Genomics-driven immunoproteomics (GDI) is a platform that helps identify antigenic protein targets of mutations and other deoxyribonucleic acid (DNA) variations that are commonly associated with pathological states. This platform utilizes data generated from deep sequencing of exomic DNA or ribonucleic acid (RNA) as input to synthesize mutant peptides into microarrays, which then can be used to detect antigenic proteins that invoke immune response in patients. The technology has been used to detect antigenic targets of multiple sclerosis, an autoimmune disease [1], and cancer to identify mutant proteins that invoke immune response in breast cancer patients [2]. This technology has many potential applications to select genomic changes that are specifically recognized by the immune system in a rapid and efficient manner.
Rekombinante Antikörper: Lehrbuch und Kompendium für Studium und Praxis
Dübel, Stefan; Breitling, Frank; Frenzel, André; Jostock, Thomas; Marschall, Andrea L. J.; Schirrmann, Thomas; Hust, Michael
Today, lithographic methods enable combinatorial synthesis of >50,000 oligonucleotides per cm2, an advance that has revolutionized the whole field of genomics. A similar development is expected for the field of proteomics, provided that affordable, very high-density peptide arrays are available. However, peptide arrays lag behind oligonucleotide arrays. This is mainly due to the monomer-by-monomer repeated consecutive coupling of 20 different amino acids associated with lithography, which adds up to an excessive number of coupling cycles. A combinatorial synthesis based on electrically charged solid amino acid particles resolves this problem. A computer chip consecutively addresses the different charged particles to a solid support, where, when completed, the whole layer of solid amino acid particles is melted at once. This frees hitherto immobilized amino acids to couple all 20 different amino acids in one single coupling reaction to the support. The method should allow for the translation of entire genomes into a set of overlapping peptides to be used in proteome research.
