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.
This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods