PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity

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

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

AbImmPred: An immunogenicity prediction method for therapeutic antibodies using AntiBERTy-based sequence features

Due to the unnecessary immune responses induced by therapeutic antibodies in clinical applications, immunogenicity is an important factor to be considered in the development of antibody therapeutics. To a certain extent, there is a lag in using wet-lab experiments to test the immunogenicity in the development process of antibody therapeutics. Developing a computational method to predict the immunogenicity at once the antibody sequence is designed, is of great significance for the screening in the early stage and reducing the risk of antibody therapeutics development. In this study, a computational immunogenicity prediction method was proposed on the basis of AntiBERTy-based features of amino sequences in the antibody variable region. The AntiBERTy-based sequence features were first calculated using the AntiBERTy pre-trained model. Principal component analysis (PCA) was then applied to reduce the extracted feature to two dimensions to obtain the final features. AutoGluon was then used to train multiple machine learning models and the best one, the weighted ensemble model, was obtained through 5-fold cross-validation on the collected data. The data contains 199 commercial therapeutic antibodies, of which 177 samples were used for model training and 5-fold cross-validation, and the remaining 22 samples were used as an independent test dataset to evaluate the performance of the constructed model and compare it with other prediction methods. Test results show that the proposed method outperforms the comparison method with 0.7273 accuracy on the independent test dataset, which is 9.09% higher than the comparison method. The corresponding web server is available through the official website of GenScript Co., Ltd., https://www.genscript.com/tools/antibody-immunogenicity.

Structure-aware deep model for MHC-II peptide binding affinity prediction

The prediction of major histocompatibility complex (MHC)-peptide binding affinity is an important branch in immune bioinformatics, especially helpful in accelerating the design of disease vaccines and immunity therapy. Although deep learning-based solutions have yielded promising results on MHC-II molecules in recent years, these methods ignored structure knowledge from each peptide when employing the deep neural network models. Each peptide sequence has its specific combination order, so it is worth considering adding the structural information of the peptide sequence to the deep model training. In this work, we use positional encoding to represent the structural information of peptide sequences and validly combine the positional encoding with existing models by different strategies. Experiments on three datasets show that the introduction of position-coding information can further improve the performance built upon the existing model. The idea of introducing positional encoding to this field can provide important reference significance for the optimization of the deep network structure in the future.

In silico design of a promiscuous chimeric multi-epitope vaccine against Mycobacterium tuberculosis

Tuberculosis (TB) is a global health threat, killing approximately 1.5 million people each year. The eradication of Mycobacterium tuberculosis, the main causative agent of TB, is increasingly challenging due to the emergence of extensive drug-resistant strains. Vaccination is considered an effective way to protect the host from pathogens, but the only clinically approved TB vaccine, Bacillus Calmette-Guérin (BCG), has limited protection in adults. Multi-epitope vaccines have been found to enhance immunity to diseases by selectively combining epitopes from several candidate proteins. This study aimed to design a multi-epitope vaccine against TB using an immuno-informatics approach. Through functional enrichment, we identified eight proteins secreted by M. tuberculosis that are either required for pathogenesis, secreted into extracellular space, or both. We then analyzed the epitopes of these proteins and selected 16 helper T lymphocyte epitopes with interferon-γ inducing activity, 15 cytotoxic T lymphocyte epitopes, and 10 linear B-cell epitopes, and conjugated them with adjuvant and Pan HLA DR-binding epitope (PADRE) using appropriate linkers. Moreover, we predicted the tertiary structure of this vaccine, its potential interaction with Toll-Like Receptor-4 (TLR4), and the immune response it might elicit. The results showed that this vaccine had a strong affinity for TLR4, which could significantly stimulate CD4⁺ and CD8⁺ cells to secrete immune factors and B lymphocytes to secrete immunoglobulins, so as to obtain good humoral and cellular immunity. Overall, this multi-epitope protein was predicted to be stable, safe, highly antigenic, and highly immunogenic, which has the potential to serve as a global vaccine against TB.

Current Update on Rotavirus in-Silico Multiepitope Vaccine Design

Rotavirus gastroenteritis is one of the leading causes of pediatric morbidity and mortality worldwide in infants and under-five populations. The World Health Organization (WHO) recommended global incorporation of the rotavirus vaccine in national immunization programs to alleviate the burden of the disease. Implementation of the rotavirus vaccination in certain regions of the world brought about a significant and consistent reduction of rotavirus-associated hospitalizations. However, the efficacy of licensed vaccines remains suboptimal in low-income countries where the incidences of rotavirus gastroenteritis continue to happen unabated. The problem of low efficacy of currently licensed oral rotavirus vaccines in low-income countries necessitates continuous exploration, design, and development of new rotavirus vaccines. Traditional vaccine development is a complex, expensive, labor-intensive, and time-consuming process. Reverse vaccinology essentially utilizes the genome and proteome information on pathogens and has opened new avenues for in-silico multiepitope vaccine design for a plethora of pathogens, promising time reduction in the complete vaccine development pipeline by complementing the traditional vaccinology approach. A substantial number of reviews on licensed rotavirus vaccines and those under evaluation are already available in the literature. However, a collective account of rotavirus in-silico vaccines is lacking in the literature, and such an account may further fuel the interest of researchers to use reverse vaccinology to expedite the vaccine development process. Therefore, the main focus of this review is to summarize the research endeavors undertaken for the design and development of rotavirus vaccines by the reverse vaccinology approach utilizing the tools of immunoinformatics.

Refining the DC-targeting vaccination for preventing emerging infectious diseases

The development of safe, long-term, effective vaccines is still a challenge for many infectious diseases. Thus, the search of new vaccine strategies and production platforms that allow rapidly and effectively responding against emerging or reemerging pathogens has become a priority in the last years. Targeting the antigens directly to dendritic cells (DCs) has emerged as a new approach to enhance the immune response after vaccination. This strategy is based on the fusion of the antigens of choice to monoclonal antibodies directed against specific DC surface receptors such as CD40. Since time is essential, in silico approaches are of high interest to select the most immunogenic and conserved epitopes to improve the T- and B-cells responses. The purpose of this review is to present the advances in DC vaccination, with special focus on DC targeting vaccines and epitope mapping strategies and provide a new framework for improving vaccine responses against infectious diseases.

Moving beyond Titers

Vaccination to prevent and even eliminate disease is amongst the greatest achievements of modern medicine. Opportunities remain in vaccine development to improve protection across the whole population. A next step in vaccine development is the detailed molecular characterization of individual humoral immune responses against a pathogen, especially the rapidly evolving pathogens. New technologies such as sequencing the immune repertoire in response to disease, immunogenomics/vaccinomics, particularly the individual HLA variants, and high-throughput epitope characterization offer new insights into disease protection. Here, we highlight the emerging technologies that could be used to identify variation within the human population, facilitate vaccine discovery, improve vaccine safety and efficacy, and identify mechanisms of generating immunological memory. In today’s vaccine-hesitant climate, these techniques used individually or especially together have the potential to improve vaccine effectiveness and safety and thus vaccine uptake rates. We highlight the importance of using these techniques in combination to understand the humoral immune response as a whole after vaccination to move beyond neutralizing titers as the standard for immunogenicity and vaccine efficacy, especially in clinical trials.

Identification of promising CD8 and CD4 T cell epitopes for peptide vaccine formulation against SARS-CoV-2

The novel virus “severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)” has been responsible for the worldwide pandemic causing huge devastation and deaths since December 2019. The disease caused by this virus is known as COVID-19. The present study is based on immuno-informatics approach to develop a multi-epitope-loaded peptide vaccine to combat the COVID-19 menace. Here, we have reported the 9-mer CD8 T cell epitopes and 15-mer CD4 T cell epitopes, free from glycosylation sites, belonging to three proteins, viz. surface glycoprotein, membrane glycoprotein and envelope protein of this virus. Immunogenicity, aliphatic amino acid, antigenicity and hydrophilicity scores of the predicted epitopes were estimated. In addition, other physicochemical parameters, namely net charge, Boman index and amino acid contents, were also accounted. Out of all the epitopes, three CD8 T cell epitopes viz. PDPSKPSKR, DPSKPSKRS and QTQTNSPRR and three CD4 T cell epitopes viz. ASYQTQTNSPRRARS, RIGNYKLNTDHSSSS and RYRIGNYKLNTDHSS were found to be efficient targets for raising immunity in human against this virus. With the help of our identified potent epitopes, various pharma industries might initiate efforts to incorporate those epitopes with carrier protein or adjuvant to develop a multi-epitope-loaded peptide vaccine against SARS-CoV-2. The peptide vaccines are usually cost-effective and therefore, could be administered as a preventive measure to combat the spread of this disease. Proper clinical trials must be conducted prior to the use of identified epitopes as vaccine candidates.

Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review

The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety-they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.

In Silico Identification of Chikungunya Virus B- and T-Cell Epitopes with High Antigenic Potential for Vaccine Development

Reverse vaccinology is an outstanding strategy to identify antigens with high potential for vaccine development. Different parameters of five prediction programs were used to assess their sensitivity and specificity to identify B-cell epitopes of Chikungunya virus (CHIKV) strains reported in the IEDB database. The results, based on the use of 15 to 20 mer epitopes and the polyproteins to which they belong, were compared to establish the best parameters to optimize the prediction of antigenic peptides of the Mexican strain CHIKV AJV21562.1. LBtope showed the highest specificity when we used the reported epitopes and polyproteins but the worst sensitivity with polyproteins; ABCpred had similar specificity to LBtope only with the epitopes reported and showed moderate specificity when we used polyproteins for the predictions. Because LBtope was more reliable in predicting true epitopes, it was used as a reference program to predict and select six novel epitopes of the Mexican strain of CHIKV according to prediction frequency, viral genome localization, and non-homology with the human proteome. On the other hand, six bioinformatics programs were used with default parameters to predict T-cell epitopes in the CHIKV strains AJV21562.1 and AJV21561.1. The sequences of the polyproteins were analyzed to predict epitopes present in the more frequent HLA alleles of the Mexican population: DQA1*03011, DQA1*0401, DQA1*0501, DQB1*0201, DQB1*0301, DQB1*0302, and DQB1*0402. Fifteen predicted epitopes in the non-structural and 15 predicted epitopes in the structural polyprotein (9- to 16-mers) with the highest scores of each allele were compared to select epitopes with at least 80% identity. Next, the epitopes predicted with at least two programs were aligned to the human proteome, and 12 sequences without identity with the human proteome were identified as potential antigenic candidates. This strategy would be useful to evaluate vaccine candidates against other viral diseases affecting the countries of the Americas and to increase knowledge about these diseases.

Allergic Diseases: A Comprehensive Review on Risk Factors, Immunological Mechanisms, Link with COVID-19, Potential Treatments, and Role of Allergen Bioinformatics

The prevalence of allergic diseases is regarded as one of the key challenges in health worldwide. Although the precise mechanisms underlying this rapid increase in prevalence are unknown, emerging evidence suggests that genetic and environmental factors play a significant role. The immune system, microbiota, viruses, and bacteria have all been linked to the onset of allergy disorders in recent years. Avoiding allergen exposure is the best treatment option; however, steroids, antihistamines, and other symptom-relieving drugs are also used. Allergen bioinformatics encompasses both computational tools/methods and allergen-related data resources for managing, archiving, and analyzing allergological data. This study highlights allergy-promoting mechanisms, algorithms, and concepts in allergen bioinformatics, as well as major areas for future research in the field of allergology.

Immunoinformatic identification of the epitope-based vaccine candidates from Maltoporin, FepA and OmpW of Shigella Spp, with molecular docking confirmation

Shigella is a bacterial pathogen that causes shigellosis, fatal bacillary dysentery, responsible for a higher level of mortality worldwide. We adopted a number of computational approaches to predict potential epitope-based vaccine candidates of immunogenic proteins of Shigella spp. We selected three cell surface proteins of the bacterium according to their antigenicity using the VaxiJen server, including, FepA, Maltoporin, and OmpW. The sequence analyses by the IEDB server resulted in three 15-mer peptides of the core epitope, FTAEHTQSV, FLVNQTLTL, and MRAGSATVR from FepA, Maltoporin, and OmpW, respectively, as the most potential epitopes that have an affinity with both cytotoxic and helper T-cells. Moreover, the epitopes showed 73.76%, 99.0%, and 93.07% world population coverage, along with 100% conservancy among the Shigella subspecies. The molecular docking simulation studies were performed to verify the interactions between the peptides and the respective HLAs. Docking analyses showed that the Epitope-MHC complexes had a higher level of global energy score dictating strong binding. We have also predicted B-cell epitopes from the sequences of these three proteins. In vivo study of the proposed epitope might contribute to the development of a functional and efficient vaccine, which might be an effective way to elude dysentery from the world.

Designing and development of epitope-based vaccines against Helicobacter pylori

Helicobacter pylori infection is the principal cause of serious diseases (e.g. gastric cancer and peptic ulcers). Antibiotic therapy is an inadequate strategy in H. pylori eradication because of which vaccination is an inevitable approach. Despite the presence of countless vaccine candidates, current vaccines in clinical trials have performed with poor efficacy which makes vaccination extremely challenging. Remarkable advancements in immunology and pathogenic biology have provided an appropriate opportunity to develop various epitope-based vaccines. The fusion of proper antigens involved in different aspects of H. pylori colonization and pathogenesis as well as peptide linkers and built-in adjuvants results in producing epitope-based vaccines with excellent therapeutic efficacy and negligible adverse effects. Difficulties of the in vitro culture of H. pylori, high genetic variation, and unfavourable immune responses against feeble epitopes in the complete antigen are major drawbacks of current vaccine strategies that epitope-based vaccines may overcome. Besides decreasing the biohazard risk, designing precise formulations, saving time and cost, and induction of maximum immunity with minimum adverse effects are the advantages of epitope-based vaccines. The present article is a comprehensive review of strategies for designing and developing epitope-based vaccines to provide insights into the innovative vaccination against H. pylori.

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.

Predicting Immunogenicity Risk in Biopharmaceuticals

The assessment of immunogenicity of biopharmaceuticals is a crucial step in the process of their development. Immunogenicity is related to the activation of adaptive immunity. The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals. The direct experimental approaches are sometimes expensive and time consuming, or their results need to be confirmed. In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design. In this review, we analyze the use of various In silico methods and approaches for immunogenicity prediction of biomolecules: sequence alignment algorithms, predicting subcellular localization, searching for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations. Computational tools for antigenicity and allergenicity prediction also are considered.

A Proteome-Wide Immunoinformatics Tool to Accelerate T-Cell Epitope Discovery and Vaccine Design in the Context of Emerging Infectious Diseases: An Ethnicity-Oriented Approach

Emerging infectious diseases (EIDs) caused by viruses are increasing in frequency, causing a high disease burden and mortality world-wide. The COVID-19 pandemic caused by the novel SARS-like coronavirus (SARS-CoV-2) underscores the need to innovate and accelerate the development of effective vaccination strategies against EIDs. Human leukocyte antigen (HLA) molecules play a central role in the immune system by determining the peptide repertoire displayed to the T-cell compartment. Genetic polymorphisms of the HLA system thus confer a strong variability in vaccine-induced immune responses and may complicate the selection of vaccine candidates, because the distribution and frequencies of HLA alleles are highly variable among different ethnic groups. Herein, we build on the emerging paradigm of rational epitope-based vaccine design, by describing an immunoinformatics tool (Predivac-3.0) for proteome-wide T-cell epitope discovery that accounts for ethnic-level variations in immune responsiveness. Predivac-3.0 implements both CD8+ and CD4+ T-cell epitope predictions based on HLA allele frequencies retrieved from the Allele Frequency Net Database. The tool was thoroughly assessed, proving comparable performances (AUC ~0.9) against four state-of-the-art pan-specific immunoinformatics methods capable of population-level analysis (NetMHCPan-4.0, Pickpocket, PSSMHCPan and SMM), as well as a strong accuracy on proteome-wide T-cell epitope predictions for HIV-specific immune responses in the Japanese population. The utility of the method was investigated for the COVID-19 pandemic, by performing in silico T-cell epitope mapping of the SARS-CoV-2 spike glycoprotein according to the ethnic context of the countries where the ChAdOx1 vaccine is currently initiating phase III clinical trials. Potentially immunodominant CD8+ and CD4+ T-cell epitopes and population coverages were predicted for each population (the Epitope Discovery mode), along with optimized sets of broadly recognized (promiscuous) T-cell epitopes maximizing coverage in the target populations (the Epitope Optimization mode). Population-specific epitope-rich regions (T-cell epitope clusters) were further predicted in protein antigens based on combined criteria of epitope density and population coverage. Overall, we conclude that Predivac-3.0 holds potential to contribute in the understanding of ethnic-level variations of vaccine-induced immune responsiveness and to guide the development of epitope-based next-generation vaccines against emerging pathogens, whose geographic distributions and populations in need of vaccinations are often well-defined for regional epidemics.

An Overview of Current Uses and Future Opportunities for Computer-Assisted Design of Vaccines for Neglected Tropical Diseases

Neglected tropical diseases are infectious diseases that impose high morbidity and mortality rates over 1.5 billion people worldwide. Originally restricted to tropical and subtropical regions, changing climate conditions have increased their potential to emerge elsewhere. Control of their impact suffers from shortages like poor epidemiological surveillance or irregular drug distribution, and some NTDs still lack of appropriate diagnostics and/or efficient therapeutics. For these, availability of vaccines to prevent new infections, or the worsening of those already established, would mean a major breakthrough. However, only dengue and rabies count with approved vaccines at present. Herein, we review the state-of-the-art of vaccination strategies for NTDs, setting the focus on third generation vaccines and the concept of reverse vaccinology. Its capability to address pathogens´ biological complexity, likely contributing to save developmental costs is discussed. The use of computational tools is a fundamental aid to analyze increasingly large datasets aimed at designing vaccine candidates with the highest, possibly, opportunities to succeed. Ultimately, we identify and analyze those studies that took an in silico approach to find vaccine candidates, and experimentally assessed their immunogenicity and/or protection capabilities.

Experimental Antiglomerular Basement Membrane GN Induced by a Peptide from Actinomyces

BACKGROUND

Antiglomerular basement membrane (anti-GBM) disease is associated with HLA-DRB1*1501 (the major predisposing genetic factor in the disease), with α3_127-148 as a nephritogenic T and B cell epitope. Although the cause of disease remains unclear, the association of infections with anti-GBM disease has been long suspected.

METHODS

To investigate whether microbes might activate autoreactive T and B lymphocytes via molecular mimicry in anti-GBM disease, we used bioinformatic tools, including BLAST, SYFPEITHI, and ABCpred, for peptide searching and epitope prediction. We used sera from patients with anti-GBM disease to assess peptides recognized by antibodies, and immunized WKY rats and a humanized mouse model (HLA-DR15 transgenic mice) with each of the peptide candidates to assess pathogenicity.

RESULTS

On the basis of the critical motif, the bioinformatic approach identified 36 microbial peptides that mimic human α3_127-148. Circulating antibodies in sera from patients with anti-GBM recognized nine of them. One peptide, B7, derived from Actinomyces species, induced proteinuria, linear IgG deposition on the GBM, and crescent formation when injected into WKY rats. The antibodies to B7 also targeted human and rat α3_127-148. B7 induced T cell activation from human α3_127-148-immunized rats. T cell responses to B7 were detected in rats immunized by Actinomyces lysate proteins or recombinant proteins. We confirmed B7's pathogenicity in HLA-DR15 transgenic mice that developed kidney injury similar to that observed in α3_135-145-immunized mice.

CONCLUSIONS

Sera from patients with anti-GBM disease recognized microbial peptides identified through a bioinformatic approach, and a peptide from Actinomyces induced experimental anti-GBM GN by T and B cell crossreactivity. These studies demonstrate that anti-GBM disease may be initiated by immunization with a microbial peptide.

Immunoinformatics-guided design of an epitope-based vaccine against severe acute respiratory syndrome coronavirus 2 spike glycoprotein

AIMS

With a large number of fatalities, coronavirus disease-2019 (COVID-19) has greatly affected human health worldwide. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes COVID-19. The World Health Organization has declared a global pandemic of this contagious disease. Researchers across the world are collaborating in a quest for remedies to combat this deadly virus. It has recently been demonstrated that the spike glycoprotein (SGP) of SARS-CoV-2 is the mediator by which the virus enters host cells.

MAIN METHODS

Our group comprehensibly analyzed the SGP of SARS-CoV-2 through multiple sequence analysis and a phylogenetic analysis. We predicted the strongest immunogenic epitopes of the SGP for both B cells and T cells.

KEY FINDINGS

We focused on predicting peptides that would bind major histocompatibility complex class I. Two optimal epitopes were identified, WTAGAAAYY and GAAAYYVGY. They interact with the HLA-B*15:01 allele, which was further validated by molecular docking simulation. This study also found that the selected epitopes are able to be recognized in a large percentage of the world's population. Furthermore, we predicted CD4+ T-cell epitopes and B-cell epitopes.

SIGNIFICANCE

Our study provides a strong basis for designing vaccine candidates against SARS-CoV-2. However, laboratory work is required to validate our theoretical results, which would lay the foundation for the appropriate vaccine manufacturing and testing processes.

Multiepitope Subunit Vaccine Design against COVID-19 Based on the Spike Protein of SARS-CoV-2: An In Silico Analysis

The global health crisis caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of COVID-19, has resulted in a negative impact on human health and on social and economic activities worldwide. Researchers around the globe need to design and develop successful therapeutics as well as vaccines against the novel COVID-19 disease. In the present study, we conducted comprehensive computer-assisted analysis on the spike glycoprotein of SARS-CoV-2 in order to design a safe and potent multiepitope vaccine. In silico epitope prioritization shortlisted six HLA I epitopes and six B-cell-derived HLA II epitopes. These high-ranked epitopes were all connected to each other via flexible GPGPG linkers, and at the N-terminus side, the sequence of Cholera Toxin β subunit was attached via an EAAAK linker. Structural modeling of the vaccine was performed, and molecular docking analysis strongly suggested a positive association of a multiepitope vaccine with Toll-like Receptor 3. The structural investigations of the vaccine-TLR3 complex revealed the formation of fifteen interchain hydrogen bonds, thus validating its integrity and stability. Moreover, it was found that this interaction was thermodynamically feasible. In conclusion, our data supports the proposition that a multiepitope vaccine will provide protective immunity against COVID-19. However, further in vivo and in vitro experiments are needed to validate the immunogenicity and safety of the candidate vaccine.

Human CD4+ T Cells Specific for Merkel Cell Polyomavirus Localize to Merkel Cell Carcinomas and Target a Required Oncogenic Domain

Although CD4⁺ T cells likely play key roles in antitumor immune responses, most immuno-oncology studies have been limited to CD8⁺ T-cell responses due to multiple technical barriers and a lack of shared antigens across patients. Merkel cell carcinoma (MCC) is an aggressive skin cancer caused by Merkel cell polyomavirus (MCPyV) oncoproteins in 80% of cases. Because MCPyV oncoproteins are shared across most patients with MCC, it is unusually feasible to identify, characterize, and potentially augment tumor-specific CD4⁺ T cells. Here, we report the identification of CD4⁺ T-cell responses against six MCPyV epitopes, one of which included a conserved, essential viral oncogenic domain that binds/disables the cellular retinoblastoma (Rb) tumor suppressor. We found that this epitope (WED_LT209-228) could be presented by three population-prevalent HLA class II alleles, making it a relevant target in 64% of virus-positive MCC patients. Cellular staining with a WED_LT209-228-HLA-DRB1*0401 tetramer indicated that specific CD4⁺ T cells were detectable in 78% (14 of 18) of evaluable MCC patients, were 250-fold enriched within MCC tumors relative to peripheral blood, and had diverse T-cell receptor sequences. We also identified a modification of this domain that still allowed recognition by these CD4⁺ T cells but disabled binding to the Rb tumor suppressor, a key step in the detoxification of a possible therapeutic vaccine. The use of these new tools for deeper study of MCPyV-specific CD4⁺ T cells may provide broader insight into cancer-specific CD4⁺ T-cell responses.

Peptide-Based Subunit Vaccine Design of T- and B-Cells Multi-Epitopes against Zika Virus Using Immunoinformatics Approaches

The Zika virus disease, also known as Zika fever is an arboviral disease that became epidemic in the Pacific Islands and had spread to 18 territories of the Americas in 2016. Zika virus disease has been linked to several health problems such as microcephaly and the Guillain-Barré syndrome, but to date, there has been no vaccine available for Zika. Problems related to the development of a vaccine include the vaccination target, which covers pregnant women and children, and the antibody dependent enhancement (ADE), which can be caused by non-neutralizing antibodies. The peptide vaccine was chosen as a focus of this study as a safer platform to develop the Zika vaccine. In this study, a collection of Zika proteomes was used to find the best candidates for T- and B-cell epitopes using the immunoinformatics approach. The most promising T-cell epitopes were mapped using the selected human leukocyte antigen (HLA) alleles, and further molecular docking and dynamics studies showed a good peptide-HLA interaction for the best major histocompatibility complex-II (MHC-II) epitope. The most promising B-cell epitopes include four linear peptides predicted to be cross-reactive with T-cells, and conformational epitopes from two proteins accessible by antibodies in their native biological assembly. It is believed that the use of immunoinformatics methods is a promising strategy against the Zika viral infection in designing an efficacious multiepitope vaccine.

Immunoinformatics Based Study of T Cell Epitopes in Zea m 1 Pollen Allergen

Background and Objectives: Zea m 1 is a pollen allergen, which is present in maize, is accountable for a type I hypersensitivity reaction in all over the world. Several effective medications are available for the disorder with various side effects. Design and verification of a peptide-based vaccine is a state-of-art technology which is more cost effective than conventional drugs. Materials and Methods: Using immunoinformatic methods, the T cell epitopes from the whole structure of this allergenic protein can be predicted. Worldwide conserved region study among the other pollen allergens has been performed for T cell predicted epitopes by using a conservancy tool. This analysis will help to identify completely conserved HLA (human leukocyte antigen) binding epitopes. Lastly, molecular docking study and MHC-oligopeptide complex binding energy calculation data are applied to determine the interacting amino acids and the affinity of the epitopes to the class II MHCmolecule. Results: The study of criteria-based analysis predicts the presence of two epitopes YVADDGDIV and WRMDTAKAL on this pollen allergen. Conclusions: The T cell epitopes identified in this study provide insight into a peptide-based vaccine for a type I hypersensitivity reaction induced by Zea m 1 grass pollen allergenic protein.

Pathogen diversity drives the evolution of generalist MHC-II alleles in human populations

Central players of the adaptive immune system are the groups of proteins encoded in the major histocompatibility complex (MHC), which shape the immune response against pathogens and tolerance to self-peptides. The corresponding genomic region is of particular interest, as it harbors more disease associations than any other region in the human genome, including associations with infectious diseases, autoimmune disorders, cancers, and neuropsychiatric diseases. Certain MHC molecules can bind to a much wider range of epitopes than others, but the functional implication of such an elevated epitope-binding repertoire has remained largely unclear. It has been suggested that by recognizing more peptide segments, such promiscuous MHC molecules promote immune response against a broader range of pathogens. If so, the geographical distribution of MHC promiscuity level should be shaped by pathogen diversity. Three lines of evidence support the hypothesis. First, we found that in pathogen-rich geographical regions, humans are more likely to carry highly promiscuous MHC class II DRB1 alleles. Second, the switch between specialist and generalist antigen presentation has occurred repeatedly and in a rapid manner during human evolution. Third, molecular positions that define promiscuity level of MHC class II molecules are especially diverse and are under positive selection in human populations. Taken together, our work indicates that pathogen load maintains generalist adaptive immune recognition, with implications for medical genetics and epidemiology.

Whereas specialist major histocompatibility complex (MHC) molecules initiate immune response against only relatively few pathogens, generalists provide protection against a broad range. Accordingly, this study shows that the geographical distribution of generalist MHC alleles in human populations reflects exposure to diverse infectious diseases.

Variation in the human genome influences our susceptibility to infectious diseases, but the causal link between disease and underlying mutation often remains enigmatic. Major histocompatibility complex II (MHC class II) molecules shape both our immune response against pathogens and our tolerance of self-peptides. The genomic region that encodes MHC molecules is of particular interest, as it is home to more genetic disease associations than any other region in the human genome, including associations with infectious diseases, autoimmune disorders, cancers, and neuropsychiatric diseases. Here, we propose that MHC class II molecules can be categorized into two major types; specialists initiate effective immune response against only relatively few pathogens, while generalists provide protection against a broad range of pathogens. As support, we demonstrate that generalist MHC class II variants are more prevalent in human populations residing in pathogen-rich areas.

Polymorphism in merozoite surface protein-7E of Plasmodium vivax in Thailand: Natural selection related to protein secondary structure

Merozoite surface protein 7 (MSP-7) is a multigene family expressed during malaria blood-stage infection. MSP-7 forms complex with MSP-1 prior to merozoite egress from erythrocytes, and could affect merozoite invasion of erythrocytes. To characterize sequence variation in the orthologue in P. vivax (PvMSP-7), a gene member encoding PvMSP-7E was analyzed among 92 Thai isolates collected from 3 major endemic areas of Thailand (Northwest: Tak, Northeast: Ubon Ratchathani, and South: Yala and Narathiwat provinces). In total, 52 distinct haplotypes were found to circulate in these areas. Although population structure based on this locus was observed between each endemic area, no genetic differentiation occurred between populations collected from different periods in the same endemic area, suggesting spatial but not temporal genetic variation. Sequence microheterogeneity in both N- and C- terminal regions was predicted to display 4 and 6 α-helical domains, respectively. Signals of purifying selection were observed in α-helices II-X, suggesting structural or functional constraint in these domains. By contrast, α-helix-I spanning the putative signal peptide was under positive selection, in which amino acid substitutions could alter predicted CD4+ T helper cell epitopes. The central region of PvMSP-7E comprised the 5’-trimorphic and the 3’-dimorphic subregions. Positive selection was identified in the 3’ dimorphic subregion of the central domain. A consensus of intrinsically unstructured or disordered protein was predicted to encompass the entire central domain that contained a number of putative B cell epitopes and putative protein binding regions. Evidences of intragenic recombination were more common in the central region than the remainders of the gene. These results suggest that the extent of sequence variation, recombination events and selective pressures in the PvMSP-7E locus seem to be differentially affected by protein secondary structure.

Fundamentals and Methods for T- and B-Cell Epitope Prediction

Adaptive immunity is mediated by T- and B-cells, which are immune cells capable of developing pathogen-specific memory that confers immunological protection. Memory and effector functions of B- and T-cells are predicated on the recognition through specialized receptors of specific targets (antigens) in pathogens. More specifically, B- and T-cells recognize portions within their cognate antigens known as epitopes. There is great interest in identifying epitopes in antigens for a number of practical reasons, including understanding disease etiology, immune monitoring, developing diagnosis assays, and designing epitope-based vaccines. Epitope identification is costly and time-consuming as it requires experimental screening of large arrays of potential epitope candidates. Fortunately, researchers have developed in silico prediction methods that dramatically reduce the burden associated with epitope mapping by decreasing the list of potential epitope candidates for experimental testing. Here, we analyze aspects of antigen recognition by T- and B-cells that are relevant for epitope prediction. Subsequently, we provide a systematic and inclusive review of the most relevant B- and T-cell epitope prediction methods and tools, paying particular attention to their foundations.

Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics

The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by in silico tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel in silico resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity.

Quantification of Inter-Sample Differences in T-Cell Receptor Repertoires Using Sequence-Based Information

Inter-sample comparisons of T-cell receptor (TCR) repertoires are crucial for gaining a better understanding of the immunological states determined by different collections of T cells from different donor sites, cell types, and genetic and pathological backgrounds. For quantitative comparison, most previous studies utilized conventional methods in ecology, which focus on TCR sequences that overlap between pairwise samples. Some recent studies attempted another approach that is categorized into Poisson abundance models using the abundance distribution of observed TCR sequences. However, these methods ignore the details of the measured sequences and are consequently unable to identify sub-repertoires that might have important contributions to the observed inter-sample differences. Moreover, the sparsity of sequence data due to the huge diversity of repertoires hampers the performance of these methods, especially when few overlapping sequences exist. In this paper, we propose a new approach for REpertoire COmparison in Low Dimensions (RECOLD) based on TCR sequence information, which can estimate the low-dimensional structure by embedding the pairwise sequence dissimilarities in high-dimensional sequence space. The inter-sample differences between repertoires are then quantified by information-theoretic measures among the distributions of data estimated in the embedded space. Using datasets of mouse and human TCR repertoires, we demonstrate that RECOLD can accurately identify the inter-sample hierarchical structures, which have a good correspondence with our intuitive understanding about sample conditions. Moreover, for the dataset of transgenic mice that have strong restrictions on the diversity of their repertoires, our estimated inter-sample structure was consistent with the structure estimated by previous methods based on abundance or overlapping sequence information. For the dataset of human healthy donors and Sézary syndrome patients, our method also showed robust estimation performance even under the condition of high sparsity in TCR sequences, while previous studies failed to estimate the structure. In addition, we identified the sequences that contribute to the pairwise-sample differences between the repertoires with the different genetic backgrounds of mice. Such identification of the sequences contributing to variation in immune cell repertoires may provide substantial insight for the development of new immunotherapies and vaccines.

Discovery of T-cell Driven Subunit Vaccines from Zika Virus Genome: An Immunoinformatics Approach

The recent outbreaks of Zika virus and the absence of a specific therapy have necessitated to identify T-cell-stimulating antigenic peptides as potential subunit vaccine candidates. The translated ssRNA (+) genome of Zika virus was explored in EMBOSS antigenic and VaxiJen to predict 63 peptides as potential antigens. Three MHC-II binding peptide prediction tools, viz. NetMHCIIpan, PREDIVAC and immune epitope database (IEDB) were employed in consensus on 63 antigenic peptides to propose 14 T-helper cell epitopes. Similarly, analysis on 63 antigenic peptides through NetMHC, NetCTL and IEDB MHC-I binding peptide prediction tool led to identification of 14 CTL epitopes. Seven T-cell epitopes, C:44-66, M:135-149, NS2A:124-144, NS3:421-453, NS3:540-554, NS4B:90-134 and NS4B:171-188, are observed to share overlapping MHC-I and MHC-II binding motifs and hence, are being proposed to constitute minimum T-cell antigens to elicit protective T-cell immune response against Zika. Three of them, C:44-66, NS3:421-453 and NS3:540-554 are identified to be conserved across all the selected strains of Zika virus. Moreover, the 21 T-cell epitopes are non-self to humans and exhibited good coverage in variable populations of 14 geographical locations. Therefore, 21 T-cell epitopes are proposed as potential subunit vaccines against Zika virus.

Identification of putative unique immunogenic ZIKV and DENV1-4 peptides for diagnostic cellular based tests

Since the re-emergence of Zika virus in 2014 and subsequent association with microcephaly, much work has focused on the development of a vaccine to halt its spread throughout the world. The mosquito vector that transmits this virus is widespread and responsible for the spread of other arboviridae including Dengue. Current diagnostic methods rely on serologic testing that are complicated by cross reactivity and therefore unable to distinguish Zika from Dengue infection in the absence of virus isolation. We performed an in silico analysis to identify potential epitopes that may stimulate a unique T-lymphocyte response to distinguish prior infection with Zika or Dengue. From this analysis, we not only identified epitopes unique to Zika and Dengue, but also identified epitopes unique to each Dengue serotype. These peptides contribute to a pool of peptides identified for vaccine development that can be tested in vitro to confirm immunogenicity, absence of homology and global population coverage. The current lack of accurate diagnostic testing hampers our ability to understand the scope of the epidemic, implications for vaccine implementation and complications related to monoinfection and co-infection with these two closely related viruses.

Epitope design of L1 protein for vaccine production against Human Papilloma Virus types 16 and 18

Cervical cancer accounts for about two-thirds of all cancer cases linked etiologically to Human Papilloma Virus (HPV). 15 oncogenic HPV types can cause cervical cancer, of which HPV16 and HPV18 combinedly account for about 70% of it. So, effective epitope design for the clinically relevant HPV types 16 and 18 would be of major medical benefit. Here, a comprehensive analysis is carried out to predict the epitopes against HPV types 16 and 18 through "reverse vaccinology" approach. We attempted to identify the evolutionarily conserved regions of major capsid protein (L1) as well as minor capsid protein (L2) of HPV and designed epitopes within these regions. In this study, we analyzed about 49 and 27 sequences of HPV L2 and L1 proteins respectively. Since we found that the intertype variability of L2 is higher than for L1 proteins, our analysis was emphasized on epitopes of L1 of HPV types 16 and 18. We had selected HLA-A*0201, DRB1*1501, DQB1*0602, DRB1*0401 and DQB1*0301 alleles for the prediction of T cell epitopes of L1 of HPV 16 and 18. Finally, we reported that predicted epitope sequences EEYDLQFIFQLCKITLTA, and RHGEEYDLQFIFQLCKITLTA of L1 protein of HPV 16, and LPDPNKF, PETQRLVWAC, PVPGQYDA, YNPETQRLVWAC, DTGYGAMD, PVPGQYDATK, KQDIPKVSAYQYRVFRV, RDNVSVDYKQTQLCI and YSRHVEEYDLQFIF of L1 protein of HPV 18 could be therapeutic tools for vaccine design against HPV.

From ZikV genome to vaccine: in silico approach for the epitope-based peptide vaccine against Zika virus envelope glycoprotein

Zika virus (ZikV) has emerged as a potential threat to human health worldwide. A member of the Flaviviridae, ZikV is transmitted to humans by mosquitoes. It is related to other pathogenic vector-borne flaviviruses including dengue, West Nile and Japanese encephalitis viruses, but produces a comparatively mild disease in humans. As a result of its epidemic outbreak and the lack of potential medication, there is a need for improved vaccine/drugs. Computational techniques will provide further information about this virus. Comparative analysis of ZikV genomes should lead to the identification of the core characteristics that define a virus family, as well as its unique properties, while phylogenetic analysis will show the evolutionary relationships and provide clues about the protein's ancestry. Envelope glycoprotein of ZikV was obtained from a protein database and the most immunogenic epitope for T cells and B cells involved in cell-mediated immunity, whereas B cells are primarily responsible for humoral immunity. We mainly focused on MHC class I potential peptides. YRIMLSVHG, VLIFLSTAV and MMLELDPPF, GLDFSDLYY are the most potent peptides predicted as epitopes for CD4⁺ and CD8⁺ T cells, respectively, whereas MMLELDPPF and GLDFSDLYY had the highest pMHC-I immunogenicity score and these are further tested for interaction against the HLA molecules, using in silico docking techniques to verify the binding cleft epitope. However, this is an introductory approach to design an epitope-based peptide vaccine against ZikV; we hope that this model will be helpful in designing and predicting novel vaccine candidates.

Combination of In Silico Methods in the Search for Potential CD4(+) and CD8(+) T Cell Epitopes in the Proteome of Leishmania braziliensis

The leishmaniases are neglected tropical diseases widespread throughout the globe, which are caused by protozoans from the genus Leishmania and are transmitted by infected phlebotomine flies. The development of a safe and effective vaccine against these diseases has been seen as the best alternative to control and reduce the number of cases. To support vaccine development, this work has applied an in silico approach to search for high potential peptide epitopes able to bind to different major histocompatibility complex Class I and Class II (MHC I and MHC II) molecules from different human populations. First, the predicted proteome of Leishmania braziliensis was compared and analyzed by modern linear programs to find epitopes with the capacity to trigger an immune response. This approach resulted in thousands of epitopes derived from 8,000 proteins conserved among different Leishmania species. Epitopes from proteins similar to those found in host species were excluded, and epitopes from proteins conserved between different Leishmania species and belonging to surface proteins were preferentially selected. The resulting epitopes were then clustered, to avoid redundancies, resulting in a total of 230 individual epitopes for MHC I and 2,319 for MHC II. These were used for molecular modeling and docking with MHC structures retrieved from the Protein Data Bank. Molecular docking then ranked epitopes based on their predicted binding affinity to both MHC I and II. Peptides corresponding to the top 10 ranked epitopes were synthesized and evaluated in vitro for their capacity to stimulate peripheral blood mononuclear cells (PBMC) from post-treated cutaneous leishmaniasis patients, with PBMC from healthy donors used as control. From the 10 peptides tested, 50% showed to be immunogenic and capable to stimulate the proliferation of lymphocytes from recovered individuals.

A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines

The growing incidences of new viral diseases and increasingly frequent viral epidemics have strained therapeutic and preventive measures; the high mutability of viral genes puts additional strains on developmental efforts. Given the high cost and time requirements for new drugs development, vaccines remain as a viable alternative, but there too traditional techniques of live-attenuated or inactivated vaccines have the danger of allergenic reactions and others. Peptide vaccines have, over the last several years, begun to be looked on as more appropriate alternatives, which are economically affordable, require less time for development and hold the promise of multi-valent dosages. The developments in bioinformatics, proteomics, immunogenomics, structural biology and other sciences have spurred the growth of vaccinomics where computer assisted approaches serve to identify suitable peptide targets for eventual development of vaccines. In this mini-review we give a brief overview of some of the recent trends in computer assisted vaccine development with emphasis on the primary selection procedures of probable peptide candidates for vaccine development.

Current methods of epitope identification for cancer vaccine design

The importance of the immune system in tumor development and progression has been emerging in many cancers. Previous cancer vaccines have not shown long-term clinical benefit possibly because were not designed to avoid eliciting regulatory T-cell responses that inhibit the anti-tumor immune response. This review will examine different methods of identifying epitopes derived from tumor associated antigens suitable for immunization and the steps used to design and validate peptide epitopes to improve efficacy of anti-tumor peptide-based vaccines. Focusing on in silico prediction algorithms, we survey the advantages and disadvantages of current cancer vaccine prediction tools.

Computer-aided design of T-cell epitope-based vaccines: addressing population coverage

Epitope-based vaccines (EVs) make use of short antigen-derived peptides corresponding to immune epitopes, which are administered to trigger a protective humoral and/or cellular immune response. EVs potentially allow for precise control over the immune response activation by focusing on the most relevant - immunogenic and conserved - antigen regions. Experimental screening of large sets of peptides is time-consuming and costly; therefore, in silico methods that facilitate T-cell epitope mapping of protein antigens are paramount for EV development. The prediction of T-cell epitopes focuses on the peptide presentation process by proteins encoded by the major histocompatibility complex (MHC). Because different MHCs have different specificities and T-cell epitope repertoires, individuals are likely to respond to a different set of peptides from a given pathogen in genetically heterogeneous human populations. In addition, protective immune responses are only expected if T-cell epitopes are restricted by MHC proteins expressed at high frequencies in the target population. Therefore, without careful consideration of the specificity and prevalence of the MHC proteins, EVs could fail to adequately cover the target population. This article reviews state-of-the-art algorithms and computational tools to guide EV design through all the stages of the process: epitope prediction, epitope selection and vaccine assembly, while optimizing vaccine immunogenicity and coping with genetic variation in humans and pathogens.

Optimization of multi-epitopic HIV-1 recombinant protein expression in prokaryote system and conjugation to mouse DEC-205 monoclonal antibody: implication for in-vivo targeted delivery of dendritic cells

OBJECTIVES

Multi-epitopic protein vaccines and direction of vaccine delivery to dendritic cells (DCs) are promising approaches for enhancing immune responses against mutable pathogens. Escherichia coli is current host for expression of recombinant proteins, and it is important to optimize expression condition. The aim of this study was the optimization of multi-epitopic HIV-1 tat/pol/gag/env recombinant protein (HIVtop4) expression by E. coli and conjugation of purified protein to anti DEC-205 monoclonal antibody as candidate vaccine.

MATERIALS AND METHODS

In this study, expression was induced in BL21 (DE3) E. coli cells by optimization of induction condition, post induction incubation time, temperature and culture medium formula. Some culture mediums were used for cell culture, and isopropyl-beta-D-thiogalactopyranoside was used for induction of expression. Protein was purified by Ni-NTA column chromatography and confirmed against anti-His antibody in western-blotting. To exploit DCs properties for immunization purposes, recombinant protein chemically coupled to αDEC-205 monoclonal antibody and confirmed against anti-His antibody in western-blotting.

RESULTS

The optimum condition for expression was 1 mM IPTG during 4 hr cultures in 2XYT medium, and final protein produced in soluble form. Conjugation of purified protein to αDEC-205 antibody resulted in smears of protein: antibodies conjugate in different molecular weights.

CONCLUSION

The best cultivation condition for production of HIVtop4 protein is induction by 1 mM IPTG during 4 hr in 2XYT medium. The final concentration of purified protein was 500 µg/ml.

A bioinformatics tool for epitope-based vaccine design that accounts for human ethnic diversity: application to emerging infectious diseases

BACKGROUND

Peptide vaccination based on multiple T-cell epitopes can be used to target well-defined ethnic populations. Because the response to T-cell epitopes is restricted by HLA proteins, the HLA specificity of T-cell epitopes becomes a major consideration for epitope-based vaccine design. We have previously shown that CD4+ T-cell epitopes restricted by 95% of human MHC class II proteins can be predicted with high-specificity.

METHODS

We describe here the integration of epitope prediction with population coverage and epitope selection algorithms. The population coverage assessment makes use of the Allele Frequency Net Database. We present the computational platform Predivac-2.0 for HLA class II-restricted epitope-based vaccine design, which accounts comprehensively for human genetic diversity.

RESULTS

We validated the performance of the tool on the identification of promiscuous and immunodominant CD4+ T-cell epitopes from the human immunodeficiency virus (HIV) protein Gag. We further describe an application for epitope-based vaccine design in the context of emerging infectious diseases associated with Lassa, Nipah and Hendra viruses. Putative CD4+ T-cell epitopes were mapped on the surface glycoproteins of these pathogens and are good candidates to be experimentally tested, as they hold potential to provide cognate help in vaccination settings in their respective target populations.

CONCLUSION

Predivac-2.0 is a novel approach in epitope-based vaccine design, particularly suited to be applied to virus-related emerging infectious diseases, because the geographic distributions of the viruses are well defined and ethnic populations in need of vaccination can be determined ("ethnicity-oriented approach"). Predivac-2.0 is accessible through the website http://predivac.biosci.uq.edu.au/.

T-cell epitope prediction methods: an overview

The scientific community is overwhelmed by the voluminous increase in the quantum of data on biological systems, including but not limited to the immune system. Consequently, immunoinformatics databases are continually being developed to accommodate this ever increasing data and analytical tools are continually being developed to analyze the same. Therefore, researchers are now equipped with numerous databases, analytical and prediction tools, in anticipation of better means of prevention of and therapeutic intervention in diseases of humans and other animals. Epitope is a part of an antigen, recognized either by B- or T-cells and/or molecules of the host immune system. Since only a few amino acid residues that comprise an epitope (instead of the whole protein) are sufficient to elicit an immune response, attempts are being made to identify or predict this critical stretch or patch of amino acid residues, i.e., T-cell epitopes and B-cell epitopes to be included in multiple-subunit vaccines. T-cell epitope prediction is a challenge owing to the high degree of MHC polymorphism and disparity in the volume of data on various steps encountered in the generation and presentation of T-cell epitopes in the living systems. Many algorithms/methods developed to predict T-cell epitopes and Web servers incorporating the same are available. These are based on approaches like considering amphipathicity profiles of proteins, sequence motifs, quantitative matrices (QM), artificial neural networks (ANN), support vector machines (SVM), quantitative structure activity relationship (QSAR) and molecular docking simulations, etc. This chapter aims to introduce the reader to the principle(s) underlying some of these methods/algorithms as well as procedural and practical aspects of using the same.

In silico analysis and experimental validation of Mycobacterium tuberculosis -specific proteins and peptides of Mycobacterium tuberculosis for immunological diagnosis and vaccine development

Comparative analyses of the Mycobacterium tuberculosis genome with the genomes of other mycobacteria have led to the identification of several genomic regions of difference (RDs) between M. tuberculosis and M. bovis BCG. The identification of immunodominant and HLA-promiscuous antigens and peptides encoded by these RDs could be useful for diagnosis and the development of new vaccines against tuberculosis. The analysis of RD proteins and peptides by in silico methods (using computational programs to predict major and HLA-promiscuous antigenic proteins and peptides) and experimental validations (using peripheral blood mononuclear cells and sera from tuberculosis patients and BCG-vaccinated healthy subjects to assess antigen-specific cellular and humoral immune responses in vitro) identified several major antigens and peptides. To evaluate the in vivo potentials, the genes of immunodominant antigens were cloned and expressed in DNA vaccine vectors. Immunizations of experimental animals with the recombinant constructs induced antigen-specific cellular responses. Further experiments showed that each of these proteins had several T and B cell epitopes scattered throughout their sequence, which confirmed their strong immunogenicity. In conclusion, the bioinformatics-based in silico identification of promiscuous antigens and peptides of M. tuberculosis is a useful approach to identify new candidates important for diagnosis and vaccine applications.