Author(s): Goldberg D, Bern M, North SJ, Haslam SM, Dell A
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Abstract MOTIVATION: In the past few years, mass spectrometry (MS) has emerged as the premier tool for identification and quantification of biological molecules such as peptides and glycans. There are two basic strategies: single-MS, which uses a single round of mass analysis, and MS/MS (or higher order MS(n)), which adds one or more additional rounds of mass analysis, interspersed with fragmentation steps. Single-MS offers higher throughput, broader mass coverage and more direct quantitation, but generally much weaker identification. Single-MS, however, does work fairly well for the case of N-glycan identification, which are more constrained than other biological polymers. We previously demonstrated single-MS identification of N-glycans to the level of 'cartoons' (monosaccharide composition and topology) by a system that incorporates an expert's detailed knowledge of the biological sample. In this article, we explore the possibility of ab initio single-MS N-glycan identification, with the goal of extending single-MS, or primarily-single-MS, identification to non-expert users, novel conditions and unstudied tissues. RESULTS: We propose and test three cartoon-assignment algorithms that make inferences informed by biological knowledge about glycan synthesis. To test the algorithms, we used 71 single-MS spectra from a variety of tissues and organisms, containing more than 2800 manually annotated peaks. The most successful of the algorithms computes the most richly connected subgraph within a 'cartoon graph'. This algorithm uniquely assigns the correct cartoon to more than half of the peaks in 41 out of the 71 spectra.
This article was published in Bioinformatics
and referenced in Journal of Proteomics & Bioinformatics