Although a T-cell response in human cytomegalovirus (HCMV)-immune individuals exists against the most abundantly expressed protein pp65 of the virus matrix, less is known about the determinants that evoke this response. The aim of the study was to identify regions within HCMV pp65 (ppUL83) that contain sequences for the cellular immune response by the use of three recombinant overlapping beta-galactosidase pp65 fusion proteins (C74, C35, and C47), covering the C-terminal 265 amino acids of the entire pp65 sequence. Two T-cell epitope determinants were recognized by human lymphocytes of healthy, HCMV-seropositive, human leukocyte antigen (HLA)-typed individuals. One T-cell determinant (amino acids [aa] 303-388) was localized in the mid-region of the entire pp65 sequence and a second T-cell determinant (aa 477-561) within the C-terminal region. By fine mapping with synthetic hexadecamer peptides three T-cell epitopes were identified within these two regions: P10-I (aa 361-376) in the mid-region, P3-II (aa 485-499), and P6-II (aa 509-524) in the C-terminal region. Inhibition studies with monoclonal antibodies to HLA class I or class II revealed a class II restricted response to peptides P10-I or P6-II, respectively. P10-I responders shared the HLA-DR11 allele and P6-II responders the -DR3 allele. Therefore, these T-cell epitopes of HCMV pp65 might be presented in association with particular HLA class II alleles.
A variety of algorithms have been successful in predicting human leukocyte antigen (HLA)-peptide binding for HLA variants for which plentiful experimental binding data exist. Although predicting binding for only the most common HLA variants may provide sufficient population coverage for vaccine design, successful prediction for as many HLA variants as possible is necessary to understand the immune response in transplantation and immunotherapy. However, the high cost of obtaining peptide binding data limits the acquisition of binding data. Therefore, a prediction algorithm, which applies the binding information from well-studied HLA variants to HLA variants, for which no peptide data exist, is necessary. To this end, a modular concept of class I HLA-peptide binding prediction was developed. Accurate predictions were made for several alleles without using experimental peptide binding data specific to those alleles. We include a comparison of module-based prediction and supertype-based prediction. The modular concept increased the number of predictable alleles from 15 to 75 of HLA-A and 12 to 36 of HLA-B proteins. Under the modular concept, binding data of certain HLA alleles can make prediction possible for numerous additional alleles. We report here a ranking of HLA alleles, which have been identified to be the most informative. Modular peptide binding prediction is freely available to researchers on the web at http://www.peptidecheck.org .
http://www.peptidecheck.org.
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