title: In silico identification of supertypes for class II MHCs.
abstract: The development of epitope-based vaccines, which have wide population coverage, is greatly complicated by MHC polymorphism. The grouping of alleles into supertypes, on the basis of common structural and functional features, addresses this problem directly. In the present study we applied a combined bioinformatics approach, based on analysis of both protein sequence and structure, to identify similarities in the peptide binding sites of 2225 human class II MHC molecules, and thus define supertypes and supertype fingerprints. Two chemometric techniques were used: hierarchical clustering using three-dimensional Comparative Similarity Indices Analysis fields and nonhierarchical k-means clustering using sequence-based z-descriptors. An average consensus of 84% was achieved, i.e., 1872 of 2225 class II molecules were classified in the same supertype by both techniques. Twelve class II supertypes were defined: five DRs, three DQs, and four DPs. The HLA class II supertypes and their fingerprints given in parenthesis are DR1 (Trp(9beta)), DR3 (Glu(9beta), Gln(70beta), and Gln/Arg(74beta)), DR4 (Glu(9beta), Gln/Arg(70beta), and Glu/Ala(74beta)), DR5 (Glu(9beta), Asp(70beta)), and DR9 (Lys/Gln(9beta)); DQ1 (Ala/Gly(86beta)), DQ2 (Glu(86beta), Lys(71beta)), and DQ3 (Glu(86beta), Thr/Asp(71beta)); DPw1 (Asp(84beta) and Lys(69beta)), DPw2 (Gly/Val(84beta) and Glu(69beta)), DPw4 (Gly/Val(84beta) and Lys(69beta)), and DPw6 (Asp(84beta) and Glu(69beta)). Apart from the good agreement between known binding motifs and our classification, several new supertypes, and corresponding thematic binding motifs, were also defined.
authors.affiliation: Edward Jenner Institute for Vaccine Research, Compton, Berkshire, UK.
authors.forename: Irini A
authors.initials: IA
authors.lastname: Doytchinova
authors.forename: Darren R
authors.initials: DR
authors.lastname: Flower
mesh_terms: D000483:Alleles; D001665:Binding Sites / Q000276:immunology; D020650:Combinatorial Chemistry Techniques / Q000379:methods; D019295:Computational Biology / Q000379:methods; D006681:HLA-D Antigens / Q000145:classification* / Q000235:genetics / Q000378:metabolism*; D006682:HLA-DP Antigens / Q000145:classification / Q000235:genetics / Q000378:metabolism; D006683:HLA-DQ Antigens / Q000145:classification / Q000235:genetics / Q000378:metabolism; D006684:HLA-DR Antigens / Q000145:classification / Q000235:genetics / Q000378:metabolism; D006801:Humans; D008958:Models, Molecular; D005810:Multigene Family; D010449:Peptide Mapping / Q000379:methods; D016415:Sequence Alignment; D040681:Structural Homology, Protein
chemical_list: D006681:HLA-D Antigens; D006682:HLA-DP Antigens; D006683:HLA-DQ Antigens; D006684:HLA-DR Antigens