PACKMAN Compliance Analysis Tutorial

IMPORTANT NOTE: This tutorial is for the publication version (v1.1.1). For the latest tutorial, please check the tutorial sections on the following link: https://py-packman.readthedocs.io/en/latest/

Step 1.1 Import the 'molecule' submodule from PACKMAN. (If PACKMAN is not installed, please follow the link: https://github.com/Pranavkhade/PACKMAN)

Step 1.2 Download the structure from PDB and save it with the appropriate extension.

Step 1.3 Load the structure using the submodule

#Step 1.1
from packman import molecule

#Step 1.2
molecule.download_structure('1LF7','1LF7.pdb')

#Step 1.3
mol=molecule.load_structure('1LF7.pdb')

Step 2.1 Import 'anm' submodule from PACKMAN.

Step 2.2 Load the C-Alpha atoms of the molecule from the first model. For further information about the atoms selection and 'molecule' object functions, please read the PACKMAN 'molecule' tutorials.

Step 2.3 Get the C-Alpha atom coordinates.

Step 2.4 Build the ANM model of the selected atoms. With appropriate power (Please refer to the Publication)

#Step 2.1
from packman import anm

#Step 2.2
c_alpha=mol[0].get_calpha()

#Step 2.3
coords=[i.get_location() for i in c_alpha]

#Step 2.4
ANM_MODEL=anm.ANM(coords,pf=True,dr=float('Inf'),power=3)

Step 3 Calculate the Hessian Matrix, Decompose it and Run the stiffness and compliance analysis.

#Step 3
ANM_MODEL.calculate_hessian()
ANM_MODEL.calculate_decomposition()
ANM_MODEL.calculate_stiffness_compliance()

True

Step 4 Extract and store the maps and profiles for the visualization.

stiffness_map  = ANM_MODEL.get_stiffness_map()
compliance_map = ANM_MODEL.get_compliance_map()


b_factors          = [i.get_bfactor() for i in c_alpha]
fluctuations       = ANM_MODEL.get_fluctuations()
stiffness_profile  = ANM_MODEL.get_stiffness_profile()
compliance_profile = ANM_MODEL.get_compliance_profile()

Step 5 Import any visualization package such as seaborn and visualize the stiffness profile.

import seaborn as sns

sns.heatmap(stiffness_map)

<matplotlib.axes._subplots.AxesSubplot at 0x1f8f2aa7e10>

Step 6 Similarly, for the compliance profile

sns.heatmap(compliance_map)

<matplotlib.axes._subplots.AxesSubplot at 0x1f8f29610b8>

Step 7 Visualize the profiles and compare them with the experimental B-factors.

from matplotlib import pyplot as plt
import numpy

plt.plot(b_factors/numpy.linalg.norm(b_factors)                   ,color= 'blue')
plt.plot(compliance_profile/numpy.linalg.norm(compliance_profile) ,color=  'red')
plt.plot(stiffness_profile/numpy.linalg.norm(stiffness_profile)   ,color='green')
plt.show()

Step 8 Calculate the Pearson Correlation Coefficient between any profile and the experimental B-factors.

from scipy.stats import pearsonr

pearsonr(b_factors,compliance_profile)

(0.7879402882589035, 6.05510642055883e-36)