Determination of nitroxide spin label conformations via PELDOR and X-ray crystallography

文献情報

出版日 2016-03-14

DOI 10.1039/C6CP01307D

インパクトファクター 3.676

著者

D. Abdullin, G. Hagelueken, O. Schiemann

原文を見る

要旨

Pulsed electron–electron double resonance (PELDOR or DEER) in combination with site-directed spin labelling has emerged as an important method for measuring nanometer distance constraints that are used to obtain coarse-grained structures of biomolecules or to follow their conformational changes. Translating measured spin–spin distances between spin labels into structural information requires taking the conformational flexibility of spin label side chains into account. Here, we present an analysis of orientation selective PELDOR data recorded on six singly MTSSL-labelled azurin mutants. The analysis yielded conformational MTSSL ensembles, which are considerably narrower than those predicted using in silico spin labeling methods but match well with spin label conformations found in the corresponding crystal structures. The possible reasons and consequences for predicting spin label conformers in the fold of biomolecules are discussed.

掲載誌

Physical Chemistry Chemical Physics

CiteScore: 5.5

自己引用率: 10.3%

年間論文数: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.