Literature DB >> 26146955

Mapping Protein Conformational Landscapes under Strongly Native Conditions with Hydrogen Exchange Mass Spectrometry.

Jacob Witten1, Amy Ruschak2, Timothy Poterba1, Alexis Jaramillo1, Andrew D Miranker2, Sheila S Jaswal1,2.   

Abstract

The thermodynamic stability and kinetic barriers separating protein conformations under native conditions are critical for proper protein function and for understanding dysfunction in diseases of protein conformation. Traditional methods to probe protein unfolding and folding employ denaturants and highly non-native conditions, which may destabilize intermediate species or cause irreversible aggregation, especially at the high protein concentrations typically required. Hydrogen exchange (HX) is ideal for detecting conformational behavior under native conditions without the need for denaturants, but detection by NMR is limited to small highly soluble proteins. Mass spectrometry (MS) can, in principle, greatly extend the applicability of native-state HX to larger proteins and lower concentrations. However, quantitative analysis of HXMS profiles is currently limited by experimental and theoretical challenges. Here we address both limitations, by proposing an approach based on using standards to eliminate the systematic experimental artifacts in HXMS profiles, and developing the theoretical framework to describe HX behavior across all regimes based on the Linderstrøm-Lang formalism. We demonstrate proof of principle by a practical application to native-state HX of a globular protein. The framework and the practical tools developed advance the ability of HXMS to extract thermodynamic and kinetic conformational parameters of proteins under native conditions.

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Year:  2015        PMID: 26146955     DOI: 10.1021/acs.jpcb.5b04528

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  1 in total

1.  Lessons from a quarter century of being human in protein science.

Authors:  Sheila S Jaswal
Journal:  Protein Sci       Date:  2022-02-02       Impact factor: 6.725

  1 in total

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