Generic Workflow for Mapping of Complex Disulfide Bonds Using In-Source Reduction and Extracted Ion Chromatograms from Data-Dependent Mass Spectrometry
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Disulfide bond mapping is a critical task in protein characterization as protein stability, structure and function is dependent on correct cysteine connectivities. Mass spectrometry (MS) is the method of choice for this, providing fast and accurate characterization of simple disulfide bonds. Disulfide mapping by liquid chromatography tandem mass spectrometry (LC-MS/MS) is performed by identifying disulfide-bonded partner peptides following proteolytic digestion. With the recently introduced ability to assign complex disulfide patterns by online postcolumn partial disulfide reduction by in-source reduction (ISR) in a LC-ISR-MS/MS methodology, the main challenge is data analysis to ensure detection of both expected and unexpected disulfide species. In this study, we introduced a workflow for confident and unbiased mapping of complex disulfide bonds using the powerful combination of extracted ion chromatograms (XICs) of LC-ISR-MS/MS data. With postcolumn partial reduction, identical LC retention times of intact disulfide-bonded species, their constituting free peptides and partially reduced variants were observed. Subsequent selective MS/MS fragmentation of all reduction products allowed confident identification of free cysteine-containing peptides using a classical shotgun proteomics database search. Matching XICs of the identified cysteine-containing peptides allowed identification of both predicted and unpredicted disulfide species, including unforeseen proteolytic specificities, missed cleavage sites, scrambled disulfide variants and the presence of disulfide-entangled complexes. Applying this workflow, we successfully mapped the complex disulfide bonds of tertiapin and the epidermal growth factor (EGF) family members transforming growth factor α (TGFα) and EGF. In addition, we were able to characterize the disulfide patterns of the special disulfide fold of the TGFβ superfamily in an all-online methodology.
|Publication status||Published - 2018|