11 research outputs found

    EpiToolKit—a web server for computational immunomics

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    Predicting the T-cell-mediated immune response is an important task in vaccine design and thus one of the key problems in computational immunomics. Various methods have been developed during the last decade and are available online. We present EpiToolKit, a web server that has been specifically designed to offer a problem-solving environment for computational immunomics. EpiToolKit offers a variety of different prediction methods for major histocompatibility complex class I and II ligands as well as minor histocompatibility antigens. These predictions are embedded in a user-friendly interface allowing refining, editing and constraining the searches conveniently. We illustrate the value of the approach with a set of novel tumor-associated peptides. EpiToolKit is available online at www.epitoolkit.org

    FRED—a framework for T-cell epitope detection

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    Summary: Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different methods become increasingly important. We have developed FRED, an extendable, open source software framework for key tasks in immunoinformatics. In this, its first version, FRED offers easily accessible prediction methods for MHC binding and antigen processing as well as general infrastructure for the handling of antigen sequence data and epitopes. FRED is implemented in Python in a modular way and allows the integration of external methods

    Vaccines meet big data: State-ofthe- Art and future prospects. From the classical 3is ("isolate-inactivate- inject") vaccinology 1.0 to vaccinology 3.0, vaccinomics, and Beyond: A historical overview

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    Vaccines are public health interventions aimed at preventing infections-related mortality, morbidity, and disability. While vaccines have been successfully designed for those infectious diseases preventable by preexisting neutralizing specific antibodies, for other communicable diseases, additional immunological mechanisms should be elicited to achieve a full protection. "New vaccines" are particularly urgent in the nowadays society, in which economic growth, globalization, and immigration are leading to the emergence/ reemergence of old and new infectious agents at the animal-human interface. Conventional vaccinology (the so-called "vaccinology 1.0") was officially born in 1796 thanks to the contribution of Edward Jenner. Entering the twenty-first century, vaccinology has shifted from a classical discipline in which serendipity and the Pasteurian principle of the three Is (isolate, inactivate, and inject) played a major role to a science, characterized by a rational design and plan ("vaccinology 3.0"). This shift has been possible thanks to Big Data, characterized by different dimensions, such as high volume, velocity, and variety of data. Big Data sources include new cutting-edge, high-throughput technologies, electronic registries, social media, and social networks, among others. The current mini-review aims at exploring the potential roles as well as pitfalls and challenges of Big Data in shaping the future vaccinology, moving toward a tailored and personalized vaccine design and administration. © 2018 Bragazzi, Gianfredi, Villarini, Rosselli, Nasr, Hussein, Martini and Behzadifar

    Vaccines meet big data: State-ofthe- Art and future prospects. From the classical 3is ("isolate-inactivate- inject") vaccinology 1.0 to vaccinology 3.0, vaccinomics, and Beyond: A historical overview

    Get PDF
    Vaccines are public health interventions aimed at preventing infections-related mortality, morbidity, and disability. While vaccines have been successfully designed for those infectious diseases preventable by preexisting neutralizing specific antibodies, for other communicable diseases, additional immunological mechanisms should be elicited to achieve a full protection. "New vaccines" are particularly urgent in the nowadays society, in which economic growth, globalization, and immigration are leading to the emergence/ reemergence of old and new infectious agents at the animal-human interface. Conventional vaccinology (the so-called "vaccinology 1.0") was officially born in 1796 thanks to the contribution of Edward Jenner. Entering the twenty-first century, vaccinology has shifted from a classical discipline in which serendipity and the Pasteurian principle of the three Is (isolate, inactivate, and inject) played a major role to a science, characterized by a rational design and plan ("vaccinology 3.0"). This shift has been possible thanks to Big Data, characterized by different dimensions, such as high volume, velocity, and variety of data. Big Data sources include new cutting-edge, high-throughput technologies, electronic registries, social media, and social networks, among others. The current mini-review aims at exploring the potential roles as well as pitfalls and challenges of Big Data in shaping the future vaccinology, moving toward a tailored and personalized vaccine design and administration. © 2018 Bragazzi, Gianfredi, Villarini, Rosselli, Nasr, Hussein, Martini and Behzadifar

    Programi i baze podataka o peptidima i proteolitičkim enzimima na internetu – kratak osvrt na 2007. i 2008. godinu

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    Bioinformatics methods have become one of the most important tools in peptide science. The number of available peptide databases is growing rapidly. The number of online programs able to process peptide sequences to extract information concerning their structure, physicochemical and biological properties is also increasing. Many of such programs were designed to manipulate protein sequences, but they have no built-in restrictions disabling their application to process oligopeptides containing less than 20 amino acid residues. Publications addressing these programs cannot be found in literature databases using the keyword \u27peptide\u27 or \u27peptides\u27, in connection with the term \u27bioinformatics\u27 or related terms, thus a reference source summarizing data from such publications seems necessary. This paper provides a brief review of bioactive peptide databases and sequence alignment programs enabling the search for peptide motifs, determination of physicochemical properties of amino acid residues, prediction of peptide structure, the occurrence of posttranslational glycosylation and immunogenicity, as well as the support of peptide design process. The review also includes databases and programs providing information about proteolytic enzymes. The databases and programs discussed in this paper were developed or updated between September 2007 and December 2008.Bioinformatičke metode postale su jedan od najvažnijih alata u području istraživanja peptida. Sve je veći broj dostupnih baza podataka o peptidima, a i „online” programa koji obradom aminokiselinskih sljedova daju informacije o strukturi peptida te njihovim fizikalno-kemijskim i biološkim svojstvima. Mnogi od tih programa dizajnirani su za obradu aminokiselinskih sljedova proteina, ali nemaju ugrađenu restrikciju njihove primjene na oligopeptide koji sadrže manje od 20 aminokiselinskih ostataka. Radovi o takvim programima ne mogu se pronaći u bazama publikacija uporabom ključnih riječi „peptid” ili „peptidi”, u kombinaciji s pojmom „bioinformatika” ili sličnim terminima. Stoga je važno sažeti rezultate objavljenih radova u jednom izvoru. U ovom je radu dan kratak pregled baza podataka o bioaktivnim peptidima i programima za analizu aminokiselinskoga slijeda koji omogućuju: pronalazak peptidnih motiva; određivanje fizikalno-kemijskih svojstava aminokiselinskih ostataka; predviđanje strukture peptida, pojave posttranslacijske glikozilacije i imunogenosti; te programsku podršku za dizajn peptida. Također su prikazani programi i baze podataka o proteolitičkim enzimima. Svi navedeni programi i baze razvijeni su i ažurirani od rujna 2007. do prosinca 2008

    The Impact of Bioinformatics on Vaccine Design and Development

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    Vaccines are the pharmaceutical products that offer the best cost‐benefit ratio in the prevention or treatment of diseases. In that a vaccine is a pharmaceutical product, vaccine development and production are costly and it takes years for this to be accomplished. Several approaches have been applied to reduce the times and costs of vaccine development, mainly focusing on the selection of appropriate antigens or antigenic structures, carriers, and adjuvants. One of these approaches is the incorporation of bioinformatics methods and analyses into vaccine development. This chapter provides an overview of the application of bioinformatics strategies in vaccine design and development, supplying some successful examples of vaccines in which bioinformatics has furnished a cutting edge in their development. Reverse vaccinology, immunoinformatics, and structural vaccinology are described and addressed in the design and development of specific vaccines against infectious diseases caused by bacteria, viruses, and parasites. These include some emerging or re‐emerging infectious diseases, as well as therapeutic vaccines to fight cancer, allergies, and substance abuse, which have been facilitated and improved by using bioinformatics tools or which are under development based on bioinformatics strategies

    Advanced Immunoinformatics Approaches for Precision Medicine

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    Genomic sequencing and other ’-omic’ technologies are slowly changing biomedical practice. As a result, patients now can be treated based on their molecular profile. Especially the immune system’s variability, in particular that of the human leukocyte antigen (HLA) gene cluster, makes such a paradigm indispensable when treating illnesses such as cancer, autoimmune diseases, or infectious diseases. It can be, however, costly and time-consuming to determine the HLA genotype with traditional means, as these methods do not utilize often pre-existing sequencing data. We therefore proposed an algorithmic approach that can use these data sources to infer the HLA genotype. HLA genotyping inference can be cast into a set covering problem under special biological constraints and can be solved efficiently via integer linear programming. Our proposed approach outperformed previously published methods and remains one of the most accurate methods to date. We then introduced two applications in which a HLA-based stratification is vital for the efficacy of the treatment and the reduction of its adverse effects. In the first example, we dealt with the optimal design of string-of-beads vaccines (SOB). We developed a mathematical model that maximizes the efficacy of such vaccines while minimizing their side effects based on a given HLA distribution. Comparisons of our optimally designed SOB with experimentally tested designs yielded promising results. In the second example, we considered the problem of anti-drug antibody (ADA) formation of biotherapeutics caused by HLA presented peptides. We combined a new statistical model for mutation effect prediction together with a quantitative measure of immunogenicity to formulate an optimization problem that finds alterations to reduce the risk of ADA formation. To efficiently solve this bi-objective problem, we developed a distributed solver that is up to 25-times faster than state-of-the art solvers. We used our approach to design the C2 domain of factor VIII, which is linked to ADA formation in hemophilia A. Our experimental evaluations of the proposed designs are encouraging and demonstrate the prospects of our approach. Bioinformatics is an integral part of modern biomedical research. The translation of advanced methods into clinical use is often complicated. To ease the translation, we developed a programming library for computational immunology and used it to implement a Galaxy-based web server for vaccine design and a KNIME extension for desktop PCs. These platforms allow researchers to develop their own immunoinformatics workflows utilizing the platform’s graphical programming capabilities

    Sequence analysis methods for the design of cancer vaccines that target tumor-specific mutant antigens (neoantigens)

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    The human adaptive immune system is programmed to distinguish between self and non-self proteins and if trained to recognize markers unique to a cancer, it may be possible to stimulate the selective destruction of cancer cells. Therapeutic cancer vaccines aim to boost the immune system by selectively increasing the population of T cells specifically targeted to the tumor-unique antigens, thereby initiating cancer cell death.. In the past, this approach has primarily focused on targeted selection of ‘shared’ tumor antigens, found across many patients. The advent of massively parallel sequencing and specialized analytical approaches has enabled more efficient characterization of tumor-specific mutant antigens, or neoantigens. Specifically, methods to predict which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell recognition improve predictions of immune checkpoint therapy response and identify one or more neoantigens as targets for personalized vaccines. Selecting the best/most immunogenic neoantigens from a large number of mutations is an important challenge, in particular in cancers with a high mutational load, such as melanomas and smoker-associated lung cancers. To address such a challenging task, Chapter 1 of this thesis describes a genome-guided in silico approach to identifying tumor neoantigens that integrates tumor mutation and expression data (DNA- and RNA-Seq). The cancer vaccine design process, from read alignment to variant calling and neoantigen prediction, typically assumes that the genotype of the Human Reference Genome sequence surrounding each somatic variant is representative of the patient’s genome sequence, and does not account for the effect of nearby variants (somatic or germline) in the neoantigenic peptide sequence. Because the accuracy of neoantigen identification has important implications for many clinical trials and studies of basic cancer immunology, Chapter 2 describes and supports the need for patient-specific inclusion of proximal variants to address this previously oversimplified assumption in the identification of neoantigens. The method of neoantigen identification described in Chapter 1 was subsequently extended (Chapter 3) and improved by the addition of a modular workflow that aids in each component of the neoantigen prediction process from neoantigen identification, prioritization, data visualization, and DNA vaccine design. These chapters describe massively parallel sequence analysis methods that will help in the identification and subsequent refinement of patient-specific antigens for use in personalized immunotherapy

    Computational Methods towards Personalized Cancer Vaccines and their Application through a Web-based Platform

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    Cancer immunotherapy is a treatment option that involves or uses components of a patient’s immune system. Today, it is heading towards becoming an integral part of treatment plans together with chemotherapy, surgery, and radiotherapy. Personalized epitope-based vaccines (EVs) serve as one strategy that is truly personalized. Each patient possesses a distinct immune system, and each tumor is unique, rendering the design of a potent vaccine challenging and dependent on the patient and the tumor. The potency of a vaccine is reliant on the ability of its constituent epitopes – short, immunogenic antigen fragments – to trigger an immune response. To assess this ability, one has to take into account the individuality of the immune system, among others conditioned by the variability of the human leukocyte antigen (HLA) gene cluster. Determining the HLA genotype with traditional experimental techniques can be time- and cost-intensive. We proposed a novel HLA genotyping algorithm based on integer linear programming that is independent of dedicated data generation for the sole purpose of HLA typing. On publicly available next-generation sequencing (NGS) data, our method outperformed previously published approaches. HLA binding is a prerequisite for T-cell recognition, and precise prediction algorithms exist. However, this information is not sufficient to assess the immunogenic potential of a peptide. To induce an immune response, reactive T-cell clones with receptors specific for a peptide-HLA complex have to be present. We suggested a method for the prediction of immunogenicity that includes peripheral tolerance models, based on gut microbiome data, in addition to central tolerance, previously shown to increase performance. The comparison to a previously published method suggests that the incorporation of gut microbiome data and HLA-binding stability estimates do not enhance prediction performance. High-throughput sequencing provides the basis for the design of personalized EVs. Through genome and transcriptome sequencing of tumor and matched non-malignant tissue samples, cancer-specific mutations can be identified, which can be further validated using other technologies such as mass spectrometry (MS). Multi-omics approaches can result in the acquisition of several hundreds of gigabytes of data. Handling and analysis of such data usually require data management solutions and high-performance computing (HPC) infrastructures. We developed the web-based platform qPortal for data-driven biomedical research that allows users to manage and analyze quantitative biological data intuitively. To emphasize the advantages of our data-driven approach with an integrated workflow system, we conducted a comparison to Galaxy. Building on qPortal, we implemented the web-based platform iVacPortal for the design of personalized EVs to facilitate data management and data analysis in such projects. Further, we applied the implemented methods through iVacPortal in two studies of two distinct cancer entities, indicating the added value of our platform for the assessment of personalized EV candidates and alternative targets for cancer immunotherapy
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