Abstract
Elastic networks have been used as simple models of proteins to study their slow structural dynamics. They consist of point-like particles connected by linear Hookean springs and hence are convenient for linear normal mode analysis around a given reference structure. Furthermore, dynamic simulations using these models can provide new insights. As the computational cost associated with these models is considerably lower compared to that of all-atom models, they are also convenient for comparative studies between multiple protein structures. In this review, we introduce examples of coarse-grained molecular dynamics studies using elastic network models and their derivatives, focusing on the nonlinear phenomena, and discuss their applicability to large-scale macromolecular assemblies.
Highlights
Cells are made up of soft materials, and proteins are one of the major components of cells
Probing Dynamical Basis of Allostery. In these simulations using elastic network model (ENM), in the same way as other models, we can apply any external forces in Molecular dynamics (MD) simulations, and measure forces acting on any particle or spring
ENMs are of wide use for analysis of structural fluctuations using normal mode analysis (NMA) and for reproducing dynamic processes using molecular dynamics, and even entire machine operation cycles
Summary
Cells are made up of soft materials, and proteins are one of the major components of cells. A large number of experimental studies have focused on the elucidation of the dynamic and mechanical aspects of various proteins. Atomic force microscopy (AFM) can even visualize the motion of each protein in physiological conditions as a molecular movie [1,2]. It is, difficult to pursue both live motion at a high temporal resolution (using a high enough frame rate) and high spatial resolution in a single experiment. It is possible to reproduce conformational changes over entire operation cycles Because of their low computational costs, ENMs are convenient for comparative studies of many different protein structures. We discuss the improvements in modeling, and further applications beyond single proteins
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