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A Scoring Algorithm for the Automated Analysis of Glycosaminoglycan MS/MS Data

  • Jiana Duan
  • Lauren Pepi
  • I. Jonathan AmsterEmail author
Research Article

Abstract

The role of glycosaminoglycans (GAGs) in major biological functions is numerous and diverse, yet structural characterization of them by mass spectrometric techniques proves to be challenging. Characterization of GAG structure from tandem mass spectrometry is a tedious and time-consuming process but one that can be automated in a database-independent, high-throughput fashion through the assistance of software implementing a genetic algorithm (J. Am. Soc. Mass Spectrom. 29, 1802–1911, 2018). This work presents the manner in which this data is interpreted by the software, specifically addressing the development of a scoring algorithm. The significance of glycosidic and cross-ring fragment ions and the implications that specific fragments provide for assigning the positions of modifications are discussed. The scoring algorithm is tested for statistical merit using the widely accepted expectation value as the criterion for quality. Using MS/MS data for well-characterized standards, this scoring approach is shown to assign the correct structure, with a low likelihood (1 in 1012 chances) that the assigned structure matches the data due to random chance. The integrated software that automates the structure assignment is called Glycosaminoglycan-Unambiguous Identification Technology (G-UNIT).

Keywords

Glycosaminoglycans Fourier transform mass spectrometry Automation Software Automated data analysis 

Notes

Acknowledgements

The authors are grateful for the generous support of the National Institutes of Health (grant numbers R21HL136271, U01CA231074, and P41GM103390). The authors would also like to acknowledge Pradeep Chopra and Geert-Jan Boons (University of Georgia) for providing the hexasaccharide samples and David Kilgour (Nottingham Trent University) for the insight on utilizing the genetic algorithm.

Supplementary material

13361_2019_2338_MOESM1_ESM.docx (873 kb)
ESM 1 (DOCX 872 kb)

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Copyright information

© American Society for Mass Spectrometry 2019

Authors and Affiliations

  1. 1.Department of ChemistryUniversity of GeorgiaAthensUSA

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