Major histocompatibility complex class I binding predictions as a tool in epitope discovery

HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders

HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders in the antigens to develop immunotherapies and vaccines. Current prediction methods are often limited by their reliance on the small datasets. This study presents HLA-DR4Pred2, developed on a large dataset containing 12,676 binders and an equal number of non-binders. It's an improved version of HLA-DR4Pred, which was trained on a small dataset, containing 576 binders and an equal number of non-binders. All models were trained, optimized, and tested on 80 % of the data using five-fold cross-validation and evaluated on the remaining 20 %. A range of machine learning techniques was employed, achieving maximum AUROC of 0.90 and 0.87, using composition and binary profile features, respectively. The performance of the composition-based model increased to 0.93, when combined with BLAST search. Additionally, models developed on the realistic dataset containing 12,676 binders and 86,300 non-binders, achieved a maximum AUROC of 0.99. Our proposed method outperformed existing methods when we compared the performance of our best model to that of existing methods on the independent dataset. Finally, we developed a standalone tool and a webserver for HLADR4Pred2, enabling the prediction, design, and virtual scanning of HLA-DRB1*04:01 binding peptides, and we also released a Python package available on the Python Package Index (https://webs.iiitd.edu.in/raghava/hladr4pred2/; https://github.com/raghavagps/hladr4pred2; https://pypi.org/project/hladr4pred2/).

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model

Peptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this field. We developed ConvNeXt-MHC, a method for predicting MHC-I-peptide binding affinity. It introduces a degenerate encoding approach to enhance well-established panspecific methods and integrates transfer learning and semi-supervised learning methods into the cutting-edge deep learning framework ConvNeXt. Comprehensive benchmark results demonstrate that ConvNeXt-MHC outperforms state-of-the-art methods in terms of accuracy. We expect that ConvNeXt-MHC will help us foster new discoveries in the field of immunoinformatics in the distant future. We constructed a user-friendly website at http://www.combio-lezhang.online/predict/, where users can access our data and application.

A Continuous Extension of Gene Set Enrichment Analysis Using the Likelihood Ratio Test Statistics Identifies Vascular Endothelial Growth Factor as a Candidate Pathway for Alzheimer's Disease via ITGA5

BACKGROUND

Alzheimer's disease (AD) involves brain neuropathologies such as amyloid plaque and hyperphosphorylated tau tangles and is accompanied by cognitive decline. Identifying the biological mechanisms underlying disease onset and progression based on quantifiable phenotypes will help understand disease etiology and devise therapies.

OBJECTIVE

Our objective was to identify molecular pathways associated with hallmark AD biomarkers and cognitive status, accounting for variables such as age, sex, education, and APOE genotype.

METHODS

We introduce a pathway-based statistical approach, extending the gene set likelihood ratio test to continuous phenotypes. We first analyzed independently each of the three phenotypes (amyloid-β, tau, cognition) using continuous gene set likelihood ratio tests to account for covariates, including age, sex, education, and APOE genotype. The analysis involved 634 subjects with data available for all three phenotypes, allowing for the identification of common pathways.

RESULTS

We identified 14 pathways significantly associated with amyloid-β; 5 associated with tau; and 174 associated with cognition, which showed a larger number of pathways compared to biomarkers. A single pathway, vascular endothelial growth factor receptor binding (VEGF-RB), exhibited associations with all three phenotypes. Mediation analysis showed that among the VEGF-RB family genes, ITGA5 mediates the relationship between cognitive scores and pathological biomarkers.

CONCLUSIONS

We presented a new statistical approach linking continuous phenotypes, gene expression across pathways, and covariates like sex, age, and education. Our results reinforced VEGF RB2's role in AD cognition and demonstrated ITGA5's significant role in mediating the AD pathology-cognition connection.

A Continuous Extension of Gene Set Enrichment Analysis using the Likelihood Ratio Test Statistics Identifies VEGF as a Candidate Pathway for Alzheimer's disease

Background

Alzheimer's disease involves brain pathologies such as amyloid plaque depositions and hyperphosphorylated tau tangles and is accompanied by cognitive decline. Identifying the biological mechanisms underlying disease onset and progression based on quantifiable phenotypes will help understand the disease etiology and devise therapies.

Objective

Our objective was to identify molecular pathways associated with AD biomarkers (Amyloid-β and tau) and cognitive status (MMSE) accounting for variables such as age, sex, education, and APOE genotype.

Methods

We introduce a novel pathway-based statistical approach, extending the gene set likelihood ratio test to continuous phenotypes. We first analyzed independently each of the three phenotypes (Amyloid-β, tau, cognition), using continuous gene set likelihood ratio tests to account for covariates, including age, sex, education, and APOE genotype. The analysis involved a large sample size with data available for all three phenotypes, allowing for the identification of common pathways.

Results

We identified 14 pathways significantly associated with Amyloid-β, 5 associated with tau, and 174 associated with MMSE. Surprisingly, the MMSE outcome showed a larger number of significant pathways compared to biomarkers. A single pathway, vascular endothelial growth factor receptor binding (VEGF-RB), exhibited significant associations with all three phenotypes.

Conclusions

The study's findings highlight the importance of the VEGF signaling pathway in aging in AD. The complex interactions within the VEGF signaling family offer valuable insights for future therapeutic interventions.

Quantitative measurement of the requirement of diverse protein degradation pathways in MHC class I peptide presentation

Peptides from degradation of intracellular proteins are continuously displayed by major histocompatibility complex (MHC) class I. To better understand origins of these peptides, we performed a comprehensive census of the class I peptide repertoire in the presence and absence of ubiquitin-proteasome system (UPS) activity upon developing optimized methodology to enrich for and quantify these peptides. Whereas most class I peptides are dependent on the UPS for their generation, a surprising 30%, enriched in peptides of mitochondrial origin, appears independent of the UPS. A further ~10% of peptides were found to be dependent on the proteasome but independent of ubiquitination for their generation. Notably, clinically achievable partial inhibition of the proteasome resulted in display of atypical peptides. Our results suggest that generation of MHC class I•peptide complexes is more complex than previously recognized, with UPS-dependent and UPS-independent components; paradoxically, alternative protein degradation pathways also generate class I peptides when canonical pathways are impaired.

Tumor antigen-unbiased variable epitope library contains mimotopes with antitumor effect in a mouse model of breast cancer

Breast cancer is one of the leading causes of death that affects the female population worldwide. Despite advances in treatments and a greater understanding of the disease, there are still difficulties in successfully treating patients. Currently, the main challenge in the field of cancer vaccines is antigenic variability which can reduce antigen-specific T- cell response efficacy. The search for and validation of immunogenic antigen targets increased dramatically over the past few decades and, with the advent of modern sequencing techniques, permitting the fast and accurate identification of the neoantigen landscape of tumor cells, will undoubtedly continue to grow exponentially for years to come. We have previously implemented Variable Epitope Libraries (VEL) as an unconventional vaccine strategy in preclinical models and for identifying and selecting mutant epitope variants. Here, we used an alanine-based sequence to generate a 9-mer VEL-like combinatorial mimotope library G3d as a new class of vaccine immunogen. An in silico analysis of the 16,000 G3d-derived sequences revealed potential MHC-I binders and immunogenic mimotopes. We demonstrated the antitumor effect of treatment with G3d in the 4T1 murine model of breast cancer. Moreover, two different T cell proliferation screening assays against a panel of randomly selected G3d-derived mimotopes allowed the isolation of both stimulatory and inhibitory mimotopes showing differential therapeutic vaccine efficacy. Thus, the mimotope library is a promising vaccine immunogen and a reliable source for isolating molecular cancer vaccine components.

Role of MHC class I pathways in Mycobacterium tuberculosis antigen presentation

MHC class I antigen processing is an underappreciated area of nonviral host–pathogen interactions, bridging both immunology and cell biology, where the pathogen’s natural life cycle involves little presence in the cytoplasm. The effective response to MHC-I foreign antigen presentation is not only cell death but also phenotypic changes in other cells and stimulation of the memory cells ready for the next antigen reoccurrence. This review looks at the MHC-I antigen processing pathway and potential alternative sources of the antigens, focusing on Mycobacterium tuberculosis (Mtb) as an intracellular pathogen that co-evolved with humans and developed an array of decoy strategies to survive in a hostile environment by manipulating host immunity to its own advantage. As that happens via the selective antigen presentation process, reinforcement of the effective antigen recognition on MHC-I molecules may stimulate subsets of effector cells that act earlier and more locally. Vaccines against tuberculosis (TB) could potentially eliminate this disease, yet their development has been slow, and success is limited in the context of this global disease’s spread. This review’s conclusions set out potential directions for MHC-I-focused approaches for the next generation of vaccines.

A Multistage Antigen Complex Epera013 Promotes Efficient and Comprehensive Immune Responses in BALB/c Mice

Tuberculosis (TB) remains a serious global health problem. Despite the widespread use of the Mycobacterium bovis bacillus Calmette-Guerin (BCG) vaccine, the primary factor for the TB pandemic and deaths is adult TB, which mainly result from endogenous reactivation of latent Mycobacterium tuberculosis (MTB) infection. Improved new TB vaccines with eligible safety and long-lasting protective efficacy remains a crucial step toward the prevention and control of TB. In this study, five immunodominant antigens, including three early secreted antigens and two latency associated antigens, were used to construct a single recombinant fusion protein (Epera013f) and a protein mixture (Epera013m). When formulated with aluminum adjuvant, the two subunit vaccines Epera013m and Epera013f were administered to BALB/c mice. The humoral immune responses, cellular responses and MTB growth inhibiting capacity elicited after Epera013m and Epera013f immunization were analyzed. In the present study, we demonstrated that both the Epera013f and Epera013m were capable of inducing a considerable immune response and protective efficacy against H37Rv infection compared with BCG groups. In addition, Epera013f generated a more comprehensive and balanced immune status, including Th1, Th2 and innate immune response, over Epera013f and BCG. The multistage antigen complex Epera013f possesses considerable immunogenicity and protective efficacy against MTB infection ex vivo indicating its potential and promising applications in further TB vaccine development.

The Intracellular Proteome as a Source for Novel Targets in CAR-T and T-Cell Engagers-Based Immunotherapy

The impressive clinical success of cancer immunotherapy has motivated the continued search for new targets that may serve to guide potent effector functions in an attempt to efficiently kill malignant cells. The intracellular proteome is an interesting source for such new targets, such as neo-antigens and others, with growing interest in their application for cell-based immunotherapies. These intracellular-derived targets are peptides presented by MHC class I molecules on the cell surface of malignant cells. These disease-specific class I HLA-peptide complexes can be targeted by specific TCRs or by antibodies that mimic TCR-specificity, termed TCR-like (TCRL) antibodies. Adoptive cell transfer of TCR engineered T cells and T-cell-receptor-like based CAR-T cells, targeted against a peptide-MHC of interest, are currently tested as cancer therapeutic agents in pre-clinical and clinical trials, along with soluble TCR- and TCRL-based agents, such as immunotoxins and bi-specific T cell engagers. Targeting the intracellular proteome using TCRL- and TCR-based molecules shows promising results in cancer immunotherapy, as exemplified by the success of the anti-gp100/HLA-A2 TCR-based T cell engager, recently approved by the FDA for the treatment of unresectable or metastatic uveal melanoma. This review is focused on the selection and isolation processes of TCR- and TCRL-based targeting moieties, with a spotlight on pre-clinical and clinical studies, examining peptide-MHC targeting agents in cancer immunotherapy.

Improving MHC class I antigen-processing predictions using representation learning and cleavage site-specific kernels

In this work, we propose a new deep-learning model, MHCrank, to predict the probability that a peptide will be processed for presentation by MHC class I molecules. We find that the performance of our model is significantly higher than that of two previously published baseline methods: MHCflurry and netMHCpan. This improvement arises from utilizing both cleavage site-specific kernels and learned embeddings for amino acids. By visualizing site-specific amino acid enrichment patterns, we observe that MHCrank's top-ranked peptides exhibit enrichments at biologically relevant positions and are consistent with previous work. Furthermore, the cosine similarity matrix derived from MHCrank's learned embeddings for amino acids correlates highly with physiochemical properties that have been experimentally demonstrated to be instrumental in determining a peptide's favorability for processing. Altogether, the results reported in this work indicate that MHCrank demonstrates strong performance compared with existing methods and could have vast applicability in aiding drug and vaccine development.

Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution

Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date MHC I binding prediction tools developed over the last decade, thoroughly evaluating feature representation methods, prediction algorithms and model training strategies on a benchmark dataset from Immune Epitope Database. A common limitation was identified during the review that all existing tools can only handle a fixed peptide sequence length. To overcome this limitation, we developed a bilateral and variable long short-term memory (BVLSTM)-based approach, named BVLSTM-MHC. It is the first variable-length MHC class I binding predictor. In comparison to the 10 mainstream prediction tools on an independent validation dataset, BVLSTM-MHC achieved the best performance in six out of eight evaluated metrics. A web server based on the BVLSTM-MHC model was developed to enable accurate and efficient MHC class I binder prediction in human, mouse, macaque and chimpanzee.

Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein-Barr virus

BACKGROUND

The ongoing search for viable treatment options to curtail Epstein Barr Virus (EBV) pathogenicity has necessitated a paradigmatic shift towards the design of peptide-based vaccines. Potential B-cell and T-cell epitopes were predicted for nine antigenic EBV proteins that mediate epithelial cell-attachment and spread, capsid self-assembly, DNA replication and processivity.

METHODS

Predictive algorithms incorporated in the Immune Epitope Database (IEDB) resources were used to determine potential B-cell epitopes based on their physicochemical attributes. These were combined with a string-kernel method and an antigenicity predictive AlgPred tool to enhance accuracy in the end-point selection of highly potential antigenic EBV B-cell epitopes. NetCTL 1.2 algorithms enabled the prediction of probable T-cell epitopes which were structurally modeled and subjected to blind peptide-protein docking with HLA-A*02:01. All-atom molecular dynamics (MD) simulation and Molecular Mechanics Generalized-Born Surface Area methods were used to investigate interaction dynamics and affinities of predicted T-cell peptide-protein complexes.

RESULTS

Computational predictions and sequence overlapping analysis yielded 18 linear (continuous) and discontinuous (conformational) subunit epitopes from the antigenic proteins with characteristic surface accessibility, flexibility and antigenicity, and predictive scores above the threshold value (1) set. A novel site was identified on HLA-A*02:01 with preferential affinity binding for modeled BMRF2, BXLF1 and BGLF4 T-cell epitopes. Interaction dynamics and energies were also computed in addition to crucial residues that mediated complex formation and stability.

CONCLUSION

This study implemented an integrative meta-analytical approach to model highly probable B-cell and T-cell epitopes as potential peptide-vaccine candidates for the treatment of EBV-related diseases.

Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules

Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.

Identification of SARS-CoV-2 CTL epitopes for development of a multivalent subunit vaccine for COVID-19

An immunoinformatics-based approach was used to identify potential multivalent subunit CTL vaccine candidates for SARS-CoV-2. Criteria for computational screening included antigen processing, antigenicity, allergenicity, and toxicity. A total of 2604 epitopes were found to be strong binders to MHC class I molecules when analyzed using IEDB tools. Further testing for antigen processing yielded 826 peptides of which 451 were 9-mers that were analyzed for potential antigenicity. Antigenic properties were predicted for 102 of the 451 peptides. Further assessment for potential allergenicity and toxicity narrowed the number of candidate CTL epitopes to 50 peptide sequences, 45 of which were present in all strains of SARS-CoV-2 that were tested. The predicted CTL epitopes were then tested to eliminate those with MHC class II binding potential, a property that could induce hyperinflammatory responses mediated by T_H2 cells in immunized hosts. Eighteen of the 50 epitopes did not show class II binding potential. To our knowledge this is the first comprehensive analysis on the proteome of SARS-CoV-2 for prediction of CTL epitopes lacking binding properties that could stimulate unwanted T_H2 responses. Future studies will be needed to assess these epitopes as multivalent subunit vaccine candidates which stimulate protective CTL responses against SARS-COV-2.

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.

A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.

Development of tumour peptide vaccines: From universalization to personalization

In recent years, relying on the human immune system to kill tumour cells has become an effective means of cancer treatment. The development of peptide vaccines, which not only break the immune tolerance of a tumour but also attack malignant cells via specific antitumour immunity, has received increased attention in tumour immunization therapy due to their safety and easy preparation. The use of large-scale sequencing technology enables the continuous discovery of new tumour antigens. With improved accuracy of epitope prediction by computer simulation and the usage of a tetramer assay, cytotoxic lymphocyte epitopes can be screened and identified more easily. Transmembrane peptide and nanoparticle technologies promote more effective intake and delivery of antigens. Consequently, considerable evolution from universal to personalized peptide vaccines has taken place, and such vaccines induce an efficient and specific immune response targeting tumour neoantigens. Recently, genomic analysis and bioinformatics approaches have greatly facilitated the breakthrough of personalized peptide vaccines targeting neoantigens, resulting in a renewed interest in this field. Further, the combination of tumour peptide vaccines with checkpoint blockades may improve patient outcomes. In this review, we discuss the development of tumour peptide vaccines and the new technological progress, from universalization to personalization, to highlight the substantial promise of tumour peptide vaccines in clinical cancer immunotherapy.

Immunoinformatics and Vaccine Development: An Overview

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

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method that predicts peptide-MHC binding. MHCnuggets can predict binding for common or rare alleles of MHC class I or II with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is faster than other methods. When compared with methods that integrate binding affinity and MHC-bound peptide (HLAp) data from mass spectrometry, MHCnuggets yields a 4-fold increase in positive predictive value on independent HLAp data. We applied MHCnuggets to 26 cancer types in The Cancer Genome Atlas, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique predicted immunogenic missense mutations (IMM). Predicted IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (P < 2 × 10^-16), including CD8⁺ T cells. Only 0.16% of predicted IMMs were observed in more than 2 patients, with 61.7% of these derived from driver mutations. Thus, we describe a method for neoantigen prediction and its performance characteristics and demonstrate its utility in data sets representing multiple human cancers.

Inhibitors of ER Aminopeptidase 1 and 2: From Design to Clinical Application

Endoplasmic Reticulum aminopeptidase 1 and 2 are two homologous enzymes that help generate peptide ligands for presentation by Major Histocompatibility Class I molecules. Their enzymatic activity influences the antigenic peptide repertoire and indirectly controls adaptive immune responses. Accumulating evidence suggests that these two enzymes are tractable targets for the regulation of immune responses with possible applications ranging from cancer immunotherapy to treating inflammatory autoimmune diseases. Here, we review the state-of-the-art in the development of inhibitors of ERAP1 and ERAP2 as well as their potential and limitations for clinical applications.

Immunoinformatics: In Silico Approaches and Computational Design of a Multi-epitope, Immunogenic Protein

Immunoinformatics is a new critical field with several tools and databases that conduct the eyesight of experimental selection and facilitate analysis of the great amount of immunologic data obtained from experimental researches and helps to design and introducing new hypothesis. Given these visages, immunoinformatics seems to be the way that develop and progress the immunological research. Bioinformatics methods and applications are successfully employed in vaccine informatics to assist different sites of the preclinical, clinical, and post-licensure vaccine enterprises. On the other hand, the progression of molecular biology and immunology caused epitope vaccines have become the focus of research on molecular vaccines. Moreover, reverse vaccinology could improve vaccine production and vaccination protocols by in silico prediction of protein-vaccine candidates from genome sequences. B- and T-cell immune epitopes could be predicted by immunoinformatics algorithms and computational methods to improve the vaccine design, protective immunity analysis, assessment of vaccine safety and efficacy, and immunization modeling. This review aims to discuss the power of computational approaches in vaccine design and their relevance to the development of effective vaccines. Furthermore, the various divisions of this field and available tools in each item are introduced and reviewed.

Broad Spectrum Peptide Vaccine Design Against Hepatitis C Virus

BACKGROUND

Hepatitis C virus (HCV) infection is a global burden. There is no peptide vaccine found as modality to cure the disease is available due to the weak cellular immune response and the limitation to induce humoral immune response.

METHODS

Five predominated HCV subtypes in Indonesia (1a, 1b, 1c, 3a, and 3k) were aligned and the conserved regions were selected. Twenty alleles of class I MHC including HLA-A, HLA-B, and HLAC types were used to predict the potential epitopes by using NetMHCPan and IEDB. Eight alleles of HLA-DRB1, together with a combination of 3 alleles of HLA-DQA1 and 5 alleles of HLA-DQB1 were utilized for Class II MHC epitopes prediction using NetMHCIIPan and IEDB. LBtope and Ig- Pred were used to predict B cells epitopes. Moreover, proteasome analysis was performed by NetCTL and the stability of the epitopes in HLA was calculated using NetMHCStabPan for Class I. All predicted epitopes were analyzed for its antigenicity, toxicity, and stability. Population coverage, molecular docking and molecular dynamics were performed for several best epitopes.

RESULTS

The results showed that two best epitopes from envelop protein, GHRMAWDMMMNWSP (E1) and PALSTGLIHLHQN (E2) were selected as promising B cell and CD8+ T cell inducers. Other two peptides, LGIGTVLDQAETAG and VLVLNPSVAATLGF, taken from NS3 protein were selected as CD4+ T cell inducer.

CONCLUSION

This study suggested the utilization of all four peptides to make a combinational peptide vaccine for in vivo study to prove its ability in inducing secondary response toward HCV.

APOBEC3G Regulation of the Evolutionary Race Between Adaptive Immunity and Viral Immune Escape Is Deeply Imprinted in the HIV Genome

APOBEC3G (A3G) is a host enzyme that mutates the genomes of retroviruses like HIV. Since A3G is expressed pre-infection, it has classically been considered an agent of innate immunity. We and others previously showed that the impact of A3G-induced mutations on the HIV genome extends to adaptive immunity also, by generating cytotoxic T cell (CTL) escape mutations. Accordingly, HIV genomic sequences encoding CTL epitopes often contain A3G-mutable “hotspot” sequence motifs, presumably to channel A3G action toward CTL escape. Here, we studied the depths and consequences of this apparent viral genome co-evolution with A3G. We identified all potential CTL epitopes in Gag, Pol, Env, and Nef restricted to several HLA class I alleles. We simulated A3G-induced mutations within CTL epitope-encoding sequences, and flanking regions. From the immune recognition perspective, we analyzed how A3G-driven mutations are predicted to impact CTL-epitope generation through modulating proteasomal processing and HLA class I binding. We found that A3G mutations were most often predicted to result in diminishing/abolishing HLA-binding affinity of peptide epitopes. From the viral genome evolution perspective, we evaluated enrichment of A3G hotspots at sequences encoding CTL epitopes and included control sequences in which the HIV genome was randomly shuffled. We found that sequences encoding immunogenic epitopes exhibited a selective enrichment of A3G hotspots, which were strongly biased to translate to non-synonymous amino acid substitutions. When superimposed on the known mutational gradient across the entire length of the HIV genome, we observed a gradient of A3G hotspot enrichment, and an HLA-specific pattern of the potential of A3G hotspots to lead to CTL escape mutations. These data illuminate the depths and extent of the co-evolution of the viral genome to subvert the host mutator A3G.

Reduced bonobo MHC class I diversity predicts a reduced viral peptide binding ability compared to chimpanzees

BACKGROUND

The highly polymorphic genes of the major histocompatibility complex (MHC) class I are involved in defense against viruses and other intracellular pathogens. Although several studies found reduced MHC class I diversity in bonobos in comparison to the closely related chimpanzee, it is unclear if this lower diversity also influences the functional ability of MHC class I molecules in bonobos. Here, we use a bioinformatic approach to analyze the viral peptide binding ability of all published bonobo MHC class I molecules (n = 58) in comparison to all published chimpanzee MHC class I molecules (n = 161) for the class I loci A, B, C and A-like.

RESULTS

We examined the peptide binding ability of all 219 different MHC class I molecules to 5,788,712 peptides derived from 1432 different primate viruses and analyzed the percentage of bound peptides and the overlap of the peptide binding repertoires of the two species. We conducted multiple levels of analysis on the "species"-, "population"- and "individual"-level to account for the characterization of MHC variation in a larger number of chimpanzees and their broader geographic distribution. We found a lower percentage of bound peptides in bonobos at the B locus in the "population"-level comparison and at the B and C loci in the "individual"-level comparison. Furthermore, we found evidence of a limited peptide binding repertoire in bonobos by tree-based visualization of functional clustering of MHC molecules, as well as an analysis of peptides bound by both species.

CONCLUSION

Our results suggest a reduced MHC class I viral peptide binding ability at the B and C loci in bonobos compared to chimpanzees. The effects of this finding on the immune defense against viruses in wild living bonobos are unclear. However, special caution is needed to prevent introduction and spread of new viruses to bonobos, as their defensive ability to cope with new viruses could be limited compared to chimpanzees.

Exploring dengue proteome to design an effective epitope-based vaccine against dengue virus

Dengue, a mosquito-borne disease, is caused by four known dengue serotypes. This infection causes a range of symptoms from a mild fever to a sever homorganic fever and death. It is a serious public health problem in subtropical and tropical countries. There is no specific vaccine currently available for clinical use and study on this issue is ongoing. In this study, bioinformatics approaches were used to predict antigenic, immunogenic, non-allergenic, and conserved B and T-cell epitopes as promising targets to design an effective peptide-based vaccine against dengue virus. Molecular docking analysis indicated the deep binding of the identified epitopes in the binding groove of the most popular human MHC I allele (human leukocyte antigens [HLA] A*0201). The final vaccine construct was created by conjugating the B and T-cell identified epitopes using proper linkers and adding an appropriate adjuvant at the N-terminal. The characteristics of the new subunit vaccine demonstrated that the epitope-based vaccine was antigenic, non-toxic, stable, and soluble. Other physicochemical properties of the new designed construct including isoelectric point value, aliphatic index, and grand average of hydropathicity were biologically considerable. Molecular docking of the engineered vaccine with Toll-like receptor 2 (TLR2) model revealed the hydrophobic interaction between the adjuvant and the ligand binding regions in the hydrophobic channel of TLR2. The study results indicated the high potential capability of the new multi-epitope vaccine to induce cellular and humoral immune responses against the dengue virus. Further experimental tests are required to investigate the immune protection capacity of the new vaccine construct in animal models. Communicated by Ramaswamy H. Sarma.

Predicting T cell recognition of MHC class I restricted neoepitopes

Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope's immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used. Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4.

Identification of immunodominant CD8 epitope in the stalk domain of influenza B viral hemagglutinin

Human infections by type B influenza virus constitute about 25% of all influenza cases. The viral hemagglutinin is comprised of two subunits, HA1 and HA2. While HA1 is constantly evolving in an unpredictable fashion, the HA2 subunit is highly conserved, making it a potential candidate for a universal vaccine. However, immunodominant epitopes in the HA2 subunit remain largely unknown. To delineate MHC Class I epitopes, we first identified 9-mer H-2K^d-restricted CD8 T cell epitopes in the HA2 domain by in silico analyses, followed by evaluating the immunodominance of these peptides in mice challenged with the virus. Of three peptides selected through in silico analysis, the universally conserved peptide, YYSTAASSL (B/HA2-190), possessed the highest predicted binding affinity to MHC Class I and was most effective in inducing IL-2 and TNF-α in mouse splenocytes. Importantly, the peptide demonstrated best capability of stimulating peptide-specific ex-vivo cytotoxicity against target cells. Taken together, this finding would be of value for assessment of cell-mediated immune responses elicited by vaccines based on the highly conserved HA2 stalk domain.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

Glypican-3-specific cytotoxic T lymphocytes induced by human leucocyte antigen-A*0201-restricted peptide effectively kill hepatocellular carcinoma cells in vitro

OBJECTIVE

To investigate potential human leucocyte antigen (HLA)-A2-restricted epitope peptides of glypican-3 (GPC3) and determine the cytotoxicity of peptide-specific cytotoxic T lymphocytes (CTLs) against hepatocellular carcinoma (HCC) cells.

METHODS

The potential HLA-A*0201-restricted GPC3 peptides were screened using computer algorithms, T2 cell-binding affinity and stability of peptide/HLA-A*0201 complex assay. The peptide-specific CTLs were generated and their cytotoxicity against GPC3⁺ SMMC 7721 and HepG2 cells was detected using IFN-γ based enzyme-linked immunospot and lactate dehydrogenase release assays in vitro.

RESULTS

A total of six peptides were identified for bindings to HAL-A2 and the GPC3 522-530 and GPC3 229-237 peptides with HLA-A*0201 molecules displayed high binding affinity and stability. The CTLs induced by the GPC3 522-530 or positive control GPC3 144-152 peptide responded to the peptide by producing IFN-γ, which were abrogated by treatment with anti-HLA-A2 antibody. The GPC3 522-530-specific CTLs responded to and killed SMMC 7721 and HepG2 cells, instead of GPC3-silenced SMMC 7721 or HepG2 cells. GPC3 522-530-specific CTLs response to HCC cells was blocked by anti-HLA-A2 antibody.

CONCLUSIONS

The GPC3 522-530 peptide contains antigen-determinant and its specific CTLs can effectively kill HCC in a HLA-A2-restricted and peptide-dependent manner. Our findings suggest that this peptide may be valuable for development of therapeutic vaccine.

Host genotype and time dependent antigen presentation of viral peptides: predictions from theory

The rate of progression of HIV infected individuals to AIDS is known to vary with the genotype of the host, and is linked to their allele of human leukocyte antigen (HLA) proteins, which present protein degradation products at the cell surface to circulating T-cells. HLA alleles are associated with Gag-specific T-cell responses that are protective against progression of the disease. While Pol is the most conserved HIV sequence, its association with immune control is not as strong. To gain a more thorough quantitative understanding of the factors that contribute to immunodominance, we have constructed a model of the recognition of HIV infection by the MHC class I pathway. Our model predicts surface presentation of HIV peptides over time, demonstrates the importance of viral protein kinetics, and provides evidence of the importance of Gag peptides in the long-term control of HIV infection. Furthermore, short-term dynamics are also predicted, with simulation of virion-derived peptides suggesting that efficient processing of Gag can lead to a 50% probability of presentation within 3 hours post-infection, as observed experimentally. In conjunction with epitope prediction algorithms, this modelling approach could be used to refine experimental targets for potential T-cell vaccines, both for HIV and other viruses.

Emerging Concepts in TCR Specificity: Rationalizing and (Maybe) Predicting Outcomes

T cell specificity emerges from a myriad of processes, ranging from the biological pathways that control T cell signaling to the structural and physical mechanisms that influence how TCRs bind peptides and MHC proteins. Of these processes, the binding specificity of the TCR is a key component. However, TCR specificity is enigmatic: TCRs are at once specific but also cross-reactive. Although long appreciated, this duality continues to puzzle immunologists and has implications for the development of TCR-based therapeutics. In this review, we discuss TCR specificity, emphasizing results that have emerged from structural and physical studies of TCR binding. We show how the TCR specificity/cross-reactivity duality can be rationalized from structural and biophysical principles. There is excellent agreement between predictions from these principles and classic predictions about the scope of TCR cross-reactivity. We demonstrate how these same principles can also explain amino acid preferences in immunogenic epitopes and highlight opportunities for structural considerations in predictive immunology.

Modeling Sequence-Dependent Peptide Fluctuations in Immunologic Recognition

In cellular immunity, T cells recognize peptide antigens bound and presented by major histocompatibility complex (MHC) proteins. The motions of peptides bound to MHC proteins play a significant role in determining immunogenicity. However, existing approaches for investigating peptide/MHC motional dynamics are challenging or of low throughput, hindering the development of algorithms for predicting immunogenicity from large databases, such as those of tumor or genetically unstable viral genomes. We addressed this by performing extensive molecular dynamics simulations on a large structural database of peptides bound to the most commonly expressed human class-I MHC protein, HLA-A*0201. The simulations reproduced experimental indicators of motion and were used to generate simple models for predicting site-specific, rapid motions of bound peptides through differences in their sequence and chemical composition alone. The models can easily be applied on their own or incorporated into immunogenicity prediction algorithms. Beyond their predictive power, the models provide insight into how amino acid substitutions can influence peptide and protein motions and how dynamic information is communicated across peptides. They also indicate a link between peptide rigidity and hydrophobicity, two features known to be important in influencing cellular immune responses.

Neoepitopes as cancer immunotherapy targets: key challenges and opportunities

Over the last half century, it has become well established that cancers can elicit a host immune response that can target them with high specificity. Only within the last decade, with the advances in high-throughput gene sequencing and bioinformatics approaches, are we now on the forefront of harnessing the host's immune system to treat cancer. Recently, some strides have been taken toward understanding effective tumor-specific MHC I restricted epitopes or neoepitopes. However, many fundamental questions still remain to be addressed before this therapy can live up to its full clinical potential. In this review, we discuss the major hurdles that lie ahead and the work being done to address them.

Novel CTL epitopes identified through a Y. pestis proteome-wide analysis in the search for vaccine candidates against plague

The causative agent of Plague, Yersinia pestis, is a highly virulent pathogen and a potential bioweapon. Depending on the route of infection, two prevalent occurrences of the disease are known, bubonic and pneumonic. The latter has a high fatality rate. In the absence of a licensed vaccine, intense efforts to develop a safe and efficacious vaccine have been conducted, and humoral-driven subunit vaccines containing the F1 and LcrV antigens are currently under clinical trials. It is well known that a cellular immune response might have an essential additive value to immunity and protection against Y. pestis infection. Nevertheless, very few documented epitopes eliciting a protective T-cell response have been reported. Here, we present a combined high throughput computational and experimental effort towards identification of CD8 T-cell epitopes. All 4067 proteins of Y. pestis were analyzed with state-of-the-art recently developed prediction algorithms aimed at mapping potential MHC class I binders. A compilation of the results obtained from several prediction methods revealed a total of 238,000 peptide candidates, which necessitated downstream filtering criteria. Our previously established and proven approach for enrichment of true positive CTL epitopes, which relies on mapping clusters rich in tandem or overlapping predicted MHC binders ("hotspots"), was applied, as well as considerations of predicted binding affinity. A total of 1532 peptides were tested for their ability to elicit a specific T-cell response by following the production of IFNγ from splenocytes isolated from vaccinated mice. Altogether, the screen resulted in 178 positive responders (11.8%), all novel Y. pestis CTL epitopes. These epitopes span 113 Y. pestis proteins. Substantial enrichment of membrane-associated proteins was detected for epitopes selected from hotspots of predicted MHC binders. These results considerably expand the repertoire of known CTL epitopes in Y. pestis and pave the way to attest their protective potential, and hence their contribution to a future potent subunit vaccine.

Identification and evaluation of the novel immunodominant antigen Rv2351c from Mycobacterium tuberculosis

There is an urgent need for new immunodominant antigens to improve the diagnosis of tuberculosis (TB) and the efficacy of the TB vaccine to control the disease worldwide. In this study, we evaluated the diagnostic potential of a novel Mycobacterium tuberculosis (MTB)-specific antigen, Rv2351c, from region of difference (RD) 7 of the MTB genome, and investigated the potency of the vaccine by identifying its immunological function in human and animal immunological experiments. Twenty T-cell epitopes were identified using TEpredict and prediction tools from the Immune Epitope Database and Analysis Resource. A total of 159 subjects, including 61 patients with pulmonary TB, 38 patients with no TB and 55 healthy donors, were recruited and analyzed with an enzyme-linked immunospot (ELISpot) assay. The ELISpot assay using Rv2351c to detect TB infection, as compared with bacteriological tests as the gold standard, had a sensitivity and specificity of 61.4% (35/57) and 91.4% (85/93), respectively. The ELISpot assay using Rv2351c had a good conformance (κ=0.554) as compared with the bacteriological test. Rv2351c also elicited a potent cellular immune response with a high expression of cytokines (IFN-γ (4978±596.7 μg/mL) and IL-4 (68.3±15.5 μg/mL)) and a potent humoral immune response with a high concentration of IgG (1:2.2 × 10⁶), IgG1 (1:4.5 × 10⁵) and IgG2a (1:1.6 × 10⁶) in immunized BALB/c mice. In addition, the ratio of IgG2a/IgG1 indicated that Rv2351c induced cellular immunity in the mice. The results of this study indicated that Rv2351c is an antigen with good immunogenicity that may potentially be used to develop diagnostic techniques and new TB vaccines.

MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory

Multispecificity-the ability of a single receptor protein molecule to interact with multiple substrates-is a hallmark of molecular recognition at protein-protein and protein-peptide interfaces, including enzyme-substrate complexes. The ability to perform structure-based prediction of multispecificity would aid in the identification of novel enzyme substrates, protein interaction partners, and enable design of novel enzymes targeted towards alternative substrates. The relatively slow speed of current biophysical, structure-based methods limits their use for prediction and, especially, design of multispecificity. Here, we develop a rapid, flexible-backbone self-consistent mean field theory-based technique, MFPred, for multispecificity modeling at protein-peptide interfaces. We benchmark our method by predicting experimentally determined peptide specificity profiles for a range of receptors: protease and kinase enzymes, and protein recognition modules including SH2, SH3, MHC Class I and PDZ domains. We observe robust recapitulation of known specificities for all receptor-peptide complexes, and comparison with other methods shows that MFPred results in equivalent or better prediction accuracy with a ~10-1000-fold decrease in computational expense. We find that modeling bound peptide backbone flexibility is key to the observed accuracy of the method. We used MFPred for predicting with high accuracy the impact of receptor-side mutations on experimentally determined multispecificity of a protease enzyme. Our approach should enable the design of a wide range of altered receptor proteins with programmed multispecificities.

Towards the knowledge-based design of universal influenza epitope ensemble vaccines

MOTIVATION

Influenza A viral heterogeneity remains a significant threat due to unpredictable antigenic drift in seasonal influenza and antigenic shifts caused by the emergence of novel subtypes. Annual review of multivalent influenza vaccines targets strains of influenza A and B likely to be predominant in future influenza seasons. This does not induce broad, cross protective immunity against emergent subtypes. Better strategies are needed to prevent future pandemics. Cross-protection can be achieved by activating CD8+ and CD4+ T cells against highly conserved regions of the influenza genome. We combine available experimental data with informatics-based immunological predictions to help design vaccines potentially able to induce cross-protective T-cells against multiple influenza subtypes.

RESULTS

To exemplify our approach we designed two epitope ensemble vaccines comprising highly conserved and experimentally verified immunogenic influenza A epitopes as putative non-seasonal influenza vaccines; one specifically targets the US population and the other is a universal vaccine. The USA-specific vaccine comprised 6 CD8+ T cell epitopes (GILGFVFTL, FMYSDFHFI, GMDPRMCSL, SVKEKDMTK, FYIQMCTEL, DTVNRTHQY) and 3 CD4+ epitopes (KGILGFVFTLTVPSE, EYIMKGVYINTALLN, ILGFVFTLTVPSERG). The universal vaccine comprised 8 CD8+ epitopes: (FMYSDFHFI, GILGFVFTL, ILRGSVAHK, FYIQMCTEL, ILKGKFQTA, YYLEKANKI, VSDGGPNLY, YSHGTGTGY) and the same 3 CD4+ epitopes. Our USA-specific vaccine has a population protection coverage (portion of the population potentially responsive to one or more component epitopes of the vaccine, PPC) of over 96 and 95% coverage of observed influenza subtypes. The universal vaccine has a PPC value of over 97 and 88% coverage of observed subtypes.

AVAILABILITY AND IMPLEMENTATION

http://imed.med.ucm.es/Tools/episopt.html CONTACT: d.r.flower@aston.ac.uk.

Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation

Binding between major histocompatibility complex (MHC) class I molecules and immunogenic epitopes is one of the most important processes for cell-mediated immunity. Consequently, computational prediction of amino acid sequences of MHC class I binding peptides from a given sequence may lead to important biomedical advances. In this study, an efficient structure-based method for predicting peptide binding to MHC class I molecules was developed, in which the binding free energy of the peptide was evaluated by two individual docking simulations. An original penalty function and restriction of degrees of freedom were determined by analysis of 361 published X-ray structures of the complex and were then introduced into the docking simulations. To validate the method, calculations using a 50-amino acid sequence as a prediction target were performed. In 27 calculations, the binding free energy of the known peptide was within the top 5 of 166 peptides generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole sequence of a protein as a prediction target were performed. These data clearly demonstrate high potential of this method for predicting peptide binding to MHC class I molecules.

HLA class I molecular variation and peptide-binding properties suggest a model of joint divergent asymmetric selection

The main function of HLA class I molecules is to present pathogen-derived peptides to cytotoxic T lymphocytes. This function is assumed to drive the maintenance of an extraordinary amount of polymorphism at each HLA locus, providing an immune advantage to heterozygote individuals capable to present larger repertories of peptides than homozygotes. This seems contradictory, however, with a reduced diversity at individual HLA loci exhibited by some isolated populations. This study shows that the level of functional diversity predicted for the two HLA-A and HLA-B genes considered simultaneously is similar (almost invariant) between 46 human populations, even when a reduced diversity exists at each locus. We thus propose that HLA-A and HLA-B evolved through a model of joint divergent asymmetric selection conferring all populations an equivalent immune potential. The distinct pattern observed for HLA-C is explained by its functional evolution towards killer cell immunoglobulin-like receptor (KIR) activity regulation rather than peptide presentation.

In silico and in vivo analysis of Toxoplasma gondii epitopes by correlating survival data with peptide-MHC-I binding affinities

BACKGROUND

Protein antigens comprising peptide motifs with high binding affinity to major histocompatibility complex class I (MHC-I) molecules are expected to induce a stronger cytotoxic T-lymphocyte response and thus provide better protection against infection with microorganisms where cytotoxic T-cells are the main effector arm of the immune system.

METHODS

Data on cyst formation and survival were extracted from past studies on the DNA immunization of mice with plasmids coding for Toxoplasma gondii antigens. From in silico analyses of the vaccine antigens, the correlation was tested between the predicted affinity for MHC-I molecules of the vaccine peptides and the survival of immunized mice after challenge with T. gondii. ELISPOT analysis was used for the experimental testing of peptide immunogenicity.

RESULTS

Predictions for the Db MHC-I molecule produced a strong, negative correlation between survival and the dissociation constant of vaccine-derived peptides. The in silico analyses of nine T. gondii antigens identified peptides with a predicted dissociation constant in the interval from 10nM to 40μM. ELISPOT assays with splenocytes from T. gondii-infected mice further supported the importance of the peptide affinity for MHC-I.

CONCLUSIONS

In silico analysis clearly helped the search for protective vaccine antigens. The ELISPOT analysis confirmed that the predicted T-cell epitopes were immunogenic by their ability to release interferon gamma in spleen cells.

HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials

MOTIVATION

T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics and are critically dependent on experimentally obtained training data. Analysis of a training set from Immune Epitope Database (IEDB) for several alleles indicates that the sampling of the peptide space is extremely sparse covering a tiny fraction of the possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction.

RESULTS

We present a new epitope prediction method that has four distinct computational modules: (i) structural modelling, estimating statistical pair-potentials and constraint derivation, (ii) implicit modelling and interaction profiling, (iii) feature representation and binding affinity prediction and (iv) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles.

CONCLUSIONS

HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any Class-1 HLA allele, by estimating the binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It relies on the strength of the mechanistic understanding of peptide-HLA recognition and provides an estimate of the total ligand space for each allele. The performance of HLaffy is seen to be superior to the currently available methods.

AVAILABILITY AND IMPLEMENTATION

The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy

CONTACT

: nchandra@biochem.iisc.ernet.in

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Design of Polyepitope DNA Vaccine against Breast Carcinoma Cells and Analysis of Its Expression in Dendritic Cells

Polyepitope DNA vaccine inducing T-cell-mediated immune response against cancer-specific antigens is a promising tool for selective elimination of tumor cells. Breast cancer-specific polyepitope DNA vaccine was designed using TEpredict and PolyCTLDesigner software on the basis of immunogenic peptides of HER2 and Mammaglobin-1 (Mam) tumor antigens. LPS-free preparations of plasmid DNA encoding polyepitope T-cell antigen and full-length copies of HER2 and Mam antigens were obtained. TaqMan-PCR systems for evaluation of the expression of immunogens in cells were created. The protocol of vaccine DNA delivery into dendritic cells was optimized. Expression of the target immunogens in dendritic cells derived from human peripheral blood mononuclear fraction after transfection with plasmid DNA preparations is demonstrated.

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.