Cyclic Ion Mobility-Collision Activation Experiments Elucidate Protein Behavior in the Gas Phase.
Charles F S EldridAisha Ben-YounisJakub UjmaHannah M BrittTristan CragnoliniSymeon KalfasDale Cooper-ShepherdNick TomczykKevin GilesMike MorrisRehana AkterDaniel P RaleighKonstantinos ThalassinosPublished in: Journal of the American Society for Mass Spectrometry (2021)
Ion mobility coupled to mass spectrometry (IM-MS) is widely used to study protein dynamics and structure in the gas phase. Increasing the energy with which the protein ions are introduced to the IM cell can induce them to unfold, providing information on the comparative energetics of unfolding between different proteoforms. Recently, a high-resolution cyclic IM-mass spectrometer (cIM-MS) was introduced, allowing multiple, consecutive tandem IM experiments (IMn) to be carried out. We describe a tandem IM technique for defining detailed protein unfolding pathways and the dynamics of disordered proteins. The method involves multiple rounds of IM separation and collision activation (CA): IM-CA-IM and CA-IM-CA-IM. Here, we explore its application to studies of a model protein, cytochrome C, and dimeric human islet amyloid polypeptide (hIAPP), a cytotoxic and amyloidogenic peptide involved in type II diabetes. In agreement with prior work using single stage IM-MS, several unfolding events are observed for cytochrome C. IMn-MS experiments also show evidence of interconversion between compact and extended structures. IMn-MS data for hIAPP shows interconversion prior to dissociation, suggesting that the certain conformations have low energy barriers between them and transition between compact and extended forms.
Keyphrases
- mass spectrometry
- high resolution
- multiple sclerosis
- ms ms
- liquid chromatography
- protein protein
- amino acid
- healthcare
- endothelial cells
- type diabetes
- cardiovascular disease
- high performance liquid chromatography
- capillary electrophoresis
- skeletal muscle
- small molecule
- metabolic syndrome
- machine learning
- electronic health record
- insulin resistance
- social media
- quantum dots
- tandem mass spectrometry
- big data
- case control