From genome to vaccine: in silico predictions, ex vivo verification

Restoring immune balance with Tregitopes: A new approach to treating immunological disorders

The induction of immunological tolerance is a promising strategy for managing autoimmune diseases, allergies, and transplant rejection. Tregitopes, a class of peptides, have emerged as potential agents for this purpose. They activate regulatory T cells, which are pivotal in reducing inflammation and promoting tolerance, by binding to MHC II molecules and facilitating their processing and presentation to Treg cells, thereby encouraging their proliferation. Moreover, Tregitopes influence the phenotype of antigen-presenting cells by attenuating the expression of CD80, CD86, and MHC class II while enhancing ILT3, resulting in the inhibition of NF-kappa B signaling pathways. Various techniques, including in vitro and in silico methods, are applied to identify Tregitope candidates. Currently, Tregitopes play a vital role in balancing immune activation and tolerance in clinical applications such as Pompe disease, diabetes-related antigens, and the prevention of spontaneous abortions in autoimmune diseases. Similarly, Tregitopes can induce antigen-specific regulatory T cells. Their anti-inflammatory effects are significant in conditions such as autoimmune encephalomyelitis, inflammatory bowel disease, and Guillain-Barré syndrome. Additionally, Tregitopes have been leveraged to enhance vaccine design and efficacy. Recent advancements in understanding the potential benefits and drawbacks of IVIG and the discovery of the function and mechanism of Tregitopes have introduced Tregitopes as a popular option for immune system modulation. It is expected that they will bring about a significant revolution in the management and treatment of autoimmune and immunological diseases. This article is a comprehensive review of Tregitopes, concluding with the potential of these epitopes as a therapeutic avenue for immunological disorders.

Multi-perspectives and challenges in identifying B-cell epitopes

The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).

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.

Mycobacterium smegmatis, a Promising Vaccine Vector for Preventing TB and Other Diseases: Vaccinomics Insights and Applications

Mycobacterium smegmatis (M.sm) is frequently used as an alternative model organism in Mycobacterium tuberculosis (M.tb) studies. While containing high sequence homology with M.tb, it is considered non-pathogenic in humans. As such it has been used to study M.tb and other infections in vivo and more recently been explored for potential therapeutic applications. A body of previous research has highlighted the potential of using genetically modified M.sm displaying rapid growth and unique immunostimulatory characteristics as an effective vaccine vector. Novel systems biology techniques can further serve to optimize these delivery constructs. In this article, we review recent advancements in vaccinomics tools that support the efficacy of a M.sm-based vaccine vector. Moreover, the integration of systems biology and molecular omics techniques in these pioneering studies heralds a potential accelerated pipeline for the development of next-generation recombinant vaccines against rapidly developing diseases.

Finding epitopes of Klebsiella pneumoniae outer membrane protein-K17 (OMPK17) and introducing a 25-mer peptide of it as a vaccine candidate

No approved vaccine exists for Klebsiella pneumoniae yet. Outer membrane protein-K17 (OMPK17) is involved in K. pneumoniae pathogenesis. No information has been found about OMPK17 dominant epitopes in the literature. Therefore, this study aimed to predict both T cell and B cell epitopes of K. pneumoniae OMPK17 via immunoinformatics approaches. Both T cell (class-I and II) and B cell (linear and discontinuous) epitopes of OMPK17 were predicted. Several screening analyses were performed including clustering, immunogenicity, human similarity, toxicity, allergenicity, conservancy, docking, and structural/physicochemical suitability. The results showed that some regions of OMPK17 have more potential as epitopes. The most possible epitopes were found via several analyses including the selection of higher-scoring epitopes, the epitopes predicted with more tools, more immunogenic epitopes, the epitopes capable of producing interferon-gamma, the epitopes with more dissimilarity to human peptides, and non-toxic and non-allergenic epitopes. By comparing the best T cell and B cell epitopes, we reached a 25-mer peptide containing both T cell (class-I and class-II) and B cell (linear) epitopes and comprising appropriate physicochemical characteristics that are required for K. pneumoniae vaccine development. The in vitro/in vivo study of this peptide is recommended to clarify its actual efficiency and efficacy.

Supplementary information

The online version contains supplementary material available at 10.1007/s11756-023-01371-0.

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.

Conformational epitope matching and prediction based on protein surface spiral features

Background

A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis.

Results

We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy.

Conclusions

The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.

Validation of a yellow fever vaccine model using data from primary vaccination in children and adults, re-vaccination and dose-response in adults and studies with immunocompromised individuals

BACKGROUND

An effective yellow fever (YF) vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for a booster dose, the optimal immunisation schedule for immunocompetent, immunosuppressed, and pediatric populations, among other issues. This work aims to demonstrate that computational tools can be used to simulate different scenarios regarding YF vaccination and the immune response of individuals to this vaccine, thus assisting the response of some of these open questions.

RESULTS

This work presents the computational results obtained by a mathematical model of the human immune response to vaccination against YF. Five scenarios were simulated: primovaccination in adults and children, booster dose in adult individuals, vaccination of individuals with autoimmune diseases under immunomodulatory therapy, and the immune response to different vaccine doses. Where data were available, the model was able to quantitatively replicate the levels of antibodies obtained experimentally. In addition, for those scenarios where data were not available, it was possible to qualitatively reproduce the immune response behaviours described in the literature.

CONCLUSIONS

Our simulations show that the minimum dose to confer immunity against YF is half of the reference dose. The results also suggest that immunological immaturity in children limits the induction and persistence of long-lived plasma cells are related to the antibody decay observed experimentally. Finally, the decay observed in the antibody level after ten years suggests that a booster dose is necessary to keep immunity against YF.

Yersinia pestis Antigen F1 but Not LcrV Induced Humoral and Cellular Immune Responses in Humans Immunized with Live Plague Vaccine-Comparison of Immunoinformatic and Immunological Approaches

The recent progress in immunoinformatics provided the basis for an accelerated development of target-specific peptide vaccines as an alternative to the traditional vaccine concept. However, there is still limited information on whether the in silico predicted immunoreactive epitopes correspond to those obtained from the actual experiments. Here, humoral and cellular immune responses to two major Yersinia pestis protective antigens, F1 and LcrV, were studied in human donors immunized with the live plague vaccine (LPV) based on the attenuated Y. pestis strain EV line NIIEG. The F1 antigen provided modest specific cellular (mixed T helper 1 (Th1)/Th2 type) and humoral immune responses in vaccinees irrespective of the amount of annual vaccinations and duration of the post-vaccination period. The probing of the F1 overlapping peptide library with the F1-positive sera revealed the presence of seven linear B cell epitopes, which were all also predicted by in silico assay. The immunoinformatics study evaluated their antigenicity, toxicity, and allergenic properties. The epitope TSQDGNNH was mostly recognized by the sera from recently vaccinated donors rather than antibodies from those immunized decades ago, suggesting the usefulness of this peptide for differentiation between recent and long-term vaccinations. The in silico analysis predicted nine linear LcrV-specific B-cell epitopes; however, weak antibody and cellular immune responses prevented their experimental evaluation, indicating that LcrV is a poor marker of successful vaccination. No specific Th17 immune response to either F1 or LcrV was detected, and there were no detectable serum levels of F1-specific immunoglobulin A (IgA) in vaccinees. Overall, the general approach validated in the LPV model could be valuable for the rational design of vaccines against other neglected and novel emerging infections with high pandemic potency.

A Highly Conserved Epitope (RNNQIPQDF) of Porcine teschovirus Induced a Group-Specific Antiserum: A Bioinformatics-Predicted Model with Pan-PTV Potential

Porcine teschovirus (PTV) is an OIE-listed pathogen with 13 known PTV serotypes. Heterologous PTV serotypes frequently co-circulate and co-infect with another swine pathogen, causing various symptoms in all age groups, thus highlighting the need for a pan-PTV diagnostic tool. Here, a recombinant protein composed of a highly conserved “RNNQIPQDF” epitope on the GH loop of VP1, predicted in silico, and a tandem repeat of this epitope carrying the pan DR (PADRE) and Toxin B epitopes was constructed to serve as a PTV detection tool. This recombinant GST-PADRE-(RNNQIPQDF)ⁿ-Toxin B protein was used as an immunogen, which effectively raised non-neutralizing or undetectable neutralizing antibodies against PTV in mice. The raised antiserum was reactive against all the PTV serotypes (PTV–1–7) tested, but not against members of the closely related genera Sapelovirus and Cardiovirus, and the unrelated virus controls. This potential pan-PTV diagnostic reagent may be used to differentiate naturally infected animals from vaccinated animals that have antibodies against a subunit vaccine that does not contain this epitope or to screen for PTV before further subtyping. To our knowledge, this is the first report that utilized in silico PTV epitope prediction to find a reagent broadly reactive to various PTV serotypes.

The combination of artificial intelligence and systems biology for intelligent vaccine design

INTRODUCTION

A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development.

AREAS COVERED

This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases.

EXPERT OPINION

Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an 'in silico era' in which scientific partnerships, including a more and more increasing creation of an 'ecosystem' of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.

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.

Identification of HLA-I restricted epitopes in six vaccine candidates of Leishmania tropica using immunoinformatics and molecular dynamics simulation approaches

In spite of numerous studies on vaccination for various species of Leishmania, research on the development of an effective vaccine for L. tropica is very scarce. In silico epitope prediction is a new way to survey the best vaccine candidates. Here, we predicted the best epitopes of six L. tropica antigens with vaccine capability against this pathogen, using highly frequent HLA-I alleles. Based on the frequent HLA alleles, the protein sequences were screened individually using four different MHC prediction applications, namely SYFPEITHI, ProPredI, BIMAS, and IEDB. Several in silico assays including clustering, human similarity exclusion, epitope conservancy prediction, investigating in experimental records, immunogenicity prediction, and prediction of population coverage were performed to narrow the results and to find the best epitopes. The selected epitopes and their restricted HLA-I alleles were docked and the final epitopes with the lowest binding energy (the highest binding affinity) were chosen. Finally, the stability and the binding properties of the best epitope-HLA-I combinations were analyzed using molecular dynamics simulation studies. We found ten potential peptides with strong binding affinity to highly frequent HLA-I alleles that can be further evaluated as vaccine targets against L. tropica.

PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome

BACKGROUND

A revolutionary diversion from classical vaccinology to reverse vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into reverse vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through reverse vaccinology.

RESULTS

We have designed an integrative computational pipeline term as "PanRV" that employs both the pangenome and reverse vaccinology approaches. PanRV comprises of four functional modules including i) Pangenome Estimation Module (PGM) ii) Reverse Vaccinology Module (RVM) iii) Functional Annotation Module (FAM) and iv) Antibiotic Resistance Association Module (ARM). The pipeline is tested by using genomic data from 301 genomes of Staphylococcus aureus and the results are verified by experimentally known antigenic data.

CONCLUSION

The proposed pipeline has proved to be the first comprehensive automated pipeline that can precisely identify putative vaccine candidates exploiting the microbial pangenome. PanRV is a Linux based package developed in JAVA language. An executable installer is provided for ease of installation along with a user manual at https://sourceforge.net/projects/panrv2/ .

A qualitatively validated mathematical-computational model of the immune response to the yellow fever vaccine

Background

Although a safe and effective yellow fever vaccine was developed more than 80 years ago, several issues regarding its use remain unclear. For example, what is the minimum dose that can provide immunity against the disease? A useful tool that can help researchers answer this and other related questions is a computational simulator that implements a mathematical model describing the human immune response to vaccination against yellow fever.

Methods

This work uses a system of ten ordinary differential equations to represent a few important populations in the response process generated by the body after vaccination. The main populations include viruses, APCs, CD8+ T cells, short-lived and long-lived plasma cells, B cells and antibodies.

Results

In order to qualitatively validate our model, four experiments were carried out, and their computational results were compared to experimental data obtained from the literature. The four experiments were: a) simulation of a scenario in which an individual was vaccinated against yellow fever for the first time; b) simulation of a booster dose ten years after the first dose; c) simulation of the immune response to the yellow fever vaccine in individuals with different levels of naïve CD8+ T cells; and d) simulation of the immune response to distinct doses of the yellow fever vaccine.

Conclusions

This work shows that the simulator was able to qualitatively reproduce some of the experimental results reported in the literature, such as the amount of antibodies and viremia throughout time, as well as to reproduce other behaviors of the immune response reported in the literature, such as those that occur after a booster dose of the vaccine.

The role of regulatory T cells in immunopathogenesis and immunotherapy of viral infections

Two hepatotropic viruses hepatitis C virus (HCV) and hepatitis B virus (HBV) have been considered as the main cause of chronic viral infections. In addition, human immunodeficiency virus (HIV) attacks the immune system by eradication of some white blood cell (T-helper cell). The role of Tregs in HCV, HBV and HIV infections ranges from suppressing antiviral T cell responses to protecting tissues as liver and immune cells from immune mediated injury. In this review, we discuss the influence of regulatory T cells in immunopathology of specific viral infections including HCV, HBV and HIV by focusing on targeting Tregs as novel approach in vaccinology against viral infections.

The use of databases, data mining and immunoinformatics in vaccinology: where are we?

INTRODUCTION

Vaccinology has evolved from a sub-discipline focussed on simplistic vaccine development based on antibody-mediated protection to a separate discipline involving epidemiology, host and pathogen biology, immunology, genomics, proteomics, structure biology, protein engineering, chemical biology, and delivery systems. Data mining in combination with bioinformatics has provided a scaffold linking all these disciplines to the design of vaccines and vaccine adjuvants. Areas covered: This review provides background knowledge on immunological aspects which have been exploited with informatics for the in silico analysis of immune responses and the design of vaccine antigens. Furthermore, the article presents various databases and bioinformatics tools, and discusses B and T cell epitope predictions, antigen design, adjuvant research and systems immunology, highlighting some important examples, and challenges for the future. Expert opinion: Informatics and data mining have not only reduced the time required for experimental immunology, but also contributed to the identification and design of novel vaccine candidates and the determination of biomarkers and pathways of vaccine response. However, more experimental data is required for benchmarking immunoinformatic tools. Nevertheless, developments in immunoinformatics and reverse vaccinology, which are nascent fields, are likely to hasten vaccine discovery, although the path to regulatory approval is likely to remain a necessary impediment.

Antigenic and immunogenic evaluation of Helicobacter pylori FlaA epitopes

Objective(s):

Helicobacter pylori are among most common human pathogens affecting at least half of the world’s population. Mobility is one of the important primary factors in bacterial colonization and invasion. The purpose of this research is cloning, expression, and purification of FlaA protein specific epitopes in order to evaluate their antigenicity and immunogenicity.

Materials and Methods:

The antigenic region of the flaA gene was bioinformatically predicted using Epitope mapping software’s and the predicted epitopes were expressed in a prokaryotic expression vector. The antigen was injected into the animal model (mice BALB/c) and some indicators including IgG1, IgG2a, IgA, IFN-γ, and IL 5 were measured.

Results:

The immunogenicity studies in animal models by measuring serum antibodies (IgG1, IgG2a, and IgA) and cytokines (IFN-γ and IL5) revealed that the rFlaA induces a proper immune response in animal models.

Conclusion:

The recombinant FlaA protein is antigenic and immunogenic. Therefore, it might be used in order to design of specific diagnostic kits and recombinant vaccines against H. pylori.

An immunoinformatics-derived DNA vaccine encoding human class II T cell epitopes of Ebola virus, Sudan virus, and Venezuelan equine encephalitis virus is immunogenic in HLA transgenic mice

Immunoinformatics tools were used to predict human leukocyte antigen (HLA) class II-restricted T cell epitopes within the envelope glycoproteins and nucleocapsid proteins of Ebola virus (EBOV) and Sudan virus (SUDV) and the structural proteins of Venezuelan equine encephalitis virus (VEEV). Selected epitopes were tested for binding to soluble HLA molecules representing 5 class II alleles (DRB1*0101, DRB1*0301, DRB1*0401, DRB1*0701, and DRB1*1501). All but one of the 25 tested peptides bound to at least one of the DRB1 alleles, and 4 of the peptides bound at least moderately or weakly to all 5 DRB1 alleles. Additional algorithms were used to design a single "string-of-beads" expression construct with 44 selected epitopes arranged to avoid creation of spurious junctional epitopes. Seventeen of these 44 predicted epitopes were conserved between the major histocompatibility complex (MHC) of humans and mice, allowing initial testing in mice. BALB/c mice vaccinated with the multi-epitope construct developed statistically significant cellular immune responses to EBOV, SUDV, and VEEV peptides as measured by interferon (IFN)-γ ELISpot assays. Significant levels of antibodies to VEEV, but not EBOV, were also detected in vaccinated BALB/c mice. To assess immunogenicity in the context of a human MHC, HLA-DR3 transgenic mice were vaccinated with the multi-epitope construct and boosted with a mixture of the 25 peptides used in the binding assays. The vaccinated HLA-DR3 mice developed significant cellular immune responses to 4 of the 25 (16%) tested individual class II peptides as measured by IFN-γ ELISpot assays. In addition, these mice developed antibodies against EBOV and VEEV as measured by ELISA. While a low but significant level of protection was observed in vaccinated transgenic mice after aerosol exposure to VEEV, no protection was observed after intraperitoneal challenge with mouse-adapted EBOV. These studies provide proof of concept for the use of an informatics approach to design a multi-agent, multi-epitope immunogen and provide a basis for further testing aimed at focusing immune responses toward desired protective T cell epitopes.

Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology

New contributions that aim to accelerate the development or to improve the efficacy and safety of vaccines arise from many different areas of research and technology. One of these areas is computational science, which traditionally participates in the initial steps, such as the pre-screening of active substances that have the potential to become a vaccine antigen. In this work, we present another promising way to use computational science in vaccinology: mathematical and computational models of important cell and protein dynamics of the immune system. A system of Ordinary Differential Equations represents different immune system populations, such as B cells and T cells, antigen presenting cells and antibodies. In this way, it is possible to simulate, in silico, the immune response to vaccines under development or under study. Distinct scenarios can be simulated by varying parameters of the mathematical model. As a proof of concept, we developed a model of the immune response to vaccination against the yellow fever. Our simulations have shown consistent results when compared with experimental data available in the literature. The model is generic enough to represent the action of other diseases or vaccines in the human immune system, such as dengue and Zika virus.

Therapeutics Insight with Inclusive Immunopharmacology Explication of Human Rotavirus A for the Treatment of Diarrhea

Rotavirus is the most common cause of severe infant and childhood diarrhea worldwide, and the morbidity and mortality rate is going to be outnumbered in developing countries like Bangladesh. To mitigate this substantial burden of disease, new therapeutics such as vaccine and drug are swiftly required against rotavirus. The present therapeutics insight study was performed with comprehensive immunoinformatics and pharmacoinformatics approach. T and B-cell epitopes were assessed in the conserved region of outer capsid protein VP4 among the highly reviewed strains from different countries including Bangladesh. The results suggest that epitope SU1 (TLKNLNDNY) could be an ideal candidate among the predicted five epitopes for both T and B-cell epitopes for the development of universal vaccine against rotavirus. This research also suggests five novel drug compounds from medicinal plant Rhizophora mucronata Lamk. for better therapeutics strategies against rotavirus diarrhea based on 3D structure building, pharmacophore, ADMET, and QSAR properties. The exact mode of action between drug compounds and target protein VP4 were revealed by molecular docking analysis. Drug likeness and oral bioavailability further confirmed the effectiveness of the proposed drugs against rotavirus diarrhea. This study might be implemented for experimental validation to facilitate the novel vaccine and drug design.

Low incidence of anti-drug antibodies in patients with type 2 diabetes treated with once-weekly glucagon-like peptide-1 receptor agonist dulaglutide

Therapeutic administration of peptides may result in anti-drug antibody (ADA) formation, hypersensitivity adverse events (AEs) and reduced efficacy. As a large peptide, the immunogenicity of once-weekly glucagon-like peptide-1 (GLP-1) receptor agonist dulaglutide is of considerable interest. The present study assessed the incidence of treatment-emergent dulaglutide ADAs, hypersensitivity AEs, injection site reactions (ISRs), and glycaemic control in ADA-positive patients in nine phase II and phase III trials (dulaglutide, N = 4006; exenatide, N = 276; non-GLP-1 comparators, N = 1141). Treatment-emergent dulaglutide ADAs were detected using a solid-phase extraction acid dissociation binding assay. Neutralizing ADAs were detected using a cell-based assay derived from human endothelial kidney cells (HEK293). A total of 64 dulaglutide-treated patients (1.6% of the population) tested ADA-positive versus eight (0.7%) from the non-GLP-1 comparator group. Of these 64 patients, 34 (0.9%) had dulaglutide-neutralizing ADAs, 36 (0.9%) had native-sequence GLP-1 (nsGLP-1) cross-reactive ADAs and four (0.1%) had nsGLP-1 neutralization ADAs. The incidence of hypersensitivity AEs and ISRs was similar in the dulaglutide versus placebo groups. No dulaglutide ADA-positive patient reported hypersensitivity AEs. Because of the low incidence of ADAs, it was not possible to establish their effect on glycaemic control.

Identification of CD4+ T-cell epitope and investigation of HLA distribution for the immunogenic proteins of Burkholderia pseudomallei using in silico approaches - A key vaccine development strategy for melioidosis

Melioidosis is a serious infectious diseases affecting multi-organ system in humans with high mortality rate. The disease is caused by the bacterium, Burkholderia pseudomallei and it is intrinsically resistant to many antibiotics. Thus, there is an urgent need for protective vaccine against B. pseudomallei; which may reduce morbidity and mortality in endemic areas. The identification of peptides that bind to major histocompatibility complex II class helps in understanding the nature of immune response and identifying T-cell epitopes for the design of new vaccines. Previous studies indicate that, ompA, bipB, fliC and groEL proteins of B. pseudomallei stimulate CD4+ T-cell immune response and act as protective immunogens. However, the data for CD4+ T-cell epitopes of these immunogenic proteins are very limited. Hence, in this present study we attempted to identify CD4+ T-cell epitopes in B. pseudomallei immunogenic proteins using in silico approaches. We did population coverage analysis for these identified epitopic core sequences to identify individuals in endemic areas expected to respond to a given set of these epitopes on the basis of HLA genotype frequencies. We observed that eight epitopic core sequences, two from each immunogenic protein, were associated with the maximum number of HLA-DR binding alleles. These eight peptides are found to be immunogenic in more than 90% of population in endemic areas considered. Thus, these eight peptides containing epitopic core sequences may act as probable vaccine candidates and they may be considered for the development of epitope-based vaccines for melioidosis.

Production of IFN-γ and IL-4 Against Intact Catalase and Constructed Catalase Epitopes of Helicobacter pylori From T-Cells

Background:

Helicobacter pylori infection is highly prevalent in the developing countries. It causes gastritis, peptic ulcer disease, and gastrocarcinoma. Treatment with drugs and antibiotics is problematic due to the following reasons: cost, resistance to antibiotics, prolonged treatment and using multiple drugs. Catalase is highly conserved among the Helicobacter species and is important to the survival of the organism. It is expressed in high amounts and is exposed to the surface of this bacterium; therefore it represents a suitable candidate vaccine antigen.

Objectives:

A suitable approach in H. pylori vaccinology is the administration of epitope based vaccines. Therefore the responses of T-cells (IFN-γ and IL-4 production) against the catalase of H. pylori were determined. Then the quality of the immune responses against intact catalase and three epitopes of catalase were compared.

Materials and Methods:

In this study, a composition of three epitopes of the H. pylori catalase was selected based on Propred software. The effect of catalase epitopes on T-cells were assayed and immune responses identified.

Results:

The results of IFN-γ, IL-4 production against antigens, epitopes, and recombinant catalase by T-cells were compared for better understanding of epitope efficiency.

Conclusions:

The current research demonstrated that epitope sequence stimulates cellular immune responses effectively. In addition, increased safety and potency as well as a reduction in time and cost were advantages of this method. Authors are going to use this sequence as a suitable vaccine candidate for further research on animal models and humans in future.

Partial pathogen protection by tick-bite sensitization and epitope recognition in peptide-immunized HLA DR3 transgenic mice

Ticks are notorious vectors of disease for humans, and many species of ticks transmit multiple pathogens, sometimes in the same tick bite. Accordingly, a broad-spectrum vaccine that targets vector ticks and pathogen transmission at the tick/host interface, rather than multiple vaccines against every possible tickborne pathogen, could become an important tool for resolving an emerging public health crisis. The concept for such a tick protective vaccine comes from observations of an acquired tick resistance (ATR) that can develop in non-natural hosts of ticks following sensitization to tick salivary components. Mice are commonly used as models to study immune responses to human pathogens but normal mice are natural hosts for many species of ticks and fail to develop ATR. We evaluated HLA DR3 transgenic (tg) "humanized" mice as a potential model of ATR and assessed the possibility of using this animal model for tick protective vaccine discovery studies. Serial tick infestations with pathogen-free Ixodes scapularis ticks were used to tick-bite sensitize HLA DR3 tg mice. Sensitization resulted in a cytokine skew favoring a Th2 bias as well as partial (57%) protection to infection with Lyme disease spirochetes (Borrelia burgdorferi) following infected tick challenge when compared to tick naïve counterparts. I. scapularis salivary gland homogenate (SGH) and a group of immunoinformatic-predicted T cell epitopes identified from the I. scapularis salivary transcriptome were used separately to vaccinate HLA DR3 tg mice, and these mice also were assessed for both pathogen protection and epitope recognition. Reduced pathogen transmission along with a Th2 skew resulted from SGH vaccination, while no significant protection and a possible T regulatory bias was seen in epitope-vaccinated mice. This study provides the first proof-of-concept for using HLA DR tg "humanized" mice for studying the potential tick protective effects of immunoinformatic- or otherwise-derived tick salivary components as tickborne disease vaccines.

Exploiting the power of OMICS approaches to produce E. coli O157 vaccines

Enterohemorrhagic Escherichia coli (EHEC) strains are well-documented human pathogens and causative agents of diarrheal episodes and hemorrhagic colitis. The serotype O157:H7 is highly virulent and responsible for both outbreaks and sporadic cases of diarrhea. Because antibiotic treatment is contraindicated against this pathogen, development of a human vaccine could be an effective intervention in public health. In our recent Infection and Immunity paper, we applied integrated approaches of in silico genome wide search combined with bioinformatics tools to identify and test O157 vaccine candidates for their protective effect on a murine model of gastrointestinal infection. Using genomic/immunoinformatic approaches that are further described here, we categorized vaccine candidates as high, medium, and low priorities, and demonstrate that some high priority candidates were able to significantly induce Th2 cytokines and reduce EHEC colonization. Using the STRING database, we have recently evaluated the vaccine candidates and predict functional protein interactions, determining whether correlations exist for the development of a multi-subunit vaccine, targeting different pathways against EHEC O157:H7. The overall approach is designed to screen potential vaccine candidates against EHEC; however, the methodology can be quickly applied to many other intestinal pathogens.

Immunogenic peptide discovery in cancer genomes

As immunotherapies to treat malignancy continue to diversify along with the tumor types amenable to treatment, it will become very important to predict which treatment is most likely to benefit a given patient. Tumor neoantigens, novel peptides resulting from somatic tumor mutations and recognized by the immune system as foreign, are likely to contribute significantly to the efficacy of immunotherapy. Multiple in silico methods have been developed to predict whether peptides, including tumor neoantigens, will be presented by the major histocompatibility complex (MHC) Class I or Class II, and interact with the T cell receptor (TCR). The methods for neoantigen prediction will be reviewed here, along with the most important examples of their use in the field of oncology.

An overview of bioinformatics tools for epitope prediction: implications on vaccine development

Exploitation of recombinant DNA and sequencing technologies has led to a new concept in vaccination in which isolated epitopes, capable of stimulating a specific immune response, have been identified and used to achieve advanced vaccine formulations; replacing those constituted by whole pathogen-formulations. In this context, bioinformatics approaches play a critical role on analyzing multiple genomes to select the protective epitopes in silico. It is conceived that cocktails of defined epitopes or chimeric protein arrangements, including the target epitopes, may provide a rationale design capable to elicit convenient humoral or cellular immune responses. This review presents a comprehensive compilation of the most advantageous online immunological software and searchable, in order to facilitate the design and development of vaccines. An outlook on how these tools are supporting vaccine development is presented. HIV and influenza have been taken as examples of promising developments on vaccination against hypervariable viruses. Perspectives in this field are also envisioned.

Time for T? Immunoinformatics addresses vaccine design for neglected tropical and emerging infectious diseases

Vaccines have been invaluable for global health, saving lives and reducing healthcare costs, while also raising the quality of human life. However, newly emerging infectious diseases (EID) and more well-established tropical disease pathogens present complex challenges to vaccine developers; in particular, neglected tropical diseases, which are most prevalent among the world's poorest, include many pathogens with large sizes, multistage life cycles and a variety of nonhuman vectors. EID such as MERS-CoV and H7N9 are highly pathogenic for humans. For many of these pathogens, while their genomes are available, immune correlates of protection are currently unknown. These complexities make developing vaccines for EID and neglected tropical diseases all the more difficult. In this review, we describe the implementation of an immunoinformatics-driven approach to systematically search for key determinants of immunity in newly available genome sequence data and design vaccines. This approach holds promise for the development of 21st century vaccines, improving human health everywhere.

In silico predicted mycobacterial epitope elicits in vitro T-cell responses

Epitope-based vaccines permit the selection of only a specific subset of epitopes to induce the necessary immune response, thus providing a rational alternative to conventional design approaches. Using a range of immunoinformatics tools, we identified a novel, contiguous 28 amino acid multi-epitope cluster within the highly conserved secretory protein Ag85B of Mycobacterium tuberculosis, the causative agent of TB. This cluster, named Ep85B, is composed of epitopes which bind to three HLA Class I and 15 Class II molecules, and harbors the potential to generate 99% population coverage in TB-endemic regions. We experimentally evaluated the capacity of Ep85B to elicit T-cell immune responses using whole blood cells and, as predicted, observed significant increases in populations of both CD4+ and memory CD4+ CD45RO+ T-cells. Our results demonstrate the practical utility of an epitope-based design methodology - a strategy that, following further evaluation, may serve as an additional tool for the development of novel vaccine candidates against TB and other diseases.

Genome-derived vaccines

Vaccine research entered a new era when the complete genome of a pathogenic bacterium was published in 1995. Since then, more than 97 bacterial pathogens have been sequenced and at least 110 additional projects are now in progress. Genome sequencing has also dramatically accelerated: high-throughput facilities can draft the sequence of an entire microbe (two to four megabases) in 1 to 2 days. Vaccine developers are using microarrays, immunoinformatics, proteomics and high-throughput immunology assays to reduce the truly unmanageable volume of information available in genome databases to a manageable size. Vaccines composed by novel antigens discovered from genome mining are already in clinical trials. Within 5 years we can expect to see a novel class of vaccines composed by genome-predicted, assembled and engineered T- and Bcell epitopes. This article addresses the convergence of three forces--microbial genome sequencing, computational immunology and new vaccine technologies--that are shifting genome mining for vaccines onto the forefront of immunology research.

HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity

Prediction of HLA binding affinity is widely used to identify candidate T cell epitopes, and an affinity of 500 nM is routinely used as a threshold for peptide selection. However, the fraction (percentage) of peptides predicted to bind with affinities of 500 nM varies by allele. For example, of a large collection of ~30,000 dengue virus-derived peptides only 0.3% were predicted to bind HLA A*0101, whereas nearly 5% were predicted for A*0201. This striking difference could not be ascribed to variation in accuracy of the algorithms used, as predicted values closely correlated with affinity measured in vitro with purified HLA molecules. These data raised the question whether different alleles would also vary in terms of epitope repertoire size, defined as the number of associated epitopes or, alternatively, whether alleles vary drastically in terms of the affinity threshold associated with immunogenicity. To address this issue, strains of HLA transgenic mice with wide (A*0201), intermediate (B*0702), or narrow (A*0101) repertoires were immunized with peptides of varying binding affinity and relative percentile ranking. The results show that absolute binding capacity is a better predictor of immunogenicity, and analysis of epitopes from the Immune Epitope Database revealed that predictive efficacy is increased using allele-specific affinity thresholds. Finally, we investigated the genetic and structural basis of the phenomenon. Although no stringent correlate was defined, on average HLA B alleles are associated with significantly narrower repertoires than are HLA A alleles.

Beyond humanization and de-immunization: tolerization as a method for reducing the immunogenicity of biologics

Immune responses to some monoclonal antibodies (mAbs) and biologic proteins interfere with their efficacy due to the development of anti-drug antibodies (ADA). In the case of mAbs, most ADA target 'foreign' sequences present in the complementarity determining regions (CDRs). Humanization of the mAb sequence is one approach that has been used to render biologics less foreign to the human immune system. However, fully human mAbs can also drive immunogenicity. De-immunization (removing epitopes) has been used to reduce biologic protein immunogenicity. Here, we discuss a third approach to reducing the immunogenicity of biologics: introduction of Treg epitopes that stimulate Treg function and induce tolerance to the biologic protein. Supplementing humanization (replacing xeno-sequences with human) and de-immunization (reducing T effector epitopes) with tolerization (introducing Treg epitopes) where feasible, as a means of improving biologics 'quality by design', may lead to the development of ever more clinically effective, but less immunogenic, biologics.

T-cell dependent immunogenicity of protein therapeutics: Preclinical assessment and mitigation

Protein therapeutics hold a prominent and rapidly expanding place among medicinal products. Purified blood products, recombinant cytokines, growth factors, enzyme replacement factors, monoclonal antibodies, fusion proteins, and chimeric fusion proteins are all examples of therapeutic proteins that have been developed in the past few decades and approved for use in the treatment of human disease. Despite early belief that the fully human nature of these proteins would represent a significant advantage, adverse effects associated with immune responses to some biologic therapies have become a topic of some concern. As a result, drug developers are devising strategies to assess immune responses to protein therapeutics during both the preclinical and the clinical phases of development. While there are many factors that contribute to protein immunogenicity, T cell- (thymus-) dependent (Td) responses appear to play a critical role in the development of antibody responses to biologic therapeutics. A range of methodologies to predict and measure Td immune responses to protein drugs has been developed. This review will focus on the Td contribution to immunogenicity, summarizing current approaches for the prediction and measurement of T cell-dependent immune responses to protein biologics, discussing the advantages and limitations of these technologies, and suggesting a practical approach for assessing and mitigating Td immunogenicity.

Antigenic heterogeneity of capsid protein VP1 in foot-and-mouth disease virus (FMDV) serotype Asia 1

Foot and mouth disease virus (FMDV), with its seven serotypes, is a highly contagious virus infecting mainly cloven-hoofed animals. The serotype Asia1 occurs mainly in Asian regions. An in-silico approach was taken to reveal the antigenic heterogeneities within the capsid protein VP1 of Asia1. A total of 47 VP1 sequences of Asia1 isolates from different countries of South Asian regions were selected, retrieved from database, and were aligned. The structure of VP1 protein was modeled using a homology modeling approach. Several antigenic sites were identified and mapped onto the three-dimensional protein structure. Variations at these antigenic sites were analyzed by calculating the protein variability index and finding mutation combinations. The data suggested that vaccine escape mutants have derived from only few mutations at several antigenic sites. Five antigenic peptides have been identified as the least variable epitopes, with just fewer amino acid substitutions. Only a limited number of serotype Asia1 antigenic variants were found to be circulated within the South Asian region. This emphasizes a possibility of formulating synthetic vaccines for controlling foot-and-mouth disease by Asia1 serotypes.

Quantitative prediction of peptide binding to HLA-DP1 protein

The exogenous proteins are processed by the host antigen-processing cells. Peptidic fragments of them are presented on the cell surface bound to the major hystocompatibility complex (MHC) molecules class II and recognized by the CD4+ T lymphocytes. The MHC binding is considered as the crucial prerequisite for T-cell recognition. Only peptides able to form stable complexes with the MHC proteins are recognized by the T-cells. These peptides are known as T-cell epitopes. All T-cell epitopes are MHC binders, but not all MHC binders are T-cell epitopes. The T-cell epitope prediction is one of the main priorities of immunoinformatics. In the present study, three chemometric techniques are combined to derive a model for in silico prediction of peptide binding to the human MHC class II protein HLA-DP1. The structures of a set of known peptide binders are described by amino acid z-descriptors. Data are processed by an iterative self-consisted algorithm using the method of partial least squares, and a quantitative matrix (QM) for peptide binding prediction to HLA-DP1 is derived. The QM is validated by two sets of proteins and showed an average accuracy of 86 percent.

Low immunogenicity predicted for emerging avian-origin H7N9: implication for influenza vaccine design

A new avian-origin influenza virus emerged near Shanghai in February 2013, and by the beginning of May it had caused over 130 human infections and 36 deaths. Human-to-human transmission of avian-origin H7N9 influenza A has been limited to a few family clusters, but the high mortality rate (27%) associated with human infection has raised concern about the potential for this virus to become a significant human pathogen. European, American, and Asian vaccine companies have already initiated the process of cloning H7 antigens such as hemagglutinin (HA) into standardized vaccine production vehicles. Unfortunately, previous H7 HA-containing vaccines have been poorly immunogenic. We used well-established immunoinformatics tools to analyze the H7N9 protein sequences and compare their T cell epitope content to other circulating influenza A strains as a means of estimating the immunogenic potential of the new influenza antigen. We found that the HA proteins derived from closely related human-derived H7N9 strains contain fewer T cell epitopes than other recently circulating strains of influenza, and that conservation of T cell epitopes with other strains of influenza was very limited. Here, we provide a detailed accounting of the type and location of T cell epitopes contained in H7N9 and their conservation in other H7 and circulating (A/California/07/2009, A/Victoria/361/2011, and A/Texas/50/2012) influenza A strains. Based on this analysis, avian-origin H7N9 2013 appears to be a “stealth” virus, capable of evading human cellular and humoral immune response. Should H7N9 develop pandemic potential, this analysis predicts that novel strategies for improving vaccine immunogenicity for this unique low-immunogenicity strain of avian-origin influenza will be urgently needed.

A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes

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Combining epitope prediction methods with molecular docking techniques to identify MHC class I restricted T-cell epitopes.

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Based on available experimental data, the prediction accuracy is up to 90%.

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Providing a valuable step forward for the design of better vaccines.

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Better understanding the activation of T-cell epitopes by MHC binding peptides.

In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.

The two-faced T cell epitope: examining the host-microbe interface with JanusMatrix

Advances in the field of T cell immunology have contributed to the understanding that cross-reactivity is an intrinsic characteristic of the T cell receptor (TCR), and that each TCR can potentially interact with many different T cell epitopes. To better define the potential for TCR cross-reactivity between epitopes derived from the human genome, the human microbiome, and human pathogens, we developed a new immunoinformatics tool, JanusMatrix, that represents an extension of the validated T cell epitope mapping tool, EpiMatrix. Initial explorations, summarized in this synopsis, have uncovered what appear to be important differences in the TCR cross-reactivity of selected regulatory and effector T cell epitopes with other epitopes in the human genome, human microbiome, and selected human pathogens. In addition to exploring the T cell epitope relationships between human self, commensal and pathogen, JanusMatrix may also be useful to explore some aspects of heterologous immunity and to examine T cell epitope relatedness between pathogens to which humans are exposed (Dengue serotypes, or HCV and Influenza, for example). In Hand-Foot-Mouth disease (HFMD) for example, extensive enterovirus and human microbiome cross-reactivity (and limited cross-reactivity with the human genome) seemingly predicts immunodominance. In contrast, more extensive cross-reactivity with proteins contained in the human genome as compared to the human microbiome was observed for selected Treg epitopes. While it may be impossible to predict all immune response influences, the availability of sequence data from the human genome, the human microbiome, and an array of human pathogens and vaccines has made computationally–driven exploration of the effects of T cell epitope cross-reactivity now possible. This is the first description of JanusMatrix, an algorithm that assesses TCR cross-reactivity that may contribute to a means of predicting the phenotype of T cells responding to selected T cell epitopes. Whether used for explorations of T cell phenotype or for evaluating cross-conservation between related viral strains at the TCR face of viral epitopes, further JanusMatrix studies may contribute to developing safer, more effective vaccines.

T-cell epitope vaccine design by immunoinformatics

Vaccination is generally considered to be the most effective method of preventing infectious diseases. All vaccinations work by presenting a foreign antigen to the immune system in order to evoke an immune response. The active agent of a vaccine may be intact but inactivated ('attenuated') forms of the causative pathogens (bacteria or viruses), or purified components of the pathogen that have been found to be highly immunogenic. The increased understanding of antigen recognition at molecular level has resulted in the development of rationally designed peptide vaccines. The concept of peptide vaccines is based on identification and chemical synthesis of B-cell and T-cell epitopes which are immunodominant and can induce specific immune responses. The accelerating growth of bioinformatics techniques and applications along with the substantial amount of experimental data has given rise to a new field, called immunoinformatics. Immunoinformatics is a branch of bioinformatics dealing with in silico analysis and modelling of immunological data and problems. Different sequence- and structure-based immunoinformatics methods are reviewed in the paper.

Conservation of HIV-1 T cell epitopes across time and clades: validation of immunogenic HLA-A2 epitopes selected for the GAIA HIV vaccine

HIV genomic sequence variability has complicated efforts to generate an effective globally relevant vaccine. Regions of the viral genome conserved in sequence and across time may represent the "Achilles' heel" of HIV. In this study, highly conserved T-cell epitopes were selected using immunoinformatics tools combining HLA-A2 supertype binding predictions with relative global conservation. Analysis performed in 2002 on 10,803 HIV-1 sequences, and again in 2009, on 43,822 sequences, yielded 38 HLA-A2 epitopes. These epitopes were experimentally validated for HLA binding and immunogenicity with PBMCs from HIV-infected patients in Providence, Rhode Island, and/or Bamako, Mali. Thirty-five (92%) stimulated an IFNγ response in PBMCs from at least one subject. Eleven of fourteen peptides (79%) were confirmed as HLA-A2 epitopes in both locations. Validation of these HLA-A2 epitopes conserved across time, clades, and geography supports the hypothesis that such epitopes could provide effective coverage of virus diversity and would be appropriate for inclusion in a globally relevant HIV vaccine.