4.7 Article

GAG-ID: Heparan Sulfate (HS) and Heparin Glycosaminoglycan High-Throughput Identification Software

Journal

MOLECULAR & CELLULAR PROTEOMICS
Volume 14, Issue 6, Pages 1720-1730

Publisher

AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC
DOI: 10.1074/mcp.M114.045856

Keywords

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Funding

  1. National Institute of General Medical Sciences of the National Institutes of Health through the Research Resource for Integrated Glycotechnology [8P41 GM103390]

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Heparin and heparan sulfate are very large linear polysaccharides that undergo a complex variety of modifications and are known to play important roles in human development, cell-cell communication and disease. Sequencing of highly sulfated glycosaminoglycan oligosaccharides like heparin and heparan sulfate by liquid chromatography-tandem mass spectrometry (LC-MS/MS) remains challenging because of the presence of multiple isomeric sequences in a complex mixture of oligosaccharides, the difficulties in separation of these isomers, and the facile loss of sulfates in MS/MS. We have previously introduced a method for structural sequencing of heparin/heparan sulfate oligosaccharides involving chemical derivatizations that replace labile sulfates with stable acetyl groups. This chemical derivatization scheme allows the use of reversed phase LC for high-resolution separation and MS/MS for sequencing of isomeric heparan sulfate oligosaccharides. However, because of the large number of analytes present in complex mixtures of heparin/HS oligosaccharides, the resulting LC-MS/MS data sets are large and cannot be annotated with existing glycomics software because of the specifically designed chemical derivatization strategy. We have developed a tool, called GAG-ID, to automate the interpretation of derivatized heparin/heparan sulfate LC-MS/MS data based on a modified multivariate hypergeometric distribution to weight the annotation of more intense peaks. The software is tested on a LC-MS/MS data set collected from a mixture of 21 synthesized heparan sulfate tetrasaccharides. By testing the discrimination of scoring with this system, we show that stratifying peaks into different intensity classes benefits the discrimination of scoring, and GAG-ID is able to properly assign all 21 synthetic tetrasaccharides in a defined mixture from a single LC-MS/MS run.

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