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

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).