Protein dynamics from nuclear magnetic relaxation

Charlier, Cyril; Cousin, Samuel F.; Ferrage, Fabien

doi:10.1039/c5cs00832h

Cited by 50 publications

(56 citation statements)

References 47 publications

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“…Spin-relaxation experiments probe motions with atomic resolution over a broad range of timescales (4,5). In addition, multiple methods and applications for 1 H, 2 H, 13 C, and 15 N nuclei in proteins and nucleic acids currently exist that make such explorations possible (5)(6)(7). As systems with increasingly complex dynamical properties and as experimental methods become more powerful, correspondingly, more sophisticated approaches are necessary for analysis of data and linkage to theoretical or computational motional models (7)(8)(9).…”

Section: Introductionmentioning

confidence: 99%

Analysis of NMR Spin-Relaxation Data Using an Inverse Gaussian Distribution Function

Hsu

Ferrage

Palmer

2018

Biophysical Journal

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Spin relaxation in solution-state NMR spectroscopy is a powerful approach to explore the conformational dynamics of biological macromolecules. Probability distribution functions for overall or internal correlation times have been used previously to model spectral density functions central to spin-relaxation theory. Applications to biological macromolecules rely on transverse relaxation rate constants, and when studying nanosecond timescale motions, sampling at ultralow frequencies is often necessary. Consequently, appropriate distribution functions necessitate spectral density functions that are accurate and convergent as frequencies approach zero. In this work, the inverse Gaussian probability distribution function is derived from general properties of spectral density functions at low and high frequencies for macromolecules in solution, using the principle of maximal entropy. This normalized distribution function is first used to calculate the correlation function, followed by the spectral density function. The resulting model-free spectral density functions are finite at a frequency of zero and can be used to describe distributions of either overall or internal correlation times using the model-free ansatz. To validate the approach, 15 N spin-relaxation data for the bZip transcription factor domain of the Saccharomyces cerevisiae protein GCN4, in the absence of cognate DNA, were analyzed using the inverse Gaussian probability distribution for intramolecular correlation times. The results extend previous models for the conformational dynamics of the intrinsically disordered, DNA-binding region of the bZip transcription factor domain.

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Section: Introductionmentioning

confidence: 99%

Analysis of NMR Spin-Relaxation Data Using an Inverse Gaussian Distribution Function

Hsu

Ferrage

Palmer

2018

Biophysical Journal

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“…The protein can alter its conformation by locally changing the secondary structure and/or by the motion of selected domains; the pigments and cofactors bound to the protein can also move within or away from their binding pockets and/or can undergo structural deformations. Small displacements, such as motions of protein side chains or backbone fluctuations, can also occur much faster on the ps timescale (Charlier et al 2016).…”

Section: (Sub)μs Timescale: Fast Conformational Changes Of the Photosmentioning

confidence: 99%

Molecular dynamics simulations in photosynthesis

Liguori¹,

Croce²,

Marrink

et al. 2020

Photosynth Res

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Photosynthesis is regulated by a dynamic interplay between proteins, enzymes, pigments, lipids, and cofactors that takes place on a large spatio-temporal scale. Molecular dynamics (MD) simulations provide a powerful toolkit to investigate dynamical processes in (bio)molecular ensembles from the (sub)picosecond to the (sub)millisecond regime and from the Å to hundreds of nm length scale. Therefore, MD is well suited to address a variety of questions arising in the field of photosynthesis research. In this review, we provide an introduction to the basic concepts of MD simulations, at atomistic and coarse-grained level of resolution. Furthermore, we discuss applications of MD simulations to model photosynthetic systems of different sizes and complexity and their connection to experimental observables. Finally, we provide a brief glance on which methods provide opportunities to capture phenomena beyond the applicability of classical MD.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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“…They make good candidates for the study of side-chain motions, in particular in the hydrophobic core of proteins where they constitute an entropy reservoir [50,51], or at protein-protein and protein-ligand binding interfaces where their motions can allow a re-modeling for a better complementary interaction with the binding partner. In this context, we have recently performed a detailed analysis of the motions of isoleucine-δ 1 methylgroup on the selectively labeled protein U-[ 2 H, 15 Different models of correlation function for a wide variety of molecular systems have been suggested in the past [43,[52][53][54][55][56][57][58]. In our analysis of high field and relaxometry relaxation rates on { 13 C 1 H 2 H 2 }-methyl group of Ubiquitin, the data recorded at low fields (lower than 5 T) allowed a better characterization of the complexity of motions that can occur in a methyl-bearing side-chain, in particular χ 1 /χ 2 rotameric transitions in isoleucine residues on nanosecond timescales [25].…”

Section: Icarus Output and Mcmcmentioning

confidence: 99%

Theoretical and computational framework for the analysis of the relaxation properties of arbitrary spin systems. Application to high-resolution relaxometry

Bolik-Coulon

Kadeřávek

Pelupessy

et al. 2020

Journal of Magnetic Resonance

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A wide variety of nuclear magnetic resonance experiments rely on the prediction and analysis of relaxation processes. Recently, innovative approaches have been introduced where the sample travels through a broad range of magnetic fields in the course of the experiment, such as dissolution dynamic nuclear polarization or high-resolution relaxometry. Understanding the relaxation properties of nuclear spin systems over orders of magnitude of magnetic fields is essential to rationalize the results of these experiments. For example, during a high-resolution relaxometry experiment, the absence of control of nuclear spin relaxation pathways during the sample transfers and relaxation delays leads to systematic deviations of polarization decays from an ideal mono-exponential decay with the pure longitudinal relaxation rate. These deviations have to be taken into account to describe quantitatively the dynamics of the system. Here, we present computational tools to (1) calculate analytical expressions of relaxation rates for a broad variety of spin systems and (2) use these analytical expressions to correct the deviations arising in high-resolution relaxometry experiments. These tools lead to a better understanding of nuclear spin relaxation, which is required to improve the sensitivity of many pulse sequences, and to better characterize motions in macromolecules.

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Protein dynamics from nuclear magnetic relaxation

Cited by 50 publications

References 47 publications

Analysis of NMR Spin-Relaxation Data Using an Inverse Gaussian Distribution Function

Analysis of NMR Spin-Relaxation Data Using an Inverse Gaussian Distribution Function

Molecular dynamics simulations in photosynthesis

Theoretical and computational framework for the analysis of the relaxation properties of arbitrary spin systems. Application to high-resolution relaxometry

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