SETE: Sequence-based Ensemble learning approach for TCR Epitope binding prediction

Predicting the binding of T cell receptors (TCRs) to epitopes plays a vital role in the immunotherapy, because it guides the development of therapeutic vaccines and cancer treatments. Many prediction methods attempted to explain the relationship between TCR repertoires from different aspects such as the V(D)J gene locus and the biophysical features of amino acids molecules, but the extraction of these features is time consuming and the performance of these models are limited. Few studies have investigated how k-mers formed by adjacent amino acids in TCR sequences direct the epitope recognition, and the specific mechanism of TCR epitope binding is still unclear. Motivated by these, we presented SETE (Sequence-based Ensemble learning approach for TCR Epitope binding prediction), a novel model to predict the TCR epitope binding accurately. The model deconstructed the CDR3β sequence to short amino acid chains as features and learned the pattern of them between different TCR repertoires with gradient boosting decision tree algorithm. Experiments have demonstrated that SETE can be helpful in predicting the TCRs’ corresponding epitopes and it outperforms other state-of-the-art methods in predicting the epitope specificity of TCR on VDJdb data set. The source codes have been uploaded at https://github.com/wonanut/SETE for academic usage only.     

A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes

In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.     

Autoantibodies to thyroglobulin in health and disease

Thyroglobulin (Tg)—a heavily glycosylated, iodinated protein—isa major autoantigen in autoimmune thyroiditis. Tg also induces thyroiditis by immunization of experimental animals. Humans with chronic lymphocytic thyroiditis characteristically produce autoantibodies to thyroglobu lin, but similar autoantibodies are also found in some clinically normal, euthyroid individuals. A comparison of the fine specificity of autoantibodies in humans and in experimentally immunized mice was carried out, based on their ability to inhibit a panel of monoclonal antibodies (MAbs). Patients with autoimmune thyroid disease, as well as normal individuals, produced autoantibodies mainly to the conserved, cross-reactive determinants of thyroglobulin. Patients developed additional autoantibodies to species-restricted epitopes. The determinants recognized by patients with Graves' disease differed in some respects from epitopes recognized by thyroiditis patients or patients with differentiated thyroid carcinoma. Similarly, mice that are genetically susceptible to thyroiditis produced autoantibodies that reacted with the mouse-specific antigenic determinants. Using an autoantibody that reacts with one of the epitopes associated with thyroiditis, a reactive 15-k Da fragment of human Tg—localized at the carboxy end of the molecule—was isolated and sequenced. Iodine plays an important role in the precise specificity of the disease-associated epitope, since T cells from patients with thyroiditis react with iodinated but not noniodinated human thyroglobulin. Addition of iodine to Tg generates new or cryptic epitopes. Use of a selected MAb as a surrogate for the T-cell receptor suggests that a specific iodine-containing epitope is sometimes involved in recognition. Finally, thyroglobulin-reactive autoantibodies exhibit proteolytic activity on thyroglobulin.     

Filaggrin Peptides with β‐Hairpin Structure Bind Rheumatoid Arthritis Antibodies

In the early detection of rheumatoid arthritis (RA) synthetic filaggrin peptides serve as antigens for rheumatoid‐specific autoantibodies (anti‐citrullinated peptide antibody, ACPA) in ELISA tests. In this work we present a peptide that exhibits the binding epitope of ACPA in the form of a stable folding β‐hairpin. The homogeneity of the peptide folding was confirmed by NMR spectroscopy and might lead to the first proposed structure of the antibody‐bound conformation of the epitope.     

Prediction of T-cell epitopes based on least squares support vector machines and amino acid properties

T-lymphocyte (T-cell) is a very important component in human immune system. It possesses a receptor (TCR) that is specific for the foreign epitopes which are in a form of short peptides bound to the major histocompatibility complex (MHC). When T-cell receives the message about the peptides bound to MHC, it makes the immune system active and results in the disposal of the immunogen. The antigenic determinants recognized and bound by the T-cell receptor is known as T-cell epitope. The accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. For the first time we developed new models using least squares support vector machine (LSSVM) and amino acid properties for T-cell epitopes prediction. A dataset including 203 short peptides (167 non-epitopes and 36 epitopes) was used as the input dataset and it was randomly divided into a training set and a test set. The models based on LSSVM and amino acid properties were evaluated using leave-one-out cross-validation method and the predictive ability of the test set, and obtained the results of 0.9875 and 0.9734 under the ROC curves, respectively. This result is more satisfactory than that were reported before. Especially, the accuracy of true positive gets a marked enhancement.     

In silico identification of outer membrane protein (Omp) and subunit vaccine design against pathogenic Vibrio cholerae

Virulence-related outer membrane proteins (Omps) are expressed in bacteria (Gram-negative) such as V. cholerae and are vital to bacterial invasion in to eukaryotic cell and survival within macrophages that could be best candidate for development of vaccine against V. cholerae. Applying in silico approaches, the 3-D model of the Omp was developed using Swiss model server and validated byProSA and Procheck web server. The continuous stretch of amino acid sequences 26 mer: RTRSNSGLLTWGDKQTITLEYGDPAL and 31 mer: FFAGGDNNLRGYGYKSISPQDASGALTGAKY having B-cell binding sites were selected from sequence alignment after B cell epitopes prediction by BCPred and AAP prediction modules of BCPreds. Further, the selected antigenic sequences (having B-cell epitopes) were analyzed for T-cell epitopes (MHC I and MHC II alleles binding sequence) by using ProPred 1 and ProPred respectively. The epitope (9 mer: YKSISPQDA) that binds to both the MHC classes (MHC I and MHC II) and covers maximum MHC alleles were identified. The identified epitopes can be useful in designing comprehensive peptide vaccine development against V. cholerae by inducing optimal immune response.     

Immunogenicity of Foot-and-Mouth Disease Virus Dendrimer Peptides: Need for a T-Cell Epitope and Ability to Elicit Heterotypic Responses

An approach based on a dendrimer display of B- and T-cell epitopes relevant for antibody induction has been shown to be effective as a foot-and-mouth disease (FMD) vaccine. B₂T dendrimers combining two copies of the major FMD virus (FMDV) type O B-cell epitope (capsid proteinVP1 (140–158)) covalently linked to a heterotypic T-cell epitope from non-structural protein 3A (21–35), henceforth B₂T-3A, has previously been shown to elicit high neutralizing antibody (nAb) titers and IFN-γ-producing cells in both mice and pigs. Here, we provide evidence that the B- and T-cell epitopes need to be tethered to a single molecular platform for successful T-cell help, leading to efficient nAb induction in mice. In addition, mice immunized with a non-covalent mixture of B₂T-3A dendrimers containing the B-cell epitopes of FMDV types O and C induced similarly high nAb levels against both serotypes, opening the way for a multivalent vaccine platform against a variety of serologically different FMDVs. These findings are relevant for the design of vaccine strategies based on B- and T-cell epitope combinations.     

T-cell epitopes of the La/SSB autoantigen: prediction based on the homology modeling of HLA-DQ2/DQ7 with the insulin-B peptide/HLA-DQ8 complex

T-cell epitopes are important components of the inappropriate response of the immune system to self-proteins in autoimmune diseases. In this study, the candidate T-cell epitopes of the La/SSB autoantigen, the main target of the autoimmune response in patients with Sjogren's Syndrome (SS), and Systemic Lupus Erythematosus (SLE) were predicted using as a template the HLA-DQ2 and DQ7 molecules, which are genetically linked to patients with SS and SLE. Modeling of DQ2 and DQ7 was based on the crystal structure of HLA-DQ8, an HLA molecule of high risk factor of type I diabetes, which is also an autoimmune disease. The quality and reliability of the modeled DQ2 and DQ7 was confirmed by the Ramachandran plot and the TINKER molecular modeling software. Common and/or similar candidate T-cell epitopes, obtained by comparing three different approaches the Taylor's sequence pattern, the TEPITOPE quantitative matrices, and the MULTIPRED artificial neural network, were subjected to homology modeling with the crystal structure of the insulin-B peptide complexed with HLA-DQ8, and the best superposed candidate epitopes were placed into the modeled HLA-DQ2 and DQ7 binding grooves to perform energy minimization calculations. Six T-cell epitopes were predicted for HLA-DQ7 and nine for HLA-DQ2 covering parts of the amino-terminal and the central regions of the La/SSB autoantigen. Residues corresponding to the P1, P4, and P9 pockets of the HLA-DQ2 and DQ7 binding grooves experience very low SASA because they are less exposed to the microenvironment of the groove. The proposed T-cell epitopes complexed with HLA-DQ2/DQ7 were further evaluated for their binding efficiency according to their potential interaction energy, binding affinity, and IC50 values. Our approach constitutes the ground work for a rapid and reliable experimentation concerning the T-cell epitope mapping of autoantigens, and could lead to the development of T-cell inhibitors as immunotherapeutics in autoimmune diseases.     

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics: comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.     

MIMOTOPES,CONTINUOUS PARATOPES AND HYDROPATHIC COMPLEMENTARITY: NOVEL APPROXIMATIONS IN THE DESCRIPTION OF IMMUNOCHEMICAL SPECIFICITY

Most antigenic sites of proteins, known as discontinuous epitopes, are made up of residues on different loops that are brought together by the folding of the polypeptide chain. The individual loops are sometimes able, on their own, to bind to the antibody and they are then known as continuous epitopes. The binding sites of antibodies, known as paratopes, are built up from residues on six hypervariable loops known as complementarity determining regions (CDRs). Peptides corresponding to individual CDR loops are often able to bind the antigen and such peptides may be viewed as continuous paratopes. Using random combinatorial peptide libraries, it is possible to obtain peptides that bind to an antiprotein antibody without showing any sequence similarity with any part of the protein. Such epitope mimics are called mimotopes provided they are able also to elicit antibodies that react with the original antigen. The binding activity of mimotopes may partly be due to the phenomenon of hydropathic complementarity between epitope and paratope peptides. Although these concepts are vague in their structural connotation, they are useful for describing the immunological activity of linear peptides.     

Conformational analysis of the ΜΒΡ<Subscript>83–99</Subscript> (Phe<Superscript>91</Superscript>) and ΜΒΡ<Subscript>83–99</Subscript> (Tyr<Superscript>91</Superscript>) peptide analogues and study of their interactions with the HLA-DR2 and human TCR receptors by using Molecular Dynamics

The two new synthetic analogues of the MBP_83–99 epitope substituted at Lys⁹¹ (primary TCR contact) with Phe [MBP_83–99 (Phe⁹¹)] or Tyr [MBP_83–99 (Tyr⁹¹)], have been structurally elucidated using 1D and 2D high resolution NMR studies. The conformational analysis of the two altered peptide ligands (APLs) has been performed and showed that they adopt a linear and extended conformation which is in agreement with the structural requirements of the peptides that interact with the HLA-DR2 and TCR receptors. In addition, Molecular Dynamics (MD) simulations of the two analogues in complex with HLA-DR2 (DRA, DRB1*1501) and TCR were performed. Similarities and differences of the binding motif of the two analogues were observed which provide a possible explanation of their biological activity. Their differences in the binding mode in comparison with the MBP_83–99 epitope may also explain their antagonistic versus agonistic activity. The obtained results clearly indicate that substitutions in crucial amino acids (TCR contacts) in combination with the specific conformational characteristics of the MBP_83–99 immunodominant epitope lead to an alteration of their biological activity. These results make the rational drug design intriguing since the biological activity is very sensitive to the substitution and conformation of the mutated MBP epitopes.     

Multifunctional nanoparticles as simulants for a gravimetric immunoassay

Immunoassays are important tools for the rapid detection and identification of pathogens, both clinically and in the research laboratory. An immunoassay with the potential for the detection of influenza was developed and tested using hemagglutinin (HA), a commonly studied glycoprotein found on the surface of influenza virions. Gold nanoparticles were synthesized, which present multiple peptide epitopes, including the HA epitope, in order to increase the gravimetric response achieved with the use of a QCM immunosensor for influenza. Specifically, epitopes associated with HA and FLAG peptides were affixed to gold nanoparticles by a six-mer PEG spacer between the epitope and the terminal cysteine. The PEG spacer was shown to enhance the probability for interaction with antibodies by increasing the distance the epitope extends from the gold surface. These nanoparticles were characterized using thermogravimetric analysis, transmission electron microscopy, matrix-assisted laser desorption/ionization-time of flight, and ¹H nuclear magnetic resonance analysis. Anti-FLAG and anti-HA antibodies were adhered to the surface of a QCM, and the response of each antibody upon exposure to HA, FLAG, and dual functionalized nanoparticles was compared with binding of Au–tiopronin nanoparticles and H5 HA proteins from influenza virus (H5N1). Results demonstrate that the immunoassay was capable of differentiating between nanoparticles presenting orthogonal epitopes in real-time with minimal nonspecific binding. The detection of H5 HA protein demonstrates the logical extension of using these nanoparticle mimics as a safe positive control in the detection of influenza, making this a vital step in improving influenza detection methodology.     

Designing an efficient multi-epitope peptide vaccine against Vibrio cholerae via combined immunoinformatics and protein interaction based approaches

Cholera continues to be a major global health concern. Among different Vibrio cholerae strains, only O1 and O139 cause acute diarrheal diseases that are related to epidemic and pandemic outbreaks. The currently available cholera vaccines are mainly lived and attenuated vaccines consisting of V. cholerae virulence factors such as toxin-coregulated pili (TCP), outer membrane proteins (Omps), and nontoxic cholera toxin B subunit (CTB). Nowadays, there is a great interest in designing an efficient epitope vaccine against cholera. Epitope vaccines consisting of immunodominant epitopes and adjuvant molecules enhance the possibility of inciting potent protective immunity. In this study, V. cholerae protective antigens (OmpW, OmpU, TcpA and TcpF) and the CTB, which is broadly used as an immunostimulatory adjuvant, were analyzed using different bioinformatics and immunoinformatics tools. The common regions between promiscuous epitopes, binding to various HLA-II supertype alleles, and B-cell epitopes were defined based upon the aforementioned protective antigens. The ultimately selected epitopes and CTB adjuvant were fused together using proper GPGPG linkers to enhance vaccine immunogenicity. A three-dimensional model of the thus constructed vaccine was generated using I-TASSER. The model was structurally validated using the ProSA-web error-detection software and the Ramachandran plot. The validation results indicated that the initial 3D model needed refinement. Subsequently, a high-quality model obtained after various refinement cycles was used for defining conformational B-cell epitopes. Several linear and conformational B-cell epitopes were determined within the epitope vaccine, suggesting likely antibody triggering features of our designed vaccine. Next, molecular docking was performed between the 3D vaccine model and the tertiary structure of the toll like receptor 2 (TLR2). To gain further insight into the interaction between vaccine and TLR2, molecular dynamics simulation was performed, corroborating stable vaccine-TLR2 binding. In sum, the results suggest that our designed epitope vaccine could incite robust long-term protective immunity against V. cholera.     

Probing the binding of propranolol enantiomers to alpha1-acid glycoprotein with ligand-detected NMR experiments

Mapping the interactions of a small molecule ligand with a protein can provide information important for biochemical studies and for drug design and development. This information can be determined using the ligand-detected (1)H NMR experiments T(1rho)-NOESY, diffusion, and saturation transfer difference (STD). This work compares the results of these experiments and examines their ability to distinguish the binding epitopes of propranolol enantiomers with alpha 1-acid glycoprotein (AGP). The epitope maps for the propranolol enantiomers are fairly similar, as expected from their similar binding affinities; however, the STD epitope maps provide unique insights into the different orientations of the enantiomers with respect to the AGP binding pocket. Our results suggest that it is best to consider the data provided by several NMR epitope mapping experiments in drawing conclusions about ligand-protein binding interactions.     

In silico epitope identification of unique multidrug resistance proteins from Salmonella Typhi for vaccine development

Typhoid fever is a multisystemic illness caused by Salmonella enterica serovars Typhi and is resistant to most antibiotics and drugs. The resistance is conferred through multidrug resistance (MDR) proteins, which efflux most antibiotics and other drugs. We predicted potential candidate B-cell and T-cell epitopes using bio- and immune-informatics tools in the 11 MDR proteins - EmrA, EmrB, EmrD, MdtA, MdtB, MdtC, MdtG, MdtH, MdtK, MdtL and TolC. The antigenic potential of the MDR proteins was calculated using VaxiJen server. The B-cell and T-cell epitopes of the MDR proteins were predicted using BCPred and ProPredI and ProPred respectively. The binding affinities of the predicted T-cell epitopes were estimated using T-epitope designer and MHCPred tools. 10, 7, 5, 12, 14, 21, 26, 3, 3 and 3 B-cell epitopes were identified in EmrA, EmrB, EmrD, TolC, MdtA, MdtB, MdtC, MdtG, MdtH and MdtL respectively. We predicted 9 T-cell epitopes - YVSRRAVQP (EmrA), FGVANAISI (EmrB), MVNSQVKQA and YQGGMVNSQ (TolC), WDRTNSHKL (MdtA), FLRNIPTAI (MdtB), YVEQLGVTG (MdtG), VKWMYAIEA (MdtH) and LAHTNTVTL (MdtL) capable of eliciting both humoral and adaptive immune responses. These T-cell epitopes specifically bind to HLA alleles - DRB1*0101 and DRB1*0401. This is the first report of epitope prediction in the MDR proteins of S. Typhi. Taken together, these results indicate the MDR proteins – EmrA, MdtA and TolC are the most suitable vaccine candidates for S. Typhi. The findings of our study on the MDR proteins prove to be useful in the development of peptide-based vaccine for the prevention and/or treatment of typhoid fever.     

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity: in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide-MHC binding affinity. The ISC-PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide-MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms--q2, SEP, and NC--ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).     

A critical cross-validation of high throughput structural binding prediction methods for pMHC

T-cells recognize antigens via their T-cell receptors. The major histocompatibility complex (MHC) binds antigens in a specific way, transports them to the surface and presents the peptides to the TCR. Many in silico approaches have been developed to predict the binding characteristics of potential T-cell epitopes (peptides), with most of them being based solely on the amino acid sequence. We present a structural approach which provides insights into the spatial binding geometry. We combine different tools for side chain substitution (threading), energy minimization, as well as scoring methods for protein/peptide interfaces. The focus of this study is on high data throughput in combination with accurate results. These methods are not meant to predict the accurate binding free energy but to give a certain direction for the classification of peptides into peptides that are potential binders and peptides that definitely do not bind to a given MHC structure. In total we performed approximately 83,000 binding affinity prediction runs to evaluate interactions between peptides and MHCs, using different combinations of tools. Depending on the tools used, the prediction quality ranged from almost random to around 75% of accuracy for correctly predicting a peptide to be either a binder or a non-binder. The prediction quality strongly depends on all three evaluation steps, namely, the threading of the peptide, energy minimization and scoring.     

Group epitope mapping by saturation transfer difference NMR to identify segments of a ligand in direct contact with a protein receptor.

A protocol based on saturation transfer difference (STD) NMR spectra was developed to characterize the binding interactions at an atom level, termed group epitope mapping (GEM). As an example we chose the well-studied system of galactose binding to the 120-kDa lectin Ricinus communis agglutinin I (RCA(120)). As ligands we used methyl beta-D-galactoside and a biantennary decasaccharide. Analysis of the saturation transfer effects of methyl beta-D-galactoside showed that the H2, H3, and H4 protons are saturated to the highest degree, giving evidence of their close proximity to protons of the RCA(120) lectin. The direct interaction of the lectin with this region of the galactose is in excellent agreement with results obtained from the analysis of the binding specificities of many chemically modified galactose derivatives (Bhattacharyya, L.; Brewer, C. F. Eur. J. Biochem. 1988, 176, 207-212). This new NMR technique can identify the binding epitope of even complex ligands very quickly, which is a great improvement over time-consuming chemical modifications. Efficient GEM benefits from a relatively high off rate of the ligand and a large excess of the ligand over the receptor. Even for a ligand like the biantennary decasaccharide with micromolar binding affinity, the binding epitopes could easily be mapped to the terminal beta-D-Gal-(1-4)-beta-D-GlcNAc (beta-D-GlcNAc = N-acetyl-D-glucosamine) residues located at the nonreducing end of the two carbohydrate chains. The binding contribution of the terminal galactose residue is stronger than those of the penultimate GlcNAc residues. We could show that the GlcNAc residues bind "edge-on" with the region from H2 to H4, making contact with the protein. Analysis of STD NMR experiments performed under competitive conditions proved that the two saccharides studied bind at the same receptor site, thereby ruling out unspecific binding.     

Epitope peptides and immunotherapy

Allergic diseases affect atopic individuals, who synthesize specific Immunoglobulins E (IgE) to environmental allergens, usually proteins or glycoproteins. These allergens include grass and tree pollens, indoor allergens such as house dust mites and animal dander, and various foods. Because allergen-specific IgE antibodies are the main effector molecules in the immune response to allergens, many studies have focused on the identification of IgE-binding epitopes (called B cell epitopes), specific and minimum regions of allergen molecules that binds to IgE. Our initial studies have provided evidence that only four to five amino acid residues are enough to comprise an epitope, since pentapeptide QQQPP in wheat glutenin is minimally required for IgE binding. Afterwards, various kinds of B cell epitope structures have been clarified. Such information contributes greatly not only to the elucidation of the etiology of allergy, but also to the development of strategies for the treatment and prevention of allergy. Allergen-specific T cells also play an important role in allergy and are obvious targets for intervention in the disease. Currently, the principle approach is to modify B cell epitopes to prevent IgE binding while preserving T cell epitopes to retain the capacity for immunotherapy. There is mounting evidence that the administration of peptide(s) containing immunodominant T cell epitopes from an allergen can induce T cell nonresponsiveness (immunotherapy). There have been clinical studies of peptide immunotherapy performed, the most promising being for bee venom sensitivity. Clinical trials of immunotherapy for cat allergen peptide have also received attention. An alternative strategy for the generation of an effective but hypoallergenic preparation for immunotherapy is to modify T cell epitope peptides by, for example, single amino acid substitution. In this article, I will present an overview of epitopes related to allergic disease, particularly stress on allergen specific immunotherapy. In addition, our ongoing study of immunotherapy by 'eating' T cell epitope peptides will be described. Eating T cell epitope peptides as food provides a more practical way of inducing tolerance and a challenge to prevent allergy in daily life, as opposed to therapy by ingesting peptides as medicine.     

Human autoantibodies and their genes

We undertook an analysis of the B cell repertoire at both the germline and somatic levels. To assess the content and organization of the IgH-V and IgK-V loci in SLE, endonuclease-generated polymorphisms were used to characterize individual variations within the human V gene segments. The results are compatible with the conclusion that this disease is not caused by major abnormalities in the structure, size, or organization of the IgV loci. We propose that hyperproduction and lupus-associated autoantibodies arises through a two-stage mechanism whereby a general activation of the multireactive preimmune B-cell repertoire precedes oligoclonal expansion of selected B cell clonotypes.