A theoretical approach towards the identification of cleavage-determining amino acid motifs of the 20 S proteasome

Immunotherapy of cytomegalovirus infection by low-dose adoptive transfer of antiviral CD8 T cells relies on substantial post-transfer expansion of central memory cells but not effector-memory cells

Cytomegaloviruses (CMVs) are host species-specific in their replication. It is a hallmark of all CMVs that productive primary infection is controlled by concerted innate and adaptive immune responses in the immunocompetent host. As a result, the infection usually passes without overt clinical symptoms and develops into latent infection, referred to as "latency". During latency, the virus is maintained in a non-replicative state from which it can reactivate to productive infection under conditions of waning immune surveillance. In contrast, infection of an immunocompromised host causes CMV disease with viral multiple-organ histopathology resulting in organ failure. Primary or reactivated CMV infection of hematopoietic cell transplantation (HCT) recipients in a "window of risk" between therapeutic hemato-ablative leukemia therapy and immune system reconstitution remains a clinical challenge. Studies in the mouse model of experimental HCT and infection with murine CMV (mCMV), followed by clinical trials in HCT patients with human CMV (hCMV) reactivation, have revealed a protective function of virus-specific CD8 T cells upon adoptive cell transfer (AT). Memory CD8 T cells derived from latently infected hosts are a favored source for immunotherapy by AT. Strikingly low numbers of these cells were found to prevent CMV disease, suggesting either an immediate effector function of few transferred cells or a clonal expansion generating high numbers of effector cells. In the murine model, the memory population consists of resting central memory T cells (TCM), as well as of conventional effector-memory T cells (cTEM) and inflationary effector-memory T cells (iTEM). iTEM increase in numbers over time in the latently infected host, a phenomenon known as 'memory inflation' (MI). They thus appeared to be a promising source for use in immunotherapy. However, we show here that iTEM contribute little to the control of infection after AT, which relies almost entirely on superior proliferative potential of TCM.

NetCleave: an open-source algorithm for predicting C-terminal antigen processing for MHC-I and MHC-II

Antigens presented on the cell surface have been subjected to multiple biological processes. Among them, C-terminal antigen processing constitutes one of the main bottlenecks of the peptide presentation pathways, as it delimits the peptidome that will be subjected downstream. Here, we present NetCleave, an open-source and retrainable algorithm for the prediction of the C-terminal antigen processing for both MHC-I and MHC-II pathways. NetCleave architecture consists of a neural network trained on 46 different physicochemical descriptors of the cleavage site amino acids. Our results demonstrate that prediction of C-terminal antigen processing achieves high accuracy on MHC-I (AUC of 0.91), while it remains challenging for MHC-II (AUC of 0.66). Moreover, we evaluated the performance of NetCleave and other prediction tools for the evaluation of four independent immunogenicity datasets (H2-Db, H2-Kb, HLA-A*02:01 and HLA-B:07:02). Overall, we demonstrate that NetCleave stands out as one of the best algorithms for the prediction of C-terminal processing, and we provide one of the first evidence that C-terminal processing predictions may help in the discovery of immunogenic peptides.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

A minigene DNA vaccine encoding peptide epitopes derived from Galectin-1 has protective antitumoral effects in a model of neuroblastoma

We recently identified Galectin-1 (Gal-1), a β-galactoside-binding lectin, as a novel immune regulator in neuroblastoma (NB). Here, we characterized the tolerogenic function of Gal-1 within the CD8⁺ T cell compartment and further evaluated its relevance as an antigen for effective DNA vaccination against NB in a mouse model. NB cells with Gal-1 knockdown (NXS-2L) exhibited significantly reduced tumor growth compared to NXS-2 NB cells. Administration of anti-CD8 antibodies prevented this antitumor effect, with primary tumor growth comparable to that from Gal-1 (G1)-sufficient NB cells. Peptide epitope screening with online databases and in silico docking experiments predicted the sequences "FDQADLTI" (#1), "GDFKIKCV" (#2), and "AHGDANTI" (#3) to have superior H2-KK binding affinities and "KFPNRLNM" (#4), "DGDFKIKCV" (#5), and "LGKDSNNL" (#6) to have superior H2-DD binding affinities. Minigenes encoding G1-KK (#1-#2-#3), G1-DD (#4-#5-#6) and the triplet with the highest affinity, G1-H (#1-#2-#4), were generated and cloned into a ubiquitin-containing plasmid (pU). Mice receiving pU-G1-KK or pU-G-1H presented a reduction in the s.c. tumor volume and weight of up to 80% compared to control mice; this reduction was associated with increased cytotoxicity of isolated splenocytes from vaccinated animals. Vaccination with pUG1-DD showed a lower capability to suppress primary tumor progression. In conclusion, Gal-1 expression by NB negatively regulates CD8⁺ T cells. Vaccination with DNA plasmids encoding Gal-1 epitopes overcomes immune escape, enhances CD8⁺ T cell-dependent immunity and displays effective antitumor activity against NB.

Development of multiepitope therapeutic vaccines against the most prevalent high-risk human papillomaviruses

Aim: Our goal was the development of DNA- or peptide-based multiepitope vaccines targeting HPV E7, E6 and E5 oncoproteins in tumor mouse model. Materials & methods: After designing the multiepitope E7, E6 and E5 constructs from four types of high risk HPVs (16, 18, 31 & 45) using bioinformatics tools, mice vaccination was performed by different homologous and heterologous modalities in a prophylactic setting. Then, anti-tumor effects of the best prophylactic strategies were studied in a therapeutic setting. Results: In both prophylactic and therapeutic experiments, groups receiving homologous E7+E6+E5 polypeptide, and heterologous E7+E6+E5 DNA prime/polypeptide boost were successful in complete rejection of tumors. Conclusion: The designed multiepitope constructs can be considered as promising candidates to develop effective therapeutic HPV vaccines.

Analysis of predicted proteasomal cleavages in the methyltransferase domain from JEV

The methyltransferase (MTase, a 265 amino acid residues long region at the N-terminal end of the viral nonfunctional supermolecule NS5 domain) is key for viral replication in Japanese Encephalitis Virus (JEV). Sequence to structure to functional information with adequate knowledge on MTase from JEV is currently limited. Therefore, it is of interest to document a report on the comprehensive analysis of predicted proteasomal cleavage data in the methyltransferase domain from JEV. This data is relevant in the design and development of vaccine and other therapeutic candidates for further consideration.

Measles Vaccines Designed for Enhanced CD8+ T Cell Activation

Priming and activation of CD8⁺ T cell responses is crucial to achieve anti-viral and anti-tumor immunity. Live attenuated measles vaccine strains have been used successfully for immunization for decades and are currently investigated in trials of oncolytic virotherapy. The available reverse genetics systems allow for insertion of additional genes, including heterologous antigens. Here, we designed recombinant measles vaccine vectors for priming and activation of antigen-specific CD8⁺ T cells. For proof-of-concept, we used cytotoxic T lymphocyte (CTL) lines specific for the melanoma-associated differentiation antigen tyrosinase-related protein-2 (TRP-2), or the model antigen chicken ovalbumin (OVA), respectively. We generated recombinant measles vaccine vectors with TRP-2 and OVA epitope cassette variants for expression of the full-length antigen or the respective immunodominant CD8⁺ epitope, with additional variants mediating secretion or proteasomal degradation of the epitope. We show that these recombinant measles virus vectors mediate varying levels of MHC class I (MHC-I)-restricted epitope presentation, leading to activation of cognate CTLs, as indicated by secretion of interferon-gamma (IFNγ) in vitro. Importantly, the recombinant OVA vaccines also mediate priming of naïve OT-I CD8⁺ T cells by dendritic cells. While all vaccine variants can prime and activate cognate T cells, IFNγ release was enhanced using a secreted epitope variant and a variant with epitope strings targeted to the proteasome. The principles presented in this study will facilitate the design of recombinant vaccines to elicit CD8⁺ responses against pathogens and tumor antigens.

PCPS: A Web Server to Predict Proteasomal Cleavage Sites

The proteasome complex is mainly responsible for proteolytic degradation of cytosolic proteins, generating the C-terminus of MHC I-restricted peptide ligands and CD8 T cell epitopes. Therefore, prediction of proteasomal cleavage sites is relevant for anticipating CD8 T-cell epitopes. There are two different proteasomes, the constitutive proteasome, expressed in all types of cells, and the immunoproteasome, constitutively expressed in dendritic cells. Although both proteasome forms generate peptides for presentation by MHC I molecules, the immunoproteasome is the main form involved in providing peptide fragments for priming CD8 T cells. On the contrary, the proteasome provides peptides for presentation by MHC I molecules that can be targeted by already primed CD8 T cells. Proteasome cleavage prediction server (PCPS) is a server for predicting cleavage sites generated by both the constitutive proteasome and the immunoproteasome. Here, we illustrate the usage of PCPS to predict proteasome and immunoproteasome cleavage sites and compare the results with those provided by NetChop, a related tool available online. PCPS is implemented for free public use available online at http://imed.med.ucm.es/Tools/pcps/ .

Systems Immunology: Learning the Rules of the Immune System

Given the many cell types and molecular components of the human immune system, along with vast variations across individuals, how should we go about developing causal and predictive explanations of immunity? A central strategy in human studies is to leverage natural variation to find relationships among variables, including DNA variants, epigenetic states, immune phenotypes, clinical descriptors, and others. Here, we focus on how natural variation is used to find patterns, infer principles, and develop predictive models for two areas: (a) immune cell activation-how single-cell profiling boosts our ability to discover immune cell types and states-and (b) antigen presentation and recognition-how models can be generated to predict presentation of antigens on MHC molecules and their detection by T cell receptors. These are two examples of a shift in how we find the drivers and targets of immunity, especially in the human system in the context of health and disease.

Development of a novel immunoproteasome digestion assay for synthetic long peptide vaccine design

Recently, many autologous tumor antigens have been examined for their potential use in cancer immunotherapy. However, the success of cancer vaccines in clinical trials has been limited, partly because of the limitations of using single, short peptides in most attempts. With this in mind, we aimed to develop multivalent synthetic long peptide (SLP) vaccines containing multiple cytotoxic T-lymphocyte (CTL) epitopes. However, to confirm whether a multivalent vaccine can induce an individual epitope-specific CTL, the only viable screening strategies currently available are interferon-gamma (IFN-μ enzyme-linked immunospot (ELISPOT) assays using human peripheral blood mononuclear cells, or expensive human leukocyte antigen (HLA)-expressing mice. In this report, we evaluated the use of our developed murine-20S immunoproteasome (i20S) digestion assay, and found that it could predict the results of IFN-μ ELISPOT assays. Importantly, the murine-i20S digestion assay not only predicted CTL induction, but also antitumor activity in an HLA-expressing mouse model. We conclude that the murine-i20S digestion assay is an extremely useful tool for the development of “all functional” multivalent SLP vaccines.

Perpetual complexity: predicting human CD8+ T-cell responses to pathogenic peptides

The accurate prediction of human CD8⁺ T-cell epitopes has great potential clinical and translational implications in the context of infection, cancer and autoimmunity. Prediction algorithms have traditionally focused on calculated peptide affinity for the binding groove of MHC-I. However, over the years it has become increasingly clear that the ultimate T-cell recognition of MHC-I-bound peptides is governed by many contributing factors within the complex antigen presentation pathway. Recent advances in next-generation sequencing and immunnopeptidomics have increased the precision of HLA-I sub-allele classification, and have led to the discovery of peptide processing events and individual allele-specific binding preferences. Here, we review some of the discoveries that initiated the development of peptide prediction algorithms, and outline some of the current available online tools for CD8⁺ T-cell epitope prediction.

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.

The landscape of BRAF transcript and protein variants in human cancer

Background

The BRAF protein kinase is widely studied as a cancer driver and therapeutic target. However, the regulation of its expression is not completely understood.

Results

Taking advantage of the RNA-seq data of more than 4800 patients belonging to 9 different cancer types, we show that BRAF mRNA exists as a pool of 3 isoforms (reference BRAF, BRAF-X1, and BRAF-X2) that differ in the last part of their coding sequences, as well as in the length (BRAF-ref: 76 nt; BRAF-X1 and BRAF-X2: up to 7 kb) and in the sequence of their 3’UTRs. The expression levels of BRAF-ref and BRAF-X1/X2 are inversely correlated, while the most prevalent among the three isoforms varies from cancer type to cancer type. In melanoma cells, the X1 isoform is expressed at the highest level in both therapy-naïve cells and cells with acquired resistance to vemurafenib driven by BRAF gene amplification or expression of the Δ[3–10] splicing variant. In addition to the BRAF-ref protein, the BRAF-X1 protein (the full length as well as the Δ[3–10] variant) is also translated. The expression levels of the BRAF-ref and BRAF-X1 proteins are similar, and together they account for BRAF functional activities. In contrast, the endogenous BRAF-X2 protein is hard to detect because the C-terminal domain is selectively recognized by the ubiquitin-proteasome pathway and targeted for degradation.

Conclusions

By shedding light on the repertoire of BRAF mRNA and protein variants, and on the complex regulation of their expression, our work paves the way to a deeper understanding of a crucially important player in human cancer and to a more informed development of new therapeutic strategies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-017-0645-4) contains supplementary material, which is available to authorized users.

Immunoinformatics and epitope prediction in the age of genomic medicine

Immunoinformatics involves the application of computational methods to immunological problems. Prediction of B- and T-cell epitopes has long been the focus of immunoinformatics, given the potential translational implications, and many tools have been developed. With the advent of next-generation sequencing (NGS) methods, an unprecedented wealth of information has become available that requires more-advanced immunoinformatics tools. Based on information from whole-genome sequencing, exome sequencing and RNA sequencing, it is possible to characterize with high accuracy an individual’s human leukocyte antigen (HLA) allotype (i.e., the individual set of HLA alleles of the patient), as well as changes arising in the HLA ligandome (the collection of peptides presented by the HLA) owing to genomic variation. This has allowed new opportunities for translational applications of epitope prediction, such as epitope-based design of prophylactic and therapeutic vaccines, and personalized cancer immunotherapies. Here, we review a wide range of immunoinformatics tools, with a focus on B- and T-cell epitope prediction. We also highlight fundamental differences in the underlying algorithms and discuss the various metrics employed to assess prediction quality, comparing their strengths and weaknesses. Finally, we discuss the new challenges and opportunities presented by high-throughput data-sets for the field of epitope prediction.

Dendritic Cells Transfected with a DNA Construct Encoding Tumour-associated Antigen Epitopes Induce a Cytotoxic Immune Response Against Autologous Tumour Cells in a Culture of Mononuclear Cells from Colorectal Cancer Patients

Significant effort has been devoted to developing effective cancer vaccines based on dendritic cells (DCs) loaded with various tumour antigens, including DNA constructs that carry sequences of tumour-associated antigens (TAAs). Such vaccines efficiently and selectively activate the T cell immune response. In this study, we describe a method to induce an antitumour immune response in mononuclear cell (MNC) cultures from colorectal cancer patients using DNA-transfected DCs encoding TAA epitopes of carcinoembryonic antigen, epithelial cell adhesion molecule and mucin 4. DCs were obtained from peripheral blood monocytes of colorectal cancer patients. Magnetic-assisted transfection was used to deliver the genetic constructs to DCs. To assess the potency of the immune response, the antitumour cytotoxic response was assessed by lymphocyte intracellular perforin and the MNC cytotoxic activity against autologous tumour cells. We showed that polyepitope DNA-transfected DCs enhanced MNC antitumour activity, increasing tumour cell death and the percentage of perforin-positive lymphocytes. In addition, DNA-transfected DCs elicited a cytotoxic response that was as efficient as that of tumour lysate-loaded DCs. Taken together, the data suggest that it is feasible to induce an antitumour immune response in colorectal MNCs using transfected DCs. Thus, the DNA construct reported in this study may potentially be used in therapeutic and prophylactic DC-based vaccines.

A rational approach to select immunogenic peptides that induce IFN-γ response against Toxoplasma gondii in human leukocytes

The ideal vaccine to prevent toxoplasmosis in humans would comprise antigens that elicit a protective T cell type 1 response with high IFN-γ production. Here, we report the use of a bioinformatics pipeline to discover peptides based on biochemical characteristics that predict strong IFN-γ response by human leukocytes. We selected peptide sequences that previously were reported to induce IFN-γ to identify the biophysical characteristics that will predict HLA-A*02 high-affinity epitopes. We found that the protein motif pattern FL...L..[VL] was common in previously reported highly immunogenic sequences. We have selected new peptides with a length of 9 residues with affinities from 2 to 21 nM with peptide signal and transmembrane domains and predicted to be cleaved at the proteasome to perform ELISPOT assays with human leukocytes. Within 9 peptides with the highest scores for IFN-γ production, four peptides elicited IFN-γ levels in a range from 252 to 1763 SFC/1e6. Our pipeline uncovered Toxoplasma proteins with peptides that are processed by MHC class 1 in humans. Our results suggest that our rational strategy for the selection of immunogenic epitopes could be used to select peptides as candidates for inclusion in epitope-based vaccines.

Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs

Proteolysis is the general term to describe the process of protein degradation into peptides. Proteasomes are the main actors in cellular proteolysis, and their activity can be measured in in vitro digestion experiments. However, in vivo proteolysis can be different than what is measured in these experiments if other proteases participate or if proteasomal activity is different in vivo. The in vivo proteolysis can be measured only indirectly, by the analysis of peptides presented on MHC-I molecules. MHC-I presented peptides are protected from further degradation, thus enabling an indirect view on the underlying in vivo proteolysis. The ligands presented on different MHC-I molecules enable different views on this process; in combination, they might give a complete picture. Based on in vitro proteasome-only digestions and MHC-I ligand data, different proteolysis predictors have been developed. With new in vitro digestion and MHC-I ligand data sets, we benchmarked how well these predictors capture in vitro proteasome-only activity and in vivo whole-cell proteolysis, respectively. Even though the in vitro proteasome digestion patterns were best captured by methods trained on such data (ProteaSMM and NetChop 20S), the in vivo whole-cell proteolysis was best predicted by a method trained on MHC-I ligand data (NetChop Cterm). Follow-up analysis showed that the likely source of this difference is the activity from proteases other than the proteasome, such as TPPII. This non-proteasomal in vivo activity is captured by NetChop Cterm and should be taken into account in MHC-I ligand predictions.

The VHSE-based prediction of proteasomal cleavage sites

Prediction of proteasomal cleavage sites has been a focus of computational biology. Up to date, the predictive methods are mostly based on nonlinear classifiers and variables with little physicochemical meanings. In this paper, the physicochemical properties of 14 residues both upstream and downstream of a cleavage site are characterized by VHSE (principal component score vector of hydrophobic, steric, and electronic properties) descriptors. Then, the resulting VHSE descriptors are employed to construct prediction models by support vector machine (SVM). For both in vivo and in vitro datasets, the performance of VHSE-based method is comparatively better than that of the well-known PAProC, MAPPP, and NetChop methods. The results reveal that the hydrophobic property of 10 residues both upstream and downstream of the cleavage site is a dominant factor affecting in vivo and in vitro cleavage specificities, followed by residue’s electronic and steric properties. Furthermore, the difference in hydrophobic potential between residues flanking the cleavage site is proposed to favor substrate cleavages. Overall, the interpretable VHSE-based method provides a preferable way to predict proteasomal cleavage sites.

Peptide-based vaccinology: experimental and computational approaches to target hypervariable viruses through the fine characterization of protective epitopes recognized by monoclonal antibodies and the identification of T-cell-activating peptides

Defining immunogenic domains of viral proteins capable of eliciting a protective immune response is crucial in the development of novel epitope-based prophylactic strategies. This is particularly important for the selective targeting of conserved regions shared among hypervariable viruses. Studying postinfection and postimmunization sera, as well as cloning and characterization of monoclonal antibodies (mAbs), still represents the best approach to identify protective epitopes. In particular, a protective mAb directed against conserved regions can play a key role in immunogen design and in human therapy as well. Experimental approaches aiming to characterize protective mAb epitopes or to identify T-cell-activating peptides are often burdened by technical limitations and can require long time to be correctly addressed. Thus, in the last decade many epitope predictive algorithms have been developed. These algorithms are continually evolving, and their use to address the empirical research is widely increasing. Here, we review several strategies based on experimental techniques alone or addressed by in silico analysis that are frequently used to predict immunogens to be included in novel epitope-based vaccine approaches. We will list the main strategies aiming to design a new vaccine preparation conferring the protection of a neutralizing mAb combined with an effective cell-mediated response.

Proteasomal cleavage site prediction of protein antigen using BP neural network based on a new set of amino acid descriptor

The accurate identification of cytotoxic T lymphocyte epitopes is becoming increasingly important in peptide vaccine design. The ubiquitin-proteasome system plays a key role in processing and presenting major histocompatibility complex class I restricted epitopes by degrading the antigenic protein. To enhance the specificity and efficiency of epitope prediction and identification, the recognition mode between the ubiquitin-proteasome complex and the protein antigen must be considered. Hence, a model that accurately predicts proteasomal cleavage must be established. This study proposes a new set of parameters to characterize the cleavage window and uses a backpropagation neural network algorithm to build a model that accurately predicts proteasomal cleavage. The accuracy of the prediction model, which depends on the window sizes of the cleavage, reaches 95.454% for the N-terminus and 95.011% for the C-terminus. The results show that the identification of proteasomal cleavage sites depends on the sequence next to it and that the prediction performance of the C-terminus is better than that of the N-terminus on average. Thus, models based on the properties of amino acids can be highly reliable and reflect the structural features of interactions between proteasomes and peptide sequences.

Protein oxidation in aging and the removal of oxidized proteins

Reactive oxygen species (ROS) are generated constantly within cells at low concentrations even under physiological conditions. During aging the levels of ROS can increase due to a limited capacity of antioxidant systems and repair mechanisms. Proteins are among the main targets for oxidants due to their high rate constants for several reactions with ROS and their abundance in biological systems. Protein damage has an important influence on cellular viability since most protein damage is non-repairable, and has deleterious consequences on protein structure and function. In addition, damaged and modified proteins can form cross-links and provide a basis for many senescence-associated alterations and may contribute to a range of human pathologies. Two proteolytic systems are responsible to ensure the maintenance of cellular functions: the proteasomal (UPS) and the lysosomal system. Those degrading systems provide a last line of antioxidative protection, removing irreversible damaged proteins and recycling amino acids for the continuous protein synthesis. But during aging, both systems are affected and their proteolytic activity declines significantly. Here we highlight the recent advantages in the understanding of protein oxidation and the fate of these damaged proteins during aging. This article is part of a Special Issue entitled: Posttranslational Protein modifications in biology and Medicine.

Prediction of protein cleavage site with feature selection by random forest

Proteinases play critical roles in both intra and extracellular processes by binding and cleaving their protein substrates. The cleavage can either be non-specific as part of degradation during protein catabolism or highly specific as part of proteolytic cascades and signal transduction events. Identification of these targets is extremely challenging. Current computational approaches for predicting cleavage sites are very limited since they mainly represent the amino acid sequences as patterns or frequency matrices. In this work, we developed a novel predictor based on Random Forest algorithm (RF) using maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). The features of physicochemical/biochemical properties, sequence conservation, residual disorder, amino acid occurrence frequency, secondary structure and solvent accessibility were utilized to represent the peptides concerned. Here, we compared existing prediction tools which are available for predicting possible cleavage sites in candidate substrates with ours. It is shown that our method makes much more reliable predictions in terms of the overall prediction accuracy. In addition, this predictor allows the use of a wide range of proteinases.

Structure of the proteasome

The ubiquitin-proteasomal system is an essential element of the protein quality control machinery in cells. The central part of this system is the 20S proteasome. The proteasome is a barrel-shaped multienzyme complex, containing several active centers hidden at the inner surface of the hollow cylinder. So, the regulation of the substrate entry toward the inner proteasomal surface is a key control mechanism of the activity of this protease. This chapter outlines the knowledge on the structure of the subunits of the 20S proteasome, the binding and structure of some proteasomal regulators and inducible proteasomal subunits. Therefore, this chapter imparts the knowledge on proteasomal structure which is required for the understanding of the following chapters.

Dysfunction of constitutive and inducible ubiquitin-proteasome system in amyotrophic lateral sclerosis: implication for protein aggregation and immune response

The ubiquitin-proteasome system (UPS) is the major intracellular proteolytic mechanism controlling the degradation of misfolded/abnormal proteins. A common hallmark in amyotrophic lateral sclerosis (ALS) and in other neurodegenerative disorders is the accumulation of misfolded/abnormal proteins into the damaged neurons, leading to the formation of cellular inclusions that are mostly ubiquitin-positive. Although proteolysis is a complex mechanism requiring the participation of different pathways, the abundant accumulation of ubiquitinated proteins strongly suggests an important contribution of UPS to these neuropathological features. The use of cellular and animal models of ALS, particularly those expressing mutant SOD1, the gene mutation most represented in familiar ALS, has provided significant evidence for a role of UPS in protein inclusions formation and motor neuron death. This review will specifically discuss this piece of evidence and provide suggestions of potential strategies for therapeutic intervention. We will also discuss the finding that, unlike the constitutive proteasome subunits, the inducible subunits are overexpressed early during disease progression in SOD1 mice models of ALS. These subunits form the immunoproteasome and generate peptides for the major histocompatibility complex class I molecules, suggesting a role of this system in the immune responses associated with the pathological features of ALS. Since recent discoveries indicate that innate and adaptive immunity may influence the disease process, in this review we will also provide evidence of a possible connection between immune-inflammatory reactions and UPS function, in the attempt to better understand the etiopathology of ALS and to identify appropriate targets for novel treatment strategies of this devastating disease.

A new fluorogenic peptide determines proteasome activity in single cells

The ubiquitin-proteasome system plays a critical role in many diseases, making it an attractive biomarker and therapeutic target. However, the impact of results obtained in vitro using purified proteasome particles or whole cell extracts is limited by the lack of efficient methods to assess proteasome activity in living cells. We have engineered an internally quenched fluorogenic peptide with a proteasome-specific cleavage motif fused to TAT and linked to the fluorophores DABCYL and EDANS. This peptide penetrates cell membranes and is rapidly cleaved by the proteasomal chymotrypsin-like activity, generating a quantitative fluorescent reporter of in vivo proteasome activity as assessed by time-lapse or flow cytometry fluorescence analysis. This reporter is an innovative tool for monitoring proteasomal proteolytic activities in physiological and pathological conditions.

State of the art and challenges in sequence based T-cell epitope prediction

Sequence based T-cell epitope predictions have improved immensely in the last decade. From predictions of peptide binding to major histocompatibility complex molecules with moderate accuracy, limited allele coverage, and no good estimates of the other events in the antigen-processing pathway, the field has evolved significantly. Methods have now been developed that produce highly accurate binding predictions for many alleles and integrate both proteasomal cleavage and transport events. Moreover have so-called pan-specific methods been developed, which allow for prediction of peptide binding to MHC alleles characterized by limited or no peptide binding data. Most of the developed methods are publicly available, and have proven to be very useful as a shortcut in epitope discovery. Here, we will go through some of the history of sequence-based predictions of helper as well as cytotoxic T cell epitopes. We will focus on some of the most accurate methods and their basic background.

Amyloid-β peptide is a substrate of the human 20S proteasome

Intraneuronal accumulation of ubiquitin conjugates is a pathological feature of neurodegenerative disorders such as Alzheimer's disease (AD). Previous reports propose that accumulation of ubiquitinated species in AD is a result of inhibition of proteasomal activity by amyloid-β (Aβ) peptides, which leads to blocking of ubiquitin-dependent protein degradation by the proteasome. Here, we provide additional insight into proteasomal dysfunction by Aβ peptides by revealing that aggregated forms of Aβ(1-42) peptides (especially small oligomers) are, in fact, competitive substrates for the chymotrypsin-like activity of the human 20S (h20S) proteasome. In addition to examining the kinetics of the h20S proteasome activity in the presence or absence of Aβ peptides, we use gel electrophoresis, LC-MS, and TOF-MS/MS analyses to examine the degradation of Aβ(1-42) by the h20S proteasome. The observed peptide fragments resulting from proteolytic cleavage of Aβ were consistent with predicted cleavage sites from proteasome degradation. These results support that the interaction of Aβ peptides with the proteasome may play a mechanistic role in proteasomal dysfunction in AD pathology. These results may also reveal a previously unknown natural pathway for clearance of Aβ in normal or diseased cells.

Computational analysis and modeling of cleavage by the immunoproteasome and the constitutive proteasome

Background

Proteasomes play a central role in the major histocompatibility class I (MHCI) antigen processing pathway. They conduct the proteolytic degradation of proteins in the cytosol, generating the C-terminus of CD8 T cell epitopes and MHCI-peptide ligands (P1 residue of cleavage site). There are two types of proteasomes, the constitutive form, expressed in most cell types, and the immunoproteasome, which is constitutively expressed in mature dendritic cells. Protective CD8 T cell epitopes are likely generated by the immunoproteasome and the constitutive proteasome, and here we have modeled and analyzed the cleavage by these two proteases.

Results

We have modeled the immunoproteasome and proteasome cleavage sites upon two non-overlapping sets of peptides consisting of 553 CD8 T cell epitopes, naturally processed and restricted by human MHCI molecules, and 382 peptides eluted from human MHCI molecules, respectively, using N-grams. Cleavage models were generated considering different epitope and MHCI-eluted fragment lengths and the same number of C-terminal flanking residues. Models were evaluated in 5-fold cross-validation. Judging by the Mathew's Correlation Coefficient (MCC), optimal cleavage models for the proteasome (MCC = 0.43 ± 0.07) and the immunoproteasome (MCC = 0.36 ± 0.06) were obtained from 12-residue peptide fragments. Using an independent dataset consisting of 137 HIV1-specific CD8 T cell epitopes, the immunoproteasome and proteasome cleavage models achieved MCC values of 0.30 and 0.18, respectively, comparatively better than those achieved by related methods. Using ROC analyses, we have also shown that, combined with MHCI-peptide binding predictions, cleavage predictions by the immunoproteasome and proteasome models significantly increase the discovery rate of CD8 T cell epitopes restricted by different MHCI molecules, including A*0201, A*0301, A*2402, B*0702, B*2705.

Conclusions

We have developed models that are specific to predict cleavage by the proteasome and the immunoproteasome. These models ought to be instrumental to identify protective CD8 T cell epitopes and are readily available for free public use at http://imed.med.ucm.es/Tools/PCPS/.

Immunoinformatics and modeling perspective of T cell epitope-based cancer immunotherapy: a holistic picture

Cancer immunotherapy is fast gaining global attention with its unique position as a potential therapy showing promise in cancer prevention and cure. It utilizes the natural system of immunity as opposed to chemotherapy and radiotherapy that utilize chemical drugs and radiation, respectively. Cancer immunotherapy essentially involves treatment and/or prevention with vaccines in the form of peptide vaccines (T and B cell epitopes), DNA vaccines and vaccination using whole tumor cells, dendritic cells, viral vectors, antibodies and adoptive transfer of T cells to harness the body's own immune system towards the targeting of cancer cells for destruction. Given the time, cost and labor involved in the vaccine discovery and development, researchers have evinced interest in the novel field of immunoinformatics to cut down the escalation of these critical resources. Immunoinformatics is a relatively new buzzword in the scientific circuit that is showing its potential and delivering on its promise in expediting the development of effective cancer immunotherapeutic agents. This review attempts to present a holistic picture of our race against cancer and time using the science and technology of immunoinformatics and molecular modeling in T cell epitope-based cancer immunotherapy. It also attempts to showcase some problem areas as well as novel ones waiting to be explored where development of novel immunoinformatics tools and simulations in the context of cancer immunotherapy would be highly welcome.

Towards in silico design of epitope-based vaccines

BACKGROUND

Epitope-based vaccines (EVs) make use of immunogenic peptides (epitopes) to trigger an immune response. Due to their manifold advantages, EVs have recently been attracting growing interest. The success of an EV is determined by the choice of epitopes used as a basis. However, the experimental discovery of candidate epitopes is expensive in terms of time and money. Furthermore, for the final choice of epitopes various immunological requirements have to be considered.

METHODS

Numerous in silico approaches exist that can guide the design of EVs. In particular, computational methods for MHC binding prediction have already become standard tools in immunology. Apart from binding prediction and prediction of antigen processing, methods for epitope design and selection have been suggested. We review these in silico approaches for epitope discovery and selection along with their strengths and weaknesses. Finally, we discuss some of the obvious problems in the design of EVs.

CONCLUSION

State-of-the-art in silico approaches to MHC binding prediction yield high accuracies. However, a more thorough understanding of the underlying biological processes and significant amounts of experimental data will be required for the validation and improvement of in silico approaches to the remaining aspects of EV design.

Xenogeneic immunization with human tyrosine hydroxylase DNA vaccines suppresses growth of established neuroblastoma

Neuroblastoma (NB) is a challenging malignancy of the sympathetic nervous tissue characterized by a very poor prognosis. One important marker for NB is the expression of tyrosine hydroxylase (TH), the first-step enzyme of catecholamine biosynthesis. We could show stable and high TH gene expression in 67 NB samples independent of the clinical stage. Based on this observation, we addressed the question of whether xenogeneic TH DNA vaccination is effective in inducing an anti-NB immune response. For this purpose, we generated three DNA vaccines based on pCMV-F3Ub and pBUD-CE4.1 plasmids encoding for human (h)THcDNA (A), hTH minigene (B), and hTHcDNA in combination with the proinflammatory cytokine interleukin 12 (C), and tested prophylactic and therapeutic efficacy to suppress primary tumor growth and spontaneous metastasis. Here we report that xenogeneic TH DNA vaccination was effective in eradicating established primary tumors and inhibiting metastasis. Interestingly, this effect could not be enhanced by adding the Th1 cytokine interleukin 12. However, increased IFN-gamma production and NB cytotoxicity of effector cells harvested from vaccinated mice suggested the participation of tumor-specific CTLs in the immune response. The depletion of CD8(+)T cells completely abrogated the hTH vaccine-mediated anti-NB immune response. Furthermore, rechallenging of surviving mice resulted in reduced primary tumor growth, indicating the induction of a memory immune response. In conclusion, xenogeneic immunization with TH-derived DNA vaccines is effective against NB, and may open a new venue for a novel and effective immunotherapeutic strategy against this challenging childhood tumor.

The protective immune response against infectious bronchitis virus induced by multi-epitope based peptide vaccines

Peptide vaccine was found to be an effective and powerful approach to a variety of pathogens. To explore multi-epitope based peptide vaccines against infectious bronchitis virus (IBV), the immunogenic peptides were fused to the 3' terminal of glutathione S transferase gene (GST) and expressed in Escherichia coli. ELISA and Western blot analysis showed that the purified fusion proteins had excellent immune activity with chicken anti-IBV serum. During the vaccination course, the candidate peptide vaccines induced strong humoral and cellular response, and provided up to 80.0% immune protection, while all non-immunized chickens in the negative control group manifested obvious typical symptoms and died after virus challenge. Our finding provides a new way to develop multi-epitope based peptide vaccine against IBV.

An introduction to epitope prediction methods and software

In this paper, current prediction methods and algorithms for both T- and B cell epitopes are reviewed, and a comprehensive summary of epitope prediction software and databases currently available online is also provided. This review can offer researchers in this field a sense of direction and insights for future work. However, our main purpose is to introduce clinical and basic biomedical researchers who are not familiar with these biological analysis tools and databases to these online resources and to provide guidance on how to use them effectively.

Induction of multiepitopic and long-lasting immune responses against tumour antigens by immunization with peptides, DNA and recombinant adenoviruses expressing minigenes

The development of immunization strategies to induce strong and multiepitopic T-cell responses against tumour antigens is needed for anti-tumour immunotherapy. However, a common finding after immunization with complex antigens is the preferential induction of immune responses against immunodominant epitopes. In this study, with the aim of inducing multiepitopic responses against several common tumour antigens, we have designed a minigene construct encoding four human leucocyte antigen (HLA)-A2-restricted epitopes belonging to tumour antigens CEA (CEA-691 and CEA-571), MAGE2 (MAGE2-157) and MAGE3 (MAGE3-112), as well as the universal PADRE epitope recognized by T helper lymphocytes. To optimize the activation of immune responses against these epitopes, we have used different antigen formats (short peptides encompassing individual epitopes and DNA plasmids or adenoviral constructs expressing the minigene) in single or combined immunization schedules. A single immunization with either DNA plasmid or recombinant adenovirus induced a monospecific immune response against the immunodominant epitope CEA-571, whereas immunization with the peptide pool induced responses against all epitopes. Combination of peptide priming followed by a boost with the plasmid and the recombinant adenovirus expressing the minigene induced stronger, multi-specific and long-lasting immune responses, overcoming the immunodominance imposed by the main T-cell epitope. Moreover, these combined immunization strategies were able to induce responses that were able to recognize Mel624 HLA-A2+ tumour cells expressing MAGE2. These results suggest that heterologous immunization strategies combining peptides and DNA or recombinant adenoviruses can be useful to broaden the specificity and enhance the efficacy of subunit vaccines.

Characterizing the N-terminal processing motif of MHC class I ligands

Most peptide ligands presented by MHC class I molecules are the product of an intracellular pathway comprising protein breakdown in the cytosol, transport into the endoplasmic reticulum, and successive N-terminal trimming events. The efficiency of each of these processes depends on the amino acid sequence of the presented ligand and its precursors. Thus, relating the amino acid composition N-terminal of presented ligands to the sequence specificity of processes in the pathway gives insight into the usage of ligand precursors in vivo. Examining the amino acid composition upstream the true N terminus of MHC class I ligands, we demonstrate the existence of a distinct N-terminal processing motif comprising approximately seven residues and matching the known preferences of proteasome and TAP, two key players in ligand processing. Furthermore, we find that some residues, which are preferred by both TAP and the proteasome, are underrepresented at positions immediately preceding the N terminus of MHC class I ligands. Based on experimentally determined aminopeptidase activities, this pattern suggests trimming next to the final N terminus to take place predominantly in the endoplasmic reticulum.

Epitope-Based Immunome-Derived Vaccines: A Strategy for Improved Design and Safety

Vaccine science has extended beyond genomics to proteomics and has come to also encompass ‘immunomics,’ the study of the universe of pathogen-derived or neoplasm-derived peptides that interface with B and T cells of the host immune system. It has been theorized that effective vaccines can be developed using the minimum essential subset of T cell and B cell epitopes that comprise the ‘immunome.’ Researchers are therefore using bioinformatics sequence analysis tools, epitope-mapping tools, microarrays, and high-throughput immunology assays to discover the minimal essential components of the immunome. When these minimal components, or epitopes, are packaged with adjuvants in an appropriate delivery vehicle, the complete package comprises an epitope-based immunome-derived vaccine. Such vaccines may have a significant advantage over conventional vaccines, as the careful selection of the components may diminish undesired side effects such as have been observed with whole pathogen and protein subunit vaccines. This chapter will review the pre-clinical and anticipated clinical development of computer-driven vaccine design and the validation of epitope-based immunome-derived vaccines in animal models; it will also include an overview of heterologous immunity and other emerging issues that will need to be addressed by vaccines of all types in the future.

SYFPEITHI: database for searching and T-cell epitope prediction

Reverse immunology has been used for about 12 years in order to identify T-cell epitopes from pathogens or tumor-associated antigens. In this chapter, we discuss the advantages and pitfalls of T-cell epitope prediction compared to classical experimental procedures such as epitope mapping and cloning experiments. We introduce our three established programs, SYFPEITHI, PAProc, and SNEP, which are freely accessible at no cost in the World Wide Web for the prediction of either HLA-peptide binding or proteasomal processing of antigens. We demonstrate the performance of our epitope prediction programs with several examples and in comparison to other epitope prediction programs available. We also reflect the actual possibilities and limitations of such computer-aided work.

Precise score for the prediction of peptides cleaved by the proteasome

MOTIVATION

An 8-10mer can become a cytotoxic T lymphocyte epitope only if it is cleaved by the proteasome, transported by TAP and presented by MHC-I molecules. Thus most of the epitopes presented to cytotoxic T cells in the context of MHC-I molecules are products of intracellular proteasomal cleavage. These products are not random, as peptide production is a function of the precise sequence of the proteins processed by the proteasome.

RESULTS

We have developed a score for the probability that a given peptide results from proteasomal cleavage. High scoring peptides are those that are cleaved in their extremities and not in their center, while low scoring peptides are either cleaved in their centers or not cleaved in their extremities. The current work differs from most previous works, in that it determines the production probability of an entire peptide, rather than trying to predict specific cleavage sites. We further present different score functions for the constitutive and the immunoproteasome. Our results were validated to have low error levels against multiple epitope databases. We provide here a novel computational tool and a website to use it-http://peptibase.cs.biu.ac.il/PepCleave_II/ to assess the probability that a given peptide indeed results from proteasomal cleavage.

Modeling the adaptive immune system: predictions and simulations

MOTIVATION

Immunological bioinformatics methods are applicable to a broad range of scientific areas. The specifics of how and where they might be implemented have recently been reviewed in the literature. However, the background and concerns for selecting between the different available methods have so far not been adequately covered.

SUMMARY

Before using predictions systems, it is necessary to not only understand how the methods are constructed but also their strength and limitations. The prediction systems in humoral epitope discovery are still in their infancy, but have reached a reasonable level of predictive strength. In cellular immunology, MHC class I binding predictions are now very strong and cover most of the known HLA specificities. These systems work well for epitope discovery, and predictions of the MHC class I pathway have been further improved by integration with state-of-the-art prediction tools for proteasomal cleavage and TAP binding. By comparison, class II MHC binding predictions have not developed to a comparable accuracy level, but new tools have emerged that deliver significantly improved predictions not only in terms of accuracy, but also in MHC specificity coverage. Simulation systems and mathematical modeling are also now beginning to reach a level where these methods will be able to answer more complex immunological questions.

Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction

BACKGROUND

Reliable predictions of Cytotoxic T lymphocyte (CTL) epitopes are essential for rational vaccine design. Most importantly, they can minimize the experimental effort needed to identify epitopes. NetCTL is a web-based tool designed for predicting human CTL epitopes in any given protein. It does so by integrating predictions of proteasomal cleavage, TAP transport efficiency, and MHC class I affinity. At least four other methods have been developed recently that likewise attempt to predict CTL epitopes: EpiJen, MAPPP, MHC-pathway, and WAPP. In order to compare the performance of prediction methods, objective benchmarks and standardized performance measures are needed. Here, we develop such large-scale benchmark and corresponding performance measures and report the performance of an updated version 1.2 of NetCTL in comparison with the four other methods.

RESULTS

We define a number of performance measures that can handle the different types of output data from the five methods. We use two evaluation datasets consisting of known HIV CTL epitopes and their source proteins. The source proteins are split into all possible 9 mers and except for annotated epitopes; all other 9 mers are considered non-epitopes. In the RANK measure, we compare two methods at a time and count how often each of the methods rank the epitope highest. In another measure, we find the specificity of the methods at three predefined sensitivity values. Lastly, for each method, we calculate the percentage of known epitopes that rank within the 5% peptides with the highest predicted score.

CONCLUSION

NetCTL-1.2 is demonstrated to have a higher predictive performance than EpiJen, MAPPP, MHC-pathway, and WAPP on all performance measures. The higher performance of NetCTL-1.2 as compared to EpiJen and MHC-pathway is, however, not statistically significant on all measures. In the large-scale benchmark calculation consisting of 216 known HIV epitopes covering all 12 recognized HLA supertypes, the NetCTL-1.2 method was shown to have a sensitivity among the 5% top-scoring peptides above 0.72. On this dataset, the best of the other methods achieved a sensitivity of 0.64. The NetCTL-1.2 method is available at http://www.cbs.dtu.dk/services/NetCTL. All used datasets are available at http://www.cbs.dtu.dk/suppl/immunology/CTL-1.2.php.

Computational analysis of proteome of H5N1 avian influenza virus to define T cell epitopes with vaccine potential

The existing vaccines against influenza are based on the generation of neutralizing antibody primarily directed against surface proteins - hemagglutinin and neuraminidase. In this work, we have computationally defined conserved T cell epitopes of proteins of influenza virus H5N1 to help in the design of a vaccine with haplotype specificity for a target population. The peptides from the proteome of H5N1 virus which are predicted to bind to different HLAs, do not show similarity with peptides of human proteome and are also identified to be generated by proteolytic cleavage. These peptides could be made use of in the design of either a DNA vaccine or a subunit vaccine against influenza.

Identification of T-cell epitopes for cancer immunotherapy

The effectiveness of T-cell-mediated immunotherapy of cancer depends on both an optimal immunostimulatory context of the therapy and the proper selection with respect to quality and quantity of the targeted tumor-associated antigens (TAA), and, more precisely, the T-cell epitopes contained in these tumor proteins. Our progressing insight in human leukocyte antigen (HLA) class I and class II antigen processing and presentation mechanisms has improved the prediction by reverse immunology of novel cytotoxic T lymphocyte and T-helper cell epitopes within known antigens. Computer algorithms that in silico predict HLA class I and class II binding, proteasome cleavage patterns and transporter associated with antigen processing translocation are now available to expedite epitope identification. The advent of genomics allows a high-throughput screening for tumor-specific transcripts and mutations, with that identifying novel shared and unique TAA. The increasing power of mass spectrometry and proteomics will lead to the direct identification from the tumor cell surface of numerous novel tumor-specific HLA class I and class II presented ligands. Together, the expanded repertoire of tumor-specific T-cell epitopes will enable more precise immunomonitoring and the development of effective epitope-defined adoptive T-cell transfer and multi-epitope-based vaccination strategies targeting epitopes derived from a wider diversity of TAA presented in a broader array of HLA molecules.

Immunological validation of the EpitOptimizer program for streamlined design of heteroclitic epitopes

One strategy to generate T-cell responses to tumors is to alter subdominant epitopes through substitution of amino acids that are optimal anchors for specific MHC molecules, termed heteroclitic epitopes. This approach is manually error-prone and time-consuming. In here, we describe a computer-based algorithm (EpitOptimizer) for the streamlined design of heteroclitic epitopes. Analysis of two cancer-related proteins showed that EpitOptimizer-generated peptides have enhanced MHC-I binding compared with their wild-type counterparts; and were able to induce stronger CD8+ T-cell responses against their native epitope. These data demonstrate that this approach can serve as the basis of epitope-engineering against cancer and intracellular pathogens.

Immunoinformatics comes of age

With the burgeoning immunological data in the scientific literature, scientists must increasingly rely on Internet resources to inform and enhance their work. Here we provide a brief overview of the adaptive immune response and summaries of immunoinformatics resources, emphasizing those with Web interfaces. These resources include searchable databases of epitopes and immune-related molecules, and analysis tools for T cell and B cell epitope prediction, vaccine design, and protein structure comparisons. There is an agreeable synergy between the growing collections in immune-related databases and the growing sophistication of analysis software; the databases provide the foundation for developing predictive computational tools, which in turn enable more rapid identification of immune responses to populate the databases. Collectively, these resources contribute to improved understanding of immune responses and escape, and evolution of pathogens under immune pressure. The public health implications are vast, including designing vaccines, understanding autoimmune diseases, and defining the correlates of immune protection.

Processing sites are different in the generation of HLA-A2.1-restricted, T cell reactive tumor antigen epitopes and viral epitopes

In order to improve the processing efficiency of T cell tumor antigen epitopes, this bioinformatic study compares proteolytic sites in the generation of 47 experimentally identified HLA-A2.1-restricted immunodominant tumor antigen epitopes to those of 52 documented HLA-A2.1-restricted immunodominant viral antigen epitopes. Our results show that the amino acid frequencies in the C-terminal cleavage sites of the tumor antigen epitopes, as well as several positions within the 10 amino acid (aa) flanking regions, are significantly different from those of the viral antigen epitopes. In the 9 amino acid epitope region, frequencies differed somewhat in the secondary-anchored amino acid residues on E3 (the third aa of the epitope), E4, E6, E7 and E8; however, frequencies in the primary-anchored positions, on E2 and E9, for binding in the HLA-A2.1 groove, remained almost identical. The most frequently occurring amino acid pairs in both N-terminal and C-terminal cleavage sites in the generation of tumor antigen epitopes were different from those of the viral antigen epitopes. Our findings demonstrate for the first time that these two groups of epitopes may be cleaved by distinct sets of proteasomes and peptidases or similar enzymes with lower efficiencies for tumor epitopes. In the future, in order to more effectively generate tumor antigen epitopes, targeted activation of the immunoproteasomes and peptidases that mediate the cleavage of viral epitopes could be achieved, thus enhancing our potential for antigen-specific tumor immunotherapy.

Endoproteolytic processing of C-terminally truncated NF-kappaB2 precursors at kappaB-containing promoters

The C-terminal, partially truncated forms of the NF-kappaB2/p52 precursor p100, p100DeltaCs, manifest constitutive processing and oncogenic ability, although the responsible mechanisms remain unknown. Here, we report that p100DeltaCs are specifically processed in association with binding to promoter DNA-containing kappaB sites. In the nucleus, p100DeltaCs bind to the kappaB promoter DNA and subsequently recruit the proteasome to form a stable proteasome/p100DeltaC/DNA complex, which mediates the processing of p100DeltaCs. Notably, the processing at the kappaB promoter is initiated by a proteasome-mediated endoproteolytic cleavage at amino acid D(415) of p100DeltaCs, and the processed p52, but not the precursors themselves, is oncogenic by up-regulating a subset of target genes. Our studies demonstrate a different mechanism of p100 processing and also present evidence showing that the proteasome modulates the action of transcription factors at promoter regions through endoproteolysis.