Abstract
Glycosaminoglycans (GAGs) covalently linked to proteoglycans (PGs) are characterized by repeating disaccharide units and variable sulfation patterns along the chain. GAG length and sulfation patterns impact disease etiology, cellular signaling, and structural support for cells. We and others have demonstrated the usefulness of tandem mass spectrometry (MS2) for assigning the structures of GAG saccharides; however, manual interpretation of tandem mass spectra is time-consuming, so computational methods must be employed. In the proteomics domain, the identification of monoisotopic peaks and charge states relies on algorithms that use averagine, or the average building block of the compound class being analyzed. Although these methods perform well for protein and peptide spectra, they perform poorly on GAG tandem mass spectra, because a single average building block does not characterize the variable sulfation of GAG disaccharide units. In addition, it is necessary to assign product ion isotope patterns to interpret the tandem mass spectra of GAG saccharides. To address these problems, we developed GAGfinder, the first tandem mass spectrum peak finding algorithm developed specifically for GAGs. We define peak finding as assigning experimental isotopic peaks directly to a given product ion composition, as opposed to deconvolution or peak picking, which are terms more accurately describing the existing methods previously mentioned. GAGfinder is a targeted, brute force approach to spectrum analysis that uses precursor composition information to generate all theoretical fragments. GAGfinder also performs peak isotope composition annotation, which is typically a subsequent step for averagine-based methods. Data are available via ProteomeXchange with identifier PXD009101.
Highlights
Wolff and colleagues first applied electron activated dissociation methods to GAG oligosaccharides, using both electron detachment dissociation (EDD) [8] and negative electron transfer dissociation (NETD) [9]
GAGfinder Performance Compared with Random Sampling—For each of the ten GAG saccharide tandem mass spectra tested, the GAGfinder performance score significantly outperformed that of the permutations
We concluded based on these numbers that GAGfinder significantly outperforms a random selection of peaks
Summary
Wolff and colleagues first applied electron activated dissociation methods to GAG oligosaccharides, using both electron detachment dissociation (EDD) [8] and negative electron transfer dissociation (NETD) [9]. Several computational methods for automatic recognition of isotopic patterns and assignment of charge states and neutral mass values have been developed, including THRASH [11], Decon2LS [12], and MS-Deconv [13], among others These methods assume product ion isotopic distributions will match the pattern produced by the molecule’s average building block, or averagine; performance for GAG saccharide tandem mass spectra is inadequate, because of the variable levels of sulfation along their chains and the relatively abundant 34S isotope. Averagine-based deisotoping and charge state deconvolution algorithms were developed to circumvent the combinatorial explosion of the number of possible protein sequences as the length of the chain increases Because of this expansion, brute force methods searching all possible proteins and protein product ions are not feasible. This paper describes the steps in GAGfinder and its performance as a means to identify the GAG monoisotopic product ions, charge states, and neu-
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