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HDXFinder: Automated Analysis and Data Reporting of Deuterium/Hydrogen Exchange Mass Spectrometry

Overview of attention for article published in Journal of the American Society for Mass Spectrometry, November 2011
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Title
HDXFinder: Automated Analysis and Data Reporting of Deuterium/Hydrogen Exchange Mass Spectrometry
Published in
Journal of the American Society for Mass Spectrometry, November 2011
DOI 10.1007/s13361-011-0234-5
Pubmed ID
Authors

Danny E. Miller, Charulata B. Prasannan, Maria T. Villar, Aron W. Fenton, Antonio Artigues

Abstract

Hydrogen/deuterium exchange in combination with mass spectrometry (H/D MS) is a sensitive technique for detection of changes in protein conformation and dynamics. However, wide application of H/D MS has been hindered, in part, by the lack of computational tools necessary for efficient analysis of the large data sets associated with this technique. We report a novel web-based application for automatic analysis of H/D MS experimental data. This application relies on the high resolution of mass spectrometers to extract all isotopic envelopes before correlating these envelopes with individual peptides. Although a fully automatic analysis is possible, a variety of graphical tools are included to aid in the verification of correlations and rankings of the isotopic peptide envelopes. As a demonstration, the rate constants for H/D exchange of peptides from rabbit muscle pyruvate kinase are mapped onto the structure of this protein.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 4%
Unknown 23 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 38%
Researcher 3 13%
Professor > Associate Professor 3 13%
Student > Bachelor 2 8%
Student > Master 2 8%
Other 4 17%
Unknown 1 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 33%
Chemistry 5 21%
Agricultural and Biological Sciences 3 13%
Computer Science 2 8%
Engineering 2 8%
Other 1 4%
Unknown 3 13%