Interactive-3-Dimensional-Gibbs-Free-Energy-landscape-plot-from-MD-simulation-results

Introduction:

A system's potential states are mapped out in an energy landscape. The idea is commonly applied in physics, chemistry, and biochemistry to represent, among other things, all conceivable chemical configurations, the spatial locations of molecules interacting in a system, or parameters and their corresponding energy levels, usually Gibbs free energy.

G = H – TS

Where, G stands for Gibbs fre energy, H stands for Heat content, T is absolute temperature and S represents entrophy of the system.

gromacs commands:

g_covar_mpi -f fie.xtc -s file.tpr | chose c-alpha (option3) X2

xmgrace eigenvalues.xvg

g_anaeig_mpi -v eigenvec.trr -f file.xtc -s file.tpr -eig eigenvalues.xvg -first 1 -last 1 -nframes 1000 -extr ev1.pdb | chose c-alpha (option3) X2

g_anaeig_mpi -v eigenvec.trr -f file.xtc -s file.tpr -eig eigenvalues.xvg -first 2 -last 2 -nframes 1000 -extr ev1.pdb | chose c-alpha (option3) X2

g_anaeig_mpi -v eigenvec.trr -f file.xtc -s file.tpr -eig eigenvalues.xvg -first 1 -last 2 -2d 2dproj_ev1_2.xvg | chose c-alpha (option3) X2

g_sham_mpi -f 2dproj_ev1_2.xvg -notime -ls Gibbs_1_2.xpm

xpm2ps_mpi -f Gibbs_1_2.xpm -o gibbs_plot.eps -rainbow red

Steps to perform 3-dimensional FEL studies:

1) Download and keep the Script file “xpm2txt.py” in the present working directory (pwd).

2) Open the bash command line in the pwd:

3) Type the command :

python2 xpm2txt.py -f gibbs.xpm -o fel.txt

4) Copy the content of the “fel.txt” in excel and save it as “fel.csv” format.

Python code to plot the graph is given below.

#importin the libraries: import pandas as pd import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm

#for the plot to be interactive %matplotlib notebook

Read the CSV file into a dataframe

df = pd.read_csv('file1.csv')

Extract the required columns from the dataframe

x = df[df.columns[0]] y = df[df.columns[1]] z = df[df.columns[2]]

Create a figure and a 3D axis

fig = plt.figure() ax = fig.add_subplot(111, projection='3d')

Create a surface plot with gradient colors (red, yellow, green, blue)

surf = ax.plot_trisurf(x, y, z, cmap='turbo', linewidth=0.2)

Remove grid lines

ax.grid(False)

Remove x, y, and z axis values

ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_zticklabels([])

Add a color bar to the side for the surface plot

fig.colorbar(surf, shrink=0.5, aspect=5)

Set labels and title for the plot

ax.set_xlabel('PC1') ax.set_ylabel('PC2') ax.set_zlabel('Gibbs Free Energy')

Create a scatter plot in the xy-plane

ax.scatter(x, y, z.min() - 0.1*(z.max()-z.min()), c=z, cmap='plasma')

Set limits for the xy-plane

ax.set_zlim(z.min() - 0.1*(z.max()-z.min()), z.max())

Show the plot

plt.show()

Save the image1 in higher dpi in the pwd:

plt.savefig('3dimensional_FEL.png', dpi=600, bbox_inches='tight')

Corresponding 2- dimensional representation :

import pandas as pd import matplotlib.pyplot as plt

Read the CSV file into a dataframe

df = pd.read_csv('file1.csv', header=0) # Assuming the column names are in the first row

Extract the required columns from the dataframe

x = df.iloc[:, 0] y = df.iloc[:, 1] z = df.iloc[:, 2]

Create a figure and axis

fig, ax = plt.subplots()

Create a contour plot

contour = ax.tricontourf(x, y, z, cmap='turbo')

Remove x and y axis values

ax.set_xticklabels([]) ax.set_yticklabels([])

Set labels and title

ax.set_xlabel('PC1') ax.set_ylabel('PC2') ax.set_title('Gibbs Free Energy')

Add a color bar

cbar = fig.colorbar(contour)

Show the plot

plt.show()

Save the image with desired orientation and resolution

plt.savefig('2dimensional_image_FEL.png', dpi=600, bbox_inches='tight')

find the native form of the protein residing in the energy basins and try to determine the corresponding frames from the simulation trajectory

gmx trjconv -s md100.tpr -f md_noPBC.xtc -dt 10000 -o hs_each10ns.pdb