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Published July 24, 2013 | Supplemental Material
Journal Article Open

Biomimetic Reagents for the Selective Free Radical and Acid–Base Chemistry of Glycans: Application to Glycan Structure Determination by Mass Spectrometry

Abstract

Nature excels at breaking down glycans into their components, typically via enzymatic acid–base catalysis to achieve selective cleavage of the glycosidic bond. Noting the importance of proton transfer in the active site of many of these enzymes, we describe a sequestered proton reagent for acid-catalyzed glycan sequencing (PRAGS) that derivatizes the reducing terminus of glycans with a pyridine moiety possessing moderate proton affinity. Gas-phase collisional activation of PRAGS-derivatized glycans predominately generates C1–O glycosidic bond cleavages retaining the charge on the reducing terminus. The resulting systematic PRAGS-directed deconstruction of the glycan can be analyzed to extract glycan composition and sequence. Glycans are also highly susceptible to dissociation by free radicals, mainly reactive oxygen species, which inspired our development of a free radical activated glycan sequencing (FRAGS) reagent, which combines a free radical precursor with a pyridine moiety that can be coupled to the reducing terminus of target glycans. Collisional activation of FRAGS-derivatized glycans generates a free radical that reacts to yield abundant cross-ring cleavages, glycosidic bond cleavages, and combinations of these types of cleavages with retention of charge at the reducing terminus. Branched sites are identified with the FRAGS reagent by the specific fragmentation patterns that are observed only at these locations. Mechanisms of dissociation as well as application of the reagents for both linear and highly branched glycan structure analysis are investigated and discussed. The approach developed here for glycan structure analysis offers unique advantages compared to earlier studies employing mass spectrometry for this purpose.

Additional Information

© 2013 American Chemical Society. Received: March 19, 2013; Published: June 27, 2013. This work was supported by the Beckman Institute at the California Institute of Technology. The early stages of this work were supported by National Science Foundation Grant CHE- 0416381. Computational resources were kindly provided by the Materials and Process Simulation Center at the California Institute of Technology.

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