MULTIPRED2: a computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Neoantigen identification: Technological advances and challenges

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells

Recent advances in neoantigen research have accelerated the development of tumor immunotherapies, including adoptive cell therapies (ACTs), cancer vaccines and antibody-based therapies, particularly for solid tumors. With the development of next-generation sequencing and bioinformatics technology, the rapid identification and prediction of tumor-specific antigens (TSAs) has become possible. Compared with tumor-associated antigens (TAAs), highly immunogenic TSAs provide new targets for personalized tumor immunotherapy and can be used as prospective indicators for predicting tumor patient survival, prognosis, and immune checkpoint blockade response. Here, the identification and characterization of neoantigens and the clinical application of neoantigen-based TCR-T immunotherapy strategies are summarized, and the current status, inherent challenges, and clinical translational potential of these strategies are discussed.

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

Current Update on Rotavirus in-Silico Multiepitope Vaccine Design

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

Neoantigen-specific TCR-T cell-based immunotherapy for acute myeloid leukemia

Neoantigens derived from non-synonymous somatic mutations are restricted to malignant cells and are thus considered ideal targets for T cell receptor (TCR)-based immunotherapy. Adoptive transfer of T cells bearing neoantigen-specific TCRs exhibits the ability to preferentially target tumor cells while remaining harmless to normal cells. High-avidity TCRs specific for neoantigens expressed on AML cells have been identified in vitro and verified using xenograft mouse models. Preclinical studies of these neoantigen-specific TCR-T cells are underway and offer great promise as safe and effective therapies. Additionally, TCR-based immunotherapies targeting tumor-associated antigens are used in early-phase clinical trials for the treatment of AML and show encouraging anti-leukemic effects. These clinical experiences support the application of TCR-T cells that are specifically designed to recognize neoantigens. In this review, we will provide a detailed profile of verified neoantigens in AML, describe the strategies to identify neoantigen-specific TCRs, and discuss the potential of neoantigen-specific T-cell-based immunotherapy in AML.

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.

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

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

PromPDD, a web-based tool for the prediction, deciphering and design of promiscuous peptides that bind to HLA class I molecules

Promiscuous peptides that can be presented by multiple human leukocyte antigens (HLAs) have great potential for the development of vaccines with wide population coverage. However, the current available methods for the prediction of peptides that bind to major histocompatibility complex (MHC) are mainly aimed at the rapid or mass screening of potential T cell epitopes from pathogen antigens or proteomics. The current approaches do not allow deciphering the contribution of the residue at each peptide position to the promiscuous binding ability of the peptide or obtaining guidelines for the design of promiscuous peptides. In this study, we re-evaluated and characterized four matrix-based prediction models that have been extensively used for the prediction of HLA-binding peptides and found that the prediction models generated based on the average relative binding (ARB) matrix shared a consistent and conservative threshold for all well-studied HLA class I alleles. Evaluations performed using datasets of HLA supertype-specific peptides with various cross-binding abilities and peptide mutant analogues indicated that the ARB-based binding matrices could be used to decipher and design promiscuous peptides that bind to multiple HLA molecules. A web-based tool called PromPDD was developed using ARB matrix-based models, and this tool enables the prediction, deciphering and design of promiscuous peptides that bind to multiple HLA molecules within or across HLA supertypes in a simpler and more direct manner. Furthermore, we expanded the application of PromPDD to HLA class I alleles with limited experimentally verified data by generating pan-specific matrices using a derived modular method, and 2641 HLA molecules encoded by HLA-A and HLA-B genes are available in PromPDD. PromPDD, which is freely available at http://www.immunoinformatics.net/PromPDD/, is the first tool for the deciphering and design of promiscuous peptides that bind to HLA class I molecules.

The Heterogeneity Between Lynch-Associated and Sporadic MMR Deficiency in Colorectal Cancers

Background

Previous studies demonstrated that prognosis of germline deficiency in mismatch repair protein (dMMR) was different from that of sporadic dMMR. The underlying mechanism has not been studied.

Methods

From a prospectively maintained database, we collected dMMR colorectal cancer (CRC) patients identified by postoperative immunohistochemistry screening. According to genetic test, patients were grouped as Lynch-associated or sporadic dMMR. We compared the clinical-pathological features, prognosis, and immunoreactive differences between the two groups. By whole-exome sequencing and neoantigen detection pipeline, mutational frequencies and neoantigen burdens were also compared. All statistical tests were two-sided.

Results

Sixty-seven sporadic dMMR and 85 Lynch-associated CRC patients were included in the study. Sporadic dMMR patients were older (P < .001) and their tumors were poorly differentiated (P = .03). The survival was better in the Lynch-associated group (P = .001). After adjustment, the difference still remained statistically significant (hazard ratio = 0.29, 95% confidence interval = 0.09 to 0.95, P = .04). The scores of Crohn's-like reaction (CRO; P < .001), immunoreactions in the invasive margin (IM; P = .01), tumor stroma (TS; P = .009), and cancer nest (CN; P = .02) of the Lynch-associated group were statistically significantly higher. The numbers of CD3+, CD8+, Foxp3+ tumor-infiltrating lymphocytes (TILs) in IM; CD3+, CD4+ TILs in TS; and CD3+, CD4+, CD8+ TILs in CN were statistically significantly higher in Lynch-associated dMMR patients. Based on the 16 patients who under went whole-exome sequencing, there were also more somatic mutations and neoantigen burdens in the Lynch-associated group compared with the sporadic dMMR group (439/pt vs 68/pt, P = .006; 628/pt vs 97/pt, P = .009).

Conclusions

There are heterogeneities in dMMR CRCs. Lynch-associated dMMR patients present with more somatic mutations and neoantigens compared with sporadic dMMR, which probably results in stronger immunoreactions and survival improvement.

Toward in silico Identification of Tumor Neoantigens in Immunotherapy

Cancer immunotherapy includes cancer vaccination, adoptive T cell transfer (ACT) with chimeric antigen receptor (CAR) T cells, and administration of tumor-infiltrating lymphocytes and immune-checkpoint blockade such as anti-CTLA4/anti-PD1 inhibitors that can directly or indirectly target tumor neoantigens and elicit a T cell response. Accurate, rapid, and cost-effective identification of neoantigens, however, is critical for successful immunotherapy. Here, we review computational issues for neoantigen identification by summarizing the various sources of neoantigens and their identification from high-throughput sequencing data. Several opinions are presented to inspire further discussions toward improving neoantigen identification. Continuing efforts are required to improve the sensitivity and specificity of bona fide neoantigens, taking advantage of the development of high-throughput sequencing techniques for effective and personalized cancer immunotherapy.

Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure

Designing novel antimicrobial peptides is a hot area of research in the field of therapeutics especially after the emergence of resistant strains against the conventional antibiotics. In the past number of in silico methods have been developed for predicting the antimicrobial property of the peptide containing natural residues. This study describes models developed for predicting the antimicrobial property of a chemically modified peptide. Our models have been trained, tested and evaluated on a dataset that contains 948 antimicrobial and 931 non-antimicrobial peptides, containing chemically modified and natural residues. Firstly, the tertiary structure of all peptides has been predicted using software PEPstrMOD. Structure analysis indicates that certain type of modifications enhance the antimicrobial property of peptides. Secondly, a wide range of features was computed from the structure of these peptides using software PaDEL. Finally, models were developed for predicting the antimicrobial potential of chemically modified peptides using a wide range of structural features of these peptides. Our best model based on support vector machine achieve maximum MCC of 0.84 with an accuracy of 91.62% on training dataset and MCC of 0.80 with an accuracy of 89.89% on validation dataset. To assist the scientific community, we have developed a web server called "AntiMPmod" which predicts the antimicrobial property of the chemically modified peptide. The web server is present at the following link (http://webs.iiitd.edu.in/raghava/antimpmod/).

Predicted MHC peptide binding promiscuity explains MHC class I 'hotspots' of antigen presentation defined by mass spectrometry eluted ligand data

Peptides that bind to and are presented by MHC class I and class II molecules collectively make up the immunopeptidome. In the context of vaccine development, an understanding of the immunopeptidome is essential, and much effort has been dedicated to its accurate and cost-effective identification. Current state-of-the-art methods mainly comprise in silico tools for predicting MHC binding, which is strongly correlated with peptide immunogenicity. However, only a small proportion of the peptides that bind to MHC molecules are, in fact, immunogenic, and substantial work has been dedicated to uncovering additional determinants of peptide immunogenicity. In this context, and in light of recent advancements in mass spectrometry (MS), the existence of immunological hotspots has been given new life, inciting the hypothesis that hotspots are associated with MHC class I peptide immunogenicity. We here introduce a precise terminology for defining these hotspots and carry out a systematic analysis of MS and in silico predicted hotspots. We find that hotspots defined from MS data are largely captured by peptide binding predictions, enabling their replication in silico. This leads us to conclude that hotspots, to a great degree, are simply a result of promiscuous HLA binding, which disproves the hypothesis that the identification of hotspots provides novel information in the context of immunogenic peptide prediction. Furthermore, our analyses demonstrate that the signal of ligand processing, although present in the MS data, has very low predictive power to discriminate between MS and in silico defined hotspots.

Identification of T cell and B cell epitopes against Indian HCV-genotype-3a for vaccine development- An in silico analysis

Hepatitis C virus (HCV) infects almost 150 million people and is a leading cause of liver disease worldwide. It has been classified into seven genotypes; the most common genotype affecting Indian population is genotype 3 (60-70%). Currently there is no vaccine for any genotype of HCV. In order to develop peptide based vaccine against HCV, it is important to identify the conservancy in the circulating genotypes, along with the Human Leucocyte Antigen (HLA) alleles in the target population. The present study aims to identify conserved CD4 and CD8 T cells and B cell epitopes against Indian HCV-genotype-3a using an in silico analysis. In the present study, 28 promiscuous CD4 T cell epitopes and some CD8 epitopes were identified. The NS4 region was predicted to be the most antigenic with maximum number of conserved and promiscuous CD4 T cell epitopes and CD8 T cell epitopes having strong and intermediate affinity towards a number of HLA alleles prevalent in Indian population. Additionally, some linear B cell epitopes were also identified, which could generate neutralizing antibodies. In order to ascertain the binding pattern of the identified epitopes with HLA alleles, molecular docking analysis was carried out. The authors suggest further experimental validation to investigate the immunogenicity of the identified epitopes.

In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs

The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.

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.

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

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

Analysis of viral diversity for vaccine target discovery

BACKGROUND

Viral vaccine target discovery requires understanding the diversity of both the virus and the human immune system. The readily available and rapidly growing pool of viral sequence data in the public domain enable the identification and characterization of immune targets relevant to adaptive immunity. A systematic bioinformatics approach is necessary to facilitate the analysis of such large datasets for selection of potential candidate vaccine targets.

RESULTS

This work describes a computational methodology to achieve this analysis, with data of dengue, West Nile, hepatitis A, HIV-1, and influenza A viruses as examples. Our methodology has been implemented as an analytical pipeline that brings significant advancement to the field of reverse vaccinology, enabling systematic screening of known sequence data in nature for identification of vaccine targets. This includes key steps (i) comprehensive and extensive collection of sequence data of viral proteomes (the virome), (ii) data cleaning, (iii) large-scale sequence alignments, (iv) peptide entropy analysis, (v) intra- and inter-species variation analysis of conserved sequences, including human homology analysis, and (vi) functional and immunological relevance analysis.

CONCLUSION

These steps are combined into the pipeline ensuring that a more refined process, as compared to a simple evolutionary conservation analysis, will facilitate a better selection of vaccine targets and their prioritization for subsequent experimental validation.

Recombinant and epitope-based vaccines on the road to the market and implications for vaccine design and production

Novel vaccination approaches based on rational design of B- and T-cell epitopes - epitope-based vaccines - are making progress in the clinical trial pipeline. The epitope-focused recombinant protein-based malaria vaccine (termed RTS,S) is a next-generation approach that successfully reached phase-III trials, and will potentially become the first commercial vaccine against a human parasitic disease. Progress made on methods such as recombinant DNA technology, advanced cell-culture techniques, immunoinformatics and rational design of immunogens are driving the development of these novel concepts. Synthetic recombinant proteins comprising both B- and T-cell epitopes can be efficiently produced through modern biotechnology and bioprocessing methods, and can enable the induction of large repertoires of immune specificities. In particular, the inclusion of appropriate CD4+ T-cell epitopes is increasingly considered a key vaccine component to elicit robust immune responses, as suggested by results coming from HIV-1 clinical trials. In silico strategies for vaccine design are under active development to address genetic variation in pathogens and several broadly protective "universal" influenza and HIV-1 vaccines are currently at different stages of clinical trials. Other methods focus on improving population coverage in target populations by rationally considering specificity and prevalence of the HLA proteins, though a proof-of-concept in humans has not been demonstrated yet. Overall, we expect immunoinformatics and bioprocessing methods to become a central part of the next-generation epitope-based vaccine development and production process.

Circulating Memory CD4+ T Cells Target Conserved Epitopes of Rhinovirus Capsid Proteins and Respond Rapidly to Experimental Infection in Humans

Rhinovirus (RV) is a major cause of common cold and an important trigger of acute episodes of chronic lung diseases. Antigenic variation across the numerous RV strains results in frequent infections and a lack of durable cross-protection. Because the nature of human CD4⁺ T cells that target RV is largely unknown, T cell epitopes of RV capsid proteins were analyzed, and cognate T cells were characterized in healthy subjects and those infected by intranasal challenge. Peptide epitopes of the RV-A16 capsid proteins VP1 and VP2 were identified by peptide/MHC class II tetramer-guided epitope mapping, validated by direct ex vivo enumeration, and interrogated using a variety of in silico methods. Among noninfected subjects, those circulating RV-A16-specific CD4⁺ T cells detected at the highest frequencies targeted 10 unique epitopes that bound to diverse HLA-DR molecules. T cell epitopes localized to conserved molecular regions of biological significance to the virus were enriched for HLA class I and II binding motifs, and constituted both species-specific (RV-A) and pan-species (RV-A, -B, and -C) varieties. Circulating epitope-specific T cells comprised both memory Th1 and T follicular helper cells, and were rapidly expanded and activated after intranasal challenge with RV-A16. Cross-reactivity was evidenced by identification of a common *0401-restricted epitope for RV-A16 and RV-A39 by tetramer-guided epitope mapping and the ability for RV-A16-specific Th1 cells to proliferate in response to their RV-A39 peptide counterpart. The preferential persistence of high-frequency RV-specific memory Th1 cells that recognize a limited set of conserved epitopes likely arises from iterative priming by previous exposures to different RV strains.

A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information

MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter α in the weight formula. By changing α value, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules.

αβ TCR-mediated recognition: relevance to tumor-antigen discovery and cancer immunotherapy

αβ T lymphocytes sense perturbations in host cellular body components induced by infectious pathogens, oncogenic transformation, or chemical or physical damage. Millions to billions of these lymphocytes are generated through T-lineage development in the thymus, each endowed with a clonally restricted surface T-cell receptor (TCR). An individual TCR has the capacity to recognize a distinct "foreign" peptide among the myriad of antigens that the mammalian host must be capable of detecting. TCRs explicitly distinguish foreign from self-peptides bound to major histocompatibility complex (MHC) molecules. This is a daunting challenge, given that the MHC-linked peptidome consists of thousands of distinct peptides with a relevant nonself target antigen often embedded at low number, among orders of magnitude higher frequency self-peptides. In this Masters of Immunology article, I review how TCR structure and attendant mechanobiology involving nonlinear responses affect sensitivity as well as specificity to meet this requirement. Assessment of human tumor-cell display using state-of-the-art mass spectrometry physical detection methods that quantify epitope copy number can help to provide information about requisite T-cell functional avidity affording protection and/or therapeutic immunity. Future rational CD8 cytotoxic T-cell-based vaccines may follow, targeting virally induced cancers, other nonviral immunogenic tumors, and potentially even nonimmunogenic tumors whose peptide display can be purposely altered by MHC-binding drugs to stimulate immune attack.

Antigenic Determinants of the Bilobal Cockroach Allergen Bla g 2

Bla g 2 is a major indoor cockroach allergen associated with the development of asthma. Antigenic determinants on Bla g 2 were analyzed by mutagenesis based on the structure of the allergen alone and in complex with monoclonal antibodies that interfere with IgE antibody binding. The structural analysis revealed mechanisms of allergen-antibody recognition through cation-π interactions. Single and multiple Bla g 2 mutants were expressed in Pichia pastoris and purified. The triple mutant K132A/K251A/F162Y showed an ∼100-fold reduced capacity to bind IgE, while preserving the native molecular fold, as proven by x-ray crystallography. This mutant was still able to induce mast cell release. T-cell responses were assessed by analyzing Th1/Th2 cytokine production and the CD4(+) T-cell phenotype in peripheral blood mononuclear cell cultures. Although T-cell activating capacity was similar for the KKF mutant and Bla g 2 based on CD25 expression, the KKF mutant was a weaker inducer of the Th2 cytokine IL-13. Furthermore, this mutant induced IL-10 from a non-T-cell source at higher levels that those induced by Bla g 2. Our findings demonstrate that a rational design of site-directed mutagenesis was effective in producing a mutant with only 3 amino acid substitutions that maintained the same fold as wild type Bla g 2. These residues, which were involved in IgE antibody binding, endowed Bla g 2 with a T-cell modulatory capacity. The antigenic analysis of Bla g 2 will be useful for the subsequent development of recombinant allergen vaccines.

Major histocompatibility complex linked databases and prediction tools for designing vaccines

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

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.

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

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

Characterization of Plasmodium falciparum Proteome at Asexual Blood Stages for Screening of Effective Vaccine Candidates: An Immunoinformatics Approach

Malaria is a complex parasitic disease that is currently causing great concerns globally owing to the resistance to antimalarial drugs and lack of an effective vaccine. The present study involves the characterization of extracellular secretory proteins as vaccine candidates derived from proteome analysis of Plasmodium falciparum at asexual blood stages of malaria. Among the screened 32 proteins, 31 were predicted as antigens by the VaxiJen program, and 26 proteins had less than two transmembrane spanning regions predicted using the THMMM program. Moreover, 10 and 5 proteins were predicted to contain secretory signals by SignalP and TargetP, respectively. T-cell epitope prediction using MULTIPRED2 and NetCTL programs revealed that most of the predicted antigens are immunogenic and contain more than 10% supertype and 5% promiscuous epitopes of HLA-A, -B, or -DR. We anticipate that T-cell immune responses against asexual blood stages of Plasmodium are dispersed on a relatively large number of parasite antigens. This is the first report, to the best of our knowledge, offering new insights, at the proteome level, for the putative screening of effective vaccine candidates against the malaria pathogen. The findings also suggest new ways forward for the modern omics-guided vaccine target discovery using reverse vaccinology.

Physical detection of influenza A epitopes identifies a stealth subset on human lung epithelium evading natural CD8 immunity

Vaccines eliciting immunity against influenza A viruses (IAVs) are currently antibody-based with hemagglutinin-directed antibody titer the only universally accepted immune correlate of protection. To investigate the disconnection between observed CD8 T-cell responses and immunity to IAV, we used a Poisson liquid chromatography data-independent acquisition MS method to physically detect PR8/34 (H1N1), X31 (H3N2), and Victoria/75 (H3N2) epitopes bound to HLA-A*02:01 on human epithelial cells following in vitro infection. Among 32 PR8 peptides (8-10mers) with predicted IC50 < 60 nM, 9 were present, whereas 23 were absent. At 18 h postinfection, epitope copies per cell varied from a low of 0.5 for M13-11 to a high of >500 for M1(58-66) with PA, HA, PB1, PB2, and NA epitopes also detected. However, aside from M1(58-66), natural CD8 memory responses against conserved presented epitopes were either absent or only weakly observed by blood Elispot. Moreover, the functional avidities of the immunodominant M1(58-66)/HLA-A*02:01-specific T cells were so poor as to be unable to effectively recognize infected human epithelium. Analysis of T-cell responses to primary PR8 infection in HLA-A*02:01 transgenic B6 mice underscores the poor avidity of T cells recognizing M1(58-66). By maintaining high levels of surface expression of this epitope on epithelial and dendritic cells, the virus exploits the combination of immunodominance and functional inadequacy to evade HLA-A*02:01-restricted T-cell immunity. A rational approach to CD8 vaccines must characterize processing and presentation of pathogen-derived epitopes as well as resultant immune responses. Correspondingly, vaccines may be directed against "stealth" epitopes, overriding viral chicanery.

Immunogenic peptide discovery in cancer genomes

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

Comparison of experimental fine-mapping to in silico prediction results of HIV-1 epitopes reveals ongoing need for mapping experiments

Methods for identifying physiologically relevant CD8 T-cell epitopes are critically important not only for the development of T-cell-based vaccines but also for understanding host-pathogen interactions. As experimentally mapping an optimal CD8 T-cell epitope is a tedious procedure, many bioinformatic tools have been developed that predict which peptides bind to a given MHC molecule. We assessed the ability of the CD8 T-cell epitope prediction tools syfpeithi, ctlpred and iedb to foretell nine experimentally mapped optimal HIV-specific epitopes. Randomly - for any of the subjects' HLA type and with any matching score - the optimal epitope was predicted in seven of nine epitopes using syfpeithi, in three of nine epitopes using ctlpred and in all nine of nine epitopes using iedb. The optimal epitope within the three highest ranks was given in four of nine epitopes applying syfpeithi, in two of nine epitopes applying ctlpred and in seven of nine epitopes applying iedb when screening for all of the subjects' HLA types. Knowing the HLA restriction of the peptide of interest improved the ranking of the optimal epitope within the predicted results. Epitopes restricted by common HLA alleles were more likely to be predicted than those restricted by uncommon HLA alleles. Epitopes with aberrant lengths compared with the usual HLA-class I nonamers were most likely not predicted. Application of epitope prediction tools together with literature searches for already described optimal epitopes narrows down the possibilities of optimal epitopes within a screening peptide of interest. However, in our opinion, the actual fine-mapping of a CD8 T-cell epitope cannot yet be replaced.

Forward Vaccinology: CTL Targeting Based upon Physical Detection of HLA-Bound Peptides

Vaccine-elicited cytotoxic T lymphocytes (CTL) recognizing conserved fragments of a pathogen's proteome could greatly impact infectious diseases and cancers. Enabling this potential are recent advances in mass spectrometry that identify specific target peptides among the myriad HLA-bound peptides on altered cells. Ultrasensitivity of these physical detection methods allows for the direct assessment of peptide presentation on small numbers of tissue-derived cells. In addition, concurrent advances in immunobiology suggest ways to induce CTLs with requisite functional avidity and tissue deployment. Elicitation of high-avidity resident-memory T cells through vaccination may shift the vaccinology paradigm both for preventive and therapeutic approaches to human disease control.

Epitope distribution in ordered and disordered protein regions - part A. T-cell epitope frequency, affinity and hydropathy

Highly disordered protein regions are prevalently hydrophilic, extremely sensitive to proteolysis in vitro, and are expected to be under-represented as T-cell epitopes. The aim of this research was to find out whether disorder and hydropathy prediction methods could help in understanding epitope processing and presentation. According to the pan-specific T-cell epitope predictors NetMHCpan and NetMHCIIpan and 9 publicly available disorder predictors, frequency of epitopes presented by human leukocyte antigens (HLA) class-I or -II was found to be more than 2.5 times higher in ordered than in disordered protein regions (depending on the disorder predictor). Both HLA class-I and HLA class-II binding epitopes are prevalently hydrophilic in disordered and prevalently hydrophobic in ordered protein regions, whereas epitopes recognized by HLA class-II alleles are more hydrophobic than those recognized by HLA class-I. As regards both classes of HLA molecules, high-affinity binding epitopes display more hydrophobicity than low affinity-binding epitopes (in both ordered and disordered regions). Epitopes belonging to disordered protein regions were not predicted to have poor affinity to HLA class-II molecules, as expected from disorder intrinsic proteolytic instability. The relation of epitope hydrophobicity and order/disorder location was also valid if alleles were grouped according to the HLA class-I and HLA class-II supertypes, except for the class-I supertype A3 in which the main part of recognized epitopes was prevalently hydrophilic. Regarding specific supertypes, the affinity of epitopes belonging to ordered regions varies only slightly (depending on the disorder predictor) compared to the affinity of epitopes in corresponding disordered regions. The distribution of epitopes in ordered and disordered protein regions has revealed that the curves of order-epitope distribution were convex-like while the curves of disorder-epitope distribution were concave-like. The percentage of prevalently hydrophobic epitopes increases with the enhancement of epitope promiscuity level and moving from disordered to ordered regions. These data suggests that reverse vaccinology, oriented towards promiscuous and high-affinity epitopes, is also oriented towards prevalently hydrophobic, ordered regions. The analysis of predicted and experimentally evaluated epitopes of cancer-testis antigen MAGE-A3 has confirmed that the majority of T-cell epitopes, particularly those that are promiscuous or naturally processed, was located in ordered and disorder/order boundary protein regions overlapping hydrophobic regions.

T-cell epitope prediction methods: an overview

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

Designing of interferon-gamma inducing MHC class-II binders

Background

The generation of interferon-gamma (IFN-γ) by MHC class II activated CD4⁺ T helper cells play a substantial contribution in the control of infections such as caused by Mycobacterium tuberculosis. In the past, numerous methods have been developed for predicting MHC class II binders that can activate T-helper cells. Best of author’s knowledge, no method has been developed so far that can predict the type of cytokine will be secreted by these MHC Class II binders or T-helper epitopes. In this study, an attempt has been made to predict the IFN-γ inducing peptides. The main dataset used in this study contains 3705 IFN-γ inducing and 6728 non-IFN-γ inducing MHC class II binders. Another dataset called IFNgOnly contains 4483 IFN-γ inducing epitopes and 2160 epitopes that induce other cytokine except IFN-γ. In addition we have alternate dataset that contains IFN-γ inducing and equal number of random peptides.

Results

It was observed that the peptide length, positional conservation of residues and amino acid composition affects IFN-γ inducing capabilities of these peptides. We identified the motifs in IFN-γ inducing binders/peptides using MERCI software. Our analysis indicates that IFN-γ inducing and non-inducing peptides can be discriminated using above features. We developed models for predicting IFN-γ inducing peptides using various approaches like machine learning technique, motifs-based search, and hybrid approach. Our best model based on the hybrid approach achieved maximum prediction accuracy of 82.10% with MCC of 0.62 on main dataset. We also developed hybrid model on IFNgOnly dataset and achieved maximum accuracy of 81.39% with 0.57 MCC.

Conclusion

Based on this study, we have developed a webserver for predicting i) IFN-γ inducing peptides, ii) virtual screening of peptide libraries and iii) identification of IFN-γ inducing regions in antigen (http://crdd.osdd.net/raghava/ifnepitope/).

Reviewers

This article was reviewed by Prof Kurt Blaser, Prof Laurence Eisenlohr and Dr Manabu Sugai.

An effective and effecient peptide binding prediction approach for a broad set of HLA-DR molecules based on ordered weighted averaging of binding pocket profiles

Background

The immune system must detect a wide variety of microbial pathogens, such as viruses, bacteria, fungi and parasitic worms, to protect the host against disease. Antigenic peptides displayed by MHC II (class II Major Histocompatibility Complex) molecules is a pivotal process to activate CD4+ T_H cells (Helper T cells). The activated T_H cells can differentiate into effector cells which assist various cells in activating against pathogen invasion. Each MHC locus encodes a great number of allele variants. Yet this limited number of MHC molecules are required to display enormous number of antigenic peptides. Since the peptide binding measurements of MHC molecules by biochemical experiments are expensive, only a few of the MHC molecules have suffecient measured peptides. To perform accurate binding prediction for those MHC alleles without suffecient measured peptides, a number of computational algorithms were proposed in the last decades.

Results

Here, we propose a new MHC II binding prediction approach, OWA-PSSM, which is a significantly extended version of a well known method called TEPITOPE. The TEPITOPE method is able to perform prediction for only 50 MHC alleles, while OWA-PSSM is able to perform prediction for much more, up to 879 HLA-DR molecules. We evaluate the method on five benchmark datasets. The method is demonstrated to be the best one in identifying binding cores compared with several other popular state-of-the-art approaches. Meanwhile, the method performs comparably to the TEPITOPE and NetMHCIIpan2.0 approaches in identifying HLA-DR epitopes and ligands, and it performs significantly better than TEPITOPEpan in the identification of HLA-DR ligands and MultiRTA in identifying HLA-DR T cell epitopes.

Conclusions

The proposed approach OWA-PSSM is fast and robust in identifying ligands, epitopes and binding cores for up to 879 MHC II molecules.

Hybrid biogeography based simultaneous feature selection and MHC class I peptide binding prediction using support vector machines and random forests

Accurate detection of peptides binding to specific Major Histocompatibility Complex Class I (MHC-I) molecules is extremely important for understanding the underlying process of the immune system, as well as for effective vaccine design and developing immunotherapies. Development of learning algorithms and their application for binding predictions have thus speeded up the state-of-the-art in immunological research, in a cost-effective manner. In this work, we propose the application of a hybrid filter-wrapper algorithm employing concepts from the recently developed biogeography based optimization algorithm, in conjunction with SVM and Random Forests for identification of MHC-I binding peptides. In the process, we demonstrate the effectiveness of this evolutionary technique, coupled with weighted heuristics, for the construction of improved prediction models. The experiments have been carried out for the CoEPrA competition datasets (accessible online at: http://www.coepra.org) and the results show a marked improvement over the winner results in some situations and comparably good with regard to others .We thus hope to initiate further research on the application of this new bio-inspired methodology for immunological research.

Relaxation estimation of RMSD in molecular dynamics immunosimulations

Molecular dynamics simulations have to be sufficiently long to draw reliable conclusions. However, no method exists to prove that a simulation has converged. We suggest the method of "lagged RMSD-analysis" as a tool to judge if an MD simulation has not yet run long enough. The analysis is based on RMSD values between pairs of configurations separated by variable time intervals Δt. Unless RMSD(Δt) has reached a stationary shape, the simulation has not yet converged.

Loss in CD4 T-cell responses to multiple epitopes in influenza due to expression of one additional MHC class II molecule in the host

An understanding of factors controlling CD4 T-cell immunodominance is needed to pursue CD4 T-cell epitope-driven vaccine design, yet our understanding of this in humans is limited by the complexity of potential MHC class II molecule expression. In the studies described here, we took advantage of genetically restricted, well-defined mouse strains to better understand the effect of increasing MHC class II molecule diversity on the CD4 T-cell repertoire and the resulting anti-influenza immunodominance hierarchy. Interferon-γ ELISPOT assays were implemented to directly quantify CD4 T-cell responses to I-A(b) and I-A(s) restricted peptide epitopes following primary influenza virus infection in parental and F(1) hybrid strains. We found striking and asymmetric declines in the magnitude of many peptide-specific responses in F(1) animals. These declines could not be accounted for by the lower surface density of MHC class II on the cell or by antigen-presenting cells failing to stimulate T cells with lower avidity T-cell receptors. Given the large diversity of MHC class II expressed in humans, these findings have important implications for the rational design of peptide-based vaccines that are based on the premise that CD4 T-cell epitope specificity can be predicted by a simple cataloguing of an individual's MHC class II genotype.

Conservation analysis of dengue virus T-cell epitope-based vaccine candidates using Peptide block entropy

Broad coverage of the pathogen population is particularly important when designing CD8+ T-cell epitope vaccines against viral pathogens. Traditional approaches are based on combinations of highly conserved T-cell epitopes. Peptide block entropy analysis is a novel approach for assembling sets of broadly covering antigens. Since T-cell epitopes are recognized as peptides rather than individual residues, this method is based on calculating the information content of blocks of peptides from a multiple sequence alignment of homologous proteins rather than using the information content of individual residues. The block entropy analysis provides broad coverage of variant antigens. We applied the block entropy analysis method to the proteomes of the four serotypes of dengue virus (DENV) and found 1,551 blocks of 9-mer peptides, which cover 99% of available sequences with five or fewer unique peptides. In contrast, the benchmark study by Khan et al. (2008) resulted in 165 conserved 9-mer peptides. Many of the conserved blocks are located consecutively in the proteins. Connecting these blocks resulted in 78 conserved regions. Of the 1551 blocks of 9-mer peptides 110 comprised predicted HLA binder sets. In total, 457 subunit peptides that encompass the diversity of all sequenced DENV strains of which 333 are T-cell epitope candidates.

Direct identification of an HPV-16 tumor antigen from cervical cancer biopsy specimens

Persistent infection with high-risk human papilloma viruses (HPV) is the worldwide cause of many cancers, including cervical, anal, vulval, vaginal, penile, and oropharyngeal. Since T cells naturally eliminate the majority of chronic HPV infections by recognizing epitopes displayed on virally altered epithelium, we exploited Poisson detection mass spectrometry (MS³) to identify those epitopes and inform future T cell-based vaccine design. Nine cervical cancer biopsies from HPV-16 positive HLA-A^*02 patients were obtained, histopathology determined, and E7 oncogene PCR-amplified from tumor DNA and sequenced. Conservation of E7 oncogene coding segments was found in all tumors. MS³ analysis of HLA-A^*02 immunoprecipitates detected E7_11–19 peptide (YMLDLQPET) in seven of the nine tumor biopsies. The remaining two samples were E7_11–19 negative and lacked the HLA-A^*02 binding GILT thioreductase peptide despite possessing binding-competent HLA-A^*02 alleles. Thus, the conserved E7_11–19 peptide is a dominant HLA-A^*02 binding tumor antigen in HPV-16 transformed cervical squamous and adenocarcinomas. Findings that a minority of HLA-A^*02:01 tumors lack expression of both E7_11–19 and a peptide from a thioreductase important in processing of cysteine-rich proteins like E7 underscore the value of physical detection, define a potential additional tumor escape mechanism and have implications for therapeutic cancer vaccine development.

Efficacy of HLA-DRB1∗03:01 and H2E transgenic mouse strains to correlate pathogenic thyroglobulin epitopes for autoimmune thyroiditis

Thyroglobulin (Tg), a homodimer of 660 kD comprising 2748 amino acids, is the largest autoantigen known. The prevalence of autoimmune thyroid disease, including Hashimoto's thyroiditis and Graves' disease, has provided the impetus for identifying pathogenic T cell epitopes from human Tg over two decades. With no known dominant epitopes, the search has long been a challenge for investigators. After identifying HLA-DRB1∗03:01 (HLA-DR3) and H2E(b) as susceptibility alleles for Tg-induced experimental autoimmune thyroiditis in transgenic mouse strains, we searched for naturally processed T cell epitopes with MHC class II-binding motif anchors and tested the selected peptides for pathogenicity in these mice. The thyroiditogenicity of one peptide, hTg2079, was confirmed in DR3 transgenic mice and corroborated in clinical studies. In H2E(b)-expressing transgenic mice, we identified three T cell epitopes from mouse Tg, mTg179, mTg409 and mTg2342, based on homology to epitopes hTg179, hTg410 and hTg2344, respectively, which we and others have found stimulatory or pathogenic in both DR3- and H2E-expressing mice. The high homology among these peptides with shared presentation by DR3, H2E(b) and H2E(k) molecules led us to examine the binding pocket residues of these class II molecules. Their similar binding characteristics help explain the pathogenic capacity of these T cell epitopes. Our approach of using appropriate human and murine MHC class II transgenic mice, combined with the synthesis and testing of potential pathogenic Tg peptides predicted from computational models of MHC-binding motifs, should continue to provide insights into human autoimmune thyroid disease.

Dana-Farber repository for machine learning in immunology

The immune system is characterized by high combinatorial complexity that necessitates the use of specialized computational tools for analysis of immunological data. Machine learning (ML) algorithms are used in combination with classical experimentation for the selection of vaccine targets and in computational simulations that reduce the number of necessary experiments. The development of ML algorithms requires standardized data sets, consistent measurement methods, and uniform scales. To bridge the gap between the immunology community and the ML community, we designed a repository for machine learning in immunology named Dana-Farber Repository for Machine Learning in Immunology (DFRMLI). This repository provides standardized data sets of HLA-binding peptides with all binding affinities mapped onto a common scale. It also provides a list of experimentally validated naturally processed T cell epitopes derived from tumor or virus antigens. The DFRMLI data were preprocessed and ensure consistency, comparability, detailed descriptions, and statistically meaningful sample sizes for peptides that bind to various HLA molecules. The repository is accessible at http://bio.dfci.harvard.edu/DFRMLI/.