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Abstract
Glycosaminoglycan (GAGs) are linear chain glycans consisting of repeating uronic sugar and amino sugar copolymers and play major roles in fundamental biological processes. Despite being ubiquitous in cells, the structure of intact GAG chains remains relatively elusive. GAG sequences can be determined using mass spectrometry with ion activation techniques but structure characterization is both time consuming if performed manually and requires a high degree of expertise. The structural analysis step is the bottleneck for higher-throughput methodologies, making it difficult for biological laboratories and clinics to perform routine glycan characterization. The work here is a software solution to the interpretation step, optimizing structures based on highest likelihood while using a genetic algorithm to maximize computation efficiency. This software package is put to the test by 1) determining large but well characterized glycans as well as 2) unknown structures far too complex for manual interpretation.