An algorithm to analyze PELDOR data of rigid spin label pairs
文献信息
Andriy Marko, Thomas F. Prisner
Pulsed Electron–electron Double Resonance (PELDOR) is a method frequently used to determine the distances between paramagnetic centers in biomacromolecules on the nanometer scale. A standard algorithm for determination of distances from the experimental data assumes that all possible mutual orientations of the spin labels are equally probable. However, in many applications the mobility of the spin labels attached to large molecules can be significantly restricted. In order to determine the total PELDOR signal in this case, the individual contributions of each rigid biradical should be explicitly calculated for the given frequencies of the probe and pump pulses. The solution of the inverse problem of determination of the ensemble of molecular structures that fit the experimental PELDOR data acquired at multiple microwave frequencies and magnetic fields has proven to be a non-trivial task, especially, when no information about the molecular structure under study is available. In this work we present a fitting algorithm that reconstructs experimental data by searching for an optimal combination of presimulated PELDOR time traces for nitroxide biradicals with all relative orientations and with inter-spin distances in the experimentally accessible range. The generated library of PELDOR time traces has been employed to excellently fit experimental data containing orientation selection effects gathered on model biradical systems.
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Physical Chemistry Chemical Physics

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.














