Prediction of desmoglein-3 peptides reveals multiple shared T-cell epitopes in HLA DR4- and DR6- associated Pemphigus vulgaris

BACKGROUND: Pemphigus vulgaris (PV) is a severe autoimmune blistering skin disorder that is strongly associated with major histocompatibility complex class II alleles DRB1*0402 and DQB1*0503. The target antigen of PV, desmoglein 3 (Dsg3), is crucial for initiating T-cell response in early disease. Although a number of T-cell specificities within Dsg3 have been reported, the number is limited and the role of T-cells in the pathogenesis of PV remains poorly understood. We report here a structure-based model for the prediction of peptide binding to DRB1*0402 and DQB1*0503. The scoring functions were rigorously trained, tested and validated using experimentally verified peptide sequences. RESULTS: High predictivity is obtained for both DRB1*0402 (r(2 )= 0.90, s = 1.20 kJ/mol, q(2 )= 0.82, s(press )= 1.61 kJ/mol) and DQB1*0503 (r(2 )= 0.95, s = 1.20 kJ/mol, q(2 )= 0.75, s(press )= 2.15 kJ/mol) models, compared to experimental data. We investigated the binding patterns of Dsg3 peptides and illustrate the existence of multiple immunodominant epitopes that may be responsible for both disease initiation and propagation in PV. Further analysis reveals that DRB1*0402 and DQB1*0503 may share similar specificities by binding peptides at different binding registers, thus providing a molecular mechanism for the dual HLA association observed in PV. CONCLUSION: Collectively, the results of this study provide interesting new insights into the pathology of PV. This is the first report illustrating high-level of cross-reactivity between both PV-implicated alleles, DRB1*0402 and DQB1*0503, as well as the existence of a potentially large number of T-cell epitopes throughout the entire Dsg3 extracellular domain (ECD) and transmembrane region. Our results reveal that DR4 and DR6 PV may initiate in the ECD and transmembrane region respectively, with implications for immunotherapeutic strategies for the treatment of this autoimmune disease

Prediction of MHC-peptide binding: a systematic and comprehensive overview

T cell immune responses are driven by the recognition of peptide antigens (T cell epitopes) that are bound to major histocompatibility complex (MHC) molecules. T cell epitope immunogenicity is thus contingent on several events, including appropriate and effective processing of the peptide from its protein source, stable peptide binding to the MHC molecule, and recognition of the MHC-bound peptide by the T cell receptor. Of these three hallmarks, MHC-peptide binding is the most selective event that determines T cell epitopes. Therefore, prediction of MHC-peptide binding constitutes the principal basis for anticipating potential T cell epitopes. The tremendous relevance of epitope identification in vaccine design and in the monitoring of T cell responses has spurred the development of many computational methods for predicting MHC-peptide binding that improve the efficiency and economics of T cell epitope identification. In this report, we will systematically examine the available methods for predicting MHC-peptide binding and discuss their most relevant advantages and drawbacks

Use of BONSAI decision trees for the identification of potential MHC class I peptide epitope motifs

Recognition of short peptides of 8 to 10 mer bound to MHC class I molecules by cytotoxic T lymphocytes forms the basis of cellular immunity. While the sequence motifs necessary for binding of intracellular peptides to MHC have been well studied, little is known about sequence motifs that may cause preferential affinity to the T cell receptor and/or preferential recognition and response by T cells. Here we demonstrate that computational learning systems can be useful to elucidate sequence motifs that affect T cell activation. Knowledge of T cell activation motifs could be useful for targeted vaccine design or immunotherapy. With the BONSAI computational learning algorithm, using a database of previously reported MHC bound peptides that had positive or negative T cell responses, we were able to identify sequence motif rules that explain 70 % of positive T cell responses and 84 % of negative T cell responses

Understanding peptide specificity through structural immunoinformatics

Ph.DDOCTOR OF PHILOSOPH

Protein function and inhibitor prediction by statistical learning approach

Ph.DDOCTOR OF PHILOSOPH