-cell targeted subunit vaccine. In this study, we investigated in a cohort of 22 individuals treated for persistent infection (chronic Q fever) whether they recognize the same set of epitopes or distinct epitopes that could be candidates for a therapeutic vaccine or aid in the diagnosis of persistent infection. In cultured enzyme-linked immunosorbent spot (ELISpot) assays, individuals with chronic Q fever showed strong class II epitope-specific responses that were largely overlapping with the peptide repertoire identified previously for convalescents. Five additional peptides were recognized more frequently by chronic subjects, but there was no combination of epitopes uniquely recognized by or nonreactive in subjects with chronic Q fever. Consistent with more recent/prolonged exposure, we found, however, stronger ex vivo responses by direct ELISpot to both whole-cell C. burnetii and individual peptides in chronic patients than in convalescents. In conclusion, we have validated and expanded a previously published set of candidate epitopes for a novel T-cell targeted subunit Q fever vaccine in treated patients with chronic Q fever and demonstrated that they successfully mounted a T-cell response comparable to that of convalescents. Finally, we demonstrated that individuals treated for chronic Q fever mount a broader ex vivo response to class II epitopes than convalescents, which could be explored for diagnostic purposes.