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Computing protein dynamics from protein structure with elastic network models

TLDR
The agreement between low frequency normal modes and large conformation changes is stimulating the study of anharmonicity in protein dynamics, probably one of the most interesting direction of development in ENMs.
Abstract
Elastic network models ENMs allow to analytically predict the equilibrium dynamics of proteins without the need of lengthy simulations and force fields, and they depend on a small number of parameters and choices. Despite they are valid only for small fluctuations from the mean native structure, it was observed that large functional conformation changes are well described by a small number of low frequency normal modes. This observation has greatly stimulated the application of ENMs for studying the functional dynamics of proteins, and it is prompting the question whether this functional dynamics is a target of natural selection. From a physical point of view, the agreement between low frequency normal modes and large conformation changes is stimulating the study of anharmonicity in protein dynamics, probably one of the most interesting direction of development in ENMs. ENMs have many applications, of which we will review four general types: (1) the efficient sampling of native conformation space, with applications to molecular replacement in X-ray spectroscopy, cryo electro-miscroscopy, docking and homology modeling; (2) the prediction of paths of conformation change between two known end states; (3) the comparison of the dynamics of evolutionarily related proteins; (4) the prediction of dynamical couplings that allow the allosteric regulation of the active site from a distant control regions, with possible applications in the development of allosteric drugs. These goals have important biotechnological applications that are driving more and more attention on the analytical study of protein dynamics through ENMs. WIREs Comput Mol Sci 2014, 4:488–503. Conflict of interest: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.

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References
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The role of dynamic conformational ensembles in biomolecular recognition

TL;DR: Experimental evidence supports a new molecular recognition paradigm for processes as diverse as signaling, catalysis, gene regulation and protein aggregation in disease, which has the potential to significantly impact views and strategies in drug design, biomolecular engineering and molecular evolution.
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Large Amplitude Elastic Motions in Proteins from a Single-Parameter, Atomic Analysis.

TL;DR: It is shown that a single-parameter potential is sufficient to reproduce the slow dynamics of proteins obtained with vastly more complex empirical potentials, which inevitably leads to unstable modes which must be eliminated through elaborate methods, and which cast doubts on the validity of the analysis.
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Anisotropy of Fluctuation Dynamics of Proteins with an Elastic Network Model

TL;DR: A new tool is presented and applied to the retinol-binding protein, which indicates enhanced flexibility in the region of entry to the ligand binding site and for the portion of the protein binding to its carrier protein.
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