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Modeling of motional EPR spectra using hindered Brownian rotational diffusion and the stochastic Liouville equation.

ABSTRACT: Electron paramagnetic resonance (EPR) spectra of molecular spin centers undergoing reorientational motion are commonly simulated using the stochastic Liouville equation (SLE) with a rigid-body hindered Brownian diffusion model. Current SLE theory applies to specific spin systems such as nitroxides and to high-symmetry orientational potentials. In this work, we extend the SLE theory to arbitrary spin systems with any number of spins and any type of spin Hamiltonian interaction term, as well as to arbitrarily complex orientational potentials. We also examine the limited accuracy of the frequency-to-field conversion used to obtain field-swept EPR spectra and present a more accurate approach. The extensions allow for the simulation of EPR spectra of all types of spin labels (nitroxides, copper²⁺, and gadolinium³⁺) attached to proteins in low-symmetry environments.

SUBMITTER: Lehner J

PROVIDER: S-EPMC7051866 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Modeling of motional EPR spectra using hindered Brownian rotational diffusion and the stochastic Liouville equation.

Lehner Jeremy J Stoll Stefan S

The Journal of chemical physics 20200301 9

Electron paramagnetic resonance (EPR) spectra of molecular spin centers undergoing reorientational motion are commonly simulated using the stochastic Liouville equation (SLE) with a rigid-body hindered Brownian diffusion model. Current SLE theory applies to specific spin systems such as nitroxides and to high-symmetry orientational potentials. In this work, we extend the SLE theory to arbitrary spin systems with any number of spins and any type of spin Hamiltonian interaction term, as well as to ...[more]

PMID: 33480723

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Project description:The key to obtaining the model-free description of the dynamics of a macromolecule is the optimisation of the model-free and Brownian rotational diffusion parameters using the collected R (1), R (2) and steady-state NOE relaxation data. The problem of optimising the chi-squared value is often assumed to be trivial, however, the long chain of dependencies required for its calculation complicates the model-free chi-squared space. Convolutions are induced by the Lorentzian form of the spectral density functions, the linear recombinations of certain spectral density values to obtain the relaxation rates, the calculation of the NOE using the ratio of two of these rates, and finally the quadratic form of the chi-squared equation itself. Two major topological features of the model-free space complicate optimisation. The first is a long, shallow valley which commences at infinite correlation times and gradually approaches the minimum. The most severe convolution occurs for motions on two timescales in which the minimum is often located at the end of a long, deep, curved tunnel or multidimensional valley through the space. A large number of optimisation algorithms will be investigated and their performance compared to determine which techniques are suitable for use in model-free analysis. Local optimisation algorithms will be shown to be sufficient for minimisation not only within the model-free space but also for the minimisation of the Brownian rotational diffusion tensor. In addition the performance of the programs Modelfree and Dasha are investigated. A number of model-free optimisation failures were identified: the inability to slide along the limits, the singular matrix failure of the Levenberg-Marquardt minimisation algorithm, the low precision of both programs, and a bug in Modelfree. Significantly, the singular matrix failure of the Levenberg-Marquardt algorithm occurs when internal correlation times are undefined and is greatly amplified in model-free analysis by both the grid search and constraint algorithms. The program relax ( http://www.nmr-relax.com ) is also presented as a new software package designed for the analysis of macromolecular dynamics through the use of NMR relaxation data and which alleviates all of the problems inherent within model-free analysis.

Dataset Information

Modeling of motional EPR spectra using hindered Brownian rotational diffusion and the stochastic Liouville equation.

Publications

Modeling of motional EPR spectra using hindered Brownian rotational diffusion and the stochastic Liouville equation.

Similar Datasets

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