Determination of the Anisotropic Rotational Diffusion Constant of Microcrystals Dispersed in Liquid Medium

2018 ◽  
Vol 122 (46) ◽  
pp. 9123-9127 ◽  
Author(s):  
Fumiko Kimura ◽  
Shigeru Horii ◽  
Itsuki Arimoto ◽  
Toshiya Doi ◽  
Masato Yoshimura ◽  
...  
Keyword(s):  
Liquid Medium ◽  
Rotational Diffusion ◽  
Diffusion Constant ◽  
Anisotropic Rotational Diffusion

scholarly journals How wide is the window opened by high-resolution relaxometry on the internal dynamics of proteins in solution?

2021 ◽  
Vol 75 (2-3) ◽  
pp. 119-131
Author(s):  
Albert A. Smith ◽  
Nicolas Bolik-Coulon ◽  
Matthias Ernst ◽  
Beat H. Meier ◽  
Fabien Ferrage
Keyword(s):  
Nuclear Magnetic Resonance ◽  
High Resolution ◽  
High Field ◽  
Rotational Diffusion ◽  
Relaxation Data ◽  
Internal Dynamics ◽  
Nmr Relaxometry ◽  
Analysis Of Dynamics

AbstractThe dynamics of molecules in solution is usually quantified by the determination of timescale-specific amplitudes of motions. High-resolution nuclear magnetic resonance (NMR) relaxometry experiments—where the sample is transferred to low fields for longitudinal (T1) relaxation, and back to high field for detection with residue-specific resolution—seeks to increase the ability to distinguish the contributions from motion on timescales slower than a few nanoseconds. However, tumbling of a molecule in solution masks some of these motions. Therefore, we investigate to what extent relaxometry improves timescale resolution, using the “detector” analysis of dynamics. Here, we demonstrate improvements in the characterization of internal dynamics of methyl-bearing side chains by carbon-13 relaxometry in the small protein ubiquitin. We show that relaxometry data leads to better information about nanosecond motions as compared to high-field relaxation data only. Our calculations show that gains from relaxometry are greater with increasing correlation time of rotational diffusion.

scholarly journals Rotational diffusion affects the dynamical self-assembly pathways of patchy particles

2015 ◽  
Vol 112 (50) ◽  
pp. 15308-15313 ◽  
Author(s):  
Arthur C. Newton ◽  
Jan Groenewold ◽  
Willem K. Kegel ◽  
Peter G. Bolhuis
Keyword(s):  
Self Assembly ◽  
Regulatory Networks ◽  
Rotational Diffusion ◽  
Equilibrium Constants ◽  
Diffusion Constant ◽  
Translational Diffusion ◽  
Einstein Relation ◽  
High Tech ◽  
Patchy Particles

Predicting the self-assembly kinetics of particles with anisotropic interactions, such as colloidal patchy particles or proteins with multiple binding sites, is important for the design of novel high-tech materials, as well as for understanding biological systems, e.g., viruses or regulatory networks. Often stochastic in nature, such self-assembly processes are fundamentally governed by rotational and translational diffusion. Whereas the rotational diffusion constant of particles is usually considered to be coupled to the translational diffusion via the Stokes–Einstein relation, in the past decade it has become clear that they can be independently altered by molecular crowding agents or via external fields. Because virus capsids naturally assemble in crowded environments such as the cell cytoplasm but also in aqueous solution in vitro, it is important to investigate how varying the rotational diffusion with respect to transitional diffusion alters the kinetic pathways of self-assembly. Kinetic trapping in malformed or intermediate structures often impedes a direct simulation approach of a kinetic network by dramatically slowing down the relaxation to the designed ground state. However, using recently developed path-sampling techniques, we can sample and analyze the entire self-assembly kinetic network of simple patchy particle systems. For assembly of a designed cluster of patchy particles we find that changing the rotational diffusion does not change the equilibrium constants, but significantly affects the dynamical pathways, and enhances (suppresses) the overall relaxation process and the yield of the target structure, by avoiding (encountering) frustrated states. Besides insight, this finding provides a design principle for improved control of nanoparticle self-assembly.

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