Analytical and Informatic Approach to Determine Disease-related Glycoforms of Proteins

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ABSTRACT The biomedical significance of disease-related alterations in protein glycosylation has been recognized for some time. However, significant challenges remain in translating these observations into robust disease biomarkers, and in establishing a detailed molecular understanding of how altered protein glycosylation affects a variety of biochemical interactions and signaling pathways, including protein-drug interactions. Moreover, because protein glycosylation is the end result of a complex series of non-template-driven biosynthetic and post-processing steps, a protein glycosylation profile is the product of an intricate network of biomolecular interactions and is thus exquisitely sensitive to perturbations in metabolic signaling pathways, including those accompanying disease. For these reasons, the detailed molecular characterization of protein glycosylation is of great biomedical interest in biomarker discovery for diseases and for the purpose of relating disease-induced alterations in protein glycosylation to protein function. Essential to such endeavors are powerful analytical techniques that can determine the compositions and structures of protein-linked oligosaccharides and their sites of protein attachment in complex and heterogeneous mixtures. A central tool for the site-specific characterization of protein glycosylation is the application of tandem mass spectrometry (MS/MS) to glycopeptides produced by proteolytic digestion of glycoproteins. A highly desirable outcome of such analyses is to obtain information on both the oligosaccharide topology and the polypeptide sequence. At present, this level of detail is typically achieved only by combining multiple MS/MS methods at the expense of speed and number of glycopeptides characterized. To overcome this barrier, we will develop a thorough chemical understanding of how the most common MS/MS method - collision-induced dissociation (CID) - can alone provide complete connectivity information on N-linked glycopeptides. We will develop and apply this understanding by: 1) establishing the influence of charge state, monosaccharide composition, amino acid composition, and vibrational degrees of freedom on the CID energy requirements for glycosidic and peptide bond scission in protonated N-glycopeptides; 2) compiling and mining a large database of CID spectra of protonated N-glycopeptides at various collision energies to empirically correlate precursor ion characteristics with energy-dependent dissociation characteristics; and 3) illustrating these methods and insights by examining changes in the site-specific N-glycosylation profile of human alpha1-acid glycoprotein (AGP) that occur during rheumatoid arthritis. Achievement of these aims will be enabled by the capabilities of both the Systems Biology Core facility and the Data Management and Analysis Core facility of the Nebraska Center for Integrated Biomolecular Communication (CIBC) to 1) obtain large, high-dimensional data sets, and 2) apply informatic methods to mine these data sets for insights on glycopeptide dissociation. These insights will be directly applicable to the elucidation of site-specific glycosylation patterns that arise as a result of disease.