Epitope distribution in ordered and disordered protein regions. Part B - Ordered regions and disordered binding sites are targets of T- and B-cell immunity.

JIM-11841; No of Pages 18 Journal of Immunological Methods xxx (2014) xxx–xxx

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Journal of Immunological Methods journal homepage: www.elsevier.com/locate/jim

Computational modelling

Epitope distribution in ordered and disordered protein regions. Part B — Ordered regions and disordered binding sites are targets of T- and B-cell immunity Mirjana D. Pavlović a, Davorka R. Jandrlić b, Nenad S. Mitić c,⁎ a b c

University of Belgrade, Institute of General and Physical Chemistry, Studentski trg 12/V, Belgrade, Serbia University of Belgrade, Faculty of Mechanical Engineering, Kraljice Marije 16, Belgrade, Serbia University of Belgrade, Faculty of Mathematics, P.O.B. 550, Studentski trg 16, Belgrade, Serbia

a r t i c l e

i n f o

Article history: Received 24 October 2013 Received in revised form 31 March 2014 Accepted 31 March 2014 Available online xxxx Keywords: T-cell epitope prediction Disorder prediction Epitope structural availability Disordered binding sites Cancer Autoimmunity

a b s t r a c t Intrinsically disordered proteins exist in highly flexible conformational states linked to different protein functions. In this work, we have presented evidence that HLA class-I- and class-II-binding T-cell epitopes, experimentally verified in several tumor-associated antigens and nuclear systemic autoantigens, are predominantly located in ordered protein regions or at disorder/order borderlines, defined by the majority of analyzed publicly available disorder predictors. We have also observed the overlapping of secondary structural elements and prevalently hydrophobic regions with T-cell epitopes in Epstein Barr Virus (EBV) nuclear antigen 1 (EBNA-1), cancer/testis antigen MAGE-A4, and Sm-B/B′, U1 snRNPA (U1A) and U1-70 kDa autoantigens. The results are in accordance with the clustering of the predicted HLA class-I and class-II epitopes in protein parts which encompass the consensus of ordered regions, determined by individual disorder predictors. Some HLA class-II epitopes and linear B-cell epitopes were located near the segments predicted to have elevated crystallographic B factor in EBNA-1, Sm-B/B′ and U1 snRNP A proteins, suggesting that protein flexibility could influence the structural availability of epitopes. Naturally processed T-cell epitopes and linear B-cell epitopes could also be found within putative disordered binding sites, determined by “dips” in the prevalently disordered parts of prediction profiles of the majority of disorder predictors, and peaks in ANCHOR-prediction profile. Two minor antigenic regions within EBNA-1, mapped to the residues 58–85 and 398–458, encompassing putative disordered binding sites, contain epitopes connected with anti-Ro 60 kDa and anti-Sm B/B′ autoimmunity in systemic lupus erythematosus. One of these regions overlaps residues 395–450, identified as the binding site of USP7 (HAUSP), which regulates the EBNA-1 replication function. In Sm-B/B′, one of the putative disordered binding sites (residues 114–165) encompasses the T-cell epitope 136–153, while another, residues 200–216, flanks two proline-rich B-cell epitopes (residues 190–198 and 216–222), overlapping the preferred CD2BP2–GYF-binding motif (R/K/G)XXPPGX(R/K), characteristic of splicosomal proteins. We have noticed that the same motif (residues 397–403) is mimicked in EBNA-1 and overlaps epitope 398–404, involved in anti-Sm B/B′ autoimmunity. The majority of recognized T- and B-cell epitopes in analyzed autoantigens or tumor-associated antigens appertain to the ordered or transient protein structures. The congruence between certain B- and T-cell epitopes and predicted disordered binding sites or protein-binding eukaryotic motifs in the

Abbreviations: KD scale, Kyte–Doolittle amino acid hydrophobicity scale; HW scale, Hopp–Woods amino acid hydrophobicity scale; SB, strong binding; WB, weak binding; AvgH, average hydropathy value method; MAA, majority of amino acid method. ⁎ Corresponding author. E-mail addresses: [email protected] (M.D. Pavlović), [email protected] (D.R. Jandrlić), [email protected] (N.S. Mitić).

http://dx.doi.org/10.1016/j.jim.2014.03.027 0022-1759/© 2014 Elsevier B.V. All rights reserved.

Please cite this article as: Pavlović, M.D., et al., Epitope distribution in ordered and disordered protein regions. Part B — Ordered regions and disordered binding sites are targets ..., J. Immunol. Methods (2014), http://dx.doi.org/10.1016/j.jim.2014.03.027

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M.D. Pavlović et al. / Journal of Immunological Methods xxx (2014) xxx–xxx

antigens participating in molecular complexes might influence the capture of antigens, their processing and subsequent presentation and immunodominance. © 2014 Elsevier B.V. All rights reserved.

1. Introduction The most fascinating question in autoimmunity and cancer immunity is why certain epitopes are uniquely selected among thousands of possible peptides, derived from autoantigens (auto-Ags). Defective immune tolerance mechanisms and/or environmental stimuli, able to cause the breakdown of peripheral tolerance towards specific auto-Ags, can predispose to autoimmunity by increasing the prevalence of autoreactive lymphocytes. The biochemical and immunological properties of auto-Ags, their altered expression, catabolism and fate after cell death can trigger and sustain the inflammation, which may have a key role in the induction of autoimmunity. Among the structural properties of most auto-Ags induced in cancer (Bei et al., 2009), tumor-associated antigens (TAAs) (Iakoucheva et al., 2002) and systemic nuclear auto-Ags (Carl et al., 2005; Brendel et al., 1991) are the following: highly charged segments, repetitive surface elements, bound nucleic acids and overexpression, which are also the characteristics of extremely disordered proteins (Uversky, 2010). Intrinsically disordered/unstructured proteins (IDPs) and protein regions (IDRs) (reviewed in: Tompa, 2009) lack stable 3D conformation under physiological conditions in vitro (Uversky et al., 2000). Despite the lack of a well-formed structure, disordered proteins often play important functional roles, (Uversky, 2010; Uversky and Dunker, 2010), becoming structured only when bound to other molecules. IDRs are inherently sensitive to proteolysis in vitro without the need for prior denaturation and, hence, supposed to be underrepresented as T-cell epitopes (Carl et al., 2005). However, in vivo protein degradation is regulated by the sequestration of proteases in separate compartments and by controlled substrate delivery, and no strong correlation between intrinsic protein disorder and shorter protein half-lives has been reported. Different models of IDR stabilization by functional interactions were proposed, providing a way to avoid their degradation, either by promoting disorder-to-order transition or by masking IDRs (Suskiewicz et al., 2011). MHC class-II epitope immunodominance was found to be related to epitope accessibility, i.e. the “context” of the epitope within the Ag, as a favorable location in the Ag 3D structure and the amino acid (AA) composition of the epitope-flanking endoprotease cleavage sites (Chianese-Bullock et al., 1998; Musson et al., 2003; Landry, 1997; Dai et al., 2002; Carmicle et al., 2007; Mirano-Bascos et al., 2008). The relationship between immunodominance of MHC class-II epitopes and their location in disordered or ordered regions was demonstrated in the experimental models of foreign Ags (Landry, 1997; Landry, 2000; Carmicle et al., 2007). The probability that particular protein segment will be cleaved by endosomal proteases can be estimated by structural parameters that indicate conformational flexibility, such as elevated crystallographic B-factors, solvent-accessible surface area or hydrogen-deuterium exchange (HX) for backbone amide groups. Immunodominant MHC class-II binding epitopes

are flanking or partially overlapping the unstructured loops (segments with highly elevated B-factors). However, elevated B-factors also occur at sites where T-cell epitopes have not been discovered and vice versa. Implementing the additionally sensitive NMR-based HX technique, a striking correlation was found between T-cell epitopes and regions of structural order, suggesting that the nearby regions of instability define the ends of T-cell epitopes in experimental model Ags (Landry, 2000) (Melton and Landry, 2008). In contrast to these studies, a minor role of the epitope “context” and importance of its AA composition for the CD4+ T-cell immunodominance have been found (Weaver et al., 2008; Weaver and Sant, 2009). The authors suggested that it could be the consequence of the competition for MHC class-II molecule binding to peptides derived from an abundant pool of endogenously synthesized proteins. Under these conditions, even dramatic differences in the yield of one Ag-derived peptide over another, due to a favorable location and flanking sequences, will lead to merely minor differences in the yield of the cell surface-expressed peptide–MHC class-II complexes. In addition, DM editing in APC may override any differences in the initial yield of peptides from the intact Ag, enhancing the presentation of high stability complexes over those with lower stability (Sant et al., 2005; Lazarski et al., 2005; Weaver and Sant, 2009). The results of these studies raised the question whether the immunodominance of CD4+ T-cell epitopes is independent of their localization in the Ag 3D structure and related predominantly to the epitope itself and the persistence of epitope-MHC class II complexes on Ag-presenting cells (APCs). MHC class-I epitope processing is not expected to be guided by the Ag 3D structure, since proteasomes have ATP-dependent protein unfolding activity, but disordered regions could destabilize proteins and speed up proteasomal degradation without the need for the previous unfolding (Suskiewicz et al., 2011). Secondly, a proteasome proteases cleavage determines that the clusters of MHC class-I epitopes are located in the hydrophobic protein regions (Lucchiari-Hartz et al., 2003). MHC class-II epitopes, both predicted and experimentally determined, were also found to have medium to high content of hydrophobic AA residues (Halling-Brown et al., 2009), which indicates that T-cell epitope cluster is preferently in ordered protein regions, enriched in bulky hydrophobic AA (Radivojac et al., 2007; Uversky and Dunker, 2010). Our previous article (Mitić et al., 2014) addresses the relationship between ordered and disordered structures and intrinsic T-cell epitope characteristics (hydropathy and AA composition) regarding epitope affinity and residence within Ag. Comparing epitope, hydropathy and disorder prediction methods, we have found that T-cell epitopes were more frequent in ordered than in disordered protein regions, which was also valid when epitope hydropathy and order/disorder location was approached according to HLA class-I and HLA class-II allele supertypes. Epitopes appertaining to the ordered regions were found to be more hydrophobic than those in disordered regions, which is consistent with the



clustering of naturally processed cytotoxic T-lymphocyte (CTL) epitopes of HIV-1-Nef in hydrophobic protein regions (Lucchiari-Hartz et al., 2003). HLA class-II binding epitopes were predicted to be more hydrophobic than HLA class-I epitopes. Both HLA class-I and class-II high affinity binding epitopes displayed more hydrophobicity than low affinity binding epitopes. The percentage of hydrophobic epitopes also increased with the enhancement of epitope promiscuity. Reverse vaccinology, which selects epitopes with the high affinity of binding to HLA molecules and high promiscuity level, thus appears to be oriented towards ordered, prevalently hydrophobic, regions. Hydrophobic portions of biological molecules, which are normally hidden inside the molecular structures, were previously suggested to be the most ancient alarm signals on which the modern immune systems were built (Seong and Matzinger, 2004). For the majority of supertypes, the anchor positions B and F, or at least one of these recognition positions, recognize the prevalently hydrophobic residues in HLA class-I-binding peptides (Sidney et al., 2008). The N-terminal residues of the peptides, binding to the P1 pocket of human DR1 and murine I-Ab class II molecules, are also selected strongly for bulky hydrophobic AA. Peptides with uncharged AA at P1 position will form complexes with class-II molecules with extended kinetic stability at neutral pH, compared to that at acidic pH, that are likely to survive the negative DM editing in acidic endosomal compartments. Thus, a greater fraction of these peptide-class-II complexes will be exported to the cell surface, where their spontaneous kinetic stability at neutral pH will determine their persistence and possibility to recruit CD4+ cells (Chaves et al., 2006). We have noticed that epitopes, predicted to bind to the alleles appertaining to the DR1, DR7, DR9 (HLA class-II) or to the A2, A1-A3, A1-A24 and A24 (HLA class-I) supertypes, located in ordered regions, are the most numerous and prevalently hydrophobic. Epitopes binding to DR1 and A2 supertypes have also high affinity prediction score (Mitić et al., 2014). This may be the reflection of the immune system evolution towards stable peptide–MHC complexes on the surface of APC for the focus of T cells. If the prevalence of hydrophobic AA is the determining factor of the epitope immunodominance, regarding the affinity of predicted epitopes and their stability at the surface of APC (at least for the epitopes binding to certain supertypes), the immune response against self-Ags will be expected to focus on non-tolerized prevalently hydrophilic peptides, concentrated in disordered regions. However, hydrophobic peptides, buried within protein regions with a high degree of tertiary structure or heavily glycosylated, that are more resistant to proteolysis (Brown et al., 2003), are also expected to be poorly immunogenic, due to being sequestrated from the proteolytic release and subsequent association with class-II molecules. The central point of tumor immunology is the breaking of immunological tolerance, since many TAAs are considered to be self-Ags that are normally expressed in many healthy tissues, while overexpressed in tumors. The attempts to stimulate antitumor cell immunity are focused on the activation of CTL or CD4 + T lymphocytes using different immunization techniques, as Ag or peptide loading on APC or viral vectors encoding Ag, in order to increase the yield of the chosen Ag-derived peptide on the surface of

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APC. Enhanced TAA concentration in Ag-processing cellular compartments, due to immunization techniques, is likely to increase the yield of Ag-derived peptide, due to its structural availability and flanking protease-sensitive residues. The increased yield of Ag-derived peptide is then likely to lead to the enhancement of the cell surface-expressed peptide–MHC class II complexes, that may even override the effects of DM editing. On the contrary, the autoimmune response is an adaptive immune response mounted against self-Ags, that arises spontaneously. There is evidence that defective immune tolerance mechanisms and/or environmental stimuli can predispose to autoimmunity (Bei et al., 2009). Starting from the observation that the majority of nuclear systemic autoantigens are characterized by long regions of structural disorder, Carl et al. (2005) proposed the model of inter- or intra-molecular epitope spreading. The model was proposed on the basis of the B-cell presentation of mimicking foreign epitope to T-cells, where an initial immune response, directed towards epitopes located in high-immunogenic, ordered, protein regions, can spread to low-immunogenic epitopes within disordered protein regions of self proteins, which are not cross-reactive with the initiating peptide, in general. B-cells are well-known for their dramatic increase in Ag uptake through surface immunoglobulin molecules and FcR-mediated Ag internalization, leading to increased Ag (or Ag-complex) delivery to the processing compartment and presentation of MHC–peptide complexes. B-cells are essential in the catalyzation of determinant spreading through the increased presentation of previously tolerized dominant self-determinants and in the display of previously non-tolerized, cryptic self-determinants following an altered Ag-processing activity or T cell activation, through the generation of peptides with higher affinity for TCR and delivery of costimulatory signals (Dai et al., 2005). These data suggests that immunogenicity to self-Ags determinants is influenced by Ag-proteolytic pathways and epitope location in the protein Ag sequence. In the present study, we have analyzed predicted and experimentally found epitopes from selected Ags, in putative ordered, disordered or disorder-to-order transition regions, comparing these data with the determined secondary structural elements, hydrophobic or hydrophilic regions or ligand-binding sites. Two tumor-associated Ags (TAA), which have been extensively studied for immunotherapy were selected: Epstein–Barr Virus (EBV) nuclear Ag-1 (EBNA-1) (UniProt accession no. P03211) (Frappier, 2012) and cancer/ testis Ag MAGE-A4 (UniProt accession no. P43358) (Sakurai et al., 2004). We have also analyzed several nuclear systemic auto-Ags: Sm-B/B′, U1 snRNPA (U1A), Ro 60 kDa, U1-70 kDa and Sm-D1 (UniProt accession nos. P14678, P09012, P10155, P08621, P62314), which have been mapped for B- and T-cell epitopes connected with autoimmune diseases (Carl et al., 2005; McClain et al., 2004; Harley and James, 2010; Monneaux and Muller, 2004; Talken et al., 2001). EBNA-1 is expressed in both latent and lytic modes of EBV infection and is required for the expression of other EBV latency genes important for the cell immortalization and DNA damage, which could contribute to the development of EBV-associated tumors (Frappier, 2012). There is also considerable evidence of the molecular mimicry between EBNA-1 Ag and auto-Ags, recognized in systemic lupus erithematosus: Sm-B/B′, Sm-D1


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and Sm-D3 ribonucleoproteins (UniProt accession no. P62318). Besides, there is a cross-reactivity of antibodies, induced by the peptide derived from Ro 60 kDa ribonucleoprotein, with the peptide from the EBNA1 Ag, which has no homology with the initiating epitope. Glycine–alanine repeat (GAr) sequence expressed in EBNA-1 is also found in cytoskeleton proteins (Toussirot and Roudier, 2008). Cross-reactivity of EBNA-1 with cancer/testis Ag MAGE-A4 was also suggested (Hennard et al., 2006). Identification of the structurally favorable epitope location of naturally processed or immunodominant epitopes or their implication in ligand-binding interactions will, probably, help in understanding the process of epitope spreading and immunodominance in pathogenic conditions. Disorder predictors could capture the dynamic functionrelated aspects of structural properties, which may help in explaining the influence the protein 3D structure might have on the availability of linear T- and B-cell epitopes. They could capture different structural aspects of proteins and can be classified according to the principle of their operation: into those based on the physicochemical properties of AA in proteins and those based on the alignments of homologous protein sequences (reviewed in: Lobanov and Galzitskaya, 2011; Dosztanyi et al., 2010; Tompa, 2009). The majority of these methods has been trained basically on ordered proteins (on missing electron density in a solved X-ray crystallography structure) and outside ordered regions, and should be used with caution. The predicted secondary structure, for example, does not necessarily contradict the protein disorder. These segments could correspond to transient secondarystructural elements or to the conformation adopted in the complex with a ligand. Many disordered proteins function by binding specifically to DNA, RNA or other proteins in the process termed “coupled folding and binding” (Meszaros et al., 2009). Several methods were proposed for the prediction of the interaction-prone segments of IDP, which undergo disorderto-order transition upon binding to their biological partners and are involved in important processes, including signaling and regulation. These methods calculate the pairwise interaction energy of protein regions, predicting disorder-binding regions (ANCHOR) (Meszaros et al., 2009) or find structural elements that perform molecular recognition features (MoRFs) (Oldfield et al., 2005; Mohan et al., 2006; Cheng et al., 2007). It was previously suggested that certain immunodominant AA epitope patterns can form amphipatic or other helices, but, within proteins, they can also exist in β strands (Rothbard and Taylor, 1988). The so-called “preformed structural elements” in IDR, implicated in molecular recognition, that serve as the first step for protein–protein interactions, are categorized into four different types on the basis of the prevailing secondary structure type formed upon binding (α, β, irregular or complex) (Kotta-Loizou et al., 2013). Additionally, eukaryotic linear motifs (ELMs) and short linear motifs (SLIMs), although based on consensus sequence patterns rather than structural characteristics as MoRFs (Hsu et al., 2012) correspond to the same binding elements as MoRFs, and also adopt a welldefined structure upon binding. MoRFs (disordered binding regions) and eukaryotic linear motifs were found to overlap significantly (Meszaros et al., 2012). They may also overlap structurally available epitopes in disordered protein regions.

2. Material and methods 2.1. Protein database, epitope and hydropathy prediction The research was conducted on tumor-associated Ags MAGE-A4 (UniProt Acc No: P43358-1) and EBNA-1 (UniProt Acc No: P03211) and on systemic nuclear autoantigens: Sm-B/B′ (UniProt Acc. No. P14678), U1 snRNPA (U1A) (UniProt Acc No: P09012), Ro 60 kDa (UniProt Acc No: P10155), U1-70 kDa (UniProt Acc No: P08621-1) and Sm-D1 (UniProt Acc No: P62314). For the purpose of comparing and proving the results, we have performed a research concerning the analysis of epitope positions in the disordered and ordered regions, on the set of the predicted epitopes from the database of 619 proteins. Supplementary File 1 contains the list of IDs of proteins on which the epitope prediction was performed. The process of epitope prediction and hydropathy calculating in individual proteins and peptides is implemented in the manner similar to the one in our previous article (Mitić et al., 2014). Epitope prediction was done using NetMHCpan-2.0 and NetMHCIIpan-1.0 programs http://www. cbs.dtu.dk/services/ (Nielsen et al., 2007; Nielsen et al., 2008). NetMHCpan program covers HLA-A, B, C (Cw), D and E and several non-human species alleles, while NetMHCIIpan generates predictions for HLA-DRB alleles. Some alleles were excluded from the prediction due to the redundancy of pseudo-sequences in the allele set. For the analysis of conformation epitope positions and region type, we have used the nonamer peptide sequence. The overall number of predicted epitopes was 2037890 (HLA class-I) and 1065338 (HLA class-II), and is based on 961 HLA-I and 326 HLA-II alleles with unique pseudo-sequences, found to bind nonamer epitopes. Supplementary File 2 contains the list of used and excluded alleles in the process of epitope prediction. The process of calculating hydropathy of individual proteins is based on using a sliding window of 9 residues and summing up AA scores from one of the two most commonly used hydrophobicity scales: Kyte–Doolittle (KD) (Kyte and Doolittle, 1982) and Hopp–Woods (HW) (Hopp and Woods, 1981). Peptide hydropathy was determined in two ways: as the average hydropathy value (AvgH) of the AA contained in the peptide and by counting the number of hydrophobic/hydrophilic AA in the peptide. The second method is named the majority of AA (MAA). In both cases, either KD or HW scale could be used. In the AvgH method, we have assumed that the peptide would be considered as hydrophobic, if an average hydropathy was 0 or higher in the KD scale, or 0 or lower in the HW scale. Similarly, in the MAA method, according to its definition, the peptide would be considered as hydrophobic, if the majority of AA were hydrophobic, or, otherwise, hydrophilic. 2.2. Disorder prediction In order to eliminate the impact of the individual disorder prediction algorithm on the positions of predicted epitopes, we have used 7 different disorder predictors with the total of 9 variants. Predictors have been selected according to the following criteria: (1) predictor is freely available and can be downloaded and applied locally, (2) predicting disordered regions is not a long-running task, and (3) predictors are



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based on different prediction methods. The set of predictors includes: IsUnstruct, VSL2b, DisEMBL_Remark465, DisEMBL_ Hot_loops, DISOPRED2, IUPred-L, IUPred-S, RONN and OnDCRF (Mitić et al., 2014). For each protein, nine (different) predictions for the positions of ordered and disordered regions were unified for further processing. The unification was done by determining the consensus for all 9 predictions obtained from various disorder predictors. After the unification, every protein contains three types of regions: ordered which covers protein positions with AA, predicted as ordered by all used disorder predictors, disordered which covers protein positions with AA, predicted as disordered by all used disorder predictors, and mixed which covers all protein positions with AA, predicted as ordered by at least one and as disordered by also at least one from the set of nine used disorder predictors.

designed to support various predictors; in this research, we have used the aforementioned epitope, disorder and hydropathy predictors. The main purpose of using this tool is the automated execution of prediction methods and uniform output suitable for analyses and comparison. EPDIS also provides the visualization of prediction results, as well as the positions of experimental epitopes and protein secondary structure (read from a separate file, entered manually). The positions of secondary structure elements were obtained from the crystallography data in the corresponding PDB files [www.rcsb.org/pdb/index.html]. Prediction results can be stored in the corresponding relational database (default is IBM DB2). The layout of the EPDIS visualization report is shown in Figs. 2 and 4.

2.3. Disorder-binding region prediction

Several experimental pieces of evidence demonstrate that T-cell epitopes are predominantly located in ordered protein regions. We have chosen two tumor-associated Ags with a partially solved crystal structure in the Protein Data Base (PDB) (www.rcsb.org/pdb/index.html), which have been extensively studied for immunotherapy: Epstein–Barr Virus (EBV), nuclear Ag 1 (EBNA-1) and cancer/testis Ag (CTA) MAGE A4. PDB data represents EBNA-1 residues 461–607, complexed to 18 base pairs of EBV episome (PDB file 1B3T) and MAGE-A4 cancer/testis Ag, residues 102–311, (PDB file 2WA0). The Epstein–Barr virus is one of the most common human herpes viruses, a causative agent in the development of several types of lymphomas and carcinomas, and it immortalizes cells as part of its latent infection pathway. EBVassociated malignancies can be distinguished by the patterns of latent viral gene expression. EBNA-1 is thought to be promising Ag for immunotherapy of EBV-associated cancers, as it is expressed in all latency states (Depil et al., 2007), and is the only Ag characteristic of Burkitt lymphoma, referred as latency I state. EBNA-1 is the only viral protein required to maintain the EBV genomes in proliferating cells. EBNA-1 cytotoxic lymphocyte response was long believed to be completely suppressed by the long internal G–A repeat (GAr) (residues 90–328 of EBNA-1), which has an immune evasion function, inhibiting protein unfolding in proteasomal degradation of the MHC class-I Ag presentation pathway (Sundar et al., 2004). However, it has been found that GAr does not completely block proteasomal degradation and Ag presentation by MHC class-I molecules. The poor recognition and killing of the target cells that naturally express EBNA-1, by specific CTL, (Destro et al., 2011) suggest the poor presentation of EBNA-1-derived CD 8 + epitopes, whereas the role of EBNA-1 specific CTL remains to be established. In contrast to that, nearly all healthy EBV carriers have CD4+ T cell response to EBNA-1, able to recognize EBV-transformed lymhoblastoid cell lines (in which the HLA class-II presentation pathway is active), and could act as cytotoxic effectors (Demachi-Okamura et al., 2008). CD4 + T-helper cells are critical in the initiation, regulation and maintenance of the immune response to EBV. The majority of HLA class-II epitopes, predicted by the NetMHCIIpan method, are located in the functionally important C-terminal region of EBNA-1 (as presented for the most studied EBNA-1 isolate B95-8), Fig. 1.A. The region encompasses the consensus of the

ANCHOR predictor [http://iupred.enzim.hu/] was used for the prediction of disordered protein-binding regions (segments) that undergo disorder-to-order transition upon binding, also defined as molecular recognition features (MoRFs) (Oldfield et al., 2005; Mohan et al., 2006; Cheng et al., 2007). ANCHOR method is based on the pairwise energy estimation, which predicts sites that exist in a disordered state in isolation, but can favorably interact with a globular protein and adopt a well-defined conformation upon binding. The criteria for identifying disordered-binding regions within the protein sequence were the following: (1) disordered regions were predicted using IUPred (Dosztanyi et al., 2005) and (2) disordered binding regions cannot form a sufficient amount of intrachain interactions to fold on their own, but are (3) likely to energetically gain and become stabilized upon binding to a globular protein partner. PONDR®-VLXT predictor [http://www.pondr.com] was also used for the screening of disordered protein-binding regions, or MoRFs, indicated by short ordered regions (“dips”) within longer intrinsically disordered segments. PONDR®-VLXT is the base of the specialized MoRF predictors, developed by training using the selection of the known disordered binding sites. Despite different underlying principles of ANCHOR and PONDR®-VLXT, the predicted binding sites often map to identical protein segments. The additional criterion for identifying the disordered-binding regions within the protein sequence was the consistency of the “dips” in the prediction profiles of the majority of disorder predictors, that are apparently near the ANCHOR-predicted binding site. Different underlying propensities for structural order can heavily influence the reaction of predictors to transient structures. Furthermore, they can show slight or profound dips in binding sites or no reaction (giving a maximal score of 1) (Dosztanyi et al., 2010). 2.4. EPDIS application We have developed EPDIS (EPitopes in DISorder) tool for the automated execution of the epitope, disorder and hydropathy prediction. EPDIS allows the loading of protein sequences, either from files in FASTA format relational database or from the UniProt database. The application is

3. Results and discussion


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EBNA-1 (Fig. 2.A), complexed to the episome (PDB file 1B3T). Summarized results on experimentally verified EBNA-1 epitopes revealed the prevalence of hydrophobic peptides, according to the HW AvgH and MAA methods. C-terminal region activates the viral DNA replication and segregation of EBV episomes through binding to the cis-acting DNA sequence ori-P. The DNA-binding domain of EBNA-1 contains a core domain (residues 504–606) and flanking domain (residues 459–503), with a remarkable similarity to DNA-binding and dimerization domain from the E2 protein of bovine papillomavirus. However, EBNA-1 has no sequence homology with any origin-binding protein or any other DNA-binding protein. EBNA-1 protein forms a very stable homodimer and the DNA-binding outer helix (residues 477–490) in each dimer is in the position to interact with the C-terminal helix (residues 568–584) (Bochkarev et al., 1995). Promiscuous epitope 482–496 (481–500) (Fig. 2.C), recognized by DR15, DR7, DR11 and DR4 alleles, is located at the end of the “flanking” DNA-binding domain, overlapping DNA-binding helix (residues 477–490). The epitope 482–496 was found to be naturally processed in the exogenous pathway by EBNA-1 protein-pulsed dendritic cells (Kruger et al., 2003), as well as to overlap the strong epitope 476–486, recognized by DP2 and DP5 (Demachi-Okamura et al., 2008). The epitope 482–496 is located in the ordered region nearby disorder/order borderlines, or at the borderline, depending on the used disorder predictor, Fig. 3.A, B. The predictors trained on the short disordered regions (defined by the missing regions of

Number of predicted epitopes

ordered regions of all analyzed disorder predictors. HLA class-II epitopes are not found in the consensus of disordered regions. HLA class-I epitopes, predicted by the NetMHCpan method, are also concentrated in the C terminal ordered region of EBNA-1 Ag, although they are not strictly located outside the consensus of disordered regions, Fig. 1.A. These results are in agreement with the results obtained for each individual protein from the larger data set of 619 proteins, for which also holds that the predicted epitopes are mainly concentrated in the consensus of ordered regions. Using the TEPITOPE software, Depil and colleagues have predicted and experimentally evaluated promiscuous and high-affinity-binding HLA class-II epitopes for 12 DRB alleles in the C-terminal EBNA-1 region (residues 475–552) (Depil et al., 2007) (Supplementary Table 1). Similar results were obtained using the NetMHCIIpan method, as presented for DRB1_1501 allele. Recognized epitopes for this allele (Depil et al., 2007; Leen et al., 2001) are represented by blue and red bars, Fig. 2.C. We have collected other experimental evidence that CD4+ T cell epitopes are concentrated in the C-terminal region of the EBNA-1 protein, Supplementary Table 1. They are concentrated mainly in the region between residues 475 and 619, Fig. 1.B. This region is prevalently hydrophobic, according to both the KD and HW scales, as represented, for KD scale, in Fig. 2.D. All analyzed disorder predictors are in accordance with the finding that C-terminal region is prevalently ordered (Fig. 3.A), which corresponds to the defined secondary structure of residues 470–607 of

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Protein sequence Fig. 1. Distribution of epitopes in Epstein–Barr virus protein EBNA-1 (isolate B95-8) (UniProt Acc No: P03211) relative to the disorder/order consensus regions: A) Epitopes predicted by the NetMHCpan method (HLA class-I) (green) and NetMHCIIpan (HLA class-II) (blue), for all the alleles included in the study. B) Experimentally validated HLA-I) (green) and HLA class-II (blue) epitopes (Supplementary Table 1). Consensus of order (orange) and disorder (gray) prediction for EBNA-1, according to the disorder predictors: IsUnstruct, VSL2b, DisEMBL_Remark465, DisEMBL_Hot_loops, DISOPRED2, IUPred-L, IUPred-S, RONN and OnDCRF. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)




Fig. 2. Epstein–Barr virus protein EBNA-1, isolate B95-8 (UniProt Acc No: P03211). The application displays data on: A) Disorder/order prediction, obtained using VSL2b and IsUnstruct predictors. Secondary structure, PDB-1B3T. B) Predicted: ordered regions, secondary structure, epitopes and hydropathy (Kyte–Doolittle). C) Nonamer epitope prediction for DRB1_0115 allele, using the NetMHCIIPan method. D) Hydropathy prediction, according to the Kyte–Doolittle AA scale, using a window of nine AA. The epitopes, AA 475–500 and 514–539 were determined as high-affinity DRB1_0115-binding (marked blue) and the epitopes in AA 482–496 and 563–577 as CD4+ T cells-inducing (marked red) for the same allele. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Protein sequence Fig. 3. Location of T-cell epitopes (related to cancer) and B-cell epitopes (related to autoimmunity) in EBNA-1 protein (UniProt Acc No: P03211). A. Disorder/ order prediction, obtained using disorder predictors (see Fig. 1.). Disorder/order prediction using PONDR®-VLXT and C. Prediction of disordered-binding regions, using ANCHOR. Antigenic regions (residues 58–80 and 398–458) are marked yellow. Exogenously processed CD4+ T-cell epitopes: AA 482–496, 476–486, 564–583 and 567–577) are marked red. Endogenously processed epitopes in predicted disordered-binding sites: CD4+ T-cell epitopes: AA 416–425 and 434–458 are marked pink. CD8+ T-cell epitopes, AA 407–414 and 72–80 are marked green. B-cell epitopes cross-reactive with epitopes on systemic nuclear autoantigens: AA 58–72, cross-reactive with Ro 60 kDa autoantigen, and AA 398–404, cross-reactive with SmB/B′ autoantigen, are marked violet. DNA-binding site (AA 459–607) is marked as a black bar, and USP7-binding site (AA and 395–450) as a blue bar in the ANCHOR prediction profile. The preferred motif for CD2BP2–GYF domain interactions (R/K/G)XXPPGX(R/K), AA 397–403, and USP7-contact region, AA 442–448, are indicated as short blue bars in the ANCHOR prediction profile. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)




Fig. 4. Human MAGE-A4 protein (UniProt Acc No: P43358-1). The application displays data on: A) disorder/order prediction, obtained using VSL2b and IsUnstruct predictors. Secondary structure, PDB-2WA0. B) Predicted: ordered regions, secondary structure epitopes and hydropathy (Kyte–Doolittle). C) Nonamer epitope prediction method for the HLA DRB_1403 allele, using NetMHCiiPan. D) Hydropathy prediction, according to the Kyte– Doolittle AA hydrophobicity scale, using a window of nine AA. The epitopes, AA 280–299 and 284–293, were experimentally determined as HLA DRB1_1403 binding and CD4+, Th1 cells-inducing (marked red). The B-cell epitope (AA 94–100) cross-reactive with epitope in EBNA-1 protein is marked violet. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

9

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class-II epitopes are prevalently found in secondary structural elements and at disorder/order borderline positions in the C-terminal region of EBNA-1. The overlapping of secondary structural elements (adopted in the EBNA1–episome complex, PDB file 1B3T) and HLA class II epitopes is evident in Fig. 2.A. Collected evidence indicate that CD4 + T cell clones could also recognize endogenously processed and presented Ags on EBV-transformed lymphoblastoid cell lines or Burkitt lymphoma lines through a novel proteasomal processing pathway, where indicator Ags have gained a direct intracellular entry into HLA class-II pathway (Long et al., 2005). Processed epitopes are prevalently located in the C-terminal, ordered, protein region (Supplementary Table 1). Other alternatively processed epitopes, as CD8 + T-cell epitopes of EBNA 1, processed via “cross-priming” from exogenously acquired Ag in dendritic cells: B35/B53 restricted epitope (residues 407–414 or 407–417) or B7-restricted epitope (residues 72–80) (Blake et al., 2000) are located in the two minor antigenic regions (residues 58–85 and 398–458), predicted to be in prevalently disordered regions. CD4+ T-cell epitope (residues 416–425) (Demachi-Okamura et al., 2008), endogenously processed in EBV-transformed lymphoblastoid B-cells, and epitope (residues 434–458) (Tsang et al., 2006), recognized by EBV-seropositive healthy donors, are also located in the second of these minor antigenic regions (residues 398–458). These minor antigenic regions overlap two putative disordered binding regions, predicted by ANCHOR, which correspond to predicted disorder-to-order

Number of predicted epitopes

X-ray structure) (Dosztanyi et al., 2010), like DisEMBL, DISOPPRED2 or IUPred-S, clearly emphasize that epitope 482–496 is in the ordered region. The same applies for VSL2b, which has balanced performances of the disordered regions of various lengths, and for IsUnstruct, which is programmed to search not only for disordered regions, but also for disorder-to-order-transitions. Other methods predict that this epitope is on the disorder/order borderlines. The epitope 482–496 of EBNA-1 is located in the proximity of the segments with elevated B factor, according to DisEMBL_Hot_loops predictor (Fig. 3.A). Such position is favorable to Ag processing and ultimately to the binding to HLA molecules and epitope presentation (Landry, 1997), as evident from the promiscuous nature of the epitope. This segment is also defined by ANCHOR as the disordered binding region, i.e. putative interaction-prone segment in disorder (Fig. 3.C). Other exogenously processed HLA class-II epitopes: 515–529 (Long et al., 2005), 518–530 (Voo et al., 2002), 563–577 (Leen et al., 2001), and 607–619 (Voo et al., 2002) are located nearby or at the disorder/order borderlines in the prevalently ordered C-terminal region of EBNA-1. Some of them are also defined by ANCHOR as parts of the putative disordered binding elements, Fig. 3.C, and contribute to the function of EBNA 1. The epitope 515–529 overlaps an α-helix (residues 514–527), contacting the major groove of the EBV episome, while the epitope 563–577 is derived from the β-sheet (residues 551–570) involved in dimerization of EBNA 1 (Paludan et al., 2002). Conclusively, the immunodominant (promiscuous and naturally processed) HLA

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Protein sequence Fig. 5. Distribution of epitopes in human MAGE-A4 protein (UniProt Acc No: P43358-1): A) epitopes predicted by the NetMHCpan (HLA class-I) (green) and NetMHCIIpan methods (HLA class-II) (blue), for all the alleles included in this study. B) Experimentally validated epitopes for the HLA class-I (marked green) and HLA class-II (marked blue) binding epitopes, Cancer immunity database (http://www.cancerimmunity.org/), and Supplementary Table 1. Consensus of the order (orange) and disorder (gray) prediction for human MAGE-A4 protein, according to the disorder predictors (see Fig. 1). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)



transition structures, also defined by the profound or slight dips in PONDR®-VLXT and all other analyzed disorder predictors, Fig. 3. T-cell antigenic/disordered binding region, encompassing residues 395–458 of EBNA-1, is flanked by two B-cell epitopes. B-cell epitope 398–404 (PPPGRRP), involved in autoimmunity, and hydrophilic B-cell epitope (residues 444–450) (EGPSTGP) are recognized by the mouse monoclonal Ab 2B4 (Hennard et al., 2006). MoAb 2B4 also interacts with the peptide from MAGE-A4 cancer/testis antigen, (residues 94–100, EGPSTSP), located at the borderline of the putative disorder-binding element, near the N-terminal to the long ordered region. The position of this peptide is apparently similar to the position of its homologous peptide in EBNA-1 (Fig. 4.A). B-cell epitope of EBNA-1 (residues 444–450), recognized by MoAb 2B4, and CD4+ T cell epitope 434–458 epitope (Tsang et al., 2006), recognized by EBV-seropositive healthy donors, encompass the contact site (residues 442–448) (Frappier, 2012) between EBNA-1 and cellular ubiquitin-specific protease USP7 (also called HAUSP), originally discovered to bind herpes simplex virus type 1. USP7 has been reported to regulate several proteins, important for cellular functioning, including p53 and Mdm2 (an E3 ubiquitin ligase for p53). EBNA-1, p53 and Mdm2 compete for the same binding pocket in the TRAF domain of USP7. EBNA1 region, mapped to AA 395–450, near the N-terminal to the DNA-binding domain, was identified as the USP7-binding site (Holowaty et al., 2003), and corresponds to the antigenic/disordered-binding region, predicted in this study. The subsequent crystal structure of this EBNA-1 peptide bound to the USP7 TRAF domain showed that the conserved EBNA1 residues 442–448 (PGEGPST) make contact to USP7. EBNA-1 could, in theory, destabilize either p53 or Mdm2, by blocking their interaction with USP7. Furthermore, EBNA-1 was confirmed to decrease p53 levels in some cells (Frappier, 2012). USP7 regulates the EBNA-1 replication function, possibly contributing to the EBV-induced immortalization and tumorogenesis (Holowaty et al., 2003). Considering the sequence homology of two epitopes and similar structural characteristics of the corresponding protein regions of EBNA-1 and MAGE-A4, there is a possibility that these regions may have similar functional characteristics. Notably, EBNA-1 region 395–450, identified as the USP7-binding site, is flanked at the N-terminus by a proline-rich sequence (residues 397–403), and, at the C-terminus, by the USP7-contact site, the small solvent exposed peptide sequences that may function as recognition motifs in the assembly of protein complexes. MAGE-A4 is the member of the MAGE-A family of CTA (shared) Ags (Scanlan et al., 2002). The CTAs are expressed in a variety of malignant tumors and are silent in normal tissues, except for testis and in some cases ovary and placenta, and are shown to have long regions of structural disorder (Rajagopalan et al., 2011). Several MAGE-A proteins inhibit p53 transactivation function and also interact with DNA-binding core of p53, suggesting that MAGE-A binding may occlude the interaction of p53 with p53-responsive promoters (Marcar et al., 2010). MAGE-A4 was identified in the search of binding partners for oncoprotein gankyrin and was found to suppress its oncogenic activity through the action of naturally cleaved C-terminal peptide, which induces p53-dependent and independent apoptosis (Sakurai et al., 2004). MAGE-A4 crystal structure is determined for residues 102–311 (PDB-2WA0). The secondary structure of this region

11

is shown in Fig. 4.A. All analyzed disorder predictors are in accordance with the finding that region 102–311 is predominantly ordered, as shown in Figs. 4.A and 5.A (for VSL2b and IsUnstruct predictors). Experimentally determined HLA class-I and class-II epitopes are located in the C-terminal, prevalently ordered regions of MAGE-A4 antigen, Fig. 5.B, annotated in Cancer immunity database (http://www.cancerimmunity.org/), and Supplementary Table 1. Exogenously processed HLA class-II epitopes were determined only in the C-terminal region, overlapping C-terminal 107 residues, which interact with gankyrin (Sakurai et al., 2004), as shown in Fig. 4.C. They are also found to be prevalently hydrophilic, according to the HW AvgH and KD AvgH and MAA hydropathy methods. The predicted and experimentally determined HLA class II epitopes (residues 280–299 and 284–293) for the allele DRB1_1403 (Ohkuri et al., 2009) overlap two α-helices (residues 276–281 and 284–295), flanking the borderline of the C-terminal ordered region. The rules, defining which peptides in a protein are capable of inducing an autoimmune response, are still a mystery. The structural properties of auto-Ags as: highly charged surface, repetitive surface elements or bound nucleic acids, correlate with the properties of highly disordered proteins. Using several disorder prediction algorithms, Carl et al. (2005) have observed that the majority of nuclear systemic auto-Ags contain long regions of structural disorder. The authors proposed a model, based on B-cell presentation of mimicking foreign epitope to T cells, to explain how humoral autoimmune response can spread, from the initial response, directed towards epitope located in an ordered region, to the epitope in a disordered region of the same or different auto-Ag. Disordered regions are improbable targets of conformationspecific antibodies, because of their structural flexibility and sensitivity to proteolysis, and are also predicted to have low frequency of MHC-II epitopes, i.e. they are under-represented as T-cell epitopes. The authors have also observed that some experimentally verified T-cell epitopes in nuclear systemic auto-Ags (Talken et al., 2001) are located in ordered protein regions. Several autoimmune diseases are characterized by antibodies to EBV protein EBNA-1, but only systemic lupus erythematosus (SLE) sera contain antiviral antibodies cross-reactive with auto-Ags (Riemekasten and Hahn, 2005). The initiating events for anti-Sm B/B′ and anti-Ro 60 kDa autoimmunity in SLE appear to be consequent to the formation of the heteroimmune antibodies that bind two different epitopes in EBV protein EBNA-1 (Harley and James, 2010). The immunization of rabbits with the linear epitope PPPGMRPP, from a proline-rich repeat of SmB/B′ auto-Ag, induced spreading of the B-cell response to different epitopes in the SmB/B′ Ag, spliceosomal proteins and other Ags considered typical for human SLE (James et al., 1995), including U1-specific RNP epitopes, frequently recognized in patients with mixed connective disease (Riemekasten and Hahn, 2005). Closely related peptide, PPPGRRP, from EBNA-1, was also capable of eliciting antibodies to SmB/B′ in rabbits and mice (James et al., 1997). The expression of the entire EBNA-1 protein in mice can elicit IgG antibodies to Sm and to double-stranded DNA (Sundar et al., 2004), supporting a possible association between EBV and etiology of SLE in humans (Harley and James, 2006). Thus,


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Fig. 6. Location of B- and T-cell epitopes in nuclear systemic autoantigens: SmB′ (UniProt Acc No: P14678-1) and nRNPA (U1A) (UniProt Acc No: P09012. A) Disorder/order prediction profiles, obtained using disorder predictors (see Fig. 1). B) Prediction of disordered binding regions, using ANCHOR predictor. C) Hydropathy prediction, according to the Kyte–Doolittle AA hydrophobicity scale. Autoantigen Sm B′: proline rich B-cell epitopes cross-reactive with EBNA-1, residues: 190–191, 216–222 and 230–238 are marked violet. CD4+ T-cell epitopes, AA: 16–33, 64–81 and 136–153 are marked red. Sm motives: Sm1 (residues 17–48) and Sm2 (residues 67–80) are marked as blue bars in the ANCHOR prediction profile. Autoantigen U1 snRNPA (U1A protein): B-cell epitopes, a pattern I, residues: 16–24, 20–30, 44–56, 73–84, 93–101, 103–115, 116–124 and 274–282, are marked violet. CD4+ T cell epitopes, AA 209–228 and 262–281 are marked red. RNA binding domains: 2–98 (RNA binding domain 1) and 195–282 (RNA binding domain 2) are marked as black bars in the ANCHOR prediction profile. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

the virus seems to use molecular mimicry in the infectious process attempting to evade the T-cell surveillance. To further investigate the relevance of disorder/order prediction for the model of Carl and colleagues, we have

analyzed EBNA-1 protein sequence using several disorder predictors. The majority of disorder predictors “see” B-cell epitope 398–404 (PPPGRRP) of EBNA-1 as disordered, Fig. 3.A. In the PONDR®-VLXT prediction plot, Fig. 3.B, the



epitope 398–404 is located at the borderline of the putative disorder-to-order transition structure (residues 398–450), which may correspond to the definition “relatively ordered”, proposed by Carl and colleagues, who also used PONDR®-VLXT predictor. ANCHOR prediction profile, Fig. 3.C, indicates that this region of EBNA-1 is an interaction-prone segment, and it was identified as the USP7-binding site (Holowaty et al., 2003), as previously mentioned. The epitope 398–404 is located in the prevalently hydrophilic part of the molecule, Fig. 2.D. The residue 397 is at the borderline of the region with highly elevated B factor (according to the DisEMBL_Hot_loops predictor, Fig. 3.A), indicating that T-cell epitopes, from the flanking “relatively ordered” region 398–450, are accessible to endosomal processing. However, the connection between epitope-mimetics and epitope-spreading from ordered to disordered protein structures remains unclear and may be, instead, related to proline–motif interactions connected to T-cell signaling (Kofler et al., 2004). The repeated epitope PPPGMRPP of Sm-B/B′, located in the prevalently disordered (and hydrophilic) region, as well as its EBNA-1 homologue, located in the “relatively ordered region”, can induce epitope spreading to many different structures of the same or different Ags. In SmB/B′, the proline rich epitopes are located in the C-terminal region of the protein, which is disordered, according to 8/9 of analyzed disorder predictors. This part of the SmB/ B′ corresponds to the missing residues in X-ray structure, while the secondary structure, projected from representative PDB entries (UniProt Acc. No. P14678) onto the UniProt protein sequence of Sm-B′, encompasses residues 1–87. Two prolinerich epitopes, at positions 190–198 and 216–222 of SmB′, are flanking a peak (residues 199–215), predicted by ANCHOR Fig. 6, suggesting that this segment could represent a disordered-binding region. The peak coincides with slight or profound dips in the long C-terminal disordered region, which are consistent in 8 out of 9 different disorder-prediction profiles. The C terminal region is even predicted to be prevalently ordered by DisEMBL_Hot_ loops and DisEMBL_ Remark465 methods. As already noticed in disorder-prediction profiles of EBNA-1, methods trained with short disordered regions, as DisEMBL_Remark465, IUPred-S or DISOPRED2, predict more profound dips in the region 190–222 of SmB′. Dosztanyi et al. (2010) have observed that some predictors react only to the general structural content of the protein domain as a whole, giving only a slight dip in the disordered region coinciding with the binding site, previously defined as a disorder-to-order transition element (MoRF), indicated as a short region of order within the longer region of disorder (a dip in PONDR®-VLXT prediction profile) (Mohan et al., 2006). PPPGMR motifs, contained in Sm-B/B′ auto-Ag, interact strongly in vitro and in vivo with CD2BP2–GYF domain, previously shown to be involved in T-cell signaling. The signature (R/K/G) XXPPGX (R/K) defines a preferred motif for interactions with CD2BP2–GYF domain and is found in Sm-B/B′ and several other splicosomal proteins (Kofler et al., 2004). This motif is also found in human EBNA-1 protein residues 397–403, overlapping the proline rich B-cell epitope 398–404. These results are indicative of the connection between the potential proline-rich protein binding motifs and immunodominant B-cell epitopes of the Sm-B/B′ and EBNA-1, involved in an autoimmune response. The colocalization of CD2BP2 and Sm-B proteins in the nucleus of Jurkat T-cells and HeLa cells

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suggests the function of the GYF domain of CD2BP2 in mediating protein–protein interactions within the spliceosome (Kofler et al., 2004). Nuclear location and proline-rich motif also suggest a potential interaction of the EBNA-1 protein with GYF domain of CD2BP2 or similar protein structures. Dominant T-cell epitopes, recognized by T-cell clones generated from SLE patients, identified in the study of Talken et al. (2001), are distant from the repeated B-cell epitope PPPGMRPP in the C-terminal region of Sm B/B′ antigen. Two of these epitopes (residues 16–33 and 64–81) are located in ordered and transition, structural regions, respectively, whereas the third epitope (residues 136–153) is located in the region defined by 8/9 predictors as prevalently disordered. However, all epitopes are prevalently hydrophobic, Fig. 6.A. The epitope 16–33 corresponds to the β strand (residues 16–21 and 26–33), while the epitope 64–81 overlaps the β strand (residues 61–72), 3/10 helix (residues 74–76) and the β strand (residues 77–84). The epitope 136–153 is, however, defined as a slight or profound “dip” by 8/9 disorder predictors and as a prominent peak in the ANCHOR prediction profile, Fig. 6, suggesting that this protein segment could be a part of the disordered binding region. Interestingly, T-cell epitopes 16–33 and 64–81 partially overlap the so-called Sm motifs (Sm1 and Sm2), that are shared among all Sm proteins. Sm1 (residues 17–48) and Sm2 (residues 67–80) are believed to be involved in protein–protein interactions (Monneaux and Muller, 2004). B-cell epitope 169–181 (TKYKQRNGWSHKD), which initiates lupus autoimmunity in the Ro 60 kDa system, is located in the ordered region of Ro 60 kDa auto-Ag (UniProt Acc No: P10155), according to the majority of disorder predictors (data not shown). Autoantibodies induced by this epitope cross-react with the EBNA-1 protein epitope GGSGSGPRHRDGVRR, (residues 58–72), that has no primary AA sequence homology with the initial Ro 60 kDa epitope (Harley and James, 2006). This epitope is located at the N-terminus of EBNA-1 (in the disorder-consensus region) and partially overlaps the prominent peak in the ANCHOR profile of EBNA-1 (residues 58–85), Fig. 3.C, i.e. the putative disordered binding region. Supposing that the response to this EBNA-1 epitope could initiate autoimmunity to Ro 60 kDa auto-Ag, the direction of epitope spreading, in this case, might be from disordered to ordered protein regions. Autoimmune response to the small nuclear ribonucleoprotein particle U1 snRNP A protein (also known as U1A protein) (UniProt Acc No: P09012), in the subset of SLE patients, is considered to show the pattern of epitope spreading from ordered to disordered protein regions (Carl et al., 2005). The secondary structure, projected from the representative PDB entries 1OIA and 2U1A of two RNA-binding domains onto the UniProt protein sequence of U1 snRNP A, is determined for the residues 2–98 of the N-terminal RNA binding domains 1 (Avis et al., 1996) and for the residues 195–282 of the C-terminal RNA binding domains 2. The highly conserved motifs, designated RNP1 and RNP2, in the first domain, lie side by side on the two central β strands (β3 and β1) and participate in RNA binding (Avis et al., 1996). Studies with maximally overlapping octapeptides from U1 snRNPA revealed two distinct Abbinding patterns (James and Harley, 1996). The pattern I consists of the eight major Ab-binding regions in U1 snRNPA, located in ordered, disordered or transient protein structures,


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according to the analyzed disorder predictors. Pattern II consists of only two of the eight major Ab-binding regions: A3 and A6. Four of these eight regions are prevalently hydrophobic, Fig. 6. The region A6 (residues 103–115), located in prevalently disordered (transient) region, is weakly immunogenic, compared to the region A3 (residues 44–56), located in an ordered region, although both epitope-regions are located on the protein surface and have similar high isoelectric points, which are good indicators of antigenicity in the snRNP system (McClain et al., 2004). The authors suggested that the epitope spreading from A3 to A6 may occur due to displaying the cryptic A6 epitope-region during Ag processing. The opinion of Carl and colleagues was that A6 was not previously tolerized, due to its position in the low-immunogenic disordered region, while the displaying of the dominant self determinant A3 was not discussed. As already mentioned, A6 is located in the region predicted to be in disorder or at order/disorder borderline by all disorder predictors, but also at the beginning of the region with elevated B-factor (according to the DisEMBL_Hot_loops predictor). Putative disordered binding region (residues 90–103), predicted by ANCHOR, immediately preceding the A6 region (residues 103–115), corresponds to an α-helix (residues 90–98), encompassing epitope- region No. 5, from the pattern I of Ab-binding (residues 93–101). This α-helix (helix C) is important for U1 snRNPA–U1 RNA interaction, although it is not directly involved in RNA binding, and the residues 99–114 may further modulate the specificity of binding to RNA (Avis et al., 1996). The residues between D90 and K98 make contact with the hydrophobic residues L44, F56 and I58 of the β-sheet (overlapping the A3 region), which stabilize the solution structure of the U1 snRNP A protein fragment. Upon RNA binding, helix C changes its orientation by 135° to allow contact with RNA. In conclusion, the epitope-region A3 lies in the β sheet that has a key role in either U1 RNA binding or helix C (epitope-region No. 5) binding. Helix C and the subsequent residues 99–114 (encompassing region A6) are involved in the conformational changes enabling these interactions. The form of U1 snRNP A, free from U1 RNA, plays the role in the polyadenylation of pre-mRNAs, and it was suggested that anti-A6 antibodies from immunized animals recognize the snRNA-free U1 snRNPA protein and thereby prevent the binding of U1A RNA to U1 snRNPA. Together with the data on the incapability of anti-A6 Abs to precipitate U1 snRNPA complexes coupled to U1 RNA, these data suggest that the A6 region is functionally masked in vivo by binding between U1 snRNPA and the U1 RNA (McClain et al., 2004), and the authors concluded that the lack of the autoimmune response to the A6 seems to be caused by the cryptic nature of this epitope-region. In contrast to that, the A3 (in ordered protein region) is freely available in vivo to be bound by the specific antibodies reacting with a protein–RNA complex. This supports earlier conclusions that B-cells, involved in the binding of the A3 epitope(s), process U1 snRNP A and present it on their MHC class II molecules, to the T cells which would lead to epitope spreading to A6 epitope-region (McClain et al., 2004). The N-terminal part of the RNA binding domains 1 encompasses first 4 out of eight epitopes (from the pattern I), which are predicted to be in ordered region. These domains contain a four-stranded β sheet and three α helices (Avis et al., 1996). Two epitope regions in the middle part of the protein could be defined as prevalently

disordered. The epitope-region No. 6 (which corresponds to the region A6 from pattern II), is defined as disordered by 7/9 predictors. It is apparent that the partially hydrophobic epitope-region No. 7 is located at the edge of the region consistently defined as a slight dip (or slope) in the long disordered central region by 9/9 analyzed predictors and by PONDR®-VLXT (Carl et al., 2005). This region corresponds to the most prominent peak in the ANCHOR prediction profile (residues 120–145), which suggests that the region No. 7 could be adjacent to the disordered binding region (site) (Fig. 6), as was previously suggested for the A6 region. Conclusively, from the eight major autoantibody-binding regions in the pattern I of Ab-binding to U1 snRNPA, (James and Harley, 1996), only two are located in prevalently disordered regions and even these two are in the putative disorder-to-order transition segments. Okubo and colleagues have mapped two epitopes, recognized by T-cells from patients with systemic autoimmunity disease (connective tissue disease) at residues 209–228 and 262–281 of the U1 snRNP A (cit. by Monneaux and Muller, 2004). The epitopes overlap the predicted secondary structural elements: first T cell epitope corresponds to the β-strand (residues 209–213) and α-helix (residues 221–229) and the second T cell epitope overlaps α-helices (residues 254–263 and 269–271) and β-strand (residues 278–279). These two epitopes are located at the borderlines of the C-terminal ordered region (according to all analyzed predictors), and the epitope 262–281 overlaps the major B-cell epitope 257–282, mapped by Okubo and colleagues and Barkat and colleagues (cit. by Monneaux and Muller, 2004) or 274–282, mapped by James and Harley (1996), Fig. 6. Both B-cell and T-cell epitopes in U1 snRNPA molecules are located in the N- and C-terminal RNA-binding (prevalently ordered) domains. Upon investigating single protein sequences, details regarding structural characteristics of T-cell epitope context can be extracted from the disorder and ANCHOR prediction outputs. Experimentally verified HLA class-I and class-II T-cell epitopes were found to be predominantly located in ordered regions, with the tendency to cluster in regions encompassing the consensus of ordered regions, predicted by the majority of disorder-prediction methods, or in regions with determined secondary structure, involved in DNA, RNA or protein binding, as shown in the examples above. The predictors trained on the short disordered regions (defined by the missing regions of X-ray structure), like DisEMBL or IUPred-S, as well as determined secondary structural elements, clearly emphasize the tendency of structures, flanking the consensus of ordered regions, to become ordered. In addition, the majority of disorder methods predict slight or profound dips in the disordered regions coinciding with disordered binding sites, detected as peaks in the ANCHOR prediction profile, except PONDR VSL2b, which reacts to transient structures in a limited way (Dosztanyi et al., 2010). These dips coincide with experimentally identified T cell epitopes, but also with B-cell epitopes from Ags associated with systemic autoimmunity. T-cell and B-cell epitopes are also found in proximity of the segments with highly elevated B factor (defined by the DisEMBL_Hot_ loops predictor). Such position was previously found favorable to Ag processing through the exogenous pathway and ultimately to the



binding to HLA molecules and epitope presentation (Landry, 1997). B-cell epitopes, from Ags associated with human SLE (including EBV protein EBNA-1) are found both in ordered, transition or disordered protein regions. It remains unclear whether epitope spreading in autoimmune diseases, proposed by the model of Carl et al. (2005)), is always directed from ordered to disordered protein regions. However, our previous work does not support the hypothesis that T-cell epitopes, belonging to disordered protein regions, have low affinity for HLA class-II molecules, as expected from disorder intrinsic proteolytic instability. Predicted HLA class-II epitopes in disordered regions have only slightly lower strong/weak ratio than in ordered regions, according to all analyzed predictors (Mitić et al., 2014). The fact that B- and T-cell epitopes in analyzed proteins are often coinciding with putative disordered binding regions (or sites) is suggestive of epitope participation in molecular complexes and might influence their processing and presentation to class-II molecules, i.e. antibodies could either enhance or suppress T-cell response, modulating the degradation of associated Ag fragments. Selected peptides may be preserved for downstream Ag processing (Brown et al., 2003). Current data also suggest that the humoral autoimmune response in SLE is propagated mainly by the epitopes that localize to the surface of complexed auto-Ags. Once immunity is instigated to an epitope freely available in vivo, the whole complex can be bound by autoreactive B cell, phagocytosed, and cryptic epitopes can be processed and presented to the autoreactive T cells (McClain et al., 2004). If different epitopes on complexed auto-Ag are contemporarily recognized by “primary” and “secondary” B cells, then, the activated T cell, brought into proximity to the “secondary” B cell, can provide cytokine stimulation (Carl et al., 2005). Numerous mechanisms have been proposed for the presentation of cryptic epitopes that may trigger autoimmunity, including dendritic cells modulated by cytokines and inflammatory stimuli or activated by self-Ags complexed to IgG antibodies, T cell priming by cross-reactive Ags presented by professional APC or Ag presented by nonprofessional APC at inflammatory sites (Lanzavecchia, 1995). Auto-Ags complexed to antibodies, foreign Ags, or those involved in large macromolecular ensembles frequently have a central role in autoimmune diseases. We have collected evidence that B- and T-cell epitopes, located in prevalently disordered regions of proteins appertain to the putative disordered-binding regions, although T-cell epitopes seem to have a “more ordered” structure than B-cell epitopes. The data on T cell epitopes associated with autoimmunity are scarce, but Talken and colleagues indicate that peptide comprising residues 16–33 of SmB autoantigen (prevalently hydrophobic and located in the ordered protein region) is, by far, the most frequently encountered T cell epitope in Sm-B (Talken et al., 2001). Greidinger and colleagues generated T-cell clones against the U1-70 kDa autoantigen (UniProt Acc No: P08621-1)), specific for five epitopes encompassing residues 97–111 (hydrophobic region), 112–136, 133–147, 151–165 and 173–187 within the RNA-binding domain (RBD) (residues 100–180) (cit. by Monneaux and Muller, 2004), defined as ordered by the majority of predictors (data not shown). The secondary structure of the central part of the RBD

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domain was predicted to contain hydrogen bonded turn at residues 142–150 (UniProt Acc No. P08621-1) in predominantly coiled structure (PDB 3PGW). The major conformational B-cell epitope of the U1-70 kDa is, also, identified in the RBD domain (Welin Henriksson et al., 1999). This complies with our conclusion that immunodominant T- and B-cell epitopes in auto-Ags are generally located in ordered regions or disordered regions which have a tendency to become ordered upon binding to a structured ligands. An exception might be the Sm-D1 auto-Ag (UniProt Acc No: SNRNP1), where major T- and B-cell epitope (residues 83–119) is identified in the C-terminal region, recognized as disordered by all analyzed disorder predictors. In the same protein, the other two T cell epitopes (residues 35–53 and 53–67) (Monneaux and Muller, 2004), are identified in the completely ordered region, according to 8/9 analyzed predictors (data not shown). However, the epitope 83–119 which contains the GR repeated motif (GRx region), involved in nucleic acid binding, seems to be a part of a conformational epitope, 70% specific for SLE. This repetition is highly homologous to EBNA-1 protein N-terminal residues 35–58, again suggesting the role of molecular mimicry and EBV infection in SLE (Riemekasten and Hahn, 2005). Antibody response against peptides appertaining to the N and C terminals of EBNA-1, was shown to be higher in lupus pediatric patients than in normal EBV-positive controls. The latter made higher levels of antibodies against, epitopes 101–113 (GGAGAGGGAGAGG) and 140–155 (GGAGAGGGA GAGGGAG) in the middle part of EBNA-1 (GA repeat), than did lupus patients. Epitopes 40–53, the GRx region, homologous to SmD1, and 398–404 (PPPGRRP), a preferred motif for CD2BP2–GYF domain interactions, appertaining to the putative disordered binding region, were selectively bound by the SLE pediatric sera (McClain et al., 2006), which suggest that ligand binding motifs (ELMs) participate in epitope immunodominance in SLE. 4. Conclusion Disorder-prediction algorithms capture different aspects of structural properties of proteins and discrepancy between prediction methods is evident in transiently ordered structures, distinguished by downward spikes (“dips”) in the predicted long disordered regions. These short disordered regions with “preformed structural elements” coincide with disordered binding regions (or sites), detected by ANCHOR, a method based on a pairwise energy estimation, which predict sites that exist in a disordered state in isolation, but can favorably interact with globular proteins and adopt a rigid conformation upon binding. In the previous article, we have found that the frequency of predicted HLA class-I or class-II T-cell epitopes in ordered protein regions, is significantly higher than in disordered regions, regardless of the disorder-prediction method. Here we present evidences that experimentally verified T-cell epitopes of several tumorassociated antigens and nuclear systemic autoantigens, which contain long regions of structural disorder, are predominantly located in ordered protein regions and disorder/order borderlines, defined by the majority of analyzed disorder predictors or by secondary structure. These regions are, also, prevalently hydrophobic. Certain naturally processed T-cell


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epitopes and linear B-cell epitopes in analyzed proteins are found in disordered binding regions, predicted by ANCHOR. In ordered protein regions, T-cell epitopes were found to correspond to determined secondary structural elements, involved in DNA, RNA or protein binding (i.e. episome-binding domain of EBV protein EBNA-1, RNA-binding regions of U1 snRNPA or U1-70 kDa autoantigens, or cancer/testis antigen MAGE-A4 gankyrin-binding region). The results are in accordance with clustering of predicted HLA class-I and HLA class-II epitopes in parts of proteins which overlap the consensus of ordered regions, determined by individual disorder predictors. Location near segments with elevated crystallographic B factor (predicted by DisEMBL_Hot_loops method), which was previously suggested to influence the HLA class-II epitope immunodominance, was observed among predicted and recognized naturally processed HLA class-II T-cell epitopes and linear B-cell epitopes in EBNA-1 protein and Sm-B/B′ and U1 snRNPA (U1A) autoantigens, suggesting that protein flexibility could influence epitope processing and presentation in analyzed proteins. Two minor T-cell antigenic regions (identified in EBVassociated cancer vaccine trials), mapped to the residues 58–85 and 398–458, in the prevalently disordered N-terminal part of protein EBNA-1, are encompassing putative disordered binding regions (determined as prominent peaks in ANCHOR prediction profiles, and as dips, consistently determined in the profiles of the majority of disorder predictors). EBNA-1 residues 395–450 were identified as the binding site of USP7 (HAUSP), a key regulator of p53 and Mdm2, which, also, regulates the EBNA-1 replication function, and possibly contributes to EBV-induced immortalization and tumorogenesis. USP7 TRAF domain contacts EBNA1 residues 442–448, overlapping CD4+ epitope (residues 434–458), recognized by EBVseropositive healthy donors, as well as B-cell epitope 444–450, which cross-reacts with the epitope (residues 94–100) from the MAGE-A4 antigen. MAGE-A4 epitope is positioned at the C-terminal of a putative disordered binding region, in a similar way as a homologous epitope in EBNA-1. Considering the sequence homology of two epitopes and similar structural characteristics of the corresponding protein regions of EBNA-1 and MAGE-A4, there is a possibility that these regions may have similar functional characteristics. In addition, we have identified proline-rich residues 397–403 of EBNA-1, overlapping the N-terminal end of the putative EBNA1 disordered binding region 395–450, as the preferred motif for CD2BP2–GYF domain interactions (R/K/G)XXPPGX(R/K), also found in Sm-B/B′ and several other splicosomal proteins. The bindings of antibodies to the residues 58–72 and 398–404 of EBNA-1 are identified as the initiating events for anti-Ro 60 kDa and anti-Sm B/B′ autoimmunity in systemic lupus erythematosus (SLE). Targeted epitopes belong to the disordered-binding/T-cell antigenic regions of EBNA-1. Similar putative disordered-binding/antigenic regions exist in Sm-B/B′ and U1 snRNPA autoantigens. In Sm-B/B′, one of these regions, 114–165, overlaps the T-cell epitope 136–153, while another region, 200–216, flanks two proline-rich B-cell epitopes (residues 190–198 and 216–222), overlapping CD2BP2–GYF binding motifs. The proline-rich motifs of SmB/B′ and EBNA-1 may have a role in the assembly of multi-protein complexes. CD2BP2–GYF domain, the module binding proline-rich sequences, was previously found to be involved in T-cell signaling, which may contribute to the immunodominance of

the proline-rich epitopes of EBNA-1 and SmB/B′ in SLE. In the patterns I and II of antibody binding to U1 snRNPA, two central antibody-binding regions (residues 103–115 and 116–224) are defined as prevalently disordered, while other B- and T-cell epitopes are located in prevalently ordered or transient part of the protein. These two antibody-binding regions are partially overlapping two prominent peaks in the ANCHOR profile (residues 90–103 and 120–140). Putative disordered-binding region 90–103 corresponds to an α-helix (residues 90–98), important for U1 snRNPA–U1 RNA interactions, which overlaps the epitope-region No. 5, defined by the pattern I of antibodybinding. The majority of recognized T- and B-cell epitopes in analyzed tumor-associated antigens and autoantigens appertain to the ordered or transient protein structures. The fact that B- and T-cell epitopes in analyzed proteins are often coinciding with putative disordered binding sites and protein-binding eukaryotic linear motifs (ELMs) suggests that antigen participation in molecular complexes might influence their processing and subsequent presentation of epitopes to HLA molecules, possibly contributing to epitope immunodominance. Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.jim.2014.03.027. Acknowledgments The work presented has been financially supported by the Ministry of Education, Science and Technological Development, Republic of Serbia, Project Nos. 174021, 174002, and III44006. References Avis, J.M., Allain, F.H., Howe, P.W., Varani, G., Nagai, K., Neuhaus, D., 1996. Solution structure of the N-terminal RNP domain of U1A protein: the role of C-terminal residues in structure stability and RNA binding. J. Mol. Biol. 257 (2), 398. http://dx.doi.org/10.1006/jmbi.1996.0171. Bei, R., Masuelli, L., Palumbo, C., Modesti, M., Modesti, A., 2009. A common repertoire of autoantibodies is shared by cancer and autoimmune disease patients: inflammation in their induction and impact on tumor growth. Cancer Lett. 281 (1), 8. http://dx.doi.org/10.1016/j.canlet.2008. 11.009. Blake, N., Haigh, T., Shaka'a, G., Croom-Carter, D., Rickinson, A., 2000. The importance of exogenous antigen in priming the human CD8+ T cell response: lessons from the EBV nuclear antigen EBNA1. J. Immunol. 165 (12), 7078. Bochkarev, A., Barwell, J.A., Pfuetzner, R.A., Furey Jr., W., Edwards, A.M., Frappier, L., 1995. Crystal structure of the DNA-binding domain of the Epstein–Barr virus origin-binding protein EBNA 1. Cell 83 (1), 39. Brendel, V., Dohlman, J., Blaisdell, B.E., Karlin, S., 1991. Very long charge runs in systemic lupus erythematosus-associated autoantigens. Proc. Natl. Acad. Sci. U. S. A. 88 (4), 1536. Brown, S.A., Stambas, J., Zhan, X., Slobod, K.S., Coleclough, C., Zirkel, A., Hurwitz, J.L., 2003. Clustering of Th cell epitopes on exposed regions of HIV envelope despite defects in antibody activity. J. Immunol. 171 (8), 4140. Carl, P.L., Temple, B.R., Cohen, P.L., 2005. Most nuclear systemic autoantigens are extremely disordered proteins: implications for the etiology of systemic autoimmunity. Arthritis Res. Ther. 7 (6), R1360. http://dx.doi.org/ 10.1186/ar1832. Carmicle, S., Steede, N.K., Landry, S.J., 2007. Antigen three-dimensional structure guides the processing and presentation of helper T-cell epitopes. Mol. Immunol. 44 (6), 1159. http://dx.doi.org/10.1016/j.molimm.2006.06.014. Chaves, F.A., Richards, K.A., Torelli, A., Wedekind, J., Sant, A.J., 2006. Peptidebinding motifs for the I-Ad MHC class II molecule: alternate pHdependent binding behavior. Biochemistry 45 (20), 6426. http://dx.doi. org/10.1021/bi060194g. Cheng, Y., Oldfield, C.J., Meng, J., Romero, P., Uversky, V.N., Dunker, A.K., 2007. Mining alpha-helix-forming molecular recognition features with cross species sequence alignments. Biochemistry 46 (47), 13468. http://dx.doi.org/10.1021/bi7012273.


M.D. Pavlović et al. / Journal of Immunological Methods xxx (2014) xxx–xxx Chianese-Bullock, K.A., Russell, H.I., Moller, C., Gerhard, W., Monaco, J.J., Eisenlohr, L.C., 1998. Antigen processing of two H2-IEd-restricted epitopes is differentially influenced by the structural changes in a viral glycoprotein. J. Immunol. 161 (4), 1599. Dai, G., Carmicle, S., Steede, N.K., Landry, S.J., 2002. Structural basis for helper T-cell and antibody epitope immunodominance in bacteriophage T4 Hsp10. Role of disordered loops. J. Biol. Chem. 277 (1), 161. http://dx. doi.org/10.1074/jbc.M102259200. Dai, Y.D., Carayanniotis, G., Sercarz, E., 2005. Antigen processing by autoreactive B cells promotes determinant spreading. Cell. Mol. Immunol. 2 (3), 169. Demachi-Okamura, A., Ito, Y., Akatsuka, Y., Tsujimura, K., Morishima, Y., Takahashi, T., Kuzushima, K., 2008. Epstein–Barr virus nuclear antigen 1specific CD4+ T cells directly kill Epstein–Barr virus-carrying natural killer and T cells. Cancer Sci. 99 (8), 1633. http://dx.doi.org/10.1111/j. 1349-7006.2008.00852.x. Depil, S., Morales, O., Castelli, F.A., Delhem, N., Francois, V., Georges, B., Pancre, V., 2007. Determination of a HLA II promiscuous peptide cocktail as potential vaccine against EBV latency II malignancies. J. Immunother. 30 (2), 215. http://dx.doi.org/10.1097/01.cji.0000211338.99137.4f. Destro, F., Sforza, F., Sicurella, M., Marescotti, D., Gallerani, E., Baldisserotto, A., Gavioli, R., 2011. Proteasome inhibitors induce the presentation of an Epstein–Barr virus nuclear antigen 1-derived cytotoxic T lymphocyte epitope in Burkitt's lymphoma cells. Immunology 133 (1), 105. http://dx.doi.org/10.1111/j.1365-2567.2011.03416.x. Dosztanyi, Z., Csizmok, V., Tompa, P., Simon, I., 2005. IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content. Bioinformatics 21 (16), 3433. http://dx.doi. org/10.1093/bioinformatics/bti541. Dosztanyi, Z., Meszaros, B., Simon, I., 2010. Bioinformatical approaches to characterize intrinsically disordered/unstructured proteins. Brief. Bioinform. 11 (2), 225. http://dx.doi.org/10.1093/bib/bbp061. Frappier, L., 2012. The Epstein–Barr Virus EBNA1 protein. Scientifica 2012, 1. http://dx.doi.org/10.6064/2012/438204. Halling-Brown, M., Shaban, R., Frampton, D., Sansom, C.E., Davies, M., Flower, D., Moss, D.S., 2009. Proteins accessible to immune surveillance show significant T-cell epitope depletion: implications for vaccine design. Mol. Immunol. 46 (13), 2699. http://dx.doi.org/10.1016/j.molimm.2009.05.027. Harley, J.B., James, J.A., 2006. Epstein–Barr virus infection induces lupus autoimmunity. Bull. NYU Hosp. Jt. Dis. 64 (1–2), 45. Harley, J.B., James, J.A., 2010. Everyone comes from somewhere: systemic lupus erythematosus and Epstein–Barr virus induction of host interferon and humoral anti-Epstein–Barr nuclear antigen 1 immunity. Arthritis Rheum. 62 (6), 1571. http://dx.doi.org/10.1002/art.27421. Hennard, C., Pfuhl, T., Buettner, M., Becker, K.F., Knofel, T., Middeldorp, J., Grasser, F., 2006. The antibody 2B4 directed against the Epstein–Barr Virus (EBV)-encoded nuclear antigen 1 (EBNA1) detects MAGE-4: implications for studies on the EBV association of human cancers. J. Pathol. 209 (4), 430. http://dx.doi.org/10.1002/path.1996. Holowaty, M.N., Zeghouf, M., Wu, H., Tellam, J., Athanasopoulos, V., Greenblatt, J., Frappier, L., 2003. Protein profiling with Epstein–Barr nuclear antigen-1 reveals an interaction with the herpesvirus-associated ubiquitin-specific protease HAUSP/USP7. J. Biol. Chem. 278 (32), 29987. http://dx.doi.org/10.1074/jbc.M303977200. Hopp, T.P., Woods, K.R., 1981. Prediction of protein antigenic determinants from amino acid sequences. Proc. Natl. Acad. Sci. U. S. A. 78 (6), 3824. Hsu, W.L., Oldfield, C., Meng, J., Huang, F., Xue, B., Uversky, V.N., Dunker, A.K., 2012. Intrinsic protein disorder and protein–protein interactions. Pac. Symp. Biocomput. 116. Iakoucheva, L.M., Brown, C.J., Lawson, J.D., Obradovic, Z., Dunker, A.K., 2002. Intrinsic disorder in cell-signaling and cancer-associated proteins. J. Mol. Biol. 323 (3), 573. James, J.A., Harley, J.B., 1996. Human lupus anti-spliceosome A protein autoantibodies bind contiguous surface structures and segregate into two sequential epitope binding patterns. J. Immunol. 156 (10), 4018. James, J.A., Gross, T., Scofield, R.H., Harley, J.B., 1995. Immunoglobulin epitope spreading and autoimmune disease after peptide immunization: Sm B/B′-derived PPPGMRPP and PPPGIRGP induce spliceosome autoimmunity. J. Exp. Med. 181 (2), 453. James, J.A., Scofield, R.H., Harley, J.B., 1997. Lupus humoral autoimmunity after short peptide immunization. Ann. N. Y. Acad. Sci. 815, 124. Kofler, M., Heuer, K., Zech, T., Freund, C., 2004. Recognition sequences for the GYF domain reveal a possible spliceosomal function of CD2BP2. J. Biol. Chem. 279 (27), 28292. http://dx.doi.org/10.1074/jbc.M402008200. Kotta-Loizou, I., Tsaousis, G.N., Hamodrakas, S.J., 2013. Analysis of Molecular Recognition Features (MoRFs) in membrane proteins. Biochim. Biophys. Acta 1834 (4), 798. http://dx.doi.org/10.1016/j.bbapap.2013.01.006. Kruger, S., Schroers, R., Rooney, C.M., Gahn, B., Chen, S.Y., 2003. Identification of a naturally processed HLA-DR-restricted T-helper epitope in Epstein– Barr virus nuclear antigen type 1. J. Immunother. 26 (3), 212.

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Kyte, J., Doolittle, R.F., 1982. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157 (1), 105. Landry, S.J., 1997. Local protein instability predictive of helper T-cell epitopes. Immunol. Today 18 (11), 527. Landry, S.J., 2000. Helper T-cell epitope immunodominance associated with structurally stable segments of hen egg lysozyme and HIV gp120. J. Theor. Biol. 203 (3), 189. http://dx.doi.org/10.1006/jtbi.1999.1056. Lanzavecchia, A., 1995. How can cryptic epitopes trigger autoimmunity? J. Exp. Med. 181 (6), 1945. Lazarski, C.A., Chaves, F.A., Jenks, S.A., Wu, S., Richards, K.A., Weaver, J.M., Sant, A.J., 2005. The kinetic stability of MHC class II: peptide complexes is a key parameter that dictates immunodominance. Immunity 23 (1), 29. http://dx.doi.org/10.1016/j.immuni.2005.05.009. Leen, A., Meij, P., Redchenko, I., Middeldorp, J., Bloemena, E., Rickinson, A., Blake, N., 2001. Differential immunogenicity of Epstein–Barr virus latent-cycle proteins for human CD4(+) T-helper 1 responses. J. Virol. 75 (18), 8649. Lobanov, M.Y., Galzitskaya, O.V., 2011. The Ising model for prediction of disordered residues from protein sequence alone. Phys. Biol. 8 (3), 035004. http://dx.doi.org/10.1088/1478-3975/8/3/035004. Long, H.M., Haigh, T.A., Gudgeon, N.H., Leen, A.M., Tsang, C.W., Brooks, J., Taylor, G.S., 2005. CD4+ T-cell responses to Epstein–Barr Virus (EBV) latent-cycle antigens and the recognition of EBV-transformed lymphoblastoid cell lines. J. Virol. 79 (8), 4896. http://dx.doi.org/10.1128/JVI.79.8.4896-4907.2005. Lucchiari-Hartz, M., Lindo, V., Hitziger, N., Gaedicke, S., Saveanu, L., van Endert, P.M., Niedermann, G., 2003. Differential proteasomal processing of hydrophobic and hydrophilic protein regions: contribution to cytotoxic T lymphocyte epitope clustering in HIV-1-Nef. Proc. Natl. Acad. Sci. U. S. A. 100 (13), 7755. http://dx.doi.org/10.1073/pnas.1232228100. Marcar, L., Maclaine, N.J., Hupp, T.R., Meek, D.W., 2010. Mage-A cancer/testis antigens inhibit p53 function by blocking its interaction with chromatin. Cancer Res. 70 (24), 10362. http://dx.doi.org/10.1158/0008-5472.CAN-101341. McClain, M.T., Lutz, C.S., Kaufman, K.M., Faig, O.Z., Gross, T.F., James, J.A., 2004. Structural availability influences the capacity of autoantigenic epitopes to induce a widespread lupus-like autoimmune response. Proc. Natl. Acad. Sci. U. S. A. 101 (10), 3551. http://dx.doi.org/10.1073/pnas.0306267101. McClain, M.T., Poole, B.D., Bruner, B.F., Kaufman, K.M., Harley, J.B., James, J.A., 2006. An altered immune response to Epstein–Barr nuclear antigen 1 in pediatric systemic lupus erythematosus. Arthritis Rheum. 54 (1), 360. http://dx.doi.org/10.1002/art.21682. Melton, S.J., Landry, S.J., 2008. Three dimensional structure directs T-cell epitope dominance associated with allergy. Clin. Mol. Allergy 6, 9. http://dx.doi.org/10.1186/1476-7961-6-9. Meszaros, B., Simon, I., Dosztanyi, Z., 2009. Prediction of protein binding regions in disordered proteins. PLoS Comput. Biol. 5 (5), e1000376. http://dx.doi.org/10.1371/journal.pcbi.1000376. Meszaros, B., Dosztanyi, Z., Simon, I., 2012. Disordered binding regions and linear motifs—bridging the gap between two models of molecular recognition. PLoS One 7 (10), e46829. http://dx.doi.org/10.1371/journal. pone.0046829. Mirano-Bascos, D., Tary-Lehmann, M., Landry, S.J., 2008. Antigen structure influences helper T-cell epitope dominance in the human immune response to HIV envelope glycoprotein gp120. Eur. J. Immunol. 38 (5), 1231. http://dx.doi.org/10.1002/eji.200738011. Mitić, N.S., Pavlović, M.D., Jandrlić, D.R., 2014. Epitope distribution in ordered and disordered protein regions — part A. T-cell epitope frequency, affinity and hydropathy. J. Immunol. Methods. http://dx.doi.org/10. 1016/j.jim.2014.02.012. Mohan, A., Oldfield, C.J., Radivojac, P., Vacic, V., Cortese, M.S., Dunker, A.K., Uversky, V.N., 2006. Analysis of molecular recognition features (MoRFs). J. Mol. Biol. 362 (5), 1043. http://dx.doi.org/10.1016/j.jmb.2006.07.087. Monneaux, F., Muller, S., 2004. Peptide-based immunotherapy of systemic lupus erythematosus. Autoimmun. Rev. 3 (1), 16. http://dx.doi.org/10. 1016/S1568-9972(03)00061-2. Musson, J.A., Walker, N., Flick-Smith, H., Williamson, E.D., Robinson, J.H., 2003. Differential processing of CD4 T-cell epitopes from the protective antigen of Bacillus anthracis. J. Biol. Chem. 278 (52), 52425. http://dx. doi.org/10.1074/jbc.M309034200. Nielsen, M., Lundegaard, C., Blicher, T., Lamberth, K., Harndahl, M., Justesen, S., Buus, S., 2007. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS One 2 (8), e796. http://dx.doi.org/10.1371/journal.pone.0000796. Nielsen, M., Lundegaard, C., Blicher, T., Peters, B., Sette, A., Justesen, S., Lund, O., 2008. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLoS Comput. Biol. 4 (7), e1000107. http://dx.doi.org/10.1371/journal.pcbi.1000107. Ohkuri, T., Wakita, D., Chamoto, K., Togashi, Y., Kitamura, H., Nishimura, T., 2009. Identification of novel helper epitopes of MAGE-A4 tumour antigen: useful tool for the propagation of Th1 cells. Br. J. Cancer 100 (7), 1135. http://dx.doi.org/10.1038/sj.bjc.6604966.


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Oldfield, C.J., Cheng, Y., Cortese, M.S., Brown, C.J., Uversky, V.N., Dunker, A.K., 2005. Comparing and combining predictors of mostly disordered proteins. Biochemistry 44 (6), 1989. http://dx.doi.org/10.1021/bi047993o. Paludan, C., Bickham, K., Nikiforow, S., Tsang, M.L., Goodman, K., Hanekom, W.A., Munz, C., 2002. Epstein–Barr nuclear antigen 1-specific CD4(+) Th1 cells kill Burkitt's lymphoma cells. J. Immunol. 169 (3), 1593. Radivojac, P., Iakoucheva, L.M., Oldfield, C.J., Obradovic, Z., Uversky, V.N., Dunker, A.K., 2007. Intrinsic disorder and functional proteomics. Biophys. J. 92 (5), 1439. http://dx.doi.org/10.1529/biophysj.106.094045. Rajagopalan, K., Mooney, S.M., Parekh, N., Getzenberg, R.H., Kulkarni, P., 2011. A majority of the cancer/testis antigens are intrinsically disordered proteins. J. Cell. Biochem. 112 (11), 3256. http://dx.doi.org/10.1002/jcb.23252. Riemekasten, G., Hahn, B.H., 2005. Key autoantigens in SLE. Rheumatology (Oxford) 44 (8), 975. http://dx.doi.org/10.1093/rheumatology/keh688. Rothbard, J.B., Taylor, W.R., 1988. A sequence pattern common to T cell epitopes. EMBO J. 7 (1), 93. Sakurai, T., Itoh, K., Higashitsuji, H., Nagao, T., Nonoguchi, K., Chiba, T., Fujita, J., 2004. A cleaved form of MAGE-A4 binds to Miz-1 and induces apoptosis in human cells. J. Biol. Chem. 279 (15), 15505. http://dx.doi. org/10.1074/jbc.M310437200. Sant, A.J., Chaves, F.A., Jenks, S.A., Richards, K.A., Menges, P., Weaver, J.M., Lazarski, C.A., 2005. The relationship between immunodominance, DM editing, and the kinetic stability of MHC class II:peptide complexes. Immunol. Rev. 207, 261. http://dx.doi.org/10.1111/j.0105-2896.2005.00307.x. Scanlan, M.J., Gure, A.O., Jungbluth, A.A., Old, L.J., Chen, Y.T., 2002. Cancer/ testis antigens: an expanding family of targets for cancer immunotherapy. Immunol. Rev. 188, 22. Seong, S.Y., Matzinger, P., 2004. Hydrophobicity: an ancient damageassociated molecular pattern that initiates innate immune responses. Nat. Rev. Immunol. 4 (6), 469. http://dx.doi.org/10.1038/nri1372. Sidney, J., Peters, B., Frahm, N., Brander, C., Sette, A., 2008. HLA class I supertypes: a revised and updated classification. BMC Immunol. 9, 1. http://dx.doi.org/10.1186/1471-2172-9-1. Sundar, K., Jacques, S., Gottlieb, P., Villars, R., Benito, M.E., Taylor, D.K., Spatz, L.A., 2004. Expression of the Epstein-Barr virus nuclear antigen-1 (EBNA-1) in the mouse can elicit the production of anti-dsDNA and anti-Sm antibodies. J. Autoimmun. 23 (2), 127. http://dx.doi.org/10.1016/j.jaut.2004.06.001. Suskiewicz, M.J., Sussman, J.L., Silman, I., Shaul, Y., 2011. Context-dependent resistance to proteolysis of intrinsically disordered proteins. Protein Sci. 20 (8), 1285. http://dx.doi.org/10.1002/pro.657.

Talken, B.L., Schafermeyer, K.R., Bailey, C.W., Lee, D.R., Hoffman, R.W., 2001. T cell epitope mapping of the Smith antigen reveals that highly conserved Smith antigen motifs are the dominant target of T cell immunity in systemic lupus erythematosus. J. Immunol. 167 (1), 562. Tompa, P., 2009. Structure and Function of Intrinsically Disordered Proteins. Chapman and Hall/CRC. Toussirot, E., Roudier, J., 2008. Epstein–Barr virus in autoimmune diseases. Best Pract. Res. Clin. Rheumatol. 22 (5), 883. http://dx.doi.org/10.1016/j. berh.2008.09.007. Tsang, C.W., Lin, X., Gudgeon, N.H., Taylor, G.S., Jia, H., Hui, E.P., Rickinson, A. B., 2006. CD4+ T-cell responses to Epstein–Barr virus nuclear antigen EBNA1 in Chinese populations are highly focused on novel C-terminal domain-derived epitopes. J. Virol. 80 (16), 8263. http://dx.doi.org/10. 1128/JVI.00400-06. Uversky, V.N., 2010. The mysterious unfoldome: structureless, underappreciated, yet vital part of any given proteome. J. Biomed. Biotechnol. 2010, 568068. http://dx.doi.org/10.1155/2010/568068. Uversky, V.N., Dunker, A.K., 2010. Understanding protein non-folding. Biochim. Biophys. Acta 1804 (6), 1231. http://dx.doi.org/10.1016/j.bbapap.2010.01. 017. Uversky, V.N., Gillespie, J.R., Fink, A.L., 2000. Why are “natively unfolded” proteins unstructured under physiologic conditions? Proteins 41 (3), 415. Voo, K.S., Fu, T., Heslop, H.E., Brenner, M.K., Rooney, C.M., Wang, R.F., 2002. Identification of HLA-DP3-restricted peptides from EBNA1 recognized by CD4(+) T cells. Cancer Res. 62 (24), 7195. Weaver, J.M., Sant, A.J., 2009. Understanding the focused CD4 T cell response to antigen and pathogenic organisms. Immunol. Res. 45 (2–3), 123. http://dx.doi.org/10.1007/s12026-009-8095-8. Weaver, J.M., Lazarski, C.A., Richards, K.A., Chaves, F.A., Jenks, S.A., Menges, P. R., Sant, A.J., 2008. Immunodominance of CD4 T cells to foreign antigens is peptide intrinsic and independent of molecular context: implications for vaccine design. J. Immunol. 181 (5), 3039. Welin Henriksson, E., Wahren-Herlenius, M., Lundberg, I., Mellquist, E., Pettersson, I., 1999. Key residues revealed in a major conformational epitope of the U1-70K protein. Proc. Natl. Acad. Sci. U. S. A. 96 (25), 14487.


Epitope distribution in ordered and disordered protein regions - part A. T-cell epitope frequency, affinity and hydropathy.

Disordered Regions Flanking Ordered Domains Modulate Signaling Transduction.

Intrinsically disordered proteins and intrinsically disordered protein regions.

DisBind: A database of classified functional binding sites in disordered and structured regions of intrinsically disordered proteins.

The s2D method: simultaneous sequence-based prediction of the statistical populations of ordered and disordered regions in proteins.

Visualization of exciton transport in ordered and disordered molecular solids.

Trap-free transport in ordered and disordered TiO2 nanostructures.

Epitaxial growth of ordered and disordered granular sphere packings.

IDPs: Less Disordered and More Ordered than Expected.

Rapid evolution of virus sequences in intrinsically disordered protein regions.

Nonoptimal codon usage influences protein structure in intrinsically disordered regions.

Presence and utility of intrinsically disordered regions in kinases.

Identifying intrinsically disordered protein regions likely to undergo binding-induced helical transitions.

General phase diagram of multimodal ordered and disordered lasers in closed and open cavities.

Expression and purification of the natively disordered and redox sensitive metal binding regions of Mycobacterium tuberculosis protein kinase G.

The role of disordered protein regions in the assembly of decapping complexes and RNP granules.

Physics in ordered and disordered colloidal matter composed of poly(N-isopropylacrylamide) microgel particles.

Numerical investigation of thermo-magneto-solutal flow of ferrocolloid through ordered and disordered permeable membranes.

Sequence complexity of amyloidogenic regions in intrinsically disordered human proteins.

Energetics of disordered and ordered rare earth oxide-stabilized bismuth oxide ionic conductors.

Phosphorylation in intrinsically disordered regions regulates the activity of Neurogenin2.

Forkhead followed by disordered tail: The intrinsically disordered regions of FOXO3a.

Experimental demonstration of ordered and disordered multiresonant metamaterials for lamb waves.

Methane oxidation over A-site ordered and disordered Sr(0.8)Gd(0.2)CoO(3-δ) perovskites.