Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices

• Published in: Nature biotechnology 1999-06, Vol.17 (6), p.555-561
• Main Authors: Hammer, Juergen, Sturniolo, Tiziana, Bono, Elisa, Ding, Jiayi, Raddrizzani, Laura, Tuereci, Oezlem, Sahin, Ugur, Braxenthaler, Michael, Gallazzi, Fabio, Protti, Maria Pia, Sinigaglia, Francesco
• Format: Article
• Language: English
• Subjects: Agriculture; Alleles; Amino Acid Sequence; Bioinformatics; Biological and medical sciences; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Database Management Systems; DNA - chemistry; Epitopes - chemistry; Epitopes - immunology; Fundamental and applied biological sciences. Psychology; Health. Pharmaceutical industry; HLA-DR Antigens - chemistry; HLA-DR Antigens - genetics; HLA-DR Antigens - immunology; Humans; Industrial applications and implications. Economical aspects; Life Sciences; Ligands; Molecular Sequence Data; Production of active biomolecules; research-article; Sequence Homology, Amino Acid; T-Lymphocytes - immunology; Vaccins
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/9858

Most pockets in the human leukocyte antigen–group DR (HLA-DR) groove are shaped by clusters of polymorphic residues and, thus, have distinct chemical and size characteristics in different HLA-DR alleles. Each HLA-DR pocket can be characterized by "pocket profiles," a quantitative representation of the interaction of all natural amino acid residues with a given pocket. In this report we demonstrate that pocket profiles are nearly independent of the remaining HLA-DR cleft. A small database of profiles was sufficient to generate a large number of HLA-DR matrices, representing the majority of human HLA-DR peptide-binding specificity. These virtual matrices were incorporated in software (TEPITOPE) capable of predicting promiscuous HLA class II ligands. This software, in combination with DNA microarray technology, has provided a new tool for the generation of comprehensive databases of candidate promiscuous T-cell epitopes in human disease tissues. First, DNA microarrays are used to reveal genes that are specifically expressed or upregulated in disease tissues. Second, the prediction software enables the scanning of these genes for promiscuous HLA-DR binding sites. In an example, we demonstrate that starting from nearly 20,000 genes, a database of candidate colon cancer–specific and promiscuous T-cell epitopes could be fully populated within a matter of days. Our approach has implications for the development of epitope-based vaccines.