T-cell epitope prediction methods: an overview

APTAnet: an atom-level peptide-TCR interaction affinity prediction model

The prediction of affinity between TCRs and peptides is crucial for the further development of TIL (Tumor-Infiltrating Lymphocytes) immunotherapy. Inspired by the broader research of drug-protein interaction (DPI), we propose an atom-level peptide-TCR interaction (PTI) affinity prediction model APTAnet using natural language processing methods. APTAnet model achieved an average ROC-AUC and PR-AUC of 0.893 and 0.877, respectively, in ten-fold cross-validation on 25,675 pairs of PTI data. Furthermore, experimental results on an independent test set from the McPAS database showed that APTAnet outperformed the current mainstream models. Finally, through the validation on 11 cases of real tumor patient data, we found that the APTAnet model can effectively identify tumor peptides and screen tumor-specific TCRs.

PoxiPred: An Artificial-Intelligence-Based Method for the Prediction of Potential Antigens and Epitopes to Accelerate Vaccine Development Efforts against Poxviruses

Poxviridae is a family of large, complex, enveloped, and double-stranded DNA viruses. The members of this family are ubiquitous and well known to cause contagious diseases in humans and other types of animals as well. Taxonomically, the poxviridae family is classified into two subfamilies, namely Chordopoxvirinae (affecting vertebrates) and Entomopoxvirinae (affecting insects). The members of the Chordopoxvirinae subfamily are further divided into 18 genera based on the genome architecture and evolutionary relationship. Of these 18 genera, four genera, namely Molluscipoxvirus, Orthopoxvirus, Parapoxvirus, and Yatapoxvirus, are known for infecting humans. Some of the popular members of poxviridae are variola virus, vaccine virus, Mpox (formerly known as monkeypox), cowpox, etc. There is still a pressing demand for the development of effective vaccines against poxviruses. Integrated immunoinformatics and artificial-intelligence (AI)-based methods have emerged as important approaches to design multi-epitope vaccines against contagious emerging infectious diseases. Despite significant progress in immunoinformatics and AI-based techniques, limited methods are available to predict the epitopes. In this study, we have proposed a unique method to predict the potential antigens and T-cell epitopes for multiple poxviruses. With PoxiPred, we developed an AI-based tool that was trained and tested with the antigens and epitopes of poxviruses. Our tool was able to locate 3191 antigen proteins from 25 distinct poxviruses. From these antigenic proteins, PoxiPred redundantly located up to five epitopes per protein, resulting in 16,817 potential T-cell epitopes which were mostly (i.e., 92%) predicted as being reactive to CD8+ T-cells. PoxiPred is able to, on a single run, identify antigens and T-cell epitopes for poxviruses with one single input, i.e., the proteome file of any poxvirus.

Reverse vaccinology and immunoinformatics approach to design a chimeric epitope vaccine against Orientia tsutsugamushi

Scrub typhus is a vector-borne infectious disease caused by Orientia tsutsugamushi and it is reportedly associated with up to 20 % of hospitalized cases of febrile illnesses. The major challenge of vaccine development is the lack of identified antigens that can induce both heterotypic and homotypic immunity including the production of antibodies, cytotoxic T lymphocyte, and helper T lymphocytes. We employed a comprehensive immunoinformatic prediction algorithm to identify immunogenic epitopes of the 56-kDa type-specific cell membrane surface antigen and surface cell antigen A of O. tsutsugamushi to select potential candidates for developing vaccines and diagnostic assays. We identified 35 linear and 29 continuous immunogenic B-cell epitopes and 51 and 27 strong-binding T-cell epitopes of major histocompatibility complex class I and class II molecules, respectively, in the conserved and variable regions of the 56-kDa type-specific surface antigen. The predicted B- and T-cell epitopes were used to develop immunogenic multi-epitope candidate vaccines and showed to elicit a broad-range of immune protection. A stable interactions between the multi-epitope vaccines and the host fibronectin protein were observed using docking and simulation methods. Molecular dynamics simulation studies demonstrated that the multi-epitope vaccine constructs and fibronectin docked models were stable during simulation time. Furthermore, the multi-epitope vaccine exhibited properties such as antigenicity, non-allergenicity and ability to induce interferon gamma production and had strong associations with their respective human leukocyte antigen alleles of world-wide population coverage. A correlation of immune simulations and the in-silico predicted immunogenic potential of multi-epitope vaccines implicate for further investigations to accelerate designing of epitope-based vaccine candidates and chimeric antigens for development of serological diagnostic assays for scrub typhus.

Nipah virus attachment glycoprotein ectodomain delivered by type 5 adenovirus vector elicits broad immune response against NiV and HeV

Nipah virus (NiV) and Hendra virus (HeV) are newly emerging dangerous zoonotic pathogens of the Henipavirus genus of the Paramyxoviridae family. NiV and HeV (HNVs) which are transmitted by bats cause acute respiratory disease and fatal encephalitis in humans. To date, as there is a lack of antiviral drugs or effective antiviral therapies, the development of vaccines against those two viruses is of primary importance, and the immunogen design is crucial to the success of vaccines. In this study, the full-length protein (G), the ectodomain (Ge) and the head domain (Gs) of NiV attachment glycoprotein were delivered by the replication-defective type 5 adenovirus vector (Ad5) respectively, and the recombinant Ad5-NiV vaccine candidates (Ad5-NiVG, Ad5-NiVGe and Ad5-NiVGs) were constructed and their immunogenicity were evaluated in mice. The results showed that all the vaccine candidates stimulated specific humoral and cellular immune responses efficiently and rapidly against both NiV and HeV, and the Ad5-NiVGe elicited the strongest immune responses after a single-dose immunization. Furthermore, the potent conserved T-cell epitope DTLYFPAVGFL shared by NiV and HeV was identified in the study, which may provide valid information on the mechanism of HNVs-specific cellular immunity. In summary, this study demonstrates that the Ad5-NiVGe could be a potent vaccine candidate against HNVs by inducing robust humoral and cellular immune responses.

Quantum vaccinomics platforms to advance in vaccinology

The opinion flows from Introduction to the immunological quantum that requires a historical perspective, to Quantum vaccine algorithms supported by a bibliometric analysis, to Quantum vaccinomics describing from our perspective the different vaccinomics and quantum vaccinomics algorithms. Finally, in the Discussion and conclusions we propose novel platforms and algorithms developed to further advance on quantum vaccinomics. In the paper we refer to protective epitopes or immunological quantum for the design of candidate vaccine antigens, which may elicit a protective response through both cellular and antibody mediated mechanisms of the host immune system. Vaccines are key interventions for the prevention and control of infectious diseases affecting humans and animals worldwide. Biophysics led to quantum biology and quantum immunology reflecting quantum dynamics within living systems and their evolution. In analogy to quantum of light, immune protective epitopes were proposed as the immunological quantum. Multiple quantum vaccine algorithms were developed based on omics and other technologies. Quantum vaccinomics is the methodological approach with different platforms used for the identification and combination of immunological quantum for vaccine development. Current quantum vaccinomics platforms include in vitro, in music and in silico algorithms and top trends in biotechnology for the identification, characterization and combination of candidate protective epitopes. These platforms have been applied to different infectious diseases and in the future should target prevalent and emerging infectious diseases with novel algorithms.

State of the art in epitope mapping and opportunities in COVID-19

The understanding of any disease calls for studying specific biological structures called epitopes. One important tool recently drawing attention and proving efficiency in both diagnosis and vaccine development is epitope mapping. Several techniques have been developed with the urge to provide precise epitope mapping for use in designing sensitive diagnostic tools and developing rpitope-based vaccines (EBVs) as well as therapeutics. In this review, we will discuss the state of the art in epitope mapping with a special emphasis on accomplishments and opportunities in combating COVID-19. These comprise SARS-CoV-2 variant analysis versus the currently available immune-based diagnostic tools and vaccines, immunological profile-based patient stratification, and finally, exploring novel epitope targets for potential prophylactic, therapeutic or diagnostic agents for COVID-19.

Immuno-informatics guided designing of a multi-epitope vaccine against Dengue and Zika

Dengue and zika are amongst the most prevalent mosquito-borne diseases caused by closely related members Dengue virus (DENV) and Zika virus (ZIKV), respectively, of the Flaviviridae family. DENV and ZIKV have been reported to co-infect several people, resulting in fatalities across the world. A vaccine that can safeguard against both these pathogens concurrently, can offer several advantages. This study has employed immuno-informatics for devising a multi-epitope, multi-pathogenic vaccine against both these viruses. Since, the two viruses share a common vector source, whose salivary components are reported to aid viral pathogenesis; antigenic salivary proteins from Aedes aegypti were also incorporated into the design of the vaccine along with conserved structural and non-structural viral proteins. Conserved B- and T-cell epitopes were identified for all the selected antigenic proteins. These epitopes were merged and further supplemented with β-defensin as an adjuvant, to yield an immunogenic vaccine construct. In-silico 3D modeling and structural validation of the vaccine construct was conducted, followed by its molecular docking and molecular dynamics simulation studies with human TLR2. Immune simulation study was also performed, and it further provided support that the designed vaccine can mount an effective immune response and hence provide protection against both DENV and ZIKV. Communicated by Ramaswamy H. Sarma.

An Attention Based Bidirectional LSTM Method to Predict the Binding of TCR and Epitope

The T-cell epitope prediction has always been a long-term challenge in immunoinformatics and bioinformatics. Studying the specific recognition between T-cell receptor (TCR) and peptide-major histocompatibility complex (p-MHC) complexes can help us better understand the immune mechanism, it's also make a signification contribution in developing vaccines and targeted drugs. Meanwhile, more advanced methods are needed for distinguishing TCRs binding from different epitopes. In this paper, we introduce a hybrid model composed of bidirectional long short-term memory networks (BiLSTM), attention and convolutional neural networks (CNN) that can identified the binding of TCRs to epitopes. The BiLSTM can more completely extract amino acid forward and backward information in the sequence, and attention mechanism can focus on amino acids at certain positions from complex sequences to capture the most important feature, then CNN was used to further extract salient features to predict the binding of TCR-epitope. In McPAS dataset, the AUC value (the area under ROC curve) of naive TCR-epitope binding is 0.974 and specific TCR-epitope binding is 0.887. The model has achieved better prediction results than other existing models (TCRGP, ERGO, NetTCR), and some experiments are used to analyze the advantages of our model. The algorithm is available at https://github.com/bijingshu/BiAttCNN.git.

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.

Mapping immunogenic epitopes of an adhesin-like protein from Methanobrevibacter ruminantium M1 and comparison of empirical data with in silico prediction methods

In silico prediction of epitopes is a potentially time-saving alternative to experimental epitope identification but is often subject to misidentification of epitopes and may not be useful for proteins from archaeal microorganisms. In this study, we mapped B- and T-cell epitopes of a model antigen from the methanogen Methanobrevibacter ruminantium M1, the Big_1 domain (AdLP-D1, amino acids 19–198) of an adhesin-like protein. A series of 17 overlapping 20-mer peptides was selected to cover the Big_1 domain. Peptide-specific antibodies were produced in mice and measured by ELISA, while an in vitro splenocyte re-stimulation assay determined specific T-cell responses. Overall, five peptides of the 17 peptides were shown to be major immunogenic epitopes of AdLP-D1. These immunogenic regions were examined for their localization in a homology-based model of AdLP-D1. Validated epitopes were found in the outside region of the protein, with loop like secondary structures reflecting their flexibility. The empirical data were compared with epitope predictions made by programmes based on a range of algorithms. In general, the epitopes identified by in silico predictions were not comparable to those determined empirically.

Enhanced safety and efficacy of protease-regulated CAR-T cell receptors

Regulatable CAR platforms could circumvent toxicities associated with CAR-T therapy, but existing systems have shortcomings including leakiness and attenuated activity. Here, we present SNIP CARs, a protease-based platform for regulating CAR activity using an FDA-approved small molecule. Design iterations yielded CAR-T cells that manifest full functional capacity with drug and no leaky activity in the absence of drug. In numerous models, SNIP CAR-T cells were more potent than constitutive CAR-T cells and showed diminished T cell exhaustion and greater stemness. In a ROR1-based CAR lethality model, drug cessation following toxicity onset reversed toxicity, thereby credentialing the platform as a safety switch. In the same model, reduced drug dosing opened a therapeutic window that resulted in tumor eradication in the absence of toxicity. SNIP CARs enable remote tuning of CAR activity, which provides solutions to safety and efficacy barriers that are currently limiting progress in using CAR-T cells to treat solid tumors.

Harnessing Fuzzy Rule Based System for Screening Major Histocompatibility Complex Class I Peptide Epitopes from the Whole Proteome: An Implementation on the Proteome of Leishmania donovani

The development of peptide-based vaccines is enhanced by immunoinformatics, which predicts the patterns that B cells and T cells recognize. Although several tools are available for predicting the Major histocompatibility complex (MHC-I) binding peptides, the wide variants of human leucocyte antigen allele make it challenging to choose a peptide that will induce an immune response in a majority of people. In addition, for a peptide to be considered a potential vaccine candidate, factors such as T cell affinity, proteasome cleavage, and similarity to human proteins also play a major role. Identifying peptides that satisfy the earlier cited measures across the entire proteome is, therefore, challenging. Hence, the fuzzy inference system (FIS) is proposed to detect each peptide's potential as a vaccine candidate and assign it either a very high, high, moderate, or low ranking. The FIS includes input features from 6 modules (binding of 27 major alleles, T cell propensity, pro-inflammatory response, proteasome cleavage, transporter associated with antigen processing, and similarity with human peptide) and rules derived from an observation of features on positive samples. On validation of experimentally verified peptides, a balanced accuracy of ∼80% was achieved, with a Mathew's correlation coefficient score of 0.67 and an F-1 score of 0.74. In addition, the method was implemented on complete proteome of Leishmania donovani, which contains ∼4,800,000 peptides. Lastly, a searchable database of the ranked results of the L. donovani proteome was made and is available online (MHC-FIS-LdDB). It is hoped that this method will simplify the identification of potential MHC-I binding candidates from a large proteome.

Towards the prediction of non-peptidic epitopes

In-silico methods for the prediction of epitopes can support and improve workflows for vaccine design, antibody production, and disease therapy. So far, the scope of B cell and T cell epitope prediction has been directed exclusively towards peptidic antigens. Nevertheless, various non-peptidic molecular classes can be recognized by immune cells. These compounds have not been systematically studied yet, and prediction approaches are lacking. The ability to predict the epitope activity of non-peptidic compounds could have vast implications; for example, for immunogenic risk assessment of the vast number of drugs and other xenobiotics. Here we present the first general attempt to predict the epitope activity of non-peptidic compounds using the Immune Epitope Database (IEDB) as a source for positive samples. The molecules stored in the Chemical Entities of Biological Interest (ChEBI) database were chosen as background samples. The molecules were clustered into eight homogeneous molecular groups, and classifiers were built for each cluster with the aim of separating the epitopes from the background. Different molecular feature encoding schemes and machine learning models were compared against each other. For those models where a high performance could be achieved based on simple decision rules, the molecular features were then further investigated. Additionally, the findings were used to build a web server that allows for the immunogenic investigation of non-peptidic molecules (http://tools-staging.iedb.org/np_epitope_predictor). The prediction quality was tested with samples from independent evaluation datasets, and the implemented method received noteworthy Receiver Operating Characteristic-Area Under Curve (ROC-AUC) values, ranging from 0.69–0.96 depending on the molecule cluster.

Small molecules found in cosmetics, foodstuffs, dyes, and industrial materials, but also those produced by plants, bacteria, and animals can trigger strong reactions of the human immune system and can therefore be hazardous to health. In the present work, several thousand immune-reactive small molecules (so-called non-peptidic epitopes) were classified by molecular structure and studied with the aim of identifying specific parts of the molecules responsible for such immune responses. Using a machine-learning approach (random forests and neural networks), we identified some substructures that appear strikingly often in non-peptidic epitopes and which may be responsible for the hazardous immune response. Such knowledge may help to explain allergic reactions to chemicals and also to minimize the health risks of new chemicals in industrial production. To support this endeavor, we have implemented the method in a publicly available web application. This can be used for the prediction and identification of non-peptidic epitopes and their underlying substructures.

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics

Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.

Review on Approved and Inprogress COVID-19 Vaccines

The last generation of Coronavirus named COVID-19 is responsible for the recent worldwide outbreak. Concerning the widespread and quick predominance, there is a critical requirement for designing appropriate vaccines to surmount this grave problem. Correspondingly, in this revision, COVID-19 vaccines (which are being developed until March 29th, 2021) are classified into specific and non-specific categories. Specific vaccines comprise genetic-based vaccines (mRNA, DNA), vector-based, protein/recombinant protein vaccines, inactivated viruses, live-attenuated vaccines, and novel strategies including microneedle arrays (MNAs), and nanoparticles vaccines. Moreover, specific vaccines such as BCG, MRR, and a few other vaccines are considered Non-specific. What is more, according to the significance of Bioinformatic sciences in the cutting-edge vaccine design and rapid outbreak of COVID-19, herein, Bioinformatic principles including reverse vaccinology, epitopes prediction/selection and, their further applications in the design of vaccines are discussed. Last but not least, safety, challenges, advantages, and future prospects of COVID-19 vaccines are highlighted.

Development of synthetic antigen vaccines for COVID-19

The current pandemic called COVID-19 caused by the SARS-CoV-2 virus brought the need for the search for fast alternatives to both control and fight the SARS-CoV-2 infection. Therefore, a race for a vaccine against COVID-19 took place, and some vaccines have been approved for emergency use in several countries in a record time. Ongoing prophylactic research has sought faster, safer, and precise alternatives by redirecting knowledge of other vaccines, and/or the development of new strategies using available tools, mainly in the areas of genomics and bioinformatics. The current review highlights the development of synthetic antigen vaccines, focusing on the usage of bioinformatics tools for the selection and construction of antigens on the different vaccine constructions under development, as well as strategies to optimize vaccines for COVID-19.

Immunoregulatory T cell epitope peptides for the treatment of allergic disease

Allergic diseases are type 2 inflammatory reactions with an increasing worldwide prevalence, making the search for new therapeutic options pertinent. Allergen immunotherapy is the only disease-modifying approach for allergic rhinitis, though it can result in systemic reactions. Recently, peptide immunotherapy (PIT), involving T-cell epitope peptides that bind to major histocompatibility complexes, have been developed. It is speculated that they can induce T helper cell type 2 anergy, Treg cell upregulation or immune deviation. Promising results in cat dander, honeybee venom, Japanese cedar pollen, grass pollens, ragweed and house dust mite clinical trials have shown safety, efficacy and tolerability to PIT. Hence, PIT may hold the potential to change the treatment algorithm for allergic rhinitis.

Designing a highly immunogenic multi epitope based subunit vaccine against Bacillus cereus

Objective

Serious non-gastrointestinal-tract infections and food poisoning are caused by Bacillus cereus. Vaccination against B. cereus is very important. The aim of this study was to identify and analyze B and T cell epitopes for chromate transporter protein of the bacteria.

Methods

Multiple sequence alignment with the Clustal Omega method was used to identify conserved regions and Geneious Prime was used to produce a consensus sequence. T and B cell epitopes were predicted by various computational tools from the NetCTL and Immune Epitope Database (IEDB), respectively.

Results

Altogether, 6 HTL cells and 11 CTL epitopes were predicted. This vaccine's molecular docking is done with Patch Dock and LigPlot to verify interactions. The immune server (C-IMMSIM) was used to develop In silico immune response in order to assess the multi-epitope vaccine's immunogenic profile.

Conclusion

We designed universal vaccine against B. cereus responsible for food poisoning. The disease may be avoided with the aid of the proposed epitope-based vaccine.

HBV S antigen evolution in the backdrop of HDV infection affects epitope processing and presentation

INTRODUCTION

HBV can evolve under selection pressure exerted by drugs and/or host immunity, resulting in accumulation of escape mutations that can affect the drug or the immune activity. Hepatitis delta virus (HDV) coinfection is also known to exert selection pressure on HBV, which leads to selective amplification of certain mutations, especially in genes that are required for HDV pathogenesis, such as HBsAg. However, little is known about the function of these mutations on HBV or HDV life cycle. The purpose of this study is to determine mutations selectively amplified in the backdrop of HDV, and how these mutations affect processing of CD4- and CD8-T cell epitopes.

METHODS

HBsAg was successfully amplified from 49/50 HBV mono- and 36/50 coinfected samples. The sequences were used to identify mutations specific to each study group, followed by an in silico analysis to determine the effect of these mutations on (1) proteasomal degradation, (2) MHC-I and MHC-II biding, and (3) processing of T-cell epitopes.

RESULTS

HBV-HDV coinfected sequences exhibited certain unique mutations in HBsAg genes. Some of these mutations affected the generation of proteasomal sites, binding of HBsAg epitopes to MHC-I and -II ligands, and subsequent generation of T- cell epitopes.

CONCLUSION

These observations suggest that HBV selectively amplifies certain mutations in the backdrop of HDV coinfection. Selective amplification of these mutations at certain strategic locations might not only enable HBV to counteract the inhibitory effects of HDV on HBV replication but also facilitate its survival by escaping the immune response.

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.

Discordance Between the Predicted Versus the Actually Recognized CD8+ T Cell Epitopes of HCMV pp65 Antigen and Aleatory Epitope Dominance

CD8+ T cell immune monitoring aims at measuring the size and functions of antigen-specific CD8+ T cell populations, thereby providing insights into cell-mediated immunity operational in a test subject. The selection of peptides for ex vivo CD8+ T cell detection is critical because within a complex antigen exists a multitude of potential epitopes that can be presented by HLA class I molecules. Further complicating this task, there is HLA class I polygenism and polymorphism which predisposes CD8+ T cell responses towards individualized epitope recognition profiles. In this study, we compare the actual CD8+ T cell recognition of a well-characterized model antigen, human cytomegalovirus (HCMV) pp65 protein, with its anticipated epitope coverage. Due to the abundance of experimentally defined HLA-A*02:01-restricted pp65 epitopes, and because in silico epitope predictions are most advanced for HLA-A*02:01, we elected to focus on subjects expressing this allele. In each test subject, every possible CD8+ T cell epitope was systematically covered testing 553 individual peptides that walk the sequence of pp65 in steps of single amino acids. Highly individualized CD8+ T cell response profiles with aleatory epitope recognition patterns were observed. No correlation was found between epitopes' ranking on the prediction scale and their actual immune dominance. Collectively, these data suggest that accurate CD8+ T cell immune monitoring may necessitate reliance on agnostic mega peptide pools, or brute force mapping, rather than electing individual peptides as representative epitopes for tetramer and other multimer labeling of surface antigen receptors.

Immunoinformatic analysis of immunogenic B- and T-cell epitopes of MIC4 protein to designing a vaccine candidate against Toxoplasma gondii through an in-silico approach

Purpose

Toxoplasmosis, transmitted by Toxoplasma gondii, is a worldwide parasitic disease that affects approximately one-third of the world's inhabitants. Today, there are no appropriate drugs to deter tissue cysts from developing in infected hosts. So, developing an effective vaccine would be valuable to avoid from toxoplasmosis. Considering the role of microneme antigens such as microneme protein 4 (MIC4) in T. gondii pathogenesis, it can be used as potential candidates for vaccine against T. gondii.

Materials and Methods

In this study several bioinformatics methods were used to assess the different aspects of MIC4 protein such as secondary and tertiary structure, physicochemical characteristics, the transmembrane domains, subcellular localization, B-cell, helper-T lymphocyte, cytotoxic-T lymphocyte epitopes, and other notable characteristic of this protein design a suitable vaccine against T. gondii.

Results

The studies revealed that MIC4 protein includes 59 potential post-translational modification sites without any transmembrane domains. Moreover, several probable epitopes of B- and T-cells were detected for MIC4. The secondary structure comprised 55.69% random coil, 5.86% beta-turn, 19.31% extended strand, and 19.14% alpha helix. According to the Ramachandran plot results, 87.42% of the amino acid residues were located in the favored, 9.44% in allowed, and 3.14% in outlier regions. The protein allergenicity and antigenicity revealed that it was non-allergenic and antigenic.

Conclusion

This study gives vital basic on MIC4 protein for further research and also established an effective vaccine with different techniques against acute and chronic toxoplasmosis.

Immunoinformatics Approach for the Identification and Characterization of T Cell and B Cell Epitopes towards the Peptide-Based Vaccine against SARS-CoV-2

Presently, immunoinformatics is playing a significant role in epitope identification and vaccine designing for various critical diseases. Using immunoinformatics, several scientists are trying to identify and characterize T cell and B cell epitopes as well as design peptide-based vaccine against SARS-CoV-2. In this review article, we have tried to discuss the importance in adaptive immunity and its significance for designing the SARS-CoV-2 vaccine. Moreover, we have attempted to illustrate several significant key points for utilizing immunoinformatics for vaccine designing, such as the criteria for selection and identification of epitopes, T cell epitope, and B cell epitope prediction and different emerging tools/databases for immunoinformatics. In the current scenario, a few immunoinformatics studies have been performed for various infectious pathogens and related diseases. Thus, we have also summarized and included these current immunoinformatics studies in this review article. Finally, we have discussed about the probable T cell and B cell epitopes and their identification and characterization for vaccine designing against SARS-CoV-2.

Dual-Antigen-Loaded Hepatitis B Virus Core Antigen Virus-like Particles Stimulate Efficient Immunotherapy Against Melanoma

Tumor cells are rich in antigens, which provide a reliable antigen library for the design of personalized vaccines. However, an effective tumor vaccine vector that can efficiently deliver antigens to lymphoid organs to stimulate strong CD8⁺ cytotoxic T-lymphocyte immune response is still lacking. Here we designed a dual-antigen delivery system based on hepatitis B virus core antigen virus-like particles (HBc VLPs). We first confirmed that different antigen-loaded HBc VLP monomers could be assembled into nanoparticles (hybrid VLPs). Hybrid VLPs could slightly enhance bone marrow-derived dendritic cell maturation in vitro. Strikingly, hybrid VLPs could generate antigen-specific antitumor immunity and innate immunity in vivo which could significantly inhibit tumor growth or metastatic formation in a subcutaneous tumor or lung metastatic tumor model, respectively. Moreover, dual-epitope vaccination generated enhanced T-cell responses that potently inhibited tumor growth and metastatic formation. Together, this study provides a new powerful concept for cancer immunotherapy and suggests a novel design for VLP-based personalized nanomedicine.

Computational Immune Proteomics Approach to Target COVID-19

Progress of the omics platforms widens their application to diverse fields, including immunology. This enables a deeper level of knowledge and the provision of a huge amount of data for which management and fruitful integration with the past evidence requires a steadily growing computational effort. In light of this, immunoinformatics emerges as a new discipline placed in between the traditional lab-based investigations and the computational analysis of the biological data. Immunoinformatics make use of tailored bioinformatics tools and data repositories to facilitate the analysis of data from a plurality of disciplines and help drive novel research hypotheses and in silico screening investigations in a fast, reliable, and cost-effective manner. Such computational immunoproteomics studies may as well prepare and guide lab-based investigations, representing valuable technology for the investigation of novel pathogens, to tentatively evaluate specificity of diagnostic products, to forecast on potential adverse effects of vaccines and to reduce the use of animal models. The present manuscript provides an overview of the COVID-19 pandemic and reviews the state of the art of the omics technologies employed in fighting SARS-CoV-2 infections. A comprehensive description of the immunoinformatics approaches and its potential role in contrasting COVID-19 pandemics is provided.

The role of immunoinformatics in the development of T-cell peptide-based vaccines against Mycobacterium tuberculosis

INTRODUCTION

Tuberculosis (TB) is a major health problem worldwide. The BCG, the only authorized vaccine to fight TB, shows a variable protection in the adult population highlighting the need of a new vaccine. Immunoinformatics offers a variety of tools that can predict immunogenic T-cell peptides of Mycobacterium tuberculosis (Mtb) that can be used to create a new vaccine. Immunoinformatics has made possible the identification of immunogenic T-cell peptides of Mtb that have been tested in vitro showing a potential for using these molecules as part of a new TB vaccine.

AREAS COVERED

This review summarizes the most common immunoinformatics tools to identify immunogenic T-cell peptides and presents a compilation about research studies that have identified T-cell peptides of Mtb by using immunoinformatics. Also, it is provided a summary of the TB vaccines undergoing clinical trials.

EXPERT OPINION

In the next few years, the field of peptide-based vaccines will keep growing along with the development of more efficient and sophisticated immunoinformatic tools to identify immunogenic peptides with a greater accuracy.

In silico clinical trials: concepts and early adoptions

Innovations in information and communication technology infuse all branches of science, including life sciences. Nevertheless, healthcare is historically slow in adopting technological innovation, compared with other industrial sectors. In recent years, new approaches in modelling and simulation have started to provide important insights in biomedicine, opening the way for their potential use in the reduction, refinement and partial substitution of both animal and human experimentation. In light of this evidence, the European Parliament and the United States Congress made similar recommendations to their respective regulators to allow wider use of modelling and simulation within the regulatory process. In the context of in silico medicine, the term 'in silico clinical trials' refers to the development of patient-specific models to form virtual cohorts for testing the safety and/or efficacy of new drugs and of new medical devices. Moreover, it could be envisaged that a virtual set of patients could complement a clinical trial (reducing the number of enrolled patients and improving statistical significance), and/or advise clinical decisions. This article will review the current state of in silico clinical trials and outline directions for a full-scale adoption of patient-specific modelling and simulation in the regulatory evaluation of biomedical products. In particular, we will focus on the development of vaccine therapies, which represents, in our opinion, an ideal target for this innovative approach.

Antigenic properties of dense granule antigen 12 protein using bioinformatics tools in order to improve vaccine design against Toxoplasma gondii

PURPOSE

Toxoplasma gondii is an opportunistic parasite infecting all warm-blooded animals including humans. The dense granule antigens (GRAs) play an important role in parasite survival and virulence and in forming the parasitophorous vacuole. Identification of protein characteristics increases our knowledge about them and leads to develop the vaccine and diagnostic studies.

MATERIALS AND METHODS

This paper gave a comprehensive definition of the important aspects of GRA12 protein, including physico-chemical features, a transmembrane domain, subcellular position, secondary and tertiary structure, potential epitopes of B-cells and T-cells, and other important features of this protein using different and reliable bioinformatics methods to determine potential epitopes for designing of a high-efficient vaccine.

RESULTS

The findings showed that GRA12 protein had 53 potential post-translational modification sites. Also, only one transmembrane domain was recognized for this protein. The secondary structure of GRA12 protein comprises 35.55% alpha-helix, 19.50% extended strand, and 44.95% random coil. Moreover, several potential B- and T-cell epitopes were identified for GRA12. Based on the results of the Ramachandran plot, 79.26% of amino acid residues were located in favored, 11.85% in allowed and 8.89% in outlier regions. Furthermore, the results of the antigenicity and allergenicity assessment noted that GRA12 is immunogenic and non-allergenic.

CONCLUSION

This research provided important basic and conceptual data on GRA12 to develop an effective vaccine against acute and chronic toxoplasmosis for further in vivo investigations. More studies are required on vaccine development using the GRA12 alone or combined with other antigens in the future.

INeo-Epp: A Novel T-Cell HLA Class-I Immunogenicity or Neoantigenic Epitope Prediction Method Based on Sequence-Related Amino Acid Features

In silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g., proteasomal cleavage, transporter associated with antigen processing (TAP), and major histocompatibility complex (MHC) combination. To date, however, the mechanism for the immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties, and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including the amino acid physicochemical property of the epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score, and frequency score for an immunogenic peptide. Subsequently, a random forest classifier for T-cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC = 0.81, external validation data set AUC = 0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called "neoantigens." Based on mutation information and sequence-related amino acid characteristics, a prediction model of a neoantigen was established as well (the optimal AUC = 0.78). Further, an easy-to-use web-based tool "INeo-Epp" was developed for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.

T-cell Epitope-based Vaccine Design for Nipah Virus by Reverse Vaccinology Approach

AIM AND OBJECTIVE

Nipah virus (NiV) is a zoonotic virus of the paramyxovirus family that sporadically breaks out from livestock and spreads in humans through breathing resulting in an indication of encephalitis syndrome. In the current study, T cell epitopes with the NiV W protein antigens were predicted.

MATERIALS AND METHODS

Modelling of unavailable 3D structure of W protein followed by docking studies of respective Human MHC - class I and MHC - class II alleles predicted was carried out for the highest binding rates. In the computational analysis, epitopes were assessed for immunogenicity, conservation, and toxicity analysis. T - cell-based vaccine development against NiV was screened for eight epitopes of Indian - Asian origin.

RESULTS

Two epitopes, SPVIAEHYY and LVNDGLNII, have been screened and selected for further docking study based on toxicity and conservancy analyses. These epitopes showed a significant score of -1.19 kcal/mol and 0.15 kcal/mol with HLA- B*35:03 and HLA- DRB1 * 07:03, respectively by using allele - Class I and Class II from AutoDock. These two peptides predicted by the reverse vaccinology approach are likely to induce immune response mediated by T - cells.

CONCLUSION

Simulation using GROMACS has revealed that LVNDGLNII epitope forms a more stable complex with HLA molecule and will be useful in developing the epitope-based Nipah virus vaccine.

Novel Biomarkers for Personalized Cancer Immunotherapy

Cancer immunotherapy has emerged as a novel and effective treatment strategy for several types of cancer. Immune checkpoint inhibitors (ICIs) have recently demonstrated impressive clinical benefit in some advanced cancers. Nonetheless, in the majority of patients, the successful use of ICIs is limited by a low response rate, high treatment cost, and treatment-related toxicity. Therefore, it is necessary to identify predictive and prognostic biomarkers to select the patients who are most likely to benefit from, and respond well to, these therapies. In this review, we summarize the evidence for candidate biomarkers of response to cancer immunotherapy.

Analysis of Apical Membrane Antigen (AMA)-1 characteristics using bioinformatics tools in order to vaccine design against Plasmodium vivax

Plasmodium vivax, an intracellular protozoan, causes malaria which is characterized by fever, anemia, respiratory distress, liver and spleen enlargement. In spite of attempts to design an efficient vaccine, there is not a vaccine against P. vivax. Notable advances have recently achieved in the development of malaria vaccines targeting the surface antigens such as Apical Membrane Antigens (AMA)-1. AMA-1 is a micronemal protein synthesized during the erythrocyte-stage of Plasmodium species and plays a significant role in the invasion process of the parasite into host cells. P. vivax AMA-1 (PvAMA-1) can induce strong cellular and humoral responses, indicating that it can be an ideal candidate of vaccine against malaria. Identification and prediction of proteins characteristics increase our knowledge about them and leads to develop vaccine and diagnostic studies. In the present study several valid bioinformatics tools were applied to analyze the various characteristics of AMA-1 such as physical and chemical properties, secondary and tertiary structures, B- cell and T-cell prediction and other important features in order to introduce potential epitopes for designing a high-efficient vaccine. The results demonstrated that this protein had 57 potential PTM sites and only one transmembrane domain on its sequence. Also, multiple hydrophilic regions and classical high hydrophilic domains were predicted. Secondary structure prediction revealed that the proportions of random coil, alpha-helix and extended strand in the AMA-1 sequence were 53.74%, 27.22%, and 19.4%, respectively. Moreover, 5 disulfide bonds were predicted at positions 14-21aa, 162-192aa, 208-220aa, 247-265aa and 354-363aa. The data obtained from B-cell and T-cell epitopes prediction showed that there were several potential epitopes on AMA-1 that can be proper targets for diagnostic and vaccine studies. The current study presented interesting basic and theoretical information regarding PvAMA-1, being important for further studies in order to design a high-efficiency vaccine against malaria.

Pro-inflammatory Cytokines Alter the Immunopeptidome Landscape by Modulation of HLA-B Expression

Antigen presentation on HLA molecules is a major mechanism by which the immune system monitors self and non-self-recognition. Importantly, HLA-I presentation has gained much attention through its role in eliciting anti-tumor immunity. Several determinants controlling the peptides presented on HLA have been uncovered, mainly through the study of model substrates and large-scale immunopeptidome analyses. These determinants include the relative abundances of proteins in the cell, the stability or turnover rate of these proteins and the binding affinities of a given peptide to the HLA haplotypes found in a cell. However, the regulatory principles involved in selection and regulation of specific antigens in response to tumor pro-inflammatory signals remain largely unknown. Here, we chose to examine the effect that TNFα and IFNγ stimulation may exert on the immunopeptidome landscape of lung cancer cells. We show that the expression of many of the proteins involved in the class I antigen presentation pathway are changed by pro-inflammatory cytokines. Further, we could show that increased expression of the HLA-B allomorph drives a significant change in HLA-bound antigens, independently of the significant changes observed in the cellular proteome. Finally, we observed increased HLA-B levels in correlation with tumor infiltration across the TCGA lung cancer cohorts. Taken together, our results suggest that the immunopeptidome landscape should be examined in the context of anti-tumor immunity whereby signals in the microenvironment may be critical in shaping and modulating this important aspect of host-tumor interactions.

Peptide vaccines in early breast cancer

The immune system plays a dual role of host-protecting and tumor-promoting, as elegantly expressed by the 'cancer immunoediting' hypothesis. Although breast cancer has not been traditionally considered to be immunogenic, recently there is accumulating and solid evidence on the association between immune system and breast cancer. To mount an effective anti-tumor response, host immunosurveillance must recognize tumor-specific epitopes, thus defining the antigenicity of a tumor. Neoantigens are mutant cancer peptides that arise as terminal products of the expression of somatic cancer mutations. Neoantigens and major histocompatibility complex (MHC) proteins present together to effector cells of the immune system. Neoantigen vaccines have shown promising results in inducing neoantigen-specific T-cell responses. Currently, cancer vaccines are under evaluation in breast cancer to avoid recurrences in patients at high risk despite optimal standard therapy. Given the promise of a very specific long-term antitumor immune response, the development of cancer vaccines continues is of great interest. Combinations of neoantigen vaccines and other immunotherapies are also studied to evade cancer immune escape.

Application of Support Vector Machines in Viral Biology

Novel experimental and sequencing techniques have led to an exponential explosion and spiraling of data in viral genomics. To analyse such data, rapidly gain information, and transform this information to knowledge, interdisciplinary approaches involving several different types of expertise are necessary. Machine learning has been in the forefront of providing models with increasing accuracy due to development of newer paradigms with strong fundamental bases. Support Vector Machines (SVM) is one such robust tool, based rigorously on statistical learning theory. SVM provides very high quality and robust solutions to classification and regression problems. Several studies in virology employ high performance tools including SVM for identification of potentially important gene and protein functions. This is mainly due to the highly beneficial aspects of SVM. In this chapter we briefly provide lucid and easy to understand details of SVM algorithms along with applications in virology.

Systems vaccinology and big data in the vaccine development chain

Systems vaccinology has proven a fascinating development in the last decade. Where traditionally vaccine development has been dominated by trial and error, systems vaccinology is a tool that provides novel and comprehensive understanding if properly used. Data sets retrieved from systems‐based studies endorse rational design and effective development of safe and efficacious vaccines. In this review we first describe different omics‐techniques that form the pillars of systems vaccinology. In the second part, the application of systems vaccinology in the different stages of vaccine development is described. Overall, this review shows that systems vaccinology has become an important tool anywhere in the vaccine development chain.

Preclinical and clinical development of neoantigen vaccines

Cancer neoantigens are antigens that result from somatic mutations present in individual cancers. Neoantigens are considered important targets for cancer immunotherapy because of their immunogenicity and lack of expression in normal tissues. Next-generation sequencing technologies and computational analysis have recently made neoantigen discovery possible. Although neoantigens are important targets of checkpoint blockade therapy, neoantigen vaccines are currently being investigated in preclinical models and early-phase human clinical trials. Preliminary results from these clinical trials demonstrate that dendritic cell, synthetic long peptide, and RNA-based neoantigen vaccines are safe, and capable of inducing both CD8+ and CD4+ neoantigen-specific T-cell responses. We and others are testing neoantigen vaccines in melanoma, breast cancer, non-small-cell lung cancer and other cancer types. Since cancers have evolved mechanisms to escape immune control, it is particularly important to study the efficacy of neoantigen vaccines in combination with other immunotherapies including checkpoint blockade therapy, and immune therapies targeting the immunosuppressive tumor microenvironment.

Epstein-Barr virus strain heterogeneity impairs human T-cell immunity

The Epstein-Barr virus (EBV) establishes lifelong infections in > 90% of the human population. Although contained as asymptomatic infection by the immune system in most individuals, EBV is associated with the pathogenesis of approximately 1.5% of all cancers in humans. Some of these EBV-associated tumors have been successfully treated by the infusion of virus-specific T-cell lines. Recent sequence analyses of a large number of viral isolates suggested that distinct EBV strains have evolved in different parts of the world. Here, we assessed the impact of such sequence variations on EBV-specific T-cell immunity. With the exceptions of EBNA2 and the EBNA3 family of proteins, an overall low protein sequence disparity of about 1% was noted between Asian viral isolates, including the newly characterized M81 strain, and the prototypic EBV type 1 and type 2 strains. However, when T-cell epitopes including their flanking regions were compared, a substantial proportion was found to be polymorphic in different EBV strains. Importantly, CD4+ and CD8+ T-cell clones specific for viral epitopes from one strain often showed diminished recognition of the corresponding epitopes in other strains. In addition, T-cell recognition of a conserved epitope was affected by amino acid exchanges within the epitope flanking region. Moreover, the CD8+ T-cell response against polymorphic epitopes varied between donors and often ignored antigen variants. These results demonstrate that viral strain heterogeneity may impair antiviral T-cell immunity and suggest that immunotherapeutic approaches against EBV should preferably target broad sets of conserved epitopes including their flanking regions.

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.

IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response

IL-17 cytokines are pro-inflammatory cytokines and are crucial in host defense against various microbes. Induction of these cytokines by microbial antigens has been investigated in the case of ischemic brain injury, gingivitis, candidiasis, autoimmune myocarditis, etc. In this study, we have investigated the ability of amino acid sequence of antigens to induce IL-17 response using machine-learning approaches. A total of 338 IL-17-inducing and 984 IL-17 non-inducing peptides were retrieved from Immune Epitope Database. 80% of the data were randomly selected as training dataset and rest 20% as validation dataset. To predict the IL-17-inducing ability of peptides/protein antigens, different sequence-based machine-learning models were developed. The performance of support vector machine (SVM) and random forest (RF) was compared with different parameters to predict IL-17-inducing epitopes (IIEs). The dipeptide composition-based SVM-model displayed an accuracy of 82.4% with Matthews correlation coefficient = 0.62 at polynomial (t = 1) kernel on 10-fold cross-validation and outperformed RF. Amino acid residues Leu, Ser, Arg, Asn, and Phe and dipeptides LL, SL, LK, IL, LI, NL, LR, FK, SF, and LE are abundant in IIEs. The present tool helps in the identification of IIEs using machine-learning approaches. The induction of IL-17 plays an important role in several inflammatory diseases, and identification of such epitopes would be of great help to the immunologists. It is freely available at http://metagenomics.iiserb.ac.in/IL17eScan/ and http://metabiosys.iiserb.ac.in/IL17eScan/.

Immunogenomics of gastrointestinal nematode infection in ruminants - breeding for resistance to produce food sustainably and safely

Gastrointestinal nematode (GIN) infection of ruminants represents a major health and welfare challenge for livestock producers worldwide. The emergence of anthelmintic resistance in important GIN species and the associated animal welfare concerns have stimulated interest in the development of alternative and more sustainable strategies aimed at the effective management of the impact of GINs. These integrative strategies include selective breeding using genetic/genomic tools, grazing management, biological control, nutritional supplementation, vaccination and targeted selective treatment. In this review, the logic of selecting for "resistance" to GIN infection as opposed to "resilience" or "tolerance" is discussed. This is followed by a review of the potential application of immunogenomics to genetic selection for animals that have the capacity to withstand the impact of GIN infection. Advances in relevant genomic technologies are highlighted together with how these tools can be advanced to support the integration of immunogenomic information into ruminant breeding programmes.

Computational prediction of immunodominant antigenic regions & potential protective epitopes for dengue vaccination

Background & objectives:

Epitope-based vaccines (EVs) are specific, safe and easy to produce. However, vaccine failure has been frequently reported due to variation within epitopic regions. Therefore, development of vaccines based on conserved epitopes may prevent such vaccine failure. This study was undertaken to identify highly conserved antigenic regions in the four dengue serotypes to produce an epitope-based dengue vaccine.

Methods:

Polyprotein sequences of all four dengue serotypes were collected and aligned using MAFFT multiple sequence alignment plugin with Geneious Pro v6.1. Consensus sequences of the polyproteins for all four dengue serotypes were designed and screened against experimentally proven epitopes to predict potential antigenic regions that are conserved among all four dengue serotypes.

Results:

The antigenic region VDRGWGNGCGLFGKG was 100 per cent conserved in the consensus polyprotein sequences of all four dengue serotypes. Fifteen experimentally proven epitopes were identical to the immunodominant antigenic region.

Interpretation & conclusions:

Computationally predicted antigenic regions may be considered for use in the development of EVs for protection against dengue virus. Such vaccines would be expected to provide protection against dengue infections caused by all dengue serotypes because these would contain antigenic regions highly conserved across those serotypes. Therefore, the immunodominant antigenic region (VDRGWGNGCGLFGKG) and 15 potential epitopes may be considered for use in dengue vaccines.

Immunomics: a 21st century approach to vaccine development for complex pathogens

Immunomics is a relatively new field of research which integrates the disciplines of immunology, genomics, proteomics, transcriptomics and bioinformatics to characterize the host-pathogen interface. Herein, we discuss how rapid advances in molecular immunology, sophisticated tools and molecular databases are facilitating in-depth exploration of the immunome. In our opinion, an immunomics-based approach presides over traditional antigen and vaccine discovery methods that have proved ineffective for highly complex pathogens such as the causative agents of malaria, tuberculosis and schistosomiasis that have evolved genetic and immunological host-parasite adaptations over time. By using an integrative multidisciplinary approach, immunomics offers enormous potential to advance 21st century antigen discovery and rational vaccine design against complex pathogens such as the Plasmodium parasite.

MHC-multimer guided isolation of neoepitopes specific T cells as a potent-personalized cancer treatment strategy

Analysis of genomic data from patient tumors provides valuable information as to potential T-cell targets such as neoepitopes. We developed an approach to characterize, isolate and utilize neoantigens-specific T cells using MHC/peptide tetramers from fresh tumor digests and peripheral blood. This bears important implications for the implementation of T cell-based immunotherapy.

ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins

Background

Proinflammatory immune response involves a complex series of molecular events leading to inflammatory reaction at a site, which enables host to combat plurality of infectious agents. It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by non-specific stimuli such as LPS. On counter with such antigens, the complex interaction of antigen presenting cells, T cells and inflammatory mediators like IL1α, IL1β, TNFα, IL12, IL18 and IL23 lead to proinflammatory immune response and further clearance of infection. In this study, we have tried to establish a relation between amino acid sequence of antigen and induction of proinflammatory response.

Results

A total of 729 experimentally-validated proinflammatory and 171 non-proinflammatory epitopes were obtained from IEDB database. The A, F, I, L and V amino acids and AF, FA, FF, PF, IV, IN dipeptides were observed as preferred residues in proinflammatory epitopes. Using the compositional and motif-based features of proinflammatory and non-proinflammatory epitopes, we have developed machine learning-based models for prediction of proinflammatory response of peptides. The hybrid of motifs and dipeptide-based features displayed best performance with MCC = 0.58 and an accuracy of 87.6 %.

Conclusion

The amino acid sequence-based features of peptides were used to develop a machine learning-based prediction tool for the prediction of proinflammatory epitopes. This is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations. The prediction model and tools for epitope mapping and similarity search are provided as a comprehensive web server which is freely available at http://metagenomics.iiserb.ac.in/proinflam/ and http://metabiosys.iiserb.ac.in/proinflam/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-016-0928-3) contains supplementary material, which is available to authorized users.

Autoimmune Responses to Soluble Aggregates of Amyloidogenic Proteins Involved in Neurodegenerative Diseases: Overlapping Aggregation Prone and Autoimmunogenic regions

Why do patients suffering from neurodegenerative diseases generate autoantibodies that selectively bind soluble aggregates of amyloidogenic proteins? Presently, molecular basis of interactions between the soluble aggregates and human immune system is unknown. By analyzing sequences of experimentally validated T-cell autoimmune epitopes, aggregating peptides, amyloidogenic proteins and randomly generated peptides, here we report overlapping regions that likely drive aggregation as well as generate autoantibodies against the aggregates. Sequence features, that make short peptides susceptible to aggregation, increase their incidence in human T-cell autoimmune epitopes by 4–6 times. Many epitopes are predicted to be significantly aggregation prone (aggregation propensities ≥10%) and the ones containing experimentally validated aggregating regions are enriched in hydrophobicity by 10–20%. Aggregate morphologies also influence Human Leukocyte Antigen (HLA) - types recognized by the aggregating regions containing epitopes. Most (88%) epitopes that contain amyloid fibril forming regions bind HLA-DR, while majority (63%) of those containing amorphous β-aggregating regions bind HLA-DQ. More than two-thirds (70%) of human amyloidogenic proteins contain overlapping regions that are simultaneously aggregation prone and auto-immunogenic. Such regions help clear soluble aggregates by generating selective autoantibodies against them. This can be harnessed for early diagnosis of proteinopathies and for drug/vaccine design against them.

Major histocompatibility complex linked databases and prediction tools for designing vaccines

Presently, the major histocompatibility complex (MHC) is receiving considerable interest owing to its remarkable role in antigen presentation and vaccine design. The specific databases and prediction approaches related to MHC sequences, structures and binding/nonbinding peptides have been aggressively developed in the past two decades with their own benchmarks and standards. Before using these databases and prediction tools, it is important to analyze why and how the tools are constructed along with their strengths and limitations. The current review presents insights into web-based immunological bioinformatics resources that include searchable databases of MHC sequences, epitopes and prediction tools that are linked to MHC based vaccine design, including population coverage analysis. In T cell epitope forecasts, MHC class I binding predictions are very accurate for most of the identified MHC alleles. However, these predictions could be further improved by integrating proteasome cleavage (in conjugation with transporter associated with antigen processing (TAP) binding) prediction, as well as T cell receptor binding prediction. On the other hand, MHC class II restricted epitope predictions display relatively low accuracy compared to MHC class I. To date, pan-specific tools have been developed, which not only deliver significantly improved predictions in terms of accuracy, but also in terms of the coverage of MHC alleles and supertypes. In addition, structural modeling and simulation systems for peptide-MHC complexes enable the molecular-level investigation of immune processes. Finally, epitope prediction tools, and their assessments and guidelines, have been presented to immunologist for the design of novel vaccine and diagnostics.