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Updated: Jun 3, 2025

Author Spotlight: Exploring Intrinsically Disordered Protein Dynamics Through NMR Relaxation Experiments
Published on: November 1, 2024
Field-dependent relaxation profiles of biomolecular systems
Adam Kubrak1,2,3, Rajka Pejanovic4, Kahinga Kamau5
1Magnetic Resonance Center (CERM), University of Florence, via Luigi Sacconi 6, Sesto Fiorentino, 50019, Italy. parigi@cerm.unifi.it.
Abstract:
The function of biomolecular systems, including biological macromolecules, often crucially depends on their dynamics. Nuclear magnetic resonance (NMR) is one of the most informative methods used to study biomolecules and their internal mobility, with atomic resolution, in near-physiological conditions. NMR relaxation profiles, obtained from the field dependence of the nuclear relaxation rates, in particular, offer the possibility to probe dynamic processes over a wide range of time scales. Relaxation profiles are routinely acquired using field-cycling relaxometers operating at a maximum field of the order of 1 T. These measurements however suffer from a lack of resolution. On the other hand, relaxation rates measured at the high magnetic fields (>4 T) of high resolution NMR spectrometers contain poor information on motions on timescales longer than few nanoseconds. The possibility to acquire relaxation profiles extended to low fields but with high resolution, obtained by shuttling the sample back and forth in the stray field of a high-field spectrometer, is expected to dramatically improve the potentialities of NMR relaxometry. Here, we review investigations of relaxometry in a wide range of biomolecular systems, such as proteins, phospholipids, or biological fluids. Although multiple models of motions have been developed to describe the relaxation rates and their field dependence, most experimental investigations rely on the model-free approach. A variety of relaxation profiles of both diamagnetic and paramagnetic biomolecular systems are here reviewed and analysed using point dipole-point dipole interaction models.
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