Automated detection and analysis of bimodal isotope peak distributions in H/D exchange mass spectrometry using HeXicon

• Verfasst von: Kreshuk, Anna [VerfasserIn]
• Verfasst von: Stankiewicz, Marta [VerfasserIn]
• Verfasst von: Lou, Xinghua [VerfasserIn]
• Verfasst von: Kirchner, Marc [VerfasserIn]
• Verfasst von: Hamprecht, Fred [VerfasserIn]
• Verfasst von: Mayer, Matthias P. [VerfasserIn]
• Titel: Automated detection and analysis of bimodal isotope peak distributions in H/D exchange mass spectrometry using HeXicon
• Verf.angabe: Anna Kreshuk, Marta Stankiewicz, Xinghua Lou, Marc Kirchner, Fred A. Hamprecht, Matthias P. Mayer
• Jahr: 2011
• Umfang: 7 S.
• Fussnoten: Available online 15 September 2010 ; Gesehen am 08.07.2022
• Titel Quelle: Enthalten in: International journal of mass spectrometry
• Ort Quelle: [S.l.] : Elsevier Science, 1968
• Jahr Quelle: 2011
• Band/Heft Quelle: 302(2011), 1, Seite 125-131
• ISSN Quelle: 1873-2798
• DOI: doi:10.1016/j.ijms.2010.08.025
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Amide hydrogen exchange
• Sach-SW: Automated data analysis
• Sach-SW: Bimodal isotope distribution
• Sach-SW: Coexisting conformation
• K10plus-PPN: 1809642159

Abstract

The analysis of conformational transitions provides important information on the mode of action of proteins. Amide hydrogen exchange mass spectrometry (HX-MS) allows to detect conformational changes and to resolve the kinetics if the transition occurs in the accessible time domain. However, manual data analysis of HX-MS experiments is tedious, especially for large proteins, and therefore, analysis is usually restricted to a subset of the information available from the mass spectra. In response to this problem, several software tools, including HeXicon from our group, have recently been presented, aiming to automate data analysis of HX-MS experiments and to extract all accessible information. Bimodal isotope distributions as arising from EX1 exchange mechanisms or different coexisting conformations in a protein are of specific interest, because they report on the kinetics of conformational changes. However, they also provide an even greater challenge for automated data analysis. In this study, we tuned HeXicon in order to search specifically for bimodal isotope distributions in large datasets. We applied the modified program to a dataset from the Escherichia coli Hsp90 homologue HtpG and compared the results with manual data analysis. All seven manually found bimodal cases were detected as bimodal by HeXicon. In addition, HeXicon also located nine previously unknown bimodal distributions, illustrating the benefit of automated data processing.