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- W2038436191 abstract "Human leukocyte antigens class I (HLAIs) display protein fragments (~9–13 amino acid peptides primarily generated by proteasomal cleavage of cytosolic proteins) at the cell surface to the adaptive immune system (principally CD8+ T cells). If CD8+ T cells recognize these presented protein fragments, they have the ability to destroy the contaminated cell. HLAIs represent one of the most diverse set of alleles in the human genome, probably an evolutionary safeguard that ensures comprehensive immune disease coverage across the human population. Thus, it is not surprising that particular HLAI alleles are genetically associated with susceptibility to, or protection against, a number of diseases including; HIV-1, type 1 diabetes and ankolysing spondylitis. However, the diversity of the HLAI system represents a major challenge for generating cross-population peptide vaccines that bind promiscuously to different HLAIs, as well as predicting organ rejection during transplantation. In this issue of Immunology and Cell Biology, Mukherjee et al.1 have developed a new computational method and tools to decipher this complex system and improve our understanding of HLAI-mediated immune responses. Recognition of HLAI, at least by CD8+ T cells, is dependent on the peptide cargo presented by the HLAI molecule. Ipso facto, the clonally expressed T-cell receptor (TCR), that is highly sensitive to the amino acid sequence of the bound peptide, governs the antigenic specificity of the T cell. Selection of peptides for the formation of peptide (p)HLAI complexes is mainly determined by the HLAI α1α2 domain peptide-binding groove; the sides of which comprise two parallel α-helices, with a β-sheet floor (Figure 1a).2 This groove contains a number of chemically distinct pockets that have binding preferences for different amino acid side chains. The HLAI peptide-binding groove is highly polymorphic, accounting for the majority of the >9000 different alleles that have been reported to date3 (although not all of these may be distinct functional proteins) in three different loci on chromosome 6, representing the three major HLAI classes (HLA-A, HLA-B and HLA-C). These polymorphisms govern peptide selection and presentation. In fact, we have recently shown that even single amino acid difference between different HLAI alleles can induce a conformational switch in the same peptide resulting in structurally distinct epitopes.4 Individuals express up to six different HLAI molecules (two each of HLA-A, -B and -C) presenting billions of distinct 9-mer peptide sequences for T-cell discrimination.5, 6 However, depending on the chemical characteristics of the binding groove, each HLAI has the potential to present a distinct repertoire of different peptide sequences such that there could be limited overlap between individuals depending on their HLAI type. In order to make sense of this extremely variable system, a number of classification models have been previously developed to group different HLAI alleles based on the sequence of the HLAI-binding groove and/or peptide-binding preferences.7, 8 These methods have helped to predict potential peptide epitopes for different HLAI molecules and have contributed to our understanding of HLAI-mediated disease outcome. Mukherjee et al.1 have developed a new hierarchical grouping system to decipher this complex system using 2010 HLAI sequences, combined with 150 crystal structures of 24 different HLAI alleles. Because the overall fold of HLAI is conserved, the authors were able to use structure-based sequence alignments to identify the 57 residues in the peptide binding groove of all 2010 HLAI alleles with a high degree of certainty. The authors argue that this approach provides a more complete model to classify HLAI groups compared with previous systems that have generally studied a far smaller number of alleles, and/or focused on only specific motifs in the peptide binding groove. By studying similarities (chemical composition and conformation) in the binding groove, the authors were able to determine seven distinct signatures which they call supergroups S1–S7 (Figure 1b). These supergroups compared well with HLA supertype definitions7 in some cases (A24 and B58 supertypes) but poorly in others (A01, B07 and B27), demonstrating key differences between classification systems. However, the new supergroups corresponded more closely with calculated epitope specificity, suggesting that their new system could better predict instances where a single peptide could be presented by different HLAI alleles. This approach also demonstrated that, although the predicted peptide repertoires segregated well into the different groups, there was some overlap between supergroups (Figure 1b). Interestingly, some of their groups also contained HLAIs from different classes. For example, supergroup S6 contained alleles from HLA-A, -B and -C. Finally, the authors have developed a freely available, and beautifully simple to use, online tool (http://proline.biochem.iisc.ernet.in/hlaclassify/) that can predict: (1) the supergroup of a specific HLAI allele; and (2) potential binding peptides based on the supergroup of the allele from known protein sequences. To conclude, classification of HLAI alleles and identification of peptide epitopes is a highly complex but important pursuit, with attendant implications for vaccine design, predicting tissue matches for organ transplantation and dissecting the CD8+ T-cell response in various disease settings. Computational approaches, as exemplified in this study, are key to this process and are likely to become even more important considering that the complexity of epitope selection is increasing with the recent discovery that different endoplasmic reticulum aminopeptidase 1 (ERAP1) allotypes can alter that selection of the peptide repertoire within the same HLAI allele.9 Although peptide predictions need to be validated in functional experiments, these developments help to shine new light on peptide repertoire selection by different HLAI alleles. Hierarchical grouping of HLAIs based on the composition of the peptide-binding groove. (a) A structural representation (using PDB: 1HHK) of HLAI demonstrating the conserved α3 and β2m domains (gray cartoon and surface) and the variable α1α2 domain that forms the peptide binding groove (multicolored cartoon and surface). (b) Overview of the seven HLAI supergroups identified by Mukherjee and colleagues (different colored structures) and a representation of the predicted sections of the peptide repertoire (colored circles) to which HLAIs within each supergroup might bind." @default.
- W2038436191 created "2016-06-24" @default.
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- W2038436191 date "2015-03-31" @default.
- W2038436191 modified "2023-09-27" @default.
- W2038436191 title "The ultimate mix and match: making sense of HLA alleles and peptide repertoires" @default.
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- W2038436191 doi "https://doi.org/10.1038/icb.2015.40" @default.
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