Direct Detection of Carbon and Nitrogen Nuclei for High-Resolution Analysis of Intrinsically Disordered Proteins using NMR Spectroscopy

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Direct Detection of Carbon and Nitrogen Nuclei for High-Resolution Analysis of Intrinsically Disordered Proteins using NMR Spectroscopy

Publication date: Available online 16 January 2018
Source:Methods

Author(s): E.B. Gibbs, R.W. Kriwacki

Nuclear magnetic resonance spectroscopy (NMR) is a powerful technique for characterizing the structural and dynamic properties of intrinsically disordered proteins and protein regions (IDPs & IDRs). However, the application of NMR to IDPs has been limited by poor chemical shift dispersion in two-dimensional (2D) 1H-15N heteronuclear correlation spectra. Among the various detection schemes available for heteronuclear correlation spectroscopy, 13C direct-detection has become a mainstay for investigations of IDPs owing to the favorable chemical shift dispersion in 2D 13C?-15N correlation spectra. Recent advances in cryoprobe technology have enhanced the sensitivity for direct detection of both 13C and 15N resonances at high magnetic field strengths, thus prompting the development of 15N direct-detect experiments to complement established 13C-detection experiments. However, the application of 15N-detection has not been widely explored for IDPs. Here we compare 1H, 13C, and 15N detection schemes for a variety of 2D heteronuclear correlation spectra and evaluate their performance on the basis of resolution, chemical shift dispersion, and sensitivity. We performed experiments with a variety of disordered systems ranging in size and complexity; from a small IDR (99 amino acids), to a large low complexity IDR (185 amino acids), and finally a ~73 kDa folded homopentameric protein that also contains disordered regions (133 amino acids/monomer). We conclude that, while requiring high sample concentration and long acquisition times, 15N-detection often offers enhanced resolution over other detection schemes in studies of disordered protein regions with low complexity sequences.
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