4.8.1.1. Elastic network analysis of MD trajectories — MDAnalysis.analysis.gnm

Author:

Benjamin Hall <benjamin.a.hall@ucl.ac.uk>

Year:

2011

Copyright:

GNU Public License v2 or later

Analyse a trajectory using elastic network models, following the approach of [1].

An example is provided in the MDAnalysis Cookbook, listed as GNMExample.

The basic approach is to pass a trajectory to GNMAnalysis and then run the analysis:

u = MDAnalysis.Universe(PSF, DCD)
C = MDAnalysis.analysis.gnm.GNMAnalysis(u, ReportVector="output.txt")

C.run()
output = zip(*C.results)

with open("eigenvalues.dat", "w") as outputfile:
    for item in output[1]:
        outputfile.write(item + "\n")

The results are found in GNMAnalysis.results, which can be used for further processing (see [1]).

References

4.8.1.1.1. Analysis tasks

class MDAnalysis.analysis.gnm.GNMAnalysis(universe, select='protein and name CA', cutoff=7.0, ReportVector=None, Bonus_groups=None)[source]

Basic tool for GNM analysis.

Each frame is treated as a novel structure and the GNM calculated. By default, this stores the dominant eigenvector and its associated eigenvalue; either can be used to monitor conformational change in a simulation.

Parameters:

  • universe (Universe) – Analyze the full trajectory in the universe.

  • select (str (optional)) – MDAnalysis selection string

  • cutoff (float (optional)) – Consider selected atoms within the cutoff as neighbors for the Gaussian network model.

  • ReportVector (str (optional)) – filename to write eigenvectors to, by default no output is written

  • Bonus_groups (tuple) – This is a tuple of selection strings that identify additional groups (such as ligands). The center of mass of each group will be added as a single point in the ENM (it is a popular way of treating small ligands such as drugs). You need to ensure that none of the atoms in Bonus_groups is contained in selection as this could lead to double counting. No checks are applied.

results.times

simulation times used in analysis

Type:

numpy.ndarray

results.eigenvalues

calculated eigenvalues

Type:

numpy.ndarray

results.eigenvectors

calculated eigenvectors

Type:

numpy.ndarray

See also

closeContactGNMAnalysis

Changed in version 0.16.0: Made generate_output() a private method _generate_output().

Changed in version 1.0.0: Changed selection keyword to select

Changed in version 2.0.0: Use AnalysisBase as parent class and store results as attributes times, eigenvalues and eigenvectors of the results attribute.

Changed in version 2.8.0: Enabled parallel execution with the multiprocessing and dask backends; use the new method get_supported_backends() to see all supported backends.

generate_kirchoff()[source]

Generate the Kirchhoff matrix of contacts.

This generates the neighbour matrix by generating a grid of near-neighbours and then calculating which are are within the cutoff.

Returns:

the resulting Kirchhoff matrix

Return type:

array

classmethod get_supported_backends()[source]

Tuple with backends supported by the core library for a given class. User can pass either one of these values as backend=... to run() method, or a custom object that has apply method (see documentation for run()):

  • ‘serial’: no parallelization

  • ‘multiprocessing’: parallelization using multiprocessing.Pool

  • ‘dask’: parallelization using dask.delayed.compute(). Requires installation of mdanalysis[dask]

If you want to add your own backend to an existing class, pass a backends.BackendBase subclass (see its documentation to learn how to implement it properly), and specify unsupported_backend=True.

Returns:

names of built-in backends that can be used in run(backend=...)()

Return type:

tuple

Added in version 2.8.0.

class MDAnalysis.analysis.gnm.closeContactGNMAnalysis(universe, select='protein', cutoff=4.5, ReportVector=None, weights='size')[source]

GNMAnalysis only using close contacts.

This is a version of the GNM where the Kirchoff matrix is constructed from the close contacts between individual atoms in different residues.

Parameters:

  • universe (Universe) – Analyze the full trajectory in the universe.

  • select (str (optional)) – MDAnalysis selection string

  • cutoff (float (optional)) – Consider selected atoms within the cutoff as neighbors for the Gaussian network model.

  • ReportVector (str (optional)) – filename to write eigenvectors to, by default no output is written

  • weights ({"size", None} (optional)) – If set to “size” (the default) then weight the contact by \(1/\sqrt{N_i N_j}\) where \(N_i\) and \(N_j\) are the number of atoms in the residues \(i\) and \(j\) that contain the atoms that form a contact.

results.times

simulation times used in analysis

Type:

numpy.ndarray

results.eigenvalues

calculated eigenvalues

Type:

numpy.ndarray

results.eigenvectors

calculated eigenvectors

Type:

numpy.ndarray

Notes

The MassWeight option has now been removed.

See also

GNMAnalysis

Changed in version 0.16.0: Made generate_output() a private method _generate_output().

Deprecated since version 0.16.0: Instead of MassWeight=True use weights="size".

Changed in version 1.0.0: MassWeight option (see above deprecation entry). Changed selection keyword to select

Changed in version 2.0.0: Use AnalysisBase as parent class and store results as attributes times, eigenvalues and eigenvectors of the results attribute.

generate_kirchoff()[source]

Generate the Kirchhoff matrix of contacts.

This generates the neighbour matrix by generating a grid of near-neighbours and then calculating which are are within the cutoff.

Returns:

the resulting Kirchhoff matrix

Return type:

array

4.8.1.1.2. Utility functions

The following functions are used internally and are typically not directly needed to perform the analysis.

MDAnalysis.analysis.gnm.generate_grid(positions, cutoff)[source]

Simple grid search.

An alternative to searching the entire list of each atom; divide the structure into cutoff sized boxes This way, for each particle you only need to search the neighbouring boxes to find the particles within the cutoff.

Observed a 6x speed up for a smallish protein with ~300 residues; this should get better with bigger systems.

Parameters:

  • positions (array) – coordinates of the atoms

  • cutoff (float) – find particles with distance less than cutoff from each other; the grid will consist of boxes with sides of at least length cutoff

MDAnalysis.analysis.gnm.order_list(w)[source]

Returns a dictionary showing the order of eigenvalues (which are reported scrambled normally)

Changed in version 0.16.0: removed unused function backup_file()