A Structural Variant Approach for Establishing a Detection Limit in Differential Hydrogen Exchange-Mass Spectrometry Measurements

Hydrogen exchange-mass spectrometry (HX-MS) is widely promoted for its ability to detect subtle perturbations in protein structure, but such perturbations will result in small differences in HX. However, the detection limit of HX-MS has not been widely investigated, nor is there a useful approach fo...

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Published in:Analytical chemistry (Washington) Vol. 91; no. 13; pp. 8017 - 8024
Main Authors: Hageman, Tyler S, Weis, David D
Format: Journal Article
Language:English
Published: United States American Chemical Society 02-07-2019
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Summary:Hydrogen exchange-mass spectrometry (HX-MS) is widely promoted for its ability to detect subtle perturbations in protein structure, but such perturbations will result in small differences in HX. However, the detection limit of HX-MS has not been widely investigated, nor is there a useful approach for defining the detection limit of HX-MS measurements. In this work, we designed a well-characterized structural variant spiking model to investigate the detection limit of conventional peptide-based HX-MS. The detection limit was challenged by spiking small fractions of a structural variant (modeled using maltose binding protein W169G mutant) into a reference protein (wild-type maltose binding protein). As little as 5% of the structural variant could be detected. The small structural perturbation was not resolvable by far UV circular dichroism, differential scanning calorimetry, or size exclusion chromatography. Furthermore, we validated the ability of the hybrid statistical analysis approach, presented in a companion paper (10.1021/acs.analchem.9b01325), to reliably identify small, significant differences in HX-MS measurements. With our structural variant spiking model, we demonstrate a benchmarking approach for determining a detection limit of HX-MS for detection of changes in higher-order structure that might be encountered in protein structural comparability and similarity assessment applications.
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ISSN:0003-2700
1520-6882
DOI:10.1021/acs.analchem.9b01326