Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research

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.

Design of a multi-epitope vaccine (vme-VAC/MST-1) against cholera and vibriosis based on reverse vaccinology and immunoinformatics approaches

Vibriosis and cholera are serious diseases distributed worldwide and caused by six marine bacteria of the Vibrio genus. Thousands of deaths occur each year due to these illnesses, necessitating the development of new preventive measures. Presently, the existing cholera vaccine demonstrates an effectiveness of approximately 60%. Here we describe a new multi-epitope vaccine, 'vme-VAC/MST-1' based on vaccine targets identified by reverse vaccinology and epitopes predicted by immunoinformatics, two currently effective tools for predicting new vaccines for bacterial pathogens. The vaccine was designed to combat vibriosis and cholera by incorporating epitopes predicted for CTL, HTL, and B cells. These epitopes were identified from six vaccine targets revealed through subtractive genomics, combined with reverse vaccinology, and were further filtered using immunoinformatics approaches based on their predicted immunogenicity. To construct the vaccine, 28 epitopes (24 CTL/B and 4 HTL/B) were linked to the sequence of the cholera toxin B subunit adjuvant. In silico analyses indicate that the resulting immunogen is stable, soluble, non-toxic, and non-allergenic. Furthermore, it exhibits no homology to the host and demonstrates a strong capacity to elicit innate, B-cell, and T-cell immune responses. Our analysis suggests that it is likely to elicit immune reactions mediated through the TLR5 pathway, as evidenced by the molecular docking of the vaccine with the receptor, which revealed high affinity and a favorable reaction. Thus, vme-VAC/MST-1 is predicted to be a safe and effective solution against pathogenic Vibrio spp. However, further experimental analyses are required to measure the vaccine's effects In vivo.Communicated by Ramaswamy H. Sarma.

An immunoinformatics study reveals a new BoLA-DR-restricted CD4+ T cell epitopes on the Gag protein of bovine leukemia virus

Bovine leukemia virus (BLV) is the causative agent of enzootic bovine leucosis (EBL), which has been reported worldwide. The expression of viral structural proteins: surface glycoprotein (gp51) and three core proteins - p15 (matrix), p24 (capsid), and p12 (nucleocapsid) induce a strong humoral and cellular immune response at first step of infection. CD4+ T-cell activation is generally induced by bovine leukocyte antigen (BoLA) region- positive antigen-presenting cells (APC) after processing of an exogenous viral antigen. Limited data are available on the BLV epitopes from the core proteins recognized by CD4+ T-cells. Thus, immunoinformatic analysis of Gag sequences obtained from 125 BLV isolates from Poland, Canada, Pakistan, Kazakhstan, Moldova and United States was performed to identify the presence of BoLA-DRB3 restricted CD4+ T-cell epitopes. The 379 15-mer overlapping peptides spanning the entire Gag sequence were run in BoLA-DRB3 allele-binding regions using a BoLA-DRB- peptide binding affinity prediction algorithm. The analysis identified 22 CD4+ T-cell peptide epitopes of variable length ranging from 17 to 22 amino acids. The predicted epitopes interacted with 73 different BoLA-DRB3 alleles found in BLV-infected cattle. Importantly, two epitopes were found to be linked with high proviral load in PBMC. A majority of dominant and subdominant epitopes showed high conservation across different viral strains, and therefore could be attractive targets for vaccine development.

Targeting alternative splicing in cancer immunotherapy

Tumor immunotherapy has made great progress in cancer treatment but still faces several challenges, such as a limited number of targetable antigens and varying responses among patients. Alternative splicing (AS) is an essential process for the maturation of nearly all mammalian mRNAs. Recent studies show that AS contributes to expanding cancer-specific antigens and modulating immunogenicity, making it a promising solution to the above challenges. The organoid technology preserves the individual immune microenvironment and reduces the time/economic costs of the experiment model, facilitating the development of splicing-based immunotherapy. Here, we summarize three critical roles of AS in immunotherapy: resources for generating neoantigens, targets for immune-therapeutic modulation, and biomarkers to guide immunotherapy options. Subsequently, we highlight the benefits of adopting organoids to develop AS-based immunotherapies. Finally, we discuss the current challenges in studying AS-based immunotherapy in terms of existing bioinformatics algorithms and biological technologies.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

A peptide derived from HSP60 reduces proinflammatory cytokines and soluble mediators: a therapeutic approach to inflammation

Cytokines are secretion proteins that mediate and regulate immunity and inflammation. They are crucial in the progress of acute inflammatory diseases and autoimmunity. In fact, the inhibition of proinflammatory cytokines has been widely tested in the treatment of rheumatoid arthritis (RA). Some of these inhibitors have been used in the treatment of COVID-19 patients to improve survival rates. However, controlling the extent of inflammation with cytokine inhibitors is still a challenge because these molecules are redundant and pleiotropic. Here we review a novel therapeutic approach based on the use of the HSP60-derived Altered Peptide Ligand (APL) designed for RA and repositioned for the treatment of COVID-19 patients with hyperinflammation. HSP60 is a molecular chaperone found in all cells. It is involved in a wide diversity of cellular events including protein folding and trafficking. HSP60 concentration increases during cellular stress, for example inflammation. This protein has a dual role in immunity. Some HSP60-derived soluble epitopes induce inflammation, while others are immunoregulatory. Our HSP60-derived APL decreases the concentration of cytokines and induces the increase of FOXP3+ regulatory T cells (Treg) in various experimental systems. Furthermore, it decreases several cytokines and soluble mediators that are raised in RA, as well as decreases the excessive inflammatory response induced by SARS-CoV-2. This approach can be extended to other inflammatory diseases.

Immunoinformatics-Aided Design of a Peptide Based Multiepitope Vaccine Targeting Glycoproteins and Membrane Proteins against Monkeypox Virus

Monkeypox is a self-limiting zoonotic viral disease and causes smallpox-like symptoms. The disease has a case fatality ratio of 3-6% and, recently, a multi-country outbreak of the disease has occurred. The currently available vaccines that have provided immunization against monkeypox are classified as live attenuated vaccinia virus-based vaccines, which pose challenges of safety and efficacy in chronic infections. In this study, we have used an immunoinformatics-aided design of a multi-epitope vaccine (MEV) candidate by targeting monkeypox virus (MPXV) glycoproteins and membrane proteins. From these proteins, seven epitopes (two T-helper cell epitopes, four T-cytotoxic cell epitopes and one linear B cell epitopes) were finally selected and predicted as antigenic, non-allergic, interferon-γ activating and non-toxic. These epitopes were linked to adjuvants to design a non-allergic and antigenic candidate MPXV-MEV. Further, molecular docking and molecular dynamics simulations predicted stable interactions between predicted MEV and human receptor TLR5. Finally, the immune-simulation analysis showed that the candidate MPXV-MEV could elicit a human immune response. The results obtained from these in silico experiments are promising but require further validation through additional in vivo experiments.

Rare variants and HLA haplotypes associated in patients with neuromyelitis optica spectrum disorders

Neuromyelitis optica spectrum disorders (NMOSD) are rare, debilitating autoimmune diseases of the central nervous system. Many NMOSD patients have antibodies to Aquaporin-4 (AQP4). Prior studies show associations of NMOSD with individual Human Leukocyte Antigen (HLA) alleles and with mutations in the complement pathway and potassium channels. HLA allele associations with NMOSD are inconsistent between populations, suggesting complex relationships between the identified alleles and risk of disease. We used a retrospective case-control approach to identify contributing genetic variants in patients who met the diagnostic criteria for NMOSD and their unaffected family members. Potentially deleterious variants identified in NMOSD patients were compared to members of their families who do not have the disease and to existing databases of human genetic variation. HLA sequences from patients from Belgrade, Serbia, were compared to the frequency of HLA haplotypes in the general population in Belgrade. We analyzed exome sequencing on 40 NMOSD patients and identified rare inherited variants in the complement pathway and potassium channel genes. Haplotype analysis further detected two haplotypes, HLA-A*01, B*08, DRB1*03 and HLA-A*01, B*08, C*07, DRB1*03, DQB1*02, which were more prevalent in NMOSD patients than in unaffected individuals. In silico modeling indicates that HLA molecules within these haplotypes are predicted to bind AQP4 at several sites, potentially contributing to the development of autoimmunity. Our results point to possible autoimmune and neurodegenerative mechanisms that cause NMOSD, and can be used to investigate potential NMOSD drug targets.

Design of a multi-epitope vaccine against the pathogenic fungi Candida tropicalis using an in silico approach

Background

Candida tropicalis causes tropical invasive fungal infections, with a high mortality. This fungus has been found to be resistant to antifungal classes such as azoles, echinocandins, and polyenes in several studies. As a result, it is vital to identify novel approaches to prevent and treat C. tropicalis infections. In this study, an in silico technique was utilized to deduce and evaluate a powerful multivalent epitope-based vaccine against C. tropicalis, which targets the secreted aspartic protease 2 (SAP2) protein. This protein is implicated in virulence and host invasion.

Results

By focusing on the Sap2 protein, 11 highly antigenic, non-allergic, non-toxic, and conserved epitopes were identified. These were subsequently paired with RS09 and flagellin adjuvants, as well as a pan HLA DR-binding epitope (PADRE) sequence to create a vaccine candidate that elicited both cell-mediated and humoral immune responses. It was projected that the vaccine design would be soluble, stable, antigenic, and non-allergic. Ramachandran plot analysis was applied to validate the vaccine construct’s 3-dimensional model. The vaccine construct was tested (at 100 ns) using molecular docking and molecular dynamics simulations, which demonstrated that it can stably connect with MHC-I and Toll-like receptor molecules. Based on in silico studies, we have shown that the vaccine construct can be expressed in E. coli. We surmise that the vaccine design is unrelated to any human proteins, indicating that it is safe to use.

Conclusions

The vaccine design looks to be an effective option for preventing C. tropicalis infections, based on the outcomes of the studies. A fungal vaccine can be proposed as prophylactic medicine and could provide initial protection as sometimes diagnosis of infection could be challenging. However, more in vitro and in vivo research is needed to prove the efficacy and safety of the proposed vaccine design.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43141-022-00415-3.

In Silico Designed Multi-Epitope Immunogen “Tpme-VAC/LGCM-2022” May Induce Both Cellular and Humoral Immunity against Treponema pallidum Infection

Syphilis, a sexually transmitted infection caused by the spirochete Treponema pallidum, has seen a resurgence over the past years. T. pallidum is capable of early dissemination and immune evasion, and the disease continues to be a global healthcare burden. The purpose of this study was to design a multi-epitope immunogen through an immunoinformatics-based approach. Multi-epitope immunogens constitute carefully selected epitopes belonging to conserved and essential bacterial proteins. Several physico-chemical characteristics, such as antigenicity, allergenicity, and stability, were determined. Further, molecular docking and dynamics simulations were performed, ensuring binding affinity and stability between the immunogen and TLR-2. An in silico cloning was performed using the pET-28a(+) vector and codon adaptation for E. coli. Finally, an in silico immune simulation was performed. The in silico predictions obtained in this work indicate that this construct would be capable of inducing the requisite immune response to elicit protection against T. pallidum. Through this methodology we have designed a promising potential vaccine candidate for syphilis, namely Tpme-VAC/LGCM-2022. However, it is necessary to validate these findings in in vitro and in vivo assays.

Identification of alternative splicing-derived cancer neoantigens for mRNA vaccine development

Messenger RNA (mRNA) vaccines have shown great potential for anti-tumor therapy due to the advantages in safety, efficacy and industrial production. However, it remains a challenge to identify suitable cancer neoantigens that can be targeted for mRNA vaccines. Abnormal alternative splicing occurs in a variety of tumors, which may result in the translation of abnormal transcripts into tumor-specific proteins. High-throughput technologies make it possible for systematic characterization of alternative splicing as a source of suitable target neoantigens for mRNA vaccine development. Here, we summarized difficulties and challenges for identifying alternative splicing-derived cancer neoantigens from RNA-seq data and proposed a conceptual framework for designing personalized mRNA vaccines based on alternative splicing-derived cancer neoantigens. In addition, several points were presented to spark further discussion toward improving the identification of alternative splicing-derived cancer neoantigens.

Rapid Identification of MHCII-Binding Peptides Through Microsphere-Assisted Peptide Screening (MAPS)

CD4⁺ T cells play a vital role in the immune response, and their function requires T cell receptor (TCR) recognition of peptide epitopes presented in complex with MHC class II (MHCII) molecules. Consequently, rapidly identifying peptides that bind MHCII is critical to understanding and treating infectious disease, cancer, autoimmunity, allergy, and transplant rejection. Computational methods provide a fast, ultrahigh-throughput approach to predict MHCII-binding peptides but lack the accuracy of experimental methods. In contrast, experimental methods offer accurate, quantitative results at the expense of speed. To address the gap between these two approaches, we developed a high-throughput, semiquantitative experimental screening strategy termed microsphere-assisted peptide screening (MAPS). Here, we use the Zika virus envelope protein as an example to demonstrate the rapid identification of MHCII-binding peptides from a single pathogenic protein using MAPS. This process involves several key steps including peptide library design, peptide exchange into MHCII, peptide-MHCII loading onto microspheres, flow cytometry screening, and data analysis to identify peptides that bind to one or more MHCII alleles.

An immunoinformatics-based designed multi-epitope candidate vaccine (mpme-VAC/STV-1) against Mycoplasma pneumoniae

Pneumonia is a serious global health problem that accounts for over one million deaths annually. Among the main microorganisms causing pneumonia, Mycoplasma pneumoniae is one of the most common ones for which a vaccine is immediately required. In this context, a multi-epitope vaccine against this pathogen could be the best option that can induce effective immune response avoiding any serious adverse reactions. In this study, using an immunoinformatics approach we have designed a multi-epitope vaccine (mpme-VAC/STV-1) against M. pneumoniae. Our designed mpme-VAC/STV-1 is constructed using CTL (cytotoxic T lymphocyte), HTL (Helper T lymphocyte), and B-cell epitopes. These epitopes are selected from the core proteins of 88 M. pneumoniae genomes that were previously identified through reverse vaccinology approaches. The epitopes were filtered according to their immunogenicity, population coverage, and several other criteria. Sixteen CTL/B- and thirteen HTL/B- epitopes that belong to 5 core proteins were combined together through peptide linkers to develop the mpme-VAC/STV-1. The heat-labile enterotoxin from E. coli was used as an adjuvant. The designed mpme-VAC/STV-1 is predicted to be stable, non-toxic, non-allergenic, non-host homologous, and with required antigenic and immunogenic properties. Docking and molecular dynamic simulation of mpme-VAC/STV-1 shows that it can stimulate TLR2 pathway mediated immunogenic reactions. In silico cloning of mpme-VAC/STV-1 in an expression vector also shows positive results. Finally, the mpme-VAC/STV-1 also shows promising efficacy in immune simulation tests. Therefore, our constructed mpme-VAC/STV-1 could be a safe and effective multi-epitope vaccine for immunization against pneumonia. However, it requires further experimental and clinical validations.

A simple pan-specific RNN model for predicting HLA-II binding peptides

The prediction of human leukocyte antigen (HLA) class II binding peptides plays important roles in understanding the mechanism of immune recognition and developing effective epitope-based vaccines. In this work, gated recurrent unit (GRU)-based recurrent neural network (RNN) was successfully employed to establish a pan-specific prediction model of HLA-II-binding peptides by using only the HLA and peptide sequence information. In comparison with the existing pan-specific models of HLA-II-binding peptides, the GRU-based RNN model covered a broad spectrum of HLA-II molecules including 50 HLA-DR, 47 HLA-DQ, and 19 HLA-DP molecules with peptide lengths varying from 8 to 43 mers. The results demonstrated strong discriminant capabilities of the GRU-based RNN model, of which the AUC values were 0.92, 0.88, and 0.88 for the training, validation, and test sets, respectively. Also, the GRU-based model showed state-of-the-art performances in predicting the binding peptides with the length ranging from 8-32 mers, which provides an efficient method for predicting HLA-II-binding peptides of longer lengths in comparison with the available methods. Overall, taking the advantages of the RNN architecture, the established pan-specific GRU model can be used for predicting accurately the HLA-II-binding peptides in a simple and direct manner.

From Infection to Immunity: Understanding the Response to SARS-CoV2 Through In-Silico Modeling

Background

Immune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies.

Aim

Studies of the insurgence of immunity is at the core of both SARS-CoV-2 vaccine development and therapies. This paper attempts to describe the insurgence (and the span) of immunity in COVID-19 at the population level by developing an in-silico model. We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor, and age in an artificially infected population on the course of the disease.

Methods

We use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degrees of immune competence. We use a parameter set to reproduce known inter-patient variability and general epidemiological statistics.

Results

By assuming the viremia at day 30 of the infection to be the proxy for lethality, we reproduce in-silico several clinical observations and identify critical factors in the statistical evolution of the infection. In particular, we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection are a prognostic factor for determining the clinical outcome of the infection. Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of modeling the immune response at individual and population levels. The model developed can explain and interpret observed patterns of infection and makes verifiable temporal predictions. Within the limitations imposed by the simulated environment, this work proposes quantitatively that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population. In this work, we exemplify how computational modeling of immune response provides an important view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunosenescence.

Immunoinformatics aided design of peptide-based vaccines against ebolaviruses

Ebolaviruses are at the forefront of emerging viruses and present a very perceptible threat to global peace and harmony. In the last decade, Ebola virus disease has claimed more than 90% of total lives since its inception in 1976. Owing to multiple host immune evasion methods employed by the virus and the limitations of traditional vaccine development approaches, finding a globally effective and reliable counter measure against Ebola virus remains a challenge. Highly conserved peptide fragments belonging to critical viral proteins and containing multiple epitopes which have the capacity to interact with a wide array of HLA molecules present a viable solution. Immunoinformatics or computational immunology enables rapid screening and shortlisting of plausible epitopes with a high immunogenic potential, thus, supporting expeditious elucidation of efficacious vaccine candidates. In light of above facts, we describe a computational methodology in this chapter for identification of potent peptide vaccine candidates against human infecting viruses. By applying this stringent methodology, we were able to identify multiple, immunogenic ebolavirus peptide fragments which, after verification in animal models, might be considered as part of future synthetic Ebola vaccine.

Identification and molecular profiling of a novel homolog of cystatin C from rock bream (Oplegnathus fasciatus) evidencing its transcriptional sensitivity to pathogen infections

Cystatins are reversible inhibitors of cysteine proteases which show an omnipresent distribution in the life on earth. Although, cystatins with mammalian origin were well characterized and their roles in physiology were reported in details, those from teleostean origin are still underrepresented in literature. However, role of cystatins in fish physiology and immune defense is highlighted in few recent reports. In this study, a cystatin C holmologue from rock bream (Oplegnathus fasciatus); termed RbCytC was identified and molecularly characterized. The complete coding sequence of RbCytC was 387 bp in length, which codes for a polypeptide with 129 amino acids, including a signal peptide of 19 amino acids. The consensus cystatin family signatures including a G residue, turning up towards the N-terminus region, QVVAG motif, locating at the middle of the sequence and the PW motif at the c terminal region was found to be well conserved in RbCytC. Phylogenetic analysis using different cystatin counterparts affirmed the close evolutionary relationship of RbCytC with its teleostan homologs which belong to family 2 cystatins. The predicted molecular model of RbCytC resembled most of the structural features of empirically elucidated tertiary structures for chicken egg white cystatin. According to the qPCR assays, RbCytC showed detectable expression in all fish tissues used in the experiment, with markedly pronounced expression level in liver. Moreover, its basal mRNA expression was up-regulated in liver and spleen tissues by experimental rock bream iridovirus infection, whereas down regulated in the same tissues, post live Edwardsiella tarda injection. Collectively, outcomes of our study validate the structural homology of RbCytC with known cystatin C similitudes, especially those of teleosts and suggest its potential roles in proteolytic processes of rock bream physiology as well as in host immune defense mechanisms.

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.

Repertoire-scale determination of class II MHC peptide binding via yeast display improves antigen prediction

CD4⁺ helper T cells contribute important functions to the immune response during pathogen infection and tumor formation by recognizing antigenic peptides presented by class II major histocompatibility complexes (MHC-II). While many computational algorithms for predicting peptide binding to MHC-II proteins have been reported, their performance varies greatly. Here we present a yeast-display-based platform that allows the identification of over an order of magnitude more unique MHC-II binders than comparable approaches. These peptides contain previously identified motifs, but also reveal new motifs that are validated by in vitro binding assays. Training of prediction algorithms with yeast-display library data improves the prediction of peptide-binding affinity and the identification of pathogen-associated and tumor-associated peptides. In summary, our yeast-display-based platform yields high-quality MHC-II-binding peptide datasets that can be used to improve the accuracy of MHC-II binding prediction algorithms, and potentially enhance our understanding of CD4⁺ T cell recognition.

Identifying peptides that can bind major histocompatibility complex II (MHC-II) is important for our understanding of T cell immunity and specificity. Here the authors present a yeast-display library screening approach that identifies more potential binders than various reported algorithms to help expand our understanding for antigen presentation.

Immunoinformatics: Predicting Peptide-MHC Binding

Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.

HLA Class II Specificity Assessed by High-Density Peptide Microarray Interactions

The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery, something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC class I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Because MHC-II restricted CD4⁺ T cells control and orchestrated most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide-MHC-II interaction data are key to the solution of this problem, a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse, and representative data. In this study, we have used rHLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, in casu containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated, suitable of training predictors able to predict T cell epitopes and peptides eluted from human EBV-transformed B cells. Collectively, with a cost per peptide reduced to a few cents, combined with the flexibility of rHLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide-MHC-II binding data as well as improving MHC-II prediction tools.

T Cell Epitope Predictions

Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.

Rapid microsphere-assisted peptide screening (MAPS) of promiscuous MHCII-binding peptides in Zika virus envelope protein

Despite promising developments in computational tools, peptide-class II MHC (MHCII) binding predictors continue to lag behind their peptide-class I MHC counterparts. Consequently, peptide-MHCII binding is often evaluated experimentally using competitive binding assays, which tend to sacrifice throughput for quantitative binding detail. Here, we developed a high-throughput semiquantitative peptide-MHCII screening strategy termed microsphere-assisted peptide screening (MAPS) that aims to balance the accuracy of competitive binding assays with the throughput of computational tools. Using MAPS, we screened a peptide library from Zika virus envelope (E) protein for binding to four common MHCII alleles (DR1, DR4, DR7, DR15). Interestingly, MAPS revealed a significant overlap between peptides that promiscuously bind multiple MHCII alleles and antibody neutralization sites. This overlap was also observed for rotavirus outer capsid glycoprotein VP7, suggesting a deeper relationship between B cell and CD4+ T cell specificity which can facilitate the design of broadly protective vaccines to Zika and other viruses.

In silico design of a T-cell epitope vaccine candidate for parasitic helminth infection

Trichuris trichiura is a parasite that infects 500 million people worldwide, leading to colitis, growth retardation and Trichuris dysentery syndrome. There are no licensed vaccines available to prevent Trichuris infection and current treatments are of limited efficacy. Trichuris infections are linked to poverty, reducing children's educational performance and the economic productivity of adults. We employed a systematic, multi-stage process to identify a candidate vaccine against trichuriasis based on the incorporation of selected T-cell epitopes into virus-like particles. We conducted a systematic review to identify the most appropriate in silico prediction tools to predict histocompatibility complex class II (MHC-II) molecule T-cell epitopes. These tools were used to identify candidate MHC-II epitopes from predicted ORFs in the Trichuris genome, selected using inclusion and exclusion criteria. Selected epitopes were incorporated into Hepatitis B core antigen virus-like particles (VLPs). Bone marrow-derived dendritic cells and bone marrow-derived macrophages responded in vitro to VLPs irrespective of whether the VLP also included T-cell epitopes. The VLPs were internalized and co-localized in the antigen presenting cell lysosomes. Upon challenge infection, mice vaccinated with the VLPs+T-cell epitopes showed a significantly reduced worm burden, and mounted Trichuris-specific IgM and IgG2c antibody responses. The protection of mice by VLPs+T-cell epitopes was characterised by the production of mesenteric lymph node (MLN)-derived Th2 cytokines and goblet cell hyperplasia. Collectively our data establishes that a combination of in silico genome-based CD4+ T-cell epitope prediction, combined with VLP delivery, offers a promising pipeline for the development of an effective, safe and affordable helminth vaccine.

Neoantigen prediction from genomic and transcriptomic data

Tumor cells acquire distinct genetic characteristics as a means to survive and proliferate indefinitely. Changes in the genetic code can also translate in changes at the protein level, therefore creating a distinguishable signature unique for tumor cells, and absent in normal tissues. The presence of discernable moieties in tumors is particularly attractive because it represents a therapeutic opportunity to target tumor cells with specificity, while sparing non-transformed cells. In this sense neoantigens, short peptides containing a mutated sequence, are seen attractive therapeutic targets because of their confinement within tumor cells. Neoantigens can be recognized with high affinity and specificity by tumor-targeting T cells, which consequently can initiate a potent anti-tumor immune response. While this is feasible and it has been tested in numerous cancer types including melanoma, colon and lung cancer, to mention a few, there are technical challenges in identifying immunogenic neoantigens. In this manuscript we address the topic of neoantigen identification from tumor samples, offering a technical overview of the bioinformatic methods utilized to profile the neoantigenic load of tumor samples obtained from clinical specimens. This is meant to guide readers through the steps of neoantigen identification using genomic data, by suggesting tools and methods that can provide, with a high degree of confidence, reliable results for downstream in vitro and in vivo applications.

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.

Vaccine development for enteric bacterial pathogens: Where do we stand?

Gut infections triggered by pathogenic bacteria lead to most frequently occurring diarrhea in humans accounting for million deaths annually. Currently, only a few licensed vaccines are available against these pathogens for mostly travelers moving to diarrheal endemic areas. Besides commercialized vaccines, there are many formulations that are either under clinical or pre-clinical stages of development and despite several efforts to improve safety, immunogenicity and efficacy, none of them can confer long-term protective immunity, for which repeated booster doses are always recommended. Further in many countries, financial, social and political constraints have jeopardized vaccine development program against these pathogens that enforce us to gather knowledge on safety, tolerability, immunogenicity and protective efficacy regarding the same. In this review, we analyze safety and efficacy issues of vaccines against five major gut bacteria causing enteric infections. The article also simultaneously describes several barriers for vaccine development and further discusses possible strategies to enhance immunogenicity and efficacy.

Construction and Screening of an Antigen-Derived Peptide Library Displayed on Yeast Cell Surface for CD4+ T Cell Epitope Identification

Antigenic peptides (termed T cell epitopes) are assembled with major histocompatibility complex (MHC) molecules and presented on the surface of antigen-presenting cells (APCs) for T cell recognition. T cells engage these peptide-MHCs using T cell receptors (TCRs). Because T cell epitopes determine the specificity of a T cell immune response, their prediction and identification are important steps in developing peptide-based vaccines and immunotherapies. In recent years, a number of computational methods have been developed to predict T cell epitopes by evaluating peptide-MHC binding; however, the success of these methods has been limited for MHC class II (MHCII) due to the structural complexity of MHCII antigen presentation. Moreover, while peptide-MHC binding is a prerequisite for a T cell epitope, it alone is not sufficient. Therefore, T cell epitope identification requires further functional verification of the MHC-binding peptide using professional APCs, which are difficult to isolate, expand, and maintain. To address these issues, we have developed a facile, accurate, and high-throughput method for T cell epitope mapping by screening antigen-derived peptide libraries in complex with MHC protein displayed on yeast cell surface. Here, we use hemagglutinin and influenza A virus X31/A/Aichi/68 as examples to describe the key steps in identification of CD4+ T cell epitopes from a single antigenic protein and the entire genome of a pathogen, respectively. Methods for single-chain peptide MHC vector design, yeast surface display, peptide library generation in Escherichia coli, and functional screening in Saccharomyces cerevisiae are discussed.

Improved methods for predicting peptide binding affinity to MHC class II molecules

Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2.

An automated benchmarking platform for MHC class II binding prediction methods

Motivation

Computational methods for the prediction of peptide-MHC binding have become an integral and essential component for candidate selection in experimental T cell epitope discovery studies. The sheer amount of published prediction methods-and often discordant reports on their performance-poses a considerable quandary to the experimentalist who needs to choose the best tool for their research.

Results

With the goal to provide an unbiased, transparent evaluation of the state-of-the-art in the field, we created an automated platform to benchmark peptide-MHC class II binding prediction tools. The platform evaluates the absolute and relative predictive performance of all participating tools on data newly entered into the Immune Epitope Database (IEDB) before they are made public, thereby providing a frequent, unbiased assessment of available prediction tools. The benchmark runs on a weekly basis, is fully automated, and displays up-to-date results on a publicly accessible website. The initial benchmark described here included six commonly used prediction servers, but other tools are encouraged to join with a simple sign-up procedure. Performance evaluation on 59 data sets composed of over 10 000 binding affinity measurements suggested that NetMHCIIpan is currently the most accurate tool, followed by NN-align and the IEDB consensus method.

Availability and implementation

Weekly reports on the participating methods can be found online at: http://tools.iedb.org/auto_bench/mhcii/weekly/.

Contact

mniel@bioinformatics.dtu.dk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification of broadly reactive epitopes targeting major glycoproteins of Herpes simplex virus (HSV) 1 and 2 - An immunoinformatics analysis

Infections due to both HSV-1 and HSV-2 constitute an enormous health burden worldwide. Development of vaccine against herpes infections is a WHO supported public health priority. The viral glycoproteins have always been the major hotspots for vaccine designing. The present study was aimed to identify the conserved T and B cell epitopes in the major glycoproteins of both HSV-1 and HSV-2 via rigorous computational approaches. Identification of promiscuous T cell epitopes is of utmost importance in vaccine designing as such epitopes are capable of binding to several allelic forms of HLA and could generate effective immune response in the host. The criteria designed for identification of T and B cell epitopes was that it should be conserved in both HSV-1 and 2, promiscuous, have high affinity towards HLA alleles, should be located on the surface of glycoproteins and not be present in the glycosylation sites. This study led to the identification of 17 HLA Class II and 26 HLA Class I T cell epitopes, 9 linear and some conformational B cell epitopes. The identified T cell epitopes were further subjected to molecular docking analysis to analyze their binding patterns. Altogether we have identified 4 most promising regions in glycoproteins (2-gB, 1-gD, 1-gH) of HSV-1 and 2 which are promiscuous to HLA Class II alleles and have overlapping HLA Class I and B cell epitopes, which could be very useful in generating both arms of immune response in the host i.e. adaptive as well as humoral immunity. Further the authors propose the cross-validation of the identified epitopes in experimental settings for confirming their immunogenicity to support the present findings.

Mapping HLA-A2, -A3 and -B7 supertype-restricted T-cell epitopes in the ebolavirus proteome

Background

Ebolavirus (EBOV) is responsible for one of the most fatal diseases encountered by mankind. Cellular T-cell responses have been implicated to be important in providing protection against the virus. Antigenic variation can result in viral escape from immune recognition. Mapping targets of immune responses among the sequence of viral proteins is, thus, an important first step towards understanding the immune responses to viral variants and can aid in the identification of vaccine targets. Herein, we performed a large-scale, proteome-wide mapping and diversity analyses of putative HLA supertype-restricted T-cell epitopes of Zaire ebolavirus (ZEBOV), the most pathogenic species among the EBOV family.

Methods

All publicly available ZEBOV sequences (14,098) for each of the nine viral proteins were retrieved, removed of irrelevant and duplicate sequences, and aligned. The overall proteome diversity of the non-redundant sequences was studied by use of Shannon’s entropy. The sequences were predicted, by use of the NetCTLpan server, for HLA-A2, -A3, and -B7 supertype-restricted epitopes, which are relevant to African and other ethnicities and provide for large (~86%) population coverage. The predicted epitopes were mapped to the alignment of each protein for analyses of antigenic sequence diversity and relevance to structure and function. The putative epitopes were validated by comparison with experimentally confirmed epitopes.

Results & discussion

ZEBOV proteome was generally conserved, with an average entropy of 0.16. The 185 HLA supertype-restricted T-cell epitopes predicted (82 (A2), 37 (A3) and 66 (B7)) mapped to 125 alignment positions and covered ~24% of the proteome length. Many of the epitopes showed a propensity to co-localize at select positions of the alignment. Thirty (30) of the mapped positions were completely conserved and may be attractive for vaccine design. The remaining (95) positions had one or more epitopes, with or without non-epitope variants. A significant number (24) of the putative epitopes matched reported experimentally validated HLA ligands/T-cell epitopes of A2, A3 and/or B7 supertype representative allele restrictions. The epitopes generally corresponded to functional motifs/domains and there was no correlation to localization on the protein 3D structure. These data and the epitope map provide important insights into the interaction between EBOV and the host immune system.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4328-8) contains supplementary material, which is available to authorized users.

The generation of a simian adenoviral vectored HCV vaccine encoding genetically conserved gene segments to target multiple HCV genotypes

BACKGROUND

Hepatitis C virus (HCV) genomic variability is a major challenge to the generation of a prophylactic vaccine. We have previously shown that HCV specific T-cell responses induced by a potent T-cell vaccine encoding a single strain subtype-1b immunogen target epitopes dominant in natural infection. However, corresponding viral regions are highly variable at a population level, with a reduction in T-cell reactivity to these variants. We therefore designed and manufactured second generation simian adenovirus vaccines encoding genomic segments, conserved between viral genotypes and assessed these for immunogenicity.

METHODS

We developed a computer algorithm to identify HCV genomic regions that were conserved between viral subtypes. Conserved segments below a pre-defined diversity threshold spanning the entire HCV genome were combined to create novel immunogens (1000-1500 amino-acids), covering variation in HCV subtypes 1a and 1b, genotypes 1 and 3, and genotypes 1-6 inclusive. Simian adenoviral vaccine vectors (ChAdOx) encoding HCV conserved immunogens were constructed. Immunogenicity was evaluated in C57BL6 mice using panels of genotype-specific peptide pools in ex-vivo IFN-ϒ ELISpot and intracellular cytokine assays.

RESULTS

ChAdOx1 conserved segment HCV vaccines primed high-magnitude, broad, cross-reactive T-cell responses; the mean magnitude of total HCV specific T-cell responses was 1174 SFU/10⁶ splenocytes for ChAdOx1-GT1-6 in C57BL6 mice targeting multiple genomic regions, with mean responses of 935, 1474 and 1112 SFU/10⁶ against genotype 1a, 1b and 3a peptide panels, respectively. Functional assays demonstrated IFNg and TNFa production by vaccine-induced CD4 and CD8 T-cells. In silico analysis shows that conserved immunogens contain multiple epitopes, with many described in natural HCV infection, predicting immunogenicity in humans.

CONCLUSIONS

Simian adenoviral vectored vaccines encoding genetic segments that are conserved between all major HCV genotypes contain multiple T-cell epitopes and are highly immunogenic in pre-clinical models. These studies pave the way for the assessment of multi-genotypic HCV T-cell vaccines in humans.

Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design

BACKGROUND AND OBJECTIVE

T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system.

METHODS

In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance.

RESULTS

The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95.125% and an AUC of 0.987 on the HLA-DRB1*0101 allele of the Wang benchmark dataset.

CONCLUSIONS

The results indicate that the proposed prediction technique "GAPES" is a promising technique that will help researchers and scientists to predict the protein structure and it will assist them in the intelligent design of new epitope-based vaccines.

Conserved Binding Regions Provide the Clue for Peptide-Based Vaccine Development: A Chemical Perspective

Synthetic peptides have become invaluable biomedical research and medicinal chemistry tools for studying functional roles, i.e., binding or proteolytic activity, naturally-occurring regions’ immunogenicity in proteins and developing therapeutic agents and vaccines. Synthetic peptides can mimic protein sites; their structure and function can be easily modulated by specific amino acid replacement. They have major advantages, i.e., they are cheap, easily-produced and chemically stable, lack infectious and secondary adverse reactions and can induce immune responses via T- and B-cell epitopes. Our group has previously shown that using synthetic peptides and adopting a functional approach has led to identifying Plasmodium falciparumconserved regions binding to host cells. Conserved high activity binding peptides’ (cHABPs) physicochemical, structural and immunological characteristics have been taken into account for properly modifying and converting them into highly immunogenic, protection-inducing peptides (mHABPs) in the experimental Aotus monkey model. This article describes stereo–electron and topochemical characteristics regarding major histocompatibility complex (MHC)-mHABP-T-cell receptor (TCR) complex formation. Some mHABPs in this complex inducing long-lasting, protective immunity have been named immune protection-inducing protein structures (IMPIPS), forming the subunit components in chemically synthesized vaccines. This manuscript summarizes this particular field and adds our recent findings concerning intramolecular interactions (H-bonds or π-interactions) enabling proper IMPIPS structure as well as the peripheral flanking residues (PFR) to stabilize the MHCII-IMPIPS-TCR interaction, aimed at inducing long-lasting, protective immunological memory.

Plasmodium vivax Cell-Traversal Protein for Ookinetes and Sporozoites: Naturally Acquired Humoral Immune Response and B-Cell Epitope Mapping in Brazilian Amazon Inhabitants

The cell-traversal protein for ookinetes and sporozoites (CelTOS), a highly conserved antigen involved in sporozoite motility, plays an important role in the traversal of host cells during the preerythrocytic stage of Plasmodium species. Recently, it has been considered an alternative target when designing novel antimalarial vaccines against Plasmodium falciparum. However, the potential of Plasmodium vivax CelTOS as a vaccine target is yet to be explored. This study evaluated the naturally acquired immune response against a recombinant P. vivax CelTOS (PvCelTOS) (IgG and IgG subclass) in 528 individuals from Brazilian Amazon, as well as the screening of B-cell epitopes in silico and peptide assays to associate the breadth of antibody responses of those individuals with exposition and/or protection correlates. We show that PvCelTOS is naturally immunogenic in Amazon inhabitants with 94 individuals (17.8%) showing specific IgG antibodies against the recombinant protein. Among responders, the IgG reactivity indexes (RIs) presented a direct correlation with the number of previous malaria episodes (p = 0.003; r = 0.315) and inverse correlation with the time elapsed from the last malaria episode (p = 0.031; r = −0.258). Interestingly, high responders to PvCelTOS (RI > 2) presented higher number of previous malaria episodes, frequency of recent malaria episodes, and ratio of cytophilic/non-cytophilic antibodies than low responders (RI < 2) and non-responders (RI < 1). Moreover, a high prevalence of the cytophilic antibody IgG1 over all other IgG subclasses (p < 0.0001) was observed. B-cell epitope mapping revealed five immunogenic regions in PvCelTOS, but no associations between the specific IgG response to peptides and exposure/protection parameters were found. However, the epitope (PvCelTOS_I136-E143) was validated as a main linear B-cell epitope, as 92% of IgG responders to PvCelTOS were also responders to this peptide sequence. This study describes for the first time the natural immunogenicity of PvCelTOS in Amazon individuals and identifies immunogenic regions in a full-length protein. The IgG magnitude was mainly composed of cytophilic antibodies (IgG1) and associated with recent malaria episodes. The data presented in this paper add further evidence to consider PvCelTOS as a vaccine candidate.

A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer

Due to advancement in sequencing technology, genomes of thousands of cancer tissues or cell-lines have been sequenced. Identification of cancer-specific epitopes or neoepitopes from cancer genomes is one of the major challenges in the field of immunotherapy or vaccine development. This paper describes a platform Cancertope, developed for designing genome-based immunotherapy or vaccine against a cancer cell. Broadly, the integrated resources on this platform are apportioned into three precise sections. First section explains a cancer-specific database of neoepitopes generated from genome of 905 cancer cell lines. This database harbors wide range of epitopes (e.g., B-cell, CD8+ T-cell, HLA class I, HLA class II) against 60 cancer-specific vaccine antigens. Second section describes a partially personalized module developed for predicting potential neoepitopes against a user-specific cancer genome. Finally, we describe a fully personalized module developed for identification of neoepitopes from genomes of cancerous and healthy cells of a cancer-patient. In order to assist the scientific community, wide range of tools are incorporated in this platform that includes screening of epitopes against human reference proteome (http://www.imtech.res.in/raghava/cancertope/).

TepiTool: A Pipeline for Computational Prediction of T Cell Epitope Candidates

Computational prediction of T cell epitope candidates is currently being used in several applications including vaccine discovery studies, development of diagnostics, and removal of unwanted immune responses against protein therapeutics. There have been continuous improvements in the performance of MHC binding prediction tools, but their general adoption by immunologists has been slow due to the lack of user-friendly interfaces and guidelines. Current tools only provide minimal advice on what alleles to include, what lengths to consider, how to deal with homologous peptides, and what cutoffs should be considered relevant. This protocol provides step-by-step instructions with necessary recommendations for prediction of the best T cell epitope candidates with the newly developed online tool called TepiTool. TepiTool, which is part of the Immune Epitope Database (IEDB), provides some of the top MHC binding prediction algorithms for number of species including humans, chimpanzees, bovines, gorillas, macaques, mice, and pigs. The TepiTool is freely accessible at http://tools.iedb.org/tepitool/. © 2016 by John Wiley & Sons, Inc.

PinaColada: peptide-inhibitor ant colony ad-hoc design algorithm

MOTIVATION

Design of protein-protein interaction (PPI) inhibitors is a major challenge in Structural Bioinformatics. Peptides, especially short ones (5-15 amino acid long), are natural candidates for inhibition of protein-protein complexes due to several attractive features such as high structural compatibility with the protein binding site (mimicking the surface of one of the proteins), small size and the ability to form strong hotspot binding connections with the protein surface. Efficient rational peptide design is still a major challenge in computer aided drug design, due to the huge space of possible sequences, which is exponential in the length of the peptide, and the high flexibility of peptide conformations.

RESULTS

In this article we present PinaColada, a novel computational method for the design of peptide inhibitors for protein-protein interactions. We employ a version of the ant colony optimization heuristic, which is used to explore the exponential space ([Formula: see text]) of length n peptide sequences, in combination with our fast robotics motivated PepCrawler algorithm, which explores the conformational space for each candidate sequence. PinaColada is being run in parallel, on a DELL PowerEdge 2.8 GHZ computer with 20 cores and 256 GB memory, and takes up to 24 h to design a peptide of 5-15 amino acids length.

AVAILABILITY AND IMPLEMENTATION

An online server available at: http://bioinfo3d.cs.tau.ac.il/PinaColada/.

CONTACT

danielza@post.tau.ac.il; wolfson@tau.ac.il.

Software tools for simultaneous data visualization and T cell epitopes and disorder prediction in proteins

We have developed EpDis and MassPred, extendable open source software tools that support bioinformatic research and enable parallel use of different methods for the prediction of T cell epitopes, disorder and disordered binding regions and hydropathy calculation. These tools offer a semi-automated installation of chosen sets of external predictors and an interface allowing for easy application of the prediction methods, which can be applied either to individual proteins or to datasets of a large number of proteins. In addition to access to prediction methods, the tools also provide visualization of the obtained results, calculation of consensus from results of different methods, as well as import of experimental data and their comparison with results obtained with different predictors. The tools also offer a graphical user interface and the possibility to store data and the results obtained using all of the integrated methods in the relational database or flat file for further analysis. The MassPred part enables a massive parallel application of all integrated predictors to the set of proteins. Both tools can be downloaded from http://bioinfo.matf.bg.ac.rs/home/downloads.wafl?cat=Software. Appendix A includes the technical description of the created tools and a list of supported predictors.

Major Histocompatibility Complex and Malaria: Focus on Plasmodium vivax Infection

The importance of host and parasite genetic factors in malaria resistance or susceptibility has been investigated since the middle of the last century. Nowadays, of all diseases that affect man, malaria still plays one of the highest levels of selective pressure on human genome. Susceptibility to malaria depends on exposure profile, epidemiological characteristics, and several components of the innate and adaptive immune system that influences the quality of the immune response generated during the Plasmodium lifecycle in the vertebrate host. But it is well known that the parasite's enormous capacity of genetic variation in conjunction with the host genetics polymorphism is also associated with a wide spectrum of susceptibility degrees to complicated or severe forms of the disease. In this scenario, variations in genes of the major histocompatibility complex (MHC) associated with host resistance or susceptibility to malaria have been identified and used as markers in host-pathogen interaction studies, mainly those evaluating the impact on the immune response, acquisition of resistance, or increased susceptibility to infection or vulnerability to disease. However, due to the intense selective pressure, number of cases, and mortality rates, the majority of the reported associations reported concerned Plasmodium falciparum malaria. Studies on the MHC polymorphism and its association with Plasmodium vivax, which is the most widespread Plasmodium and the most prevalent species outside the African continent, are less frequent but equally important. Despite punctual contributions, there are accumulated evidences of human genetic control in P. vivax infection and disease. Herein, we review the current knowledge in the field of MHC and derived molecules (HLA Class I, Class II, TNF-α, LTA, BAT1, and CTL4) regarding P. vivax malaria. We discuss particularly the results of P. vivax studies on HLA class I and II polymorphisms in relation to host susceptibility, naturally acquired immune response against specific antigens and the implication of this knowledge to overcome the parasite immune evasion. Finally, the potential impact of such polymorphisms on the development of vaccine candidate antigens against P. vivax will be studied.

A computational method for identification of vaccine targets from protein regions of conserved human leukocyte antigen binding

Background

Computational methods for T cell-based vaccine target discovery focus on selection of highly conserved peptides identified across pathogen variants, followed by prediction of their binding of human leukocyte antigen molecules. However, experimental studies have shown that T cells often target diverse regions in highly variable viral pathogens and this diversity may need to be addressed through redefinition of suitable peptide targets.

Methods

We have developed a method for antigen assessment and target selection for polyvalent vaccines, with which we identified immune epitopes from variable regions, where all variants bind HLA. These regions, although variable, can thus be considered stable in terms of HLA binding and represent valuable vaccine targets.

Results

We applied this method to predict CD8⁺ T-cell targets in influenza A H7N9 hemagglutinin and significantly increased the number of potential vaccine targets compared to the number of targets discovered using the traditional approach where low-frequency peptides are excluded.

Conclusions

We developed a webserver with an intuitive visualization scheme for summarizing the T cell-based antigenic potential of any given protein or proteome using human leukocyte antigen binding predictions and made a web-accessible software implementation freely available at http://met-hilab.cbs.dtu.dk/blockcons/.

Bioinformatic Analysis of Antigenic Proteins in Celiac Disease

Investigation of the chemistry of the gliadin proteins has played an important role in our comprehension of how celiac disease (CoD) develops and progresses as a response to challenge with this immune stimulus. Studies in this area have implicated gut enzymes, tissue transglutaminase-mediated deamidation, and peptide binding affinity for the HLA-DQ2 and DQ8 molecules in disease pathogenesis. As the number and availability of prolamin sequences increases, the complexity and cost of laboratory analysis will similarly increase. Freely available tools to bioinformatically analyze candidate protein sequences can be employed as a low-cost, high-return preliminary mechanism to focus one's laboratory analyses on the most rewarding sequences. This chapter describes the use of antigen prediction, deamidation prediction, and protease cleavage prediction as may be applied to CoD research.

Machine Learning Methods for Predicting HLA-Peptide Binding Activity

As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA–peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA–peptide binding prediction. We also summarize the descriptors based on which the HLA–peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA–peptide binding prediction method based on network analysis.

A limit to the divergent allele advantage model supported by variable pathogen recognition across HLA-DRB1 allele lineages

Genetic diversity in human leukocyte antigen (HLA) molecules is thought to have arisen from the co‐evolution between host and pathogen and maintained by balancing selection. Heterozygote advantage is a common proposed scenario for maintaining high levels of diversity in HLA genes, and extending from this, the divergent allele advantage (DAA) model suggests that individuals with more divergent HLA alleles bind and recognize a wider array of antigens. While the DAA model seems biologically suitable for driving HLA diversity, there is likely an upper threshold to the amount of sequence divergence. We used peptide‐binding and pathogen‐recognition capacity of DRB1 alleles as a model to further explore the DAA model; within the DRB1 locus, we examined binding predictions based on two distinct phylogenetic groups (denoted group A and B) previously identified based on non‐peptide‐binding region (PBR) nucleotide sequences. Predictions in this study support that group A allele and group B allele lineages have contrasting binding/recognition capacity, with only the latter supporting the DAA model. Furthermore, computer simulations revealed an inconsistency in the DAA model alone with observed extent of polymorphisms, supporting that the DAA model could only work effectively in combination with other mechanisms. Overall, we support that the mechanisms driving HLA diversity are non‐exclusive. By investigating the relationships among HLA alleles, and pathogens recognized, we can provide further insights into the mechanisms on how humans have adapted to infectious diseases over time.