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WIREs Comput Mol Sci
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Essential dynamics: foundation and applications

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Collective coordinates, as obtained by a principal component analysis of atomic fluctuations, are commonly used to predict a low‐dimensional subspace in which essential protein motion is expected to take place. The definition of such an essential subspace allows to characterize protein functional, and folding, motion, to provide insight into the (free) energy landscape, and to enhance conformational sampling in molecular dynamics simulations. Here, we provide an overview on the topic, giving particular attention to some methodological aspects, such as the problem of convergence, and mentioning possible new developments. © 2012 John Wiley & Sons, Ltd.

Figure 1.

Example of essential dynamics in two dimensions. With a distribution of points as depicted here, two coordinates (x, y) are required to identify a point in the cluster in (A), whereas one coordinate (x′) approximately identifies a point in (B).

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Figure 2.

Relative cumulative deviation (i.e., percentage of the cumumlative square fluctuation) up to the first 30 eigenvectors provided by the essential dynamics analysis performed on the Cα atoms of a 25‐residue peptide simulated in water. The corresponding eigenvalues are given in the inset. It can be seen that the first two eigenvectors contribute for ≈65% of the total Cα motion.

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Figure 3.

Typical root mean square inner product of the essential subspaces (10 eigenvectors) obtained from two independent subparts of increasing time length as provided by the molecular dynamics simulation of a solvated protein.

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Figure 4.

Superimposition of 10 filtered configurations obtained by projecting the Cα motion onto an essential eigenvector of fluctuation involved in the unfolding of cytochrome c.

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Figure 5.

Example of the folding free energy landscape of a peptide in solution as a function of the position along two first essential eigenvectors (q1, q2). The corresponding free energy change, ΔA(q1, q2), is given in kJ/mol and q1, q2 are given in nm.

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Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods

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