This module defines a class and a function for Gaussian network model (GNM) calculations.
A class for Gaussian Network Model (GNM) analysis of proteins ([IB97], [TH97]).
See example Gaussian Network Model (GNM).
[IB97] | Bahar I, Atilgan AR, Erman B. Direct evaluation of thermal fluctuations in protein using a single parameter harmonic potential. Folding & Design 1997 2:173-181. |
[TH97] | Haliloglu T, Bahar I, Erman B. Gaussian dynamics of folded proteins. Phys. Rev. Lett. 1997 79:3090-3093. |
Add eigen vector and eigen value pair(s) to the instance. If eigen value is omitted, it will be set to 1. Inverse eigenvalues are set as variances.
Build Kirchhoff matrix for given coordinate set.
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Instances of Gamma classes and custom functions are accepted as gamma argument.
When Scipy is available, user can select to use sparse matrices for efficient usage of memory at the cost of computation speed.
Calculate normal modes. This method uses scipy.linalg.eigh() function to diagonalize the Kirchhoff matrix. When Scipy is not found, numpy.linalg.eigh() is used.
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Return a copy of eigenvectors array.
Return covariance matrix. If covariance matrix is not set or yet calculated, it will be calculated using available modes.
Return cutoff distance.
Return eigenvalues. For PCA and EDA models built using coordinate data in Å, unit of eigenvalues is Å2. For ANM, GNM, and RTB, on the other hand, eigenvalues are in arbitrary or relative units but they correlate with stiffness of the motion along associated eigenvector.
Return a copy of eigenvectors array.
Return spring constant (or the gamma function or Gamma instance).
Return a copy of the Kirchhoff matrix.
Return self.
Return title of the model.
Return variances. For PCA and EDA models built using coordinate data in Å, unit of variance is Å2. For ANM, GNM, and RTB, on the other hand, variance is the inverse of the eigenvalue, so it has arbitrary or relative units.
Return True if model is 3-dimensional.
Return number of atoms.
Return number of degrees of freedom.
Return number of modes in the instance (not necessarily maximum number of possible modes).
Set eigen vectors and eigen values. If eigen values are omitted, they will be set to 1. Inverse eigenvalues are set as variances.
Set title of the model.