Prediction of peptides binding to MHC class I alleles by partial periodic pattern mining

MHC (Major Histocompatibility Complex) is a key player in the immune response of an organism. It is important to be able to predict which antigenic peptides will bind to a specific MHC allele and which will not, creating possibilities for controlling immune response and for the applications of immunotherapy. However, a problem for MHC class I is the presence of bulges and loops in the peptides, changing the total length. Most machine learning methods in use today require the sequences to be of same length to successfully mine the binding motifs. We propose the use of time-based data mining methods in motif mining to be able to mine motifs position-independently. Also, the information for both binding and non-binding peptides is used on the contrary to the other methods which only rely on binding peptides. The prediction results are between 60-95% for the tested alleles

Models of self-peptide sampling by developing T cells identify candidate mechanisms of thymic selection

Conventional and regulatory T cells develop in the thymus where they are exposed to samples of self-peptide MHC (pMHC) ligands. This probabilistic process selects for cells within a range of responsiveness that allows the detection of foreign antigen without excessive responses to self. Regulatory T cells are thought to lie at the higher end of the spectrum of acceptable self-reactivity and play a crucial role in the control of autoimmunity and tolerance to innocuous antigens. While many studies have elucidated key elements influencing lineage commitment, we still lack a full understanding of how thymocytes integrate signals obtained by sampling self-peptides to make fate decisions. To address this problem, we apply stochastic models of signal integration by T cells to data from a study quantifying the development of the two lineages using controllable levels of agonist peptide in the thymus. We find two models are able to explain the observations; one in which T cells continually re-assess fate decisions on the basis of multiple summed proximal signals from TCR-pMHC interactions; and another in which TCR sensitivity is modulated over time, such that contact with the same pMHC ligand may lead to divergent outcomes at different stages of development. Neither model requires that T and T are differentially susceptible to deletion or that the two lineages need qualitatively different signals for development, as have been proposed. We find additional support for the variable-sensitivity model, which is able to explain apparently paradoxical observations regarding the effect of partial and strong agonists on T and T development

Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide conservation scores

© 2014 Goodswen et al. Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein's potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response-one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine discovery pool of evidence

A Comparative Approach Linking Molecular Dynamics of Altered Peptide Ligands and MHC with In Vivo Immune Responses

The recognition of peptide in the context of MHC by T lymphocytes is a critical step in the initiation of an adaptive immune response. However, the molecular nature of the interaction between peptide and MHC and how it influences T cell responsiveness is not fully understood.We analyzed the immunological consequences of the interaction of MHC class II (I-Au) restricted 11-mer peptides of myelin basic protein with amino acid substitutions at position 4. These mutant peptides differ in MHC binding affinity, CD4+ T cell priming, and alter the severity of peptide-induced experimental allergic encephalomyelitis. Using molecular dynamics, a computational method of quantifying intrinsic movements of proteins at high resolution, we investigated conformational changes in MHC upon peptide binding. We found that irrespective of peptide binding affinity, MHC deformation appears to influence costimulation, which then leads to effective T cell priming and disease induction. Although this study compares in vivo and molecular dynamics results for three altered peptide ligands, further investigation with similar complexes is essential to determine whether spatial rearrangement of peptide-MHC and costimulatory complexes is an additional level of T cell regulation

Development of Immune-Specific Interaction Potentials and Their Application in the Multi-Agent-System VaccImm

Peptide vaccination in cancer therapy is a promising alternative to conventional methods. However, the parameters for this personalized treatment are difficult to access experimentally. In this respect, in silico models can help to narrow down the parameter space or to explain certain phenomena at a systems level. Herein, we develop two empirical interaction potentials specific to B-cell and T-cell receptor complexes and validate their applicability in comparison to a more general potential. The interaction potentials are applied to the model VaccImm which simulates the immune response against solid tumors under peptide vaccination therapy. This multi-agent system is derived from another immune system simulator (C-ImmSim) and now includes a module that enables the amino acid sequence of immune receptors and their ligands to be taken into account. The multi-agent approach is combined with approved methods for prediction of major histocompatibility complex (MHC)-binding peptides and the newly developed interaction potentials. In the analysis, we critically assess the impact of the different modules on the simulation with VaccImm and how they influence each other. In addition, we explore the reasons for failures in inducing an immune response by examining the activation states of the immune cell populations in detail

Induction of T-cell responses against mutation-specific peptides from malignant pediatric brain tumor samples

Medulloblastoma is the most common malignant brain tumor in childhood and adolescence and constitutes an important cause for cancer-related death in pediatric patients. Although standard therapy including surgery, chemotherapy and radiation can cure up to 80 % of average-risk patients, they imply severe cognitive long-term adverse effects and are unsatisfactory in advanced tumors. Therefore, alternative treatment strategies need to be established. Immunotherapeutic approaches like peptide vaccination and adoptive T-cell transfer (ATT) aim at enhancing self-protection through detection and elimination of malignant cells. Tumor-specific neoepitopes are promising targets for ATT as they are expressed exclusively by cancer tissue. Moreover, administration of mutation-derived peptide vaccines allows augmenting the endogenous immune response through abundant presentation of tumor antigen. In this proof-of-concept study we demonstrate a highly individualized approach where patient-specific neoepitopes are determined and tested for immunogenicity. Primary tumor samples from two pediatric medulloblastoma patients were analyzed in this project. Tumor-specific mutations were identified by next generation sequencing of tumor tissue and whole blood. Variants were confirmed by deep sequencing. In order to identify neoepitope peptides presented by the patients’ human leucocyte antigen (HLA) molecules, HLA binding affinity was predicted in silico by netMHC database. Respective peptides were synthesized and blood cells from healthy donors matching the patients’ HLA types were used to provide T lymphocytes and dendritic cells for antigen presentation. After seven restimulations in vitro, CD8+ cytotoxic T-cell reactivity against neoepitopes was assessed via flow-cytometric analysis of Interferon gamma and Tumor Necrosis Factor alpha release. A successful de novo T-cell response was induced for 9 of 19 tested peptides. In this proof-of-principle study we show that induction of a T-cell response against medullobastoma-derived neoantigens is feasible despite low mutational burden and low immunogenicity. In the future, this strategy can be used to synthesize individualized peptide cocktails for peptide vaccination or identify medulloblastoma-specific T-cell receptors for ATT. Long-term aims of this study are the identification of medulloblastoma/T-cell interaction and improvement of current treatment options for pediatric patients with advanced medulloblastoma

Identification of T-cell epitopes in the Hepatitis C virus genotype 4 proteome: a step towards epitope-driven vaccine development

Hepatitis C is an inflammatory infectious disease of the liver caused by the Hepatitis C Virus (HCV). It is a global pandemic, chronically inflicting 150 million people worldwide, with millions of new infections arising annually. The standard therapy of HCV is expensive, associated with severe side effects, and has variable success rates. Thus far, no HCV vaccine has been developed, owing to the challenges that faced and still face its development. Despite these challenges, several attempts have been taken to develop a vaccine, some of which have progressed to phase II clinical trials. Most of these attempts, however, have focused on HCV genotypes 1 and 2 as vaccine targets, and almost no attention has been given to HCV genotype 4 (HCV-4), the viral genotype most prevalent in the Middle East and Central Africa. In an attempt to fill this gap in HCV-4 vaccine research, this project describes the in silico identification of a group of highly conserved and immunogenic T-cell epitopes from the HCV-4 proteome, using the iVAX immunoinformatics toolkit (EpiVax Inc., RI, USA), as a first step towards the development of an epitope-driven vaccine against the viral genotype. Furthermore, it puts forth a fast and inexpensive method for the validation of the results retrospectively using the repository of empirical HCV immune epitope data on the Immune Epitope Database (IEDB). 90 HLA class I and 14 HLA class II epitopes were identified. From those, 20 HLA class I epitopes were found to be previously uncharacterized, while the in silico HLA binding predictions for 27 others (class I and class II) have been retrospectively validated. The retrospective validation results for 4 of the identified HLA class II epitopes were confirmed by a pilot HLA class II binding assay. Furthermore, an investigation of the conservancy of a selected set of the identified epitopes in newly re-sequenced HCV strains from the Egyptian population was performed. The identified and retrospectively validated set of epitopes constitutes a good target for further immunogenicity testing and epitope-driven vaccine development against HCV-4

Bridging Innate and Adaptive Antitumor Immunity Targeting Glycans

Effective immunotherapy for cancer depends on cellular responses to tumor antigens. The role of major histocompatibility complex (MHC) in T-cell recognition and T-cell receptor repertoire selection has become a central tenet in immunology. Structurally, this does not contradict earlier findings that T-cells can differentiate between small hapten structures like simple glycans. Understanding T-cell recognition of antigens as defined genetically by MHC and combinatorially by T cell receptors led to the “altered self” hypothesis. This notion reflects a more fundamental principle underlying immune surveillance and integrating evolutionarily and mechanistically diverse elements of the immune system. Danger associated molecular patterns, including those generated by glycan remodeling, represent an instance of altered self. A prominent example is the modification of the tumor-associated antigen MUC1. Similar examples emphasize glycan reactivity patterns of antigen receptors as a phenomenon bridging innate and adaptive but also humoral and cellular immunity and providing templates for immunotherapies

From Functional Genomics to Functional Immunomics: New Challenges, Old Problems, Big Rewards

The development of DNA microarray technology a decade ago led to the establishment of functional genomics as one of the most active and successful scientific disciplines today. With the ongoing development of immunomic microarray technology—a spatially addressable, large-scale technology for measurement of specific immunological response—the new challenge of functional immunomics is emerging, which bears similarities to but is also significantly different from functional genomics. Immunonic data has been successfully used to identify biological markers involved in autoimmune diseases, allergies, viral infections such as human immunodeficiency virus (HIV), influenza, diabetes, and responses to cancer vaccines. This review intends to provide a coherent vision of this nascent scientific field, and speculate on future research directions. We discuss at some length issues such as epitope prediction, immunomic microarray technology and its applications, and computation and statistical challenges related to functional immunomics. Based on the recent discovery of regulation mechanisms in T cell responses, we envision the use of immunomic microarrays as a tool for advances in systems biology of cellular immune responses, by means of immunomic regulatory network models