pygnm 0.1

参考文献

• 用单参数调和势直接评价蛋白质的热涨落 I.Bahar，A.R.Atilgan和B.Erman Folding&Design 21973-1811997年。
• 折叠蛋白质的高斯动力学 Haliloglu、T.Bahar、I.和Erman、B.Phys。修订版利特。793090-30931997年。
• Wikipedia article

许可证

这个python包是在gnu通用公共许可下发布的。

依赖关系

生物疗法 纽比 短发 matplotlib（可选，但绘图功能需要）

安装

教程

本教程也包含在pygnm包中。

"""This is an example use of PyGNM, and is distributed with PyGNM package.

This example assumes that you have installed Matplotlib.
A ubiquitin structureis used in this example.
(PDB id 2BWF, Lowe et al., 2006 Acta Crystallogr., Sect. D 62:177)

"""
# Import plotting library
>>> import matplotlib.pyplot as pl

# Import GNM module from pygnm package
>>> from pygnm import GNM

# Initialize a GNM object
# Please make sure that the PDB file is in the current working directory
# You may need to download this, or another, PDB file
# Both .pdb and pdb.gz extensions are handled
>>> gnm = GNM('2BWF.pdb.gz')

# Select residues to be included in the model
# Some examples are
#      "*" and "*:*"
#        select all chains and residues
#
#      "A:x,y-z,w D:k-l G:*"
#        select residues x, y to z (inclusive),
#                     and w in chain A
#        and residues k to l (inclusive) in chain D
#        and all residues in chain G
#
#      "A B"
#        select all residues in chains A and B
#
#      "*:5-25,50-100"
#        select same set of residues in all chains
>>> gnm.select_residues('A:1-70')

# Build Kirchhoff matrix
#   cutoff=10., gamma=1.0 are default values, and may be omitted
>>> gnm.set_kirchhoff(cutoff=10., gamma=1.0)

# Calculate GNM modes
#   An optional parameter is n_modes
#   If you want to see only 20 nonzero modes, you can set n_modes=20
#   gnm.calculate_modes(n_modes=20)
>>> gnm.calculate_modes()

# CONVENIENCE FUNCTIONS
# Get first 20 eigenvalues, including the zero eigenvalue
>>> gnm.get_eigenvalues( range(20) )
# Get first eigenvector with a nonzero eigenvalue
>>> gnm.get_eigenvectors(1)
# Get first and second modes (eigenvector^2) with nonzero eigenvalues
>>> gnm.get_modes( [1, 2] )
# Get covariance matrix (pseudo-inverse of Kirchhoff)
>>> gnm.get_covariance()
# Get cross-correlations calclated using first 9 modes (w/ nonzero eigenvalues)
>>> gnm.get_cross_correlations( range(1, 10))

# PLOTTING FUNCTIONS
# Note that before each plot command, pl.figure() function is called
# to prevent plotting over the previous figure.
# Plot eigenvector shape
>>> pl.figure()
>>> gnm.plot_eigenvector(1)

# Plot mode shape
>>> pl.figure()
>>> gnm.plot_mode(1)

# Plot bfactors from PDB file
>>> pl.figure()
>>> gnm.plot_bfactors()

# Plot GNM square-fluctuations
>>> pl.figure()
>>> gnm.plot_square_fluctuations()

# Plot GNM square-fluctuations for low frequence nonzero 10 modes
>>> pl.figure()
>>> gnm.plot_square_fluctuations(indices=range(1, 11))

# Plot GNM cross-correlations
>>> pl.figure()
>>> gnm.show_cross_correlations()

# Plot GNM cross-correlations for low frequence nonzero 10 modes
>>> pl.figure()
>>> gnm.show_cross_correlations(indices=range(1, 11))

# WRITE PDB
# Write a PDB file with square fluctuations on beta column
>>> gnm.write_pdb( bfactors=gnm.get_square_fluctuations(indices=range(1, 11)) )