Anisotropic Network Model: Difference between revisions

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Revision as of 22:28, 29 July 2011

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The '''Anisotropic Network Model''' (ANM) is a simple yet powerful tool made for [[Normal Mode]] Analysis of proteins, which has been successfully applied for exploring the relation between function and dynamics for many proteins. It is essentially an Elastic Network Model for the Cα atoms with a step function for the dependence of the force constants on the inter-particle distance.

{{orphan|date=May 2010}}

The '''Anisotropic Network Model''' (ANM) is a simple yet powerful tool made for [[Normal Mode]] Analysis of proteins, which has been successfully applied for exploring the relation between function and dynamics for many proteins. It is essentially an Elastic Network Model for the Cα atoms with a step function for the dependence of the force constants on the inter-particle distance.

== Theory ==

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<math>H = U\Lambda{U^T}</math>

The pseudo-inverse is composed of the 3N-6 eigenvectors and their respective non-zero eigen values. Where λi are the eigenvalues of H sorted by their size from small to large and Ui the corresponding eigenvectors. The eigenvectors (the columns of the matrix U) describe the vibrational direction and the relative amplitude in the different modes.

== Comparing ANM and GNM ==

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ANM and GNM are both based on an elastic network model. The GNM has proven itself to accurately describe the vibrational dynamics of proteins and their complexes in numerous studies. Whereas the GNM is limited to the evaluation of the mean-square displacements and cross-correlations between fluctuations, the motion being projected to a mode space of N dimensions, the ANM approach permits us to evaluate directional preferences and thus provides 3-D descriptions of the 3N - 6 internal modes.

It has been observed that GNM fluctuation predictions agree better with experiments than those computed with ANM. The higher performance of GNM can been attributed to its underlying potential, which takes account of orientational deformations, in addition to distance changes.

== Evaluation of the Model ==

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6. Chennubhotla,C. et al. (2005) Elastic network models for understanding biomolecular machinary: from enzymes to supramolecular assemblies. Phys Biol, 2, S173-S180.<br />

7. Cui,Q. and Bahar,I. (2006) Normal Mode Analysis: Theory and Applications to Biological and Chemical Systems. Chapman & Hall/CRC, Boca Raton, FL.

[[Category: Molecular modelling]]

== See also ==

- Gaussian Network Model [[Gaussian network model]]

[[Category: Molecular modelling]]