<jupyter_start><jupyter_text>Importing libraries<jupyter_code>import MDAnalysis as mda
from MDAnalysis.analysis import rms, diffusionmap, align
import numpy as np 
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns
import nglview as nv
plt.style.use('seaborn')<jupyter_output><empty_output><jupyter_text>Reading, loading and showing trajectories  WT  R1<jupyter_code>u_WT_R1 = mda.Universe('/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/WT/R1/MDProduction/md_1.gro','/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/WT/R1/MDProduction/WT_R1_noPBC.xtc')
u_WT_R1.trajectory
prot = u_WT_R1.select_atoms('protein', 'name ZN')
view_md_prot = nv.show_mdanalysis(prot)
view_md_prot<jupyter_output><empty_output><jupyter_text>R2<jupyter_code>u_WT_R2 = mda.Universe('/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/WT/R2/MDProduction/md_1.gro','/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/WT/R2/MDProduction/WT_R2_noPBC.xtc')
u_WT_R2.trajectory
prot = u_WT_R2.select_atoms('protein', 'name ZN')
view_md_prot = nv.show_mdanalysis(prot)
view_md_prot<jupyter_output><empty_output><jupyter_text>R3<jupyter_code>u_WT_R3 = mda.Universe('/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/WT/R3/MDProduction/md_1.gro','/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/WT/R3/MDProduction/WT_R3_noPBC.xtc')
u_WT_R3.trajectory
prot = u_WT_R3.select_atoms('protein', 'name ZN')
view_md_prot = nv.show_mdanalysis(prot)
view_md_prot<jupyter_output><empty_output><jupyter_text>C391R   R1<jupyter_code>u_C391R_R1 = mda.Universe('/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/C391R/R1/MDProduction/md_1.gro','/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/C391R/R1/MDProduction/C391R_R1_noPBC.xtc')
u_C391R_R1.trajectory
prot = u_C391R_R1.select_atoms('protein', 'name ZN')
view_md_prot = nv.show_mdanalysis(prot)
view_md_prot<jupyter_output><empty_output><jupyter_text>R2<jupyter_code>u_C391R_R2 = mda.Universe('/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/C391R/R2/MDProduction/md_1.gro','/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/C391R/R2/MDProduction/C391R_R2_noPBC.xtc')
u_C391R_R2.trajectory
prot = u_C391R_R2.select_atoms('protein', 'name ZN')
view_md_prot = nv.show_mdanalysis(prot)
view_md_prot<jupyter_output><empty_output><jupyter_text>R3<jupyter_code>u_C391R_R3 = mda.Universe('/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/C391R/R3/MDProduction/md_1.gro','/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/C391R/R3/MDProduction/C391R_R3_noPBC.xtc')
u_C391R_R3.trajectory
prot = u_C391R_R3.select_atoms('protein', 'name ZN')
view_md_prot = nv.show_mdanalysis(prot)
view_md_prot<jupyter_output><empty_output><jupyter_text>419X   R1<jupyter_code>u_419X_R1 = mda.Universe('/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/Truncated/R1/MDProduction/prod.gro','/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/Truncated/R1/MDProduction/419X_R1_noPBC.xtc')
u_419X_R1.trajectory
prot = u_419X_R1.select_atoms('protein', 'name ZN')
view_md_prot = nv.show_mdanalysis(prot)
view_md_prot<jupyter_output><empty_output><jupyter_text>R2<jupyter_code>u_419X_R2 = mda.Universe('/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/Truncated/R2/MDProduction/prod.gro','/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/Truncated/R2/MDProduction/419X_R2_noPBC.xtc')
u_419X_R2.trajectory
prot = u_419X_R2.select_atoms('protein', 'name ZN')
view_md_prot = nv.show_mdanalysis(prot)
view_md_prot<jupyter_output><empty_output><jupyter_text>R3<jupyter_code>u_419X_R3 = mda.Universe('/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/Truncated/R3/MDProduction/prod.gro','/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/Truncated/R3/MDProduction/419X_R3_noPBC.xtc')
u_419X_R3.trajectory
prot = u_419X_R3.select_atoms('protein', 'name ZN')
view_md_prot = nv.show_mdanalysis(prot)
view_md_prot<jupyter_output><empty_output><jupyter_text>Showing trajectories in tetrahedron   WT   R1<jupyter_code>tet = u_WT_R1.select_atoms("resid 271:284") + u_WT_R1.select_atoms("resid 339:352") + u_WT_R1.select_atoms("name ZN")
tet_view = nv.show_mdanalysis(tet,gui=True)
tet_view<jupyter_output><empty_output><jupyter_text>R2<jupyter_code>tet = u_WT_R2.select_atoms("resid 271:284") + u_WT_R2.select_atoms("resid 339:352") + u_WT_R2.select_atoms("name ZN")
tet_view = nv.show_mdanalysis(tet,gui=True)
tet_view<jupyter_output><empty_output><jupyter_text>R3<jupyter_code>tet = u_WT_R3.select_atoms("resid 271:284") + u_WT_R3.select_atoms("resid 339:352") + u_WT_R3.select_atoms("name ZN")
tet_view = nv.show_mdanalysis(tet,gui=True)
tet_view<jupyter_output><empty_output><jupyter_text>C391R   R1<jupyter_code>tet = u_C391R_R1.select_atoms("resid 271:284") + u_C391R_R1.select_atoms("resid 339:352") + u_C391R_R1.select_atoms("name ZN")
tet_view = nv.show_mdanalysis(tet,gui=True)
tet_view<jupyter_output><empty_output><jupyter_text>R2<jupyter_code>tet = u_C391R_R2.select_atoms("resid 271:284") + u_C391R_R2.select_atoms("resid 339:352") + u_C391R_R2.select_atoms("name ZN")
tet_view = nv.show_mdanalysis(tet,gui=True)
tet_view<jupyter_output><empty_output><jupyter_text>R3<jupyter_code>tet = u_C391R_R3.select_atoms("resid 271:284") + u_C391R_R3.select_atoms("resid 339:352") + u_C391R_R3.select_atoms("name ZN")
tet_view = nv.show_mdanalysis(tet,gui=True)
tet_view<jupyter_output><empty_output><jupyter_text>419X  R1<jupyter_code>tet = u_419X_R1.select_atoms("resid 271:284") + u_419X_R1.select_atoms("resid 339:352") + u_419X_R1.select_atoms("name ZN")
tet_view = nv.show_mdanalysis(tet,gui=True)
tet_view<jupyter_output><empty_output><jupyter_text>R2<jupyter_code>tet = u_419X_R2.select_atoms("resid 271:284") + u_419X_R2.select_atoms("resid 339:352") + u_419X_R2.select_atoms("name ZN")
tet_view = nv.show_mdanalysis(tet,gui=True)
tet_view<jupyter_output><empty_output><jupyter_text>R3<jupyter_code>tet = u_419X_R3.select_atoms("resid 271:284") + u_419X_R3.select_atoms("resid 339:352") + u_419X_R3.select_atoms("name ZN")
tet_view = nv.show_mdanalysis(tet,gui=True)
tet_view<jupyter_output><empty_output><jupyter_text>Radius of gyration  WT<jupyter_code>rgyr = []
time = []
protein = u_WT_R1.select_atoms("protein")
for ts in u_WT_R1.trajectory:
    time.append(u_WT_R1.trajectory.time)
    rgyr.append(protein.radius_of_gyration()*0.1)
rgyr_R2 = []
protein = u_WT_R2.select_atoms("protein")
for ts in u_WT_R2.trajectory:
    rgyr_R2.append(protein.radius_of_gyration()*0.1)
rgyr_R3 = []
protein = u_WT_R3.select_atoms("protein")
for ts in u_WT_R3.trajectory:
    rgyr_R3.append(protein.radius_of_gyration()*0.1)
rgyr_df = pd.DataFrame(rgyr, columns=['R1'], index=time)
rgyr_df.index.name = 'Time (ps)'
rgyr_df['R2'] = rgyr_R2
rgyr_df['R3'] = rgyr_R3
rgyr_df['Average_WT'] =  rgyr_df.mean(axis=1)
rgyr_df<jupyter_output><empty_output><jupyter_text>C391R<jupyter_code>rgyr_c391_R1 = []
protein = u_C391R_R1.select_atoms("protein")
for ts in u_C391R_R1.trajectory:
    rgyr_c391_R1.append(protein.radius_of_gyration()*0.1)
rgyr_c391_R2 = []
protein = u_C391R_R2.select_atoms("protein")
for ts in u_C391R_R2.trajectory:
    rgyr_c391_R2.append(protein.radius_of_gyration()*0.1)
rgyr_c391_R3 = []
protein = u_C391R_R3.select_atoms("protein")
for ts in u_C391R_R3.trajectory:
    rgyr_c391_R3.append(protein.radius_of_gyration()*0.1)
rgyr_c391r_df = pd.DataFrame(rgyr_c391_R1, columns=['R1'], index=time)
rgyr_c391r_df.index.name = 'Time (ps)'
rgyr_c391r_df['R2'] = rgyr_c391_R2
rgyr_c391r_df['R3'] = rgyr_c391_R3
rgyr_c391r_df['Average_C391R'] = rgyr_c391r_df.mean(axis=1)
rgyr_c391r_df<jupyter_output><empty_output><jupyter_text>419X<jupyter_code>rgyr_419X_R1 = []
protein = u_419X_R1.select_atoms("protein")
for ts in u_419X_R1.trajectory:
    rgyr_419X_R1.append(protein.radius_of_gyration()*0.1)
rgyr_419X_R2 = []
protein = u_419X_R2.select_atoms("protein")
for ts in u_419X_R2.trajectory:
    rgyr_419X_R2.append(protein.radius_of_gyration()*0.1)
rgyr_419X_R3 = []
protein = u_419X_R3.select_atoms("protein")
for ts in u_419X_R3.trajectory:
    rgyr_419X_R3.append(protein.radius_of_gyration()*0.1)
rgyr_419x_df = pd.DataFrame(rgyr_419X_R1, columns=['R1'], index=time)
rgyr_419x_df.index.name = 'Time (ps)'
rgyr_419x_df['R2'] = rgyr_419X_R2
rgyr_419x_df['R3'] = rgyr_419X_R3
rgyr_419x_df['Average_419X'] = rgyr_419x_df.mean(axis=1)
rgyr_419x_df
fig1, (ax1, ax2, ax3) = plt.subplots(nrows=1,ncols=3, figsize=(18, 6))
rgyr_df.plot(y = ['R1', 'R2', 'R3'], ax = ax1)
ax1.set(xlabel = 'Tiempo (ps)', ylabel = 'Radio de giro (nm)', title = 'WT')
ax1.set_xlim([0, 5000])
ax1.set_ylim([2.24, 2.32])
rgyr_c391r_df.plot(y = ['R1', 'R2', 'R3'], ax = ax2)
ax2.set(xlabel = 'Tiempo (ps)', ylabel = 'Radio de giro (nm)', title = 'C391R')
ax2.set_xlim([0, 5000])
ax2.set_ylim([2.23, 2.34])
rgyr_419x_df.plot( y = ['R1', 'R2', 'R3'], ax = ax3)
ax3.set(xlabel = 'Tiempo (ps)', ylabel = 'Radio de giro (nm)', title = '419X')
ax3.set_xlim([0, 5000])
ax3.set_ylim([2.24, 2.36])<jupyter_output><empty_output><jupyter_text>WT vs C391R vs 419X<jupyter_code>average_RG_df = pd.concat([rgyr_df, rgyr_c391r_df, rgyr_419x_df], axis = 1)
fig2, ax1 = plt.subplots(figsize=(12, 6))
average_RG_df.plot(y = ['Average_WT', 'Average_C391R', 'Average_419X'], ax = ax1)
ax1.set(xlabel = 'Tiempo (ps)', ylabel = 'Radio de giro (nm)')
ax1.set_xlim([0, 5000])
ax1.set_ylim([2.25, 2.33])<jupyter_output><empty_output><jupyter_text>RMSD_MDAnalysis  WT<jupyter_code>u_WT_R1.trajectory[0] # set to first frame
rmsd_analysis_WT = rms.RMSD(u_WT_R1,u_WT_R1, select='backbone', groupselections=['name CA', 'protein'], ref_frame=0)
rmsd_analysis_WT.run()
u_WT_R2.trajectory[0] # set to first frame
rmsd_analysis_WT_R2 = rms.RMSD(u_WT_R2, select='backbone', groupselections=['name CA', 'protein'])
rmsd_analysis_WT_R2.run()
u_WT_R3.trajectory[0] # set to first frame
rmsd_analysis_WT_R3 = rms.RMSD(u_WT_R3, select='backbone', groupselections=['name CA', 'protein'])
rmsd_analysis_WT_R3.run()<jupyter_output><empty_output><jupyter_text>Backbone<jupyter_code>rmsd_df_WT_BC = pd.DataFrame(rmsd_analysis_WT.rmsd[:, 2]*0.1,
                       columns=['R1'],
                       index=rmsd_analysis_WT.rmsd[:, 1])

rmsd_df_WT_BC.index.name = 'Time (ps)'
rmsd_df_WT_BC ['R2'] = rmsd_analysis_WT_R2.rmsd[:, 2]*0.1
rmsd_df_WT_BC ['R3'] = rmsd_analysis_WT_R3.rmsd[:, 2]*0.1
rmsd_df_WT_BC ['Average_BC_WT'] = rmsd_df_WT_BC.mean(axis=1)
rmsd_df_WT_BC<jupyter_output><empty_output><jupyter_text>Alpha C<jupyter_code>rmsd_df_WT_CA = pd.DataFrame(rmsd_analysis_WT.rmsd[:, 3]*0.1,
                       columns=['R1'],
                       index=rmsd_analysis_WT.rmsd[:, 1])

rmsd_df_WT_CA.index.name = 'Time (ps)'
rmsd_df_WT_CA['R2'] = rmsd_analysis_WT_R2.rmsd[:, 3]*0.1
rmsd_df_WT_CA['R3'] = rmsd_analysis_WT_R3.rmsd[:, 3]*0.1
rmsd_df_WT_CA['Average_CA_WT'] = rmsd_df_WT_CA.mean(axis=1)
rmsd_df_WT_CA<jupyter_output><empty_output><jupyter_text>Protein<jupyter_code>rmsd_df_WT_PR = pd.DataFrame(rmsd_analysis_WT.rmsd[:, 4]*0.1,
                       columns=['R1'],
                       index=rmsd_analysis_WT.rmsd[:, 1])

rmsd_df_WT_PR.index.name = 'Time (ps)'
rmsd_df_WT_PR['R2'] = rmsd_analysis_WT_R2.rmsd[:, 4]*0.1
rmsd_df_WT_PR['R3'] = rmsd_analysis_WT_R3.rmsd[:, 4]*0.1
rmsd_df_WT_PR['Average_PR_WT'] = rmsd_df_WT_PR.mean(axis=1)
rmsd_df_WT_PR
fig3, (ax1, ax2, ax3) = plt.subplots(nrows=1,ncols=3, figsize=(18, 6))
rmsd_df_WT_BC.plot(y = ['R1', 'R2', 'R3'], ax = ax1)
ax1.set(xlabel = 'Tiempo (ns)', ylabel = 'RMSD (nm)', title = 'Backbone')
rmsd_df_WT_CA.plot(y = ['R1', 'R2', 'R3'], ax = ax2)
ax2.set(xlabel = 'Tiempo (ns)', ylabel = 'RMSD (nm)', title = 'Carbono alpha')
rmsd_df_WT_PR.plot( y = ['R1', 'R2', 'R3'], ax = ax3)
ax3.set(xlabel = 'Tiempo (ns)', ylabel = 'RMSD (nm)', title = 'Proteína')<jupyter_output><empty_output><jupyter_text>C391R<jupyter_code>u_C391R_R1.trajectory[0] # set to first frame
rmsd_analysis_C391R_R1 = rms.RMSD(u_C391R_R1, select='backbone', groupselections=['name CA', 'protein'])
rmsd_analysis_C391R_R1.run()
u_C391R_R2.trajectory[0] # set to first frame
rmsd_analysis_C391R_R2 = rms.RMSD(u_C391R_R2, select='backbone', groupselections=['name CA', 'protein'])
rmsd_analysis_C391R_R2.run()
u_C391R_R3.trajectory[0] # set to first frame
rmsd_analysis_C391R_R3 = rms.RMSD(u_C391R_R3, select='backbone', groupselections=['name CA', 'protein'])
rmsd_analysis_C391R_R3.run()<jupyter_output><empty_output><jupyter_text>Backbone<jupyter_code>rmsd_df_C391R_BC = pd.DataFrame(rmsd_analysis_C391R_R1.rmsd[:, 2]*0.1,
                       columns=['R1'],
                       index=rmsd_analysis_C391R_R1.rmsd[:, 1])

rmsd_df_C391R_BC.index.name = 'Time (ps)'
rmsd_df_C391R_BC['R2'] = rmsd_analysis_C391R_R2.rmsd[:, 2]*0.1
rmsd_df_C391R_BC['R3'] = rmsd_analysis_C391R_R3.rmsd[:, 2]*0.1
rmsd_df_C391R_BC['Average_BC_C391R'] = rmsd_df_C391R_BC.mean(axis=1)
rmsd_df_C391R_BC<jupyter_output><empty_output><jupyter_text>Alpha C<jupyter_code>rmsd_df_C391R_CA = pd.DataFrame(rmsd_analysis_C391R_R1.rmsd[:, 3]*0.1,
                       columns=['R1'],
                       index=rmsd_analysis_C391R_R1.rmsd[:, 1])
rmsd_df_C391R_CA.index.name = 'Time (ps)'
rmsd_df_C391R_CA['R2'] = rmsd_analysis_C391R_R2.rmsd[:, 3]*0.1
rmsd_df_C391R_CA['R3'] = rmsd_analysis_C391R_R3.rmsd[:, 3]*0.1
rmsd_df_C391R_CA['Average_CA_C391R'] = rmsd_df_C391R_CA.mean(axis=1)
rmsd_df_C391R_CA<jupyter_output><empty_output><jupyter_text>Protein<jupyter_code>rmsd_df_C391R_PR = pd.DataFrame(rmsd_analysis_C391R_R1.rmsd[:, 4]*0.1,
                       columns=['R1'],
                       index=rmsd_analysis_C391R_R1.rmsd[:, 1])

rmsd_df_C391R_PR.index.name = 'Time (ps)'
rmsd_df_C391R_PR['R2'] = rmsd_analysis_C391R_R2.rmsd[:, 4]*0.1
rmsd_df_C391R_PR['R3'] = rmsd_analysis_C391R_R3.rmsd[:, 4]*0.1
rmsd_df_C391R_PR['Average_PR_C391R'] = rmsd_df_C391R_CA.mean(axis=1)
rmsd_df_C391R_PR
fig4, (ax1, ax2, ax3) = plt.subplots(nrows=1,ncols=3, figsize=(18, 6))
rmsd_df_C391R_BC.plot(y = ['R1', 'R2', 'R3'], ax = ax1)
ax1.set(xlabel = 'Tiempo (ns)', ylabel = 'RMSD (nm)', title = 'Backbone')
rmsd_df_C391R_CA.plot(y = ['R1', 'R2', 'R3'], ax = ax2)
ax2.set(xlabel = 'Tiempo (ns)', ylabel = 'RMSD (nm)', title = 'Carbono alpha')
rmsd_df_C391R_PR.plot( y = ['R1', 'R2', 'R3'], ax = ax3)
ax3.set(xlabel = 'Tiempo (ns)', ylabel = 'RMSD (nm)', title = 'Proteína')<jupyter_output><empty_output><jupyter_text>419X<jupyter_code>u_419X_R1.trajectory[0] # set to first frame
rmsd_analysis_419X_R1 = rms.RMSD(u_419X_R1, select='backbone', groupselections=['name CA', 'protein'])
rmsd_analysis_419X_R1.run()
u_419X_R2.trajectory[0] # set to first frame
rmsd_analysis_419X_R2 = rms.RMSD(u_419X_R2, select='backbone', groupselections=['name CA', 'protein'])
rmsd_analysis_419X_R2.run()
u_419X_R3.trajectory[0] # set to first frame
rmsd_analysis_419X_R3 = rms.RMSD(u_419X_R3, select='backbone', groupselections=['name CA', 'protein'])
rmsd_analysis_419X_R3.run()<jupyter_output><empty_output><jupyter_text>Backbone<jupyter_code>rmsd_df_419X_BC = pd.DataFrame(rmsd_analysis_419X_R1.rmsd[:, 2]*0.1,
                       columns=['R1'],
                       index=rmsd_analysis_419X_R1.rmsd[:, 1])

rmsd_df_419X_BC.index.name = 'Time (ps)'
rmsd_df_419X_BC['R2'] = rmsd_analysis_419X_R2.rmsd[:, 2]*0.1
rmsd_df_419X_BC['R3'] = rmsd_analysis_419X_R3.rmsd[:, 2]*0.1
rmsd_df_419X_BC['Average_BC_419X'] =  rmsd_df_419X_BC.mean(axis=1)
rmsd_df_419X_BC<jupyter_output><empty_output><jupyter_text>Alpha C<jupyter_code>rmsd_df_419X_CA = pd.DataFrame(rmsd_analysis_419X_R1.rmsd[:, 3]*0.1,
                       columns=['R1'],
                       index=rmsd_analysis_419X_R1.rmsd[:, 1])

rmsd_df_419X_CA.index.name = 'Time (ps)'
rmsd_df_419X_CA['R2'] = rmsd_analysis_419X_R2.rmsd[:, 3]*0.1
rmsd_df_419X_CA['R3'] = rmsd_analysis_419X_R3.rmsd[:, 3]*0.1
rmsd_df_419X_CA['Average_CA_419X'] = rmsd_df_419X_CA.mean(axis=1)
rmsd_df_419X_CA<jupyter_output><empty_output><jupyter_text>Protein<jupyter_code>rmsd_df_419X_PR = pd.DataFrame(rmsd_analysis_419X_R1.rmsd[:, 4]*0.1,
                       columns=['R1'],
                       index=rmsd_analysis_419X_R1.rmsd[:, 1])
rmsd_df_419X_PR['R2'] = rmsd_analysis_419X_R2.rmsd[:, 4]*0.1
rmsd_df_419X_PR['R3'] = rmsd_analysis_419X_R3.rmsd[:, 4]*0.1
rmsd_df_419X_PR['Average_PR_419X'] = rmsd_df_419X_PR.mean(axis=1)
rmsd_df_419X_PR
fig5, (ax1, ax2, ax3) = plt.subplots(nrows=1,ncols=3, figsize=(18, 6))
rmsd_df_419X_BC.plot(y = ['R1', 'R2', 'R3'], ax = ax1)
ax1.set(xlabel = 'Tiempo (ns)', ylabel = 'RMSD (nm)', title = 'Backbone')
rmsd_df_419X_CA.plot(y = ['R1', 'R2', 'R3'], ax = ax2)
ax2.set(xlabel = 'Tiempo (ns)', ylabel = 'RMSD (nm)', title = 'Carbono alpha')
rmsd_df_419X_PR.plot( y = ['R1', 'R2', 'R3'], ax = ax3)
ax3.set(xlabel = 'Tiempo (ns)', ylabel = 'RMSD (nm)', title = 'Proteína')<jupyter_output><empty_output><jupyter_text>WT vs C391R vs 419X<jupyter_code>average_RMSD_BC = pd.concat([rmsd_df_WT_BC, rmsd_df_C391R_BC, rmsd_df_419X_BC], axis = 1)
average_RMSD_CA = pd.concat([rmsd_df_WT_CA, rmsd_df_C391R_CA, rmsd_df_419X_CA], axis = 1)
average_RMSD_PR = pd.concat([rmsd_df_WT_PR, rmsd_df_C391R_PR, rmsd_df_419X_PR], axis = 1)

fig5, (ax1, ax2, ax3) = plt.subplots(nrows=1,ncols=3, figsize=(18, 6))
average_RMSD_BC.plot(y = ['Average_BC_WT', 'Average_BC_C391R', 'Average_BC_419X'], ax = ax1)
ax1.set(xlabel = 'Tiempo (ns)', ylabel = 'RMSD (nm)', title = 'Backbone')
average_RMSD_CA.plot(y = ['Average_CA_WT', 'Average_CA_C391R', 'Average_CA_419X'], ax = ax2)
ax2.set(xlabel = 'Tiempo (ns)', ylabel = 'RMSD (nm)', title = 'Carbono alpha')
average_RMSD_PR.plot( y = ['Average_PR_WT', 'Average_PR_C391R', 'Average_PR_419X'], ax = ax3)
ax3.set(xlabel = 'Tiempo (ns)', ylabel = 'RMSD (nm)', title = 'Proteína')<jupyter_output><empty_output><jupyter_text>2D RMSD  WT<jupyter_code>align.AlignTraj(u_WT_R1, u_WT_R1,
                select='name CA',
                filename='aligned_WT_R1.dcd').run()
aligned_WT_R1 = mda.Universe('/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/WT/R1/MDProduction/md_1.gro','/media/sebastianayalar/ADATA HD330/Projects/Saudi/Results/WT/R1/MDProduction/WT_R1_noPBC.xtc', 'aligned_WT_R1.dcd')
matrix = diffusionmapistanceMatrix(u_WT_R1, select='name CA').run()
align.AlignTraj(u_WT_R2, u_WT_R2,
                select='name CA',
                filename='aligned_WT_R2.dcd').run()
align.AlignTraj(u_WT_R3, u_WT_R3,
                select='name CA',
                filename='aligned_WT_R3.dcd').run()<jupyter_output><empty_output>
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