Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is an important tool for measuring and monitoring protein structure. A bottom-up approach to HDX-MS provides peptide level deuterium uptake values and a more refined localization of deuterium incorporation compared with global HDX-MS measurements. The degree of localization provided by HDX-MS is proportional to the number of peptides that can be identified and monitored across an exchange experiment. Ion mobility spectrometry (IMS) has been shown to improve MS-based peptide analysis of biological samples through increased separation capacity. The integration of IMS within HDX-MS workflows has been commercialized but presently its adoption has not been widespread. The potential benefits of IMS, therefore, have not yet been fully explored. We herein describe a comprehensive evaluation of traveling wave ion mobility integrated within an online-HDX-MS system and present the first reported example of UDMS acquisition for HDX analysis. Instrument settings required for optimal peptide identifications are described and the effects of detector saturation due to peak compression are discussed. A model system is utilized to confirm the comparability of HDX-IM-MS and HDX-MS uptake values prior to an evaluation of the benefits of IMS at increasing sample complexity. Interestingly, MS and IM-MS acquisitions were found to identify distinct populations of peptides that were unique to the respective methods, a property that can be utilized to increase the spatial resolution of HDX-MS experiments by >60%. Graphical Abstract ᅟ.
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http://dx.doi.org/10.1007/s13361-017-1633-z | DOI Listing |
Molecules
January 2025
Department of Chemical Technology, Institute of Chemical Sciences, Faculty of Chemistry, Maria Curie-Skłodowska University, 3 Maria Curie-Skłodowska Square, 20-031 Lublin, Poland.
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January 2025
Military Institute of Chemistry and Radiometry, gen A. Chruściela "Montera" 105, 00-910 Warsaw, Poland.
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January 2025
College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China.
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Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59717, USA.
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January 2025
College of Biosystems Engineering and Food Science, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou 310058, China.
Volatile organic compounds (VOCs) are closely associated with the maturity and variety of strawberries. However, the complexity of VOCs hinders their potential application in strawberry classification. This study developed a novel classification workflow using strawberry VOC profiles and machine learning (ML) models for precise fruit classification.
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