Prediction of proteasome cleavage motifs by neural networks

We present a predictive method that can simulate an essential step in the antigen presentation in higher vertebrates, namely the step involving the proteasomal degradation of polypeptides into fragments which have the potential to bind to MHC Class I molecules. Proteasomal cleavage prediction algorithms published so far were trained on data from in vitro digestion experiments with constitutive proteasomes. As a result, they did not take into account the characteristics of the structurally modified proteasomes--often called immunoproteasomes--found in cells stimulated by gamma-interferon under physiological conditions. Our algorithm has been trained not only on in vitro data, but also on MHC Class I ligand data, which reflect a combination of immunoproteasome and constitutive proteasome specificity. This feature, together with the use of neural networks, a non-linear classification technique, make the prediction of MHC Class I ligand boundaries more accurate: 65% of the cleavage sites and 85% of the non-cleavage sites are correctly determined. Moreover, we show that the neural networks trained on the constitutive proteasome data learns a specificity that differs from that of the networks trained on MHC Class I ligands, i.e. the specificity of the immunoproteasome is different than the constitutive proteasome. The tools developed in this study in combination with a predictor of MHC and TAP binding capacity should give a more complete prediction of the generation and presentation of peptides on MHC Class I molecules. Here we demonstrate that such an approach produces an accurate prediction of the CTL the epitopes in HIV Nef. The method is available at www.cbs.dtu.dk/services/NetChop/.     

PAProC: a prediction algorithm for proteasomal cleavages available on the WWW

The first version of PAProC (Prediction Algorithm for Proteasomal Cleavages) is now available to the general public. PAProC is a prediction tool for cleavages by human and yeast proteasomes, based on experimental cleavage data. It will be particularly useful for immunologists working on antigen processing and the prediction of major histocompatibility complex class I molecule (MHC I) ligands and cytotoxic T-lymphocyte (CTL) epitopes. Likewise, in cases in which proteasomal protein degradation has been indicated in disease, PAProC can be used to assess the general cleavability of disease-linked proteins. On its web site (http://www.paproc.de), background information and hyperlinks are provided for the user (e.g., to SYFPEITHI, the database for the prediction of MHC I ligands).     

Diversity of proteasomal missions: fine tuning of the immune response

The majority of cellular proteins are degraded by proteasomes within the ubiquitin-proteasome ATP-dependent degradation pathway. Products of proteasomal activity are short peptides that are further hydrolysed by proteases to single amino acids. However, some peptides can escape this degradation, being selected and taken up by major histocompatibility complex (MHC) class I molecules for presentation to the immune system on the cell surface. MHC class I molecules are highly selective and specific in terms of ligand binding. Variability of peptides produced in living cells arises in a variety of ways, ensuring fast and efficient immune responses. Substitution of constitutive proteasomal subunits with immunosubunits leads to conformational changes in the substrate binding channels, resulting in a modified protein cleavage pattern and consequently in the generation of new antigenic peptides. The recently discovered event of proteasomal peptide splicing opens new horizons in the understanding of additional functions that proteasomes apparently possess. Whether peptide splicing is an occasional side product of proteasomal activity still needs to be clarified. Both gamma-interferon-induced immunoproteasomes and peptide splicing represent two significant events providing increased diversity of antigenic peptides for flexible and fine-tuned immune response.     

Possible involvement of proteasomes (prosomes) in AUUUA-mediated mRNA decay

We have identified a cellular target for proteasomal endonuclease activity. Thus, 20 S proteasomes interact with the 3'-untranslated region of certain cytoplasmic mRNAs in vivo, and 20 S proteasomes isolated from Friend leukemia virus-infected mouse spleen cells were found to be associated with a mRNA fragment showing great homology to the 3'-untranslated region of tumor necrosis factor-beta mRNA that contains AUUUA sequences. We furthermore demonstrate that 20 S proteasomes destabilize oligoribonucleotides corresponding to the 3'-untranslated region of tumor necrosis factor-alpha, creating a specific cleavage pattern. The cleavage reaction is accelerated with increasing number of AUUUA motifs, and major cleavage sites are localized at the 5' side of the A residues. These results strongly suggest that 20 S proteasomes could be involved in the destabilization of cytokine mRNAs such as tumor necrosis factor mRNAs and other short-lived mRNAs containing AUUUA sequences.     

Quantitative analysis of prion-protein degradation by constitutive and immuno-20S proteasomes indicates differences correlated with disease susceptibility

The main part of cytosolic protein degradation depends on the ubiquitin-proteasome system. Proteasomes degrade their substrates into small peptide fragments, some of which are translocated into the endoplasmatic reticulum and loaded onto MHC class I molecules, which are then transported to the cell surface for inspection by CTL. A reliable prediction of proteasomal cleavages in a given protein for the identification of CTL epitopes would benefit immensely from additional cleavage data for the training of prediction algorithms. To increase the knowledge about proteasomal specificity and to gain more insight into the relation of proteasomal activity and susceptibility to prion disease, we digested sheep prion protein with human constitutive and immuno-20S proteasomes. All fragments generated in the digest were quantified. Our results underline the different cleavage specificities of constitutive and immunoproteasomes and provide data for the training of prediction programs for proteasomal cleavages. Furthermore, the kinetic analysis of proteasomal digestion of two different alleles of prion protein shows that even small changes in a protein sequence can affect the overall efficiency of proteasomal processing and thus provides more insight into the possible molecular background of allelic variations and the pathogenicity of prion proteins.     

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.     

Integrated modeling of the major events in the MHC class I antigen processing pathway

Rational design of epitope-driven vaccines is a key goal of immunoinformatics. Typically, candidate selection relies on the prediction of MHC-peptide binding only, as this is known to be the most selective step in the MHC class I antigen processing pathway. However, proteasomal cleavage and transport by the transporter associated with antigen processing (TAP) are essential steps in antigen processing as well. While prediction methods exist for the individual steps, no method has yet offered an integrated prediction of all three major processing events. Here we present WAPP, a method combining prediction of proteasomal cleavage, TAP transport, and MHC binding into a single prediction system. The proteasomal cleavage site prediction employs a new matrix-based method that is based on experimentally verified proteasomal cleavage sites. Support vector regression is used for predicting peptides transported by TAP. MHC binding is the last step in the antigen processing pathway and was predicted using a support vector machine method, SVMHC. The individual methods are combined in a filtering approach mimicking the natural processing pathway. WAPP thus predicts peptides that are cleaved by the proteasome at the C terminus, transported by TAP, and show significant affinity to MHC class I molecules. This results in a decrease in false positive rates compared to MHC binding prediction alone. Compared to prediction of MHC binding only, we report an increased overall accuracy and a lower rate of false positive predictions for the HLA-A*0201, HLA-B*2705, HLA-A*01, and HLA-A*03 alleles using WAPP. The method is available online through our prediction server at http://www-bs.informatik.uni-tuebingen.de/WAPP     

A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes

In the present study, a systematic attempt has been made to develop an accurate method for predicting MHC class I restricted T cell epitopes for a large number of MHC class I alleles. Initially, a quantitative matrix (QM)-based method was developed for 47 MHC class I alleles having at least 15 binders. A secondary artificial neural network (ANN)-based method was developed for 30 out of 47 MHC alleles having a minimum of 40 binders. Combination of these ANN-and QM-based prediction methods for 30 alleles improved the accuracy of prediction by 6% compared to each individual method. Average accuracy of hybrid method for 30 MHC alleles is 92.8%. This method also allows prediction of binders for 20 additional alleles using QM that has been reported in the literature, thus allowing prediction for 67 MHC class I alleles. The performance of the method was evaluated using jack-knife validation test. The performance of the methods was also evaluated on blind or independent data. Comparison of our method with existing MHC binder prediction methods for alleles studied by both methods shows that our method is superior to other existing methods. This method also identifies proteasomal cleavage sites in antigen sequences by implementing the matrices described earlier. Thus, the method that we discover allows the identification of MHC class I binders (peptides binding with many MHC alleles) having proteasomal cleavage site at C-terminus. The user-friendly result display format (HTML-II) can assist in locating the promiscuous MHC binding regions from antigen sequence. The method is available on the web at www.imtech.res.in/raghava/nhlapred and its mirror site is available at http://bioinformatics.uams.edu/mirror/nhlapred/.     

Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

We introduced previously an on-line resource, RANKPEP that uses position specific scoring matrices (PSSMs) or profiles for the prediction of peptide-MHC class I (MHCI) binding as a basis for CD8 T-cell epitope identification. Here, using PSSMs that are structurally consistent with the binding mode of MHC class II (MHCII) ligands, we have extended RANKPEP to prediction of peptide-MHCII binding and anticipation of CD4 T-cell epitopes. Currently, 88 and 50 different MHCI and MHCII molecules, respectively, can be targeted for peptide binding predictions in RANKPEP. Because appropriate processing of antigenic peptides must occur prior to major histocompatibility complex (MHC) binding, cleavage site prediction methods are important adjuncts for T-cell epitope discovery. Given that the C-terminus of most MHCI-restricted epitopes results from proteasomal cleavage, we have modeled the cleavage site from known MHCI-restricted epitopes using statistical language models. The RANKPEP server now determines whether the C-terminus of any predicted MHCI ligand may result from such proteasomal cleavage. Also implemented is a variability masking function. This feature focuses prediction on conserved rather than highly variable protein segments encoded by infectious genomes, thereby offering identification of invariant T-cell epitopes to thwart mutation as an immune evasion mechanism.     

Sequence signals for generation of antigenic peptides by the proteasome: implications for proteasomal cleavage mechanism

Proteasomal cleavage of proteins is the first step in the processing of most antigenic peptides that are presented to cytotoxic T cells. Still, its specificity and mechanism are not fully understood. To identify preferred sequence signals that are used for generation of antigenic peptides by the proteasome, we performed a rigorous analysis of the residues at the termini and flanking regions of naturally processed peptides eluted from MHC class I molecules. Our results show that both the C terminus (position P1 of the cleavage site) and its immediate flanking position (P1') possess significant signals. The N termini of the peptides show these signals only weakly, consistent with previous findings that antigenic peptides may be cleaved by the proteasome with N-terminal extensions. Nevertheless, we succeed to demonstrate indirectly that the N-terminal cleavage sites contain the same preferred signals at position P1'. This reinforces previous findings regarding the role of the P1' position of a cleavage site in determining the cleavage specificity, in addition to the well-known contribution of position P1. Our results apply to the generation of antigenic peptides and bare direct implications for the mechanism of proteasomal cleavage. We propose a model for proteasomal cleavage mechanism by which both ends of cleaved fragments are determined by the same cleavage signals, involving preferred residues at both P1 and P1' positions of a cleavage site. The compatibility of this model with experimental data on protein degradation products and generation of antigenic peptides is demonstrated.     

NetCTLpan: pan-specific MHC class I pathway epitope predictions

Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at .     

20S proteasome-dependent generation of an IEpp89 murine cytomegalovirus-derived H-2L(d) epitope from a recombinant protein

The majority of MHC class I epitopes is generated through the ubiquitin-proteasome system. In the present study, we have analyzed the proteasome-dependent generation of the IE pp89 MCMV-derived H-2L(d) epitope by both in vitro and in vivo experiments. As revealed by cytotoxic T-cell assays, the pp89 9mer epitope was generated with high fidelity from the recombinant IE pp89 by 20S proteasomes. In vitro processing showed that the recombinant pp89 was rapidly degraded by 20S proteasomes. Analysis of cell lysates under conditions that allowed detection of polyubiquitinated proteins provided no evidence for the presence of ubiquitin-pp89-conjugates in vivo. These findings suggest a ubiquitin-independent mechanism of proteasomal degradation for pp89.     

Antitopes define preferential proteasomal cleavage site usage

Protein degradation by proteasomes is a major source of peptides presented by major histocompatibility v complex class I proteins. Importantly, interferon gamma-induced immunoproteasomes in many cases strongly enhance the generation of antigenic peptides both in vitro and in vivo. Whether this is due to enhanced substrate turnover or to a change in proteasomal cleavage specificity is, however, largely unresolved. To overcome the problems of peptide quantification inherent to mass spectrometry, we introduced the "antitope" as substrate-specific internal standard. The antitope is a non-functional peptide that is generated by proteasomal cleavage within the epitope, resulting in partial overlaps with the functional epitope. Using antitopes as internal standards we demonstrate that the observed enhanced immunoproteasome-dependent presentation of the bacterial listeriolysin O T-cell epitope LLO(296-304) is indeed due to altered cleavage preferences. This method is also applicable to other major histocompatibility class I epitopes as is shown for two potential epitopes derived from Coxsackievirus.     

Antigen processing by proteasomes: insights into the molecular basis of crypticity

Eight to eleven amino acid residues are the sizes of predominant peptides found to be associated with MHC class I molecules. Proteasomes have been implicated in antigen processing and generation of such peptides. Advanced methodologies in peptide elution together with sequence determination have led to the characterisation of MHC class I binding motifs. More recently, screening of random peptide phage display libraries and synthetic combinatorial peptide libraries have also been successfully used. This has led to the development and use of predictive algorithms to screen antigens for potential CTL epitopes. Not all predicted epitopes will be generated in vivo and the emerging picture suggests differential presentation of predicted CTL epitopes ranging from cryptic to immunodominant. The scope of this review is to discuss antigen processing by proteasomes, and to put forward a hypothesis that the molecular basis of immunogenicity can be a function of proteasomal processing. This may explain how pathogens and tumours are able to escape immunosurveillance by altering sequences required by proteasomes for epitope generation. Abbreviations: CTL – cytotoxic T lymphocytes; DRiPs – defective ribosomal products; ER – endoplasmic reticulum; Hsps – heat shock proteins; LMP – low molecular weight peptide; MHC – major histocompatibility complex; TAP – transporter associated with antigen processing.     

Enhanced rate of degradation of basic proteins by 26S immunoproteasomes

Immunoproteasomes are alternative forms of proteasomes specialized in the generation of MHC class I antigenic peptides and important for efficient cytokine production. We have identified a new biochemical property of 26S immunoproteasomes, namely the ability to hydrolyze basic proteins at greatly increased rates compared to constitutive proteasomes. This enhanced degradative capacity is specific for basic polypeptides, since substrates with a lower content in lysine and arginine residues are hydrolyzed at comparable rates by constitutive and immunoproteasomes. Crucially, selective inhibition of the immunoproteasome tryptic subunit β2i strongly reduces degradation of basic proteins. Therefore, our data demonstrate the rate limiting function of the proteasomal trypsin-like activity in controlling turnover rates of basic protein substrates and suggest new biological roles for immunoproteasomes in maintaining cellular homeostasis by rapidly removing a potentially harmful excess of free histones that can build up under different pathophysiological conditions.     

MAPPP: MHC class I antigenic peptide processing prediction

MAPPP is a bioinformatics tool for the prediction of potential antigenic epitopes presented on the cell surface by major histocompatibility complex class I (MHC I) molecules to CD8 positive T lymphocytes. It combines existing predictions for proteasomal cleavage with peptide anchoring to MHC I molecules.     

Cell type-specific proteasomal processing of HIV-1 Gag-p24 results in an altered epitope repertoire

Proteasomes are critical for the processing of antigens for presentation through the major histocompatibility complex (MHC) class I pathway. HIV-1 Gag protein is a component of several experimental HIV-1 vaccines. Therefore, understanding the processing of HIV-1 Gag protein and the resulting epitope repertoire is essential. Purified proteasomes from mature dendritic cells (DC) and activated CD4(+) T cells from the same volunteer were used to cleave full-length Gag-p24 protein, and the resulting peptide fragments were identified by mass spectrometry. Distinct proteasomal degradation patterns and peptide fragments were unique to either mature DC or activated CD4(+) T cells. Almost half of the peptides generated were cell type specific. Two additional differences were observed in the peptides identified from the two cell types. These were in the HLA-B35-Px epitope and the HLA-B27-KK10 epitope. These epitopes have been linked to HIV-1 disease progression. Our results suggest that the source of generation of precursor MHC class I epitopes may be a critical factor for the induction of relevant epitope-specific cytotoxic T cells.     

Imatinib mesylate and nilotinib affect MHC-class I presentation by modulating the proteasomal processing of antigenic peptides

Imatinib (IM) has been described to modulate the function of dendritic cells and T lymphocytes and to affect the expression of antigen in CML cells. In our study, we investigated the effect of the tyrosine kinase inhibitors IM and nilotinib (NI) on antigen presentation and processing by analyzing the proteasomal activity in CML cell lines and patient samples. We used a biotinylated active site-directed probe, which covalently binds to the proteasomally active beta-subunits in an activity-dependent fashion. Additionally, we analyzed the cleavage and processing of HLA-A3/11- and HLA-B8-binding peptides derived from BCR-ABL by IM- or NI-treated isolated 20S immunoproteasomes using mass spectrometry. We found that IM treatment leads to a reduction in MHC-class I expression which is in line with the inhibition of proteasomal activity. This process is independent of BCR-ABL or apoptosis induction. In vitro digestion experiments using purified proteasomes showed that generation of epitope-precursor peptides was significantly altered in the presence of NI and IM. Treatment of the immunoproteasome with these compounds resulted in an almost complete reduction in the generation of long precursor peptides for the HLA-A3/A11 and ?B8 epitopes while processing of the short peptide sequences increased. Treatment of isolated 20S proteasomes with serine-/threonine- and tyrosine-specific phosphatases induced a significant downregulation of the proteasomal activity further indicating that phosphorylation of the proteasome regulates its function and antigen processing. Our results demonstrate that IM and NI can affect the immunogenicity of malignant cells by modulating proteasomal degradation and the repertoire of processed T cell epitopes.     

Expression and regulation of interferon gamma-inducible proteasomal subunits LMP7 and LMP10 in the bovine corpus luteum

The proteasome is a large, polymeric protease complex responsible for intracellular protein degradation and generation of peptides that bind to class I major histocompatibility complex (MHC) molecules. Interferon gamma (INFgamma) induces expression of alternative proteasomal subunits that affect intracellular protein degradation, thereby changing the types of peptides that bind to class I MHC molecules. These alterations in class I MHC peptides can influence whether cells and tissues are tolerated by the immune system. Expression of two INFgamma-inducible proteasomal subunits, LMP7 and LMP10, in bovine luteal tissue was examined in this study. Northern analysis revealed the presence of mRNA encoding LMP7 and LMP10 in luteal tissue. Steady-state amounts of LMP7 mRNA did not change during the estrous cycle, but LMP10 mRNA was low in early corpus luteum (CL) and elevated in midcycle and late CL. Tumor necrosis factor alpha alone and in the presence of LH and/or prostaglandin F2alpha elevated steady-state amounts of LMP10 mRNA but did not affect LMP7 mRNA in cultured luteal cells. Immunohistochemistry revealed the presence of LMP10 primarily in small luteal cells. Numbers of LMP10-positive cells were lower in early CL than in midcycle and late CL. The finding that INFgamma-inducible proteasomal subunits are expressed in luteal tissue when the CL is fully functional was unexpected and suggests that proteasomes in luteal cells may generate peptides capable of stimulating a class I MHC-dependent inflammatory response.     

Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy

Identification of MHC binding peptides is essential for understanding the molecular mechanism of immune response. However, most of the prediction methods use motifs/profiles derived from experimental peptide binding data for specific MHC alleles, thus limiting their applicability only to those alleles for which such data is available. In this work we have developed a structure-based method which does not require experimental peptide binding data for training. Our method models MHC-peptide complexes using crystal structures of 170 MHC-peptide complexes and evaluates the binding energies using two well known residue based statistical pair potentials, namely Betancourt-Thirumalai (BT) and Miyazawa-Jernigan (MJ) matrices. Extensive benchmarking of prediction accuracy on a data set of 1654 epitopes from class I and class II alleles available in the SYFPEITHI database indicate that BT pair-potential can predict more than 60% of the known binders in case of 14 MHC alleles with AUC values for ROC curves ranging from 0.6 to 0.9. Similar benchmarking on 29,522 class I and class II MHC binding peptides with known IC(50) values in the IEDB database showed AUC values higher than 0.6 for 10 class I alleles and 9 class II alleles in predictions involving classification of a peptide to be binder or non-binder. Comparison with recently available benchmarking studies indicated that, the prediction accuracy of our method for many of the class I and class II MHC alleles was comparable to the sequence based methods, even if it does not use any experimental data for training. It is also encouraging to note that the ranks of true binding peptides could further be improved, when high scoring peptides obtained from pair potential were re-ranked using all atom forcefield and MM/PBSA method.