4.7.1.1. 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 [Hall2007].

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 [Hall2007]).

References

Hall2007(1,2): Benjamin A. Hall, Samantha L. Kaye, Andy Pang, Rafael Perera, and Philip C. Biggin. Characterization of protein conformational states by normal-mode frequencies. Journal of the American Chemical Society, 129(37):11394–11401, 2007. PMID: 17715919. doi:10.1021/ja071797y.

4.7.1.1.1. 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

4.7.1.1.2. 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()

4.7.1.1. Elastic network analysis of MD trajectories — MDAnalysis.analysis.gnm

4.7.1.1.1. Analysis tasks

4.7.1.1.2. Utility functions

4.7.1.1. Elastic network analysis of MD trajectories — `MDAnalysis.analysis.gnm`