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).

中文翻译：

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一种用于糖胺聚糖 MS/MS 数据自动分析的评分算法。

糖胺聚糖 (GAG) 在主要生物学功能中的作用多种多样，但通过质谱技术对其进行结构表征证明具有挑战性。从串联质谱中表征 GAG 结构是一个繁琐且耗时的过程，但可以通过实现遗传算法的软件（J. Am. Soc. Mass Spectrom）以独立于数据库的高通量方式自动化. 29, 1802-1911, 2018)。这项工作介绍了软件解释这些数据的方式，特别是针对评分算法的开发。讨论了糖苷和跨环碎片离子的重要性以及特定碎片为分配修饰位置提供的含义。使用广泛接受的期望值作为质量标准来测试评分算法的统计价值。使用 MS/MS 数据作为充分表征的标准，显示这种评分方法分配正确的结构，由于随机机会，分配的结构与数据匹配的可能性很低（1012 次机会中的 1 次）。自动进行结构分配的集成软件称为糖胺聚糖无歧义识别技术 (G-UNIT)。