Predicting Protein Subcellular Localization: Past, Present, and Future

Functional characterization of every single protein is a major challenge of the post-genomic era. The large-scale analysis of a cell’s proteins, proteomics, seeks to provide these proteins with reliable annotations regarding their interaction partners and functions in the cellular machinery. An important step on this way is to determine the subcellular localization of each protein. Eukaryotic cells are divided into subcellular compartments, or organelles. Transport across the membrane into the organelles is a highly regulated and complex cellular process. Predicting the subcellular localization by computational means has been an area of vivid activity during recent years. The publicly available prediction methods differ mainly in four aspects: the underlying biological motivation, the computational method used, localization coverage, and reliability, which are of importance to the user. This review provides a short description of the main events in the protein sorting process and an overview of the most commonly used methods in this field

Prediction of MHC class I binding peptides, using SVMHC

BACKGROUND: T-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules. MHC-peptide complexes are potential tools for diagnosis and treatment of pathogens and cancer, as well as for the development of peptide vaccines. Only one in 100 to 200 potential binders actually binds to a certain MHC molecule, therefore a good prediction method for MHC class I binding peptides can reduce the number of candidate binders that need to be synthesized and tested. RESULTS: Here, we present a novel approach, SVMHC, based on support vector machines to predict the binding of peptides to MHC class I molecules. This method seems to perform slightly better than two profile based methods, SYFPEITHI and HLA_BIND. The implementation of SVMHC is quite simple and does not involve any manual steps, therefore as more data become available it is trivial to provide prediction for more MHC types. SVMHC currently contains prediction for 26 MHC class I types from the MHCPEP database or alternatively 6 MHC class I types from the higher quality SYFPEITHI database. The prediction models for these MHC types are implemented in a public web service available at http://www.sbc.su.se/svmhc/. CONCLUSIONS: Prediction of MHC class I binding peptides using Support Vector Machines, shows high performance and is easy to apply to a large number of MHC class I types. As more peptide data are put into MHC databases, SVMHC can easily be updated to give prediction for additional MHC class I types. We suggest that the number of binding peptides needed for SVM training is at least 20 sequences

Machine learning techniques for personalised medicine approaches in immune-mediated chronic inflammatory diseases: Applications and challenges

In the past decade, the emergence of machine learning (ML) applications has led to significant advances towards implementation of personalised medicine approaches for improved health care, due to the exceptional performance of ML models when utilising complex big data. The immune-mediated chronic inflammatory diseases are a group of complex disorders associated with dysregulated immune responses resulting in inflammation affecting various organs and systems. The heterogeneous nature of these diseases poses great challenges for tailored disease management and addressing unmet patient needs. Applying novel ML techniques to the clinical study of chronic inflammatory diseases shows promising results and great potential for precision medicine applications in clinical research and practice. In this review, we highlight the clinical applications of various ML techniques for prediction, diagnosis and prognosis of autoimmune rheumatic diseases, inflammatory bowel disease, autoimmune chronic kidney disease, and multiple sclerosis, as well as ML applications for patient stratification and treatment selection. We highlight the use of ML in drug development, including target identification, validation and drug repurposing, as well as challenges related to data interpretation and validation, and ethical concerns related to the use of artificial intelligence in clinical research

Occurrence of Anti-Drug Antibodies against Interferon-Beta and Natalizumab in Multiple Sclerosis: A Collaborative Cohort Analysis

Immunogenicity of biopharmaceutical products in multiple sclerosis is a frequent side effect which has a multifactorial etiology. Here we study associations between anti-drug antibody (ADA) occurrence and demographic and clinical factors. Retrospective data from routine ADA test laboratories in Sweden, Denmark, Austria and Germany (Dusseldorf group) and from one research study in Germany (Munich group) were gathered to build a collaborative multi-cohort dataset within the framework of the ABIRISK project. A subset of 5638 interferon-beta (IFN beta)-treated and 3440 natalizumab-treated patients having data on at least the first two years of treatment were eligible for interval-censored time-to-event analysis. In multivariate Cox regression, IFN beta-1a subcutaneous and IFN beta-1b subcutaneous treated patients were at higher risk of ADA occurrence compared to IFN beta-1a intramuscular-treated patients (pooled HR = 6.4, 95% CI 4.9-8.4 and pooled HR = 8.7, 95% CI 6.6-11.4 respectively). Patients older than 50 years at start of IFN beta therapy developed ADA more frequently than adult patients younger than 30 (pooled HR = 1.8, 95% CI 1.4-2.3). Men developed ADA more frequently than women (pooled HR = 1.3, 95% CI 1.1-1.6). Interestingly we observed that in Sweden and Germany, patients who started IFN beta in April were at higher risk of developing ADA (HR = 1.6, 95% CI 1.1-2.4 and HR = 2.4, 95% CI 1.5-3.9 respectively). This result is not confirmed in the other cohorts and warrants further investigations. Concerning natalizumab, patients older than 45 years had a higher ADA rate (pooled HR = 1.4, 95% CI 1.0-1.8) and women developed ADA more frequently than men (pooled HR = 1.4, 95% CI 1.0-2.0). We confirmed previously reported differences in immunogenicity of the different types of IFN beta. Differences in ADA occurrence by sex and age are reported here for the first time. These findings should be further investigated taking into account other exposures and biomarkers

Occurrence of Anti-Drug Antibodies against Interferon-Beta and Natalizumab in Multiple Sclerosis: A Collaborative Cohort Analysis

Immunogenicity of biopharmaceutical products in multiple sclerosis is a frequent side effect which has a multifactorial etiology. Here we study associations between anti-drug antibody (ADA) occurrence and demographic and clinical factors. Retrospective data from routine ADA test laboratories in Sweden, Denmark, Austria and Germany (Dusseldorf group) and from one research study in Germany (Munich group) were gathered to build a collaborative multi-cohort dataset within the framework of the ABIRISK project. A subset of 5638 interferon-beta (IFN beta)-treated and 3440 natalizumab-treated patients having data on at least the first two years of treatment were eligible for interval-censored time-to-event analysis. In multivariate Cox regression, IFN beta-1a subcutaneous and IFN beta-1b subcutaneous treated patients were at higher risk of ADA occurrence compared to IFN beta-1a intramuscular-treated patients (pooled HR = 6.4, 95% CI 4.9-8.4 and pooled HR = 8.7, 95% CI 6.6-11.4 respectively). Patients older than 50 years at start of IFN beta therapy developed ADA more frequently than adult patients younger than 30 (pooled HR = 1.8, 95% CI 1.4-2.3). Men developed ADA more frequently than women (pooled HR = 1.3, 95% CI 1.1-1.6). Interestingly we observed that in Sweden and Germany, patients who started IFN beta in April were at higher risk of developing ADA (HR = 1.6, 95% CI 1.1-2.4 and HR = 2.4, 95% CI 1.5-3.9 respectively). This result is not confirmed in the other cohorts and warrants further investigations. Concerning natalizumab, patients older than 45 years had a higher ADA rate (pooled HR = 1.4, 95% CI 1.0-1.8) and women developed ADA more frequently than men (pooled HR = 1.4, 95% CI 1.0-2.0). We confirmed previously reported differences in immunogenicity of the different types of IFN beta. Differences in ADA occurrence by sex and age are reported here for the first time. These findings should be further investigated taking into account other exposures and biomarkers

Clinical practice of analysis of anti-drug antibodies against interferon beta and natalizumab in multiple sclerosis patients in Europe:A descriptive study of test results

Antibodies against biopharmaceuticals (anti-drug antibodies, ADA) have been a well-integrated part of the clinical care of multiple sclerosis (MS) in several European countries. ADA data generated in Europe during the more than 10 years of ADA monitoring in MS patients treated with interferon beta (IFNβ) and natalizumab have been pooled and characterized through collaboration within a European consortium. The aim of this study was to report on the clinical practice of ADA testing in Europe, considering the number of ADA tests performed and type of ADA assays used, and to determine the frequency of ADA testing against the different drug preparations in different countries. A common database platform (tranSMART) for querying, analyzing and storing retrospective data of MS cohorts was set up to harmonize the data and compare results of ADA tests between different countries. Retrospective data from six countries (Sweden, Austria, Spain, Switzerland, Germany and Denmark) on 20,695 patients and on 42,555 samples were loaded into tranSMART including data points of age, gender, treatment, samples, and ADA results. The previously observed immunogenic difference among the four IFNβ preparations was confirmed in this large dataset. Decreased usage of the more immunogenic preparations IFNβ-1a subcutaneous (s.c.) and IFNβ-1b s.c. in favor of the least immunogenic preparation IFNβ-1a intramuscular (i.m.) was observed. The median time from treatment start to first ADA test correlated with time to first positive test. Shorter times were observed for IFNβ-1b-Extavia s.c. (0.99 and 0.94 years) and natalizumab (0.25 and 0.23 years), which were introduced on the market when ADA testing was already available, as compared to IFNβ-1a i.m. (1.41 and 2.27 years), IFNβ-1b-Betaferon s.c. (2.51 and 1.96 years) and IFNβ-1a s.c. (2.11 and 2.09 years) which were available years before routine testing began. A higher rate of anti-IFNβ ADA was observed in test samples taken from older patients. Testing for ADA varies between different European countries and is highly dependent on the policy within each country. For drugs where routine monitoring of ADA is not in place, there is a risk that some patients remain on treatment for several years despite ADA positivity. For drugs where a strategy of ADA testing is introduced with the release of the drug, there is a reduced risk of having ADA positive patients and thus of less efficient treatment. This indicates that potential savings in health cost might be achieved by routine analysis of ADA

Computational Immunology : von MHC-Peptid Bindung zur Immuntherapie

The human immune system provides effective protection against invading pathogens and cancer. Soluble antibodies can directly bind to extracellular antigens, whereas other mechanisms are needed for the recognition of virally infected or cancerous cells. Intracellular proteins are digested into smaller peptides, which are then displayed on the cell surface bound to major histocompatibility (MHC) class I molecules. Cytotoxic T (Tc) cells play an important role in the immune system since they can recognize MHC-peptide complexes and eliminate infected or abnormal cells. The intracellular events leading to MHC-peptide presentation are collectively known as antigen processing. There are three main steps in the antigen processing pathway; digestion of proteins into peptides by proteasomes in the cytosol, transport of peptides into the endoplasmic reticulum (ER) by the transporters associated with antigen processing (TAP), and MHC-peptide complex formation. A detailed understanding of these processes is a prerequisite for rational peptide vaccine design aiming to efficiently activate Tc cells. This has motivated the development of computational methods dealing with the different steps of the antigen processing pathway. Methods predicting MHC-peptide binding with relatively good accuracy exists, however, there is room for improvements. Less is known about the two preceding steps, protein cleavage and peptide transport. There is a need of methods addressing these steps. Furthermore, there is a lack of methods that consider the whole antigen processing pathway in an integrative manner. The first part of this thesis describes different methods for predicting MHC-peptide binding. Support vector machines and decision trees are used to study a wide range of different MHC alleles. In a comparative study the SVM-based method SVMHC shows better prediction accuracy compared to the well-known SYFPEITHI and BIMAS methods. Additionally, a consensus method for predicting peptide binding to HLA-A*0201 is presented. Novel methods for prediction of proteasomal cleavage and TAP transport are presented. These show improved prediction accuracy in comparison to existing methods. The prediction methods addressing the individual steps of the processing pathway are integrated in the WAPP (whole antigen processing pathway) method. WAPP shows increased accuracy of MHC-peptide binding prediction by filtering out peptides not likely to be generated by the proteasome or transported by TAP. Immunotherapy has proven useful in cancer therapy during recent years. The promising results include several successful reports using MHC-binding peptides in order to activate the immune system. In cancer immunotherapy these peptides typically originate from tumorspecifc antigens (TSAs) or tumor-associated antigens (TAAs). The second part of this thesis describes an integrative analysis system for cancer-related data. CAP is used to analyze the effects of genetic variation and gene expression levels for raising autoimmune responses in cancer. This provides insights into the characteristics of TSAs and TAAs. Furthermore, TSAs are analyzed for potential MHC-binding peptides. In conclusion, the individual methods presented here show improvement when compared to other similar methods. The integrated method WAPP modeling the whole antigen processing pathway is the first of its kind and shows promising results. Finally the combination of CAP and SVMHC prove the usefulness of integrative analysis coupled to prediction tools for finding peptide immunotherapy candidates.Das menschliche Immunsystem stellt eine effektive Barriere zum Schutz vor Pathogenen und Krebserkrankungen dar. Extrazellulärer Antigene werden von löslichen Antikörper erkannt, die die Basis der humoralen Immunantwort bilden. Die Erkennung intrazelluläre Antigene, die für eine Immunantwort auf Virusinfektionen oder Krebs notwendig ist, erfolgt mit Hilfe andere Mechanismen: intrazelluläre Proteine werden zu kleinen Peptiden verdaut, die, an die Moleküle des Histokompatibilitätskomplexes (MHC Klasse I) binden und auf der Zelloberfläche präsentiert werden. Zytotoxische T-Zellen (Tc) erkennen MHC-Peptid-Komplexe die von körperfremden oder veränderten Antigenen stammen und eliminieren diese infizierten oder abnormalen Zellen. Das intrazelluläre Vorgang, der zur MHC-Peptid-Präsentation führt, wird als Antigenprozessierung bezeichnet. Drei Schritte im Rahmen der Antigenprozessierung sind von besonderer Bedeutung: Verdau, Transport und MHC-Bindung. Im ersten Schritt verdauen Proteasen des Cytosols Proteine zu kurzen Peptiden. Diese werden im zweiten Schritt aktiv ins endoplasmatische Retikulum (ER) transportiert. Dort binden sie schließlich spezifisch an MHC-Moleküle. Ein detailliertes Verständnis und eine theoretische Modellierung dieser Schritte ist Voraussetzung für den computergestützten Entwurf von peptidbasierten Impfstoffen. Für die Vorhersage der MHC-Peptidbindung existiert eine Reihe von Verfahren mit guter Vorhersagegenauigkeit, die aber immer noch Raum für Verbesserungen bieten. Wesentlich weniger gut sind die beiden anderen Schritte (Verdau zu Peptiden und Transport ins ER) vorhersagbar. Darüber hinaus fehlen Methoden, diese drei Schritte zu einer integrierten Vorhersage der Antigenprozessierung zusammenzuführen. Der erste Teil dieser Arbeit beschreibt die unterschiedlichen Methoden zur Vorhersage der MHC-Peptidbindung. Zur Vorhersage der Bindung an eine Reihe unterschiedlicher Allele kommen Supportvektormaschinen (SVMs) und Entscheidungsbäume zum Einsatz. Die SVM-basierte Methode SVMHC bietet eine bessere Vorhersagegenauigkeit als die bekannten Methoden SYFPEITHI und BIMAS. Diese lässt sich durch Konsensusmethoden noch weiter steigern, wie am Beispiel für das Allels HLA-A*0201 gezeigt wird. Auch für die Vorhersage des Verdaus in Peptide und den Transport ins ER werden Vorhersagemodelle vorgestellt. Diese zeigen ebenfalls deutlich verbesserte Vorhersagequalität als vergleichbare Methoden. Die drei Einzelvorhersagen (Verdau, Transport, Bindung) werden schließlich in einer integrierten Vorhersage der gesamten Prozessierung zusammengeführt: WAPP (Whole Antigen Processing Pathway). WAPP zeichnet sich ebenfalls durch eine verbesserte Vorhersagegenauigkeit aus, insbesondere aufgrund seiner geringeren Rate an falsch positiven Vorhersagen. Im Gegensatz zu reinen MHC-basierten Methoden kann die Peptide, die nicht verdaut oder transportiert werden, erkannt und ausfiltriert werden. Immuntherapie hat sich in den letzten Jahren als ein vielversprechender Weg in der Krebsbekämpfung herausgestellt. Dabei wurden zum Beispiel MHC-Bindende Peptide eingesetzt, um das Immunsystem gegen Krebszellen zu aktivieren. In der Krebsimmuntherapie stammen diese Peptide üblicherweise von tumorspezifischen Antigenen (TSAs) und tumorassoziierten Antigenen (TAAs). Der zweite Teil der Arbeit beschreibt ein integriertes System zur Analyse krebsrelevanter Datensätze zur Unterstützung der Immuntherapie. Dazu kommt die in dieser Arbeit entwickelte Methode zur Vorhersage der Antigenprozessierung wieder zum Einsatz. Integriert wird diese Vorhersage in das Analysewerkzeug CAP, das die Integration und Analyse heterogener krebsrelevanter Datensätze ermöglicht. CAP wird verwendet, um den Einuss von genetischer Variabilität und Genexpression auf die Entstehung einer Immunantwort gegen Krebs zu untersuchen. Diese Daten erlauben die Identifizierung von TSAs und TAAs, die dann wieder mit Hilfe von SVMHC auf ihre Immunrelevanz untersucht werden können. Zusammenfassend zeigen die hier entwickelten Methoden gegenüber vorher bekannten Methoden deutlich verbesserte Vorhersagegenauigkeit. Die integrierte Vorhersagemethode WAPP ist die erste ihrer Art und liefert vielversprechende Ergebnisse. Die Kombination von SVMHC und CAP zeigt den Nutzen der beiden Methoden für die Identifizierung von Peptiden für die Immuntherapie

Current Perspectives on Multi-Omics Data Integration With Application on Toxicity Biomarkers Discovery

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A mathematical framework for the selection of an optimal set of peptides for epitope-based vaccines.

Epitope-based vaccines (EVs) have a wide range of applications: from therapeutic to prophylactic approaches, from infectious diseases to cancer. The development of an EV is based on the knowledge of target-specific antigens from which immunogenic peptides, so-called epitopes, are derived. Such epitopes form the key components of the EV. Due to regulatory, economic, and practical concerns the number of epitopes that can be included in an EV is limited. Furthermore, as the major histocompatibility complex (MHC) binding these epitopes is highly polymorphic, every patient possesses a set of MHC class I and class II molecules of differing specificities. A peptide combination effective for one person can thus be completely ineffective for another. This renders the optimal selection of these epitopes an important and interesting optimization problem. In this work we present a mathematical framework based on integer linear programming (ILP) that allows the formulation of various flavors of the vaccine design problem and the efficient identification of optimal sets of epitopes. Out of a user-defined set of predicted or experimentally determined epitopes, the framework selects the set with the maximum likelihood of eliciting a broad and potent immune response. Our ILP approach allows an elegant and flexible formulation of numerous variants of the EV design problem. In order to demonstrate this, we show how common immunological requirements for a good EV (e.g., coverage of epitopes from each antigen, coverage of all MHC alleles in a set, or avoidance of epitopes with high mutation rates) can be translated into constraints or modifications of the objective function within the ILP framework. An implementation of the algorithm outperforms a simple greedy strategy as well as a previously suggested evolutionary algorithm and has runtimes on the order of seconds for typical problem sizes