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google, scholar, hla, pubmed, cas, article, class, mhc, molecules, peptide, specificities, binding, supertype, immunogenetics, sette, sidney, alleles, immunol, privacy, cookies, content, search, definition, lund, nielsen, sequence, clusters, guercio, del, data, information, publish, research, supertypes, clustering, specificity, kesmir, søren, buus, functional, access, chapter, related, nucleic, acids, res, kubo, press, university, analysis,

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month download article/chapter mhc class ii epitopes hla-dr binding sites position-based sequence weights sune justesen & søren buus swiss-prot protein knowledgebase hla-dr types major hla class class ii access common hla molecules christina sylvester-hvid dk/researchgroups/immunology/supertypes del guercio mf hidden markov models designing peptide vaccines full article pdf privacy choices/manage cookies class ii molecules multiple hla alleles hla specificity space article immunogenetics aims hla facts book functional genetic variation mhc-binding peptides anders gorm petersen gibbs sampling procedure gibbs sampling strategy gibbs sampling approach reconstructing phylogenetic trees display consensus sequences quantitative elisa capable 5th framework programme mhc sequencing data representative hla molecules conditions privacy policy european economic area extremely polymorphic genes inefficient antiviral protection dutch science foundation biological sequence analysis accepting optional cookies main content log clustering sequence motifs previously observed specificities mol biol 243 specificity matrices peder worning author information authors hla class neighbor-joining method

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         headline:Definition of supertypes for HLA molecules using clustering of specificity matrices
         description:Major histocompatibility complex (MHC) proteins are encoded by extremely polymorphic genes and play a crucial role in immunity. However, not all genetically different MHC molecules are functionally different. Sette and Sidney (1999) have defined nine HLA class I supertypes and showed that with only nine main functional binding specificities it is possible to cover the binding properties of almost all known HLA class I molecules. Here we present a comprehensive study of the functional relationship between all HLA molecules with known specificities in a uniform and automated way. We have developed a novel method for clustering sequence motifs. We construct hidden Markov models for HLA class I molecules using a Gibbs sampling procedure and use the similarities among these to define clusters of specificities. These clusters are extensions of the previously suggested ones. We suggest splitting some of the alleles in the A1 supertype into a new A26 supertype, and some of the alleles in the B27 supertype into a new B39 supertype. Furthermore the B8 alleles may define their own supertype. We also use the published specificities for a number of HLA-DR types to define clusters with similar specificities. We report that the previously observed specificities of these class II molecules can be clustered into nine classes, which only partly correspond to the serological classification. We show that classification of HLA molecules may be done in a uniform and automated way. The definition of clusters allows for selection of representative HLA molecules that can cover the HLA specificity space better. This makes it possible to target most of the known HLA alleles with known specificities using only a few peptides, and may be used in construction of vaccines. Supplementary material is available at http://www.cbs.dtu.dk/researchgroups/immunology/supertypes.html .
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      headline:Definition of supertypes for HLA molecules using clustering of specificity matrices
      description:Major histocompatibility complex (MHC) proteins are encoded by extremely polymorphic genes and play a crucial role in immunity. However, not all genetically different MHC molecules are functionally different. Sette and Sidney (1999) have defined nine HLA class I supertypes and showed that with only nine main functional binding specificities it is possible to cover the binding properties of almost all known HLA class I molecules. Here we present a comprehensive study of the functional relationship between all HLA molecules with known specificities in a uniform and automated way. We have developed a novel method for clustering sequence motifs. We construct hidden Markov models for HLA class I molecules using a Gibbs sampling procedure and use the similarities among these to define clusters of specificities. These clusters are extensions of the previously suggested ones. We suggest splitting some of the alleles in the A1 supertype into a new A26 supertype, and some of the alleles in the B27 supertype into a new B39 supertype. Furthermore the B8 alleles may define their own supertype. We also use the published specificities for a number of HLA-DR types to define clusters with similar specificities. We report that the previously observed specificities of these class II molecules can be clustered into nine classes, which only partly correspond to the serological classification. We show that classification of HLA molecules may be done in a uniform and automated way. The definition of clusters allows for selection of representative HLA molecules that can cover the HLA specificity space better. This makes it possible to target most of the known HLA alleles with known specificities using only a few peptides, and may be used in construction of vaccines. Supplementary material is available at http://www.cbs.dtu.dk/researchgroups/immunology/supertypes.html .
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         Human Genetics
         Gene Function
         Cell Biology
         Allergology
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