Published in

Springer, Applied Magnetic Resonance, 1-3(55), p. 187-205, 2023

DOI: 10.1007/s00723-023-01611-1

Links

Tools

Export citation

Search in Google Scholar

Spectroscopically Orthogonal Labelling to Disentangle Site-Specific Nitroxide Label Distributions

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Green circle
Preprint: archiving allowed
Orange circle
Postprint: archiving restricted
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

AbstractBiomolecular applications of pulse dipolar electron paramagnetic resonance spectroscopy (PDS) are becoming increasingly valuable in structural biology. Site-directed spin labelling of proteins is routinely performed using nitroxides, with paramagnetic metal ions and other organic radicals gaining popularity as alternative spin centres. Spectroscopically orthogonal spin labelling using different types of labels potentially increases the information content available from a single sample. When analysing experimental distance distributions between two nitroxide spin labels, the site-specific rotamer information has been projected into the distance and is not readily available, and the contributions of individual labelling sites to the width of the distance distribution are not obvious from the PDS data. Here, we exploit the exquisite precision of labelling double-histidine (dHis) motifs with CuII chelate complexes. The contribution of this label to the distance distribution widths in model protein GB1 has been shown to be negligible. By combining a dHis CuII labelling site with cysteine-specific nitroxide labelling, we gather insights on the label rotamers at two distinct sites, comparing their contributions to distance distributions based on different in silico modelling approaches and structural models. From this study, it seems advisable to consider discrepancies between different in silico modelling approaches when selecting labelling sites for PDS studies.