def run(self):
u_coord = MDA.Universe(self.args.reference_path,
self.args.coordinate_path)
u_ref = MDA.Universe(self.args.reference_path)
R_tot = RMS.RMSD(u_coord, u_ref)
R_A = RMS.RMSD(u_coord, u_ref, select="bynum 1-70")
R_B = RMS.RMSD(u_coord, u_ref, select="bynum 71-140")
R_C = RMS.RMSD(u_coord, u_ref, select="bynum 141-210")
for R in [R_tot, R_A, R_B, R_C]:
R.run()
tot_arr = R_tot.rmsd[:, 2]
a_arr = R_A.rmsd[:, 2]
b_arr = R_B.rmsd[:, 2]
c_arr = R_C.rmsd[:, 2]
time = R_tot.rmsd[:, 1]
full_arr = numpy.vstack((time, tot_arr, a_arr, b_arr, c_arr))
numpy.save('output', full_arr)
def delta_proc(init, targ, traj, top = "/nfs/homes/tcolburn/Projects/Beckstein/Mhp1/top/2x79_r10_g470.psf",
direction = "i2occ2o", label = "TC"):
initial = mda.Universe(top, init)
target = mda.Universe(top, targ)
if type(traj) is str:
trajectory = mda.Universe(top, traj)
else:
trajectory = traj
r_init = rms.RMSD(trajectory, initial, select='name CA and protein')
r_init.run()
r_init.save("r_init_" + label)
r_targ = rms.RMSD(trajectory, target, select='name CA and protein')
r_targ.run()
r_targ.save("r_targ" + label)
rmsd_init = r_init.rmsd.T
rmsd_targ = r_targ.rmsd.T
del_rmsd = rmsd_init[2] - rmsd_targ[2]
time = rmsd_init[1]
return del_rmsd, time
def rmsd_traj(traj, ref, title):
R = rms.RMSD(traj.select_atoms('name CA'),
ref.select_atoms('name CA')).run()
R = R.rmsd.T
frame = R[0]
time = R[1]
plt.subplots(figsize=(10, 5))
fig = plt.plot(time / 1000, R[2], linewidth=0.2)
plt.ylabel('RMSD ($\AA$)')
plt.xlabel('time (ns)')
plt.title(title)
plt.show()
return R, fig
def RMSD(u, PU_directory_output):
# calcule le RMSD de la PU
print("Processing RMSD...")
Rmsd = rms.RMSD(u)
Rmsd.run()
rmsd = Rmsd.rmsd.T # utilisation de la transposée pour l'affichage
time = rmsd[1]
plt.figure()
ax2 = plt.subplot(111)
ax2.plot(time, rmsd[2], 'g-', label="RMSD")
ax2.legend(loc="best")
ax2.set_title("RMSD")
ax2.set_xlabel("Time (ps)")
ax2.set_ylabel(r"RMSD ($\AA$)")
ax2.figure.savefig(PU_directory_output + "RMSD.png")
print("Done!")
def test_rmsd_matrix_with_superimposition(self, ens1):
conf_dist_matrix = encore.confdistmatrix.conformational_distance_matrix(
ens1,
encore.confdistmatrix.set_rmsd_matrix_elements,
selection="name CA",
pairwise_align=True,
weights='mass',
n_jobs=1)
reference = rms.RMSD(ens1, select="name CA")
reference.run()
for i, rmsd in enumerate(reference.rmsd):
assert_almost_equal(
conf_dist_matrix[0, i],
rmsd[2],
decimal=3,
err_msg=
"calculated RMSD values differ from the reference implementation"
)
import MDAnalysis as mda
import matplotlib.pyplot as plt
from MDAnalysis.analysis import align
from MDAnalysis.analysis import rms
import pandas as pd
x = mda.Universe('AT3_3UIM_model1.B99990004_filt.pdb')
a = mda.Universe('outI.pdb')
align.AlignTraj(a,x,select='backbone or (resid 8 and name CB and resname ALA)',filename='aligned_AT3.I.pdb',match_atoms=True,dt=16).run()
R = rms.RMSD(a,x,select='backbone or (resid 8 and name CB and resname ALA)')
R.run()
df = pd.DataFrame(R.rmsd,columns=['Frames','Time (ns)','backbone or (resid 8 and name CB and resname ALA)'])
df.to_csv('rmsd_AT3.I.csv', index=False)
fig = df.plot(x='Frames', y=['backbone or (resid 8 and name CB and resname ALA)'],kind='hist', figsize=(20, 16), fontsize=60, color=['#000004FF'], edgecolor="white", legend=False, xticks=[4,5,6,7])
fig.set_xlabel(r'RMSD ($\AA$)')
for item in ([fig.title, fig.xaxis.label, fig.yaxis.label] +
fig.get_xticklabels() + fig.get_yticklabels()):
item.set_fontsize(60)
fig.figure.savefig('rmsd_AT3.I.pdf')
def atom_rmsd(gro, trr, out, atom_name='AU', **kwargs):
"""Computes time resolved rmsd of atom group identified by atom name.
rmsd(t) = sqrt(1/N*sum_i=1^N w_i*(x_i(t)-x_i^rev)^2)
see
https://www.mdanalysis.org/mdanalysis/documentation_pages/analysis/rms.html
Units in output textfile are default MDAnalysis units.
https://www.mdanalysis.org/mdanalysis/documentation_pages/units.html
Parameters
----------
gro: str
GROMACS gro coordinates file
trr: str
GROMACS trr trajectory file with N frames
out: str
output text file
atom_name: str, optional
defaults: 'AU'
**kwargs:
keyword arguments forwarded to MDAnalysis.analysis.rms.RMSD
Output
------
out text file contains time [ps] and rmsd [Ang] in column vectors.
"""
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
logger = logging.getLogger("%s:rank[%i/%i]" % (__name__, rank, size))
mda_trr = mda.Universe(gro, trr)
atom_group = mda_trr.atoms[mda_trr.atoms.names == atom_name]
rmsd_atom_group = mda_rms.RMSD(atom_group, ref_frame=0, **kwargs)
# in the standard case #ranks > #frames,
N = len(mda_trr.trajectory)
span = N // size
# special treatment for more ranks than frames
if span < 1: # less frames than ranks
span = 1
n1 = rank * span
n2 = (rank + 1) * span
if rank >= N: # treatment for rank > N
n1 = 0
n2 = 0
# in this case, just return empty time_resolved_rdf
elif rank == size - 1: # treatment for last rank if N >= size
n2 = N
if n2 > n1:
logger.info("RMSD for frame %i to %i." % (n1, n2))
rmsd_atom_group.run(start=n1, stop=n2)
data = rmsd_atom_group.rmsd[:, 1:] # time and rmsd in column vectors
else:
data = np.array([])
# format of rmsd:
# rmsdT = rmsd_atom_group.rmsd.T
# frame = rmsdT[0]
# time = rmsdT[1]
# rmsd = rmsdT[2]
data_list = comm.gather(data, root=0)
if rank == 0:
filtered_data_list = [l for l in data_list if len(l) > 0]
data = np.vstack(filtered_data_list)
np.savetxt(
out,
data,
fmt='%.8e',
header='\n'.join((
'{modulename:s}, {username:s}@{hostname:s}, {timestamp:s}'.
format(
modulename=__name__,
username=getpass.getuser(),
hostname=socket.gethostname(),
timestamp=str(datetime.datetime.now()),
),
'https://www.mdanalysis.org/mdanalysis/documentation_pages/analysis/rms.html',
'rmsd(t) = sqrt(1/N*sum_i=1^N w_i*(x_i(t)-x_i^rev)^2)',
'time [ps], rmsd [Ang]')))
def setup(self, select, weights):
self.u = MDAnalysis.Universe(PSF, DCD)
self.RMSD_inst = rms.RMSD(atomgroup=self.u,
reference=None,
select=select,
weights=weights)
BCA = md.Universe('/home/cod33/foldon/templates/reference/BCA.pdb')
BAC = md.Universe('/home/cod33/foldon/templates/reference/BAC.pdb')
CAB = md.Universe('/home/cod33/foldon/templates/reference/CAB.pdb')
CBA = md.Universe('/home/cod33/foldon/templates/reference/CBA.pdb')
epsilons = ['0.20','0.21','0.22','0.23','0.24','0.25']
state = ['bound', 'unbound']
file_coords = ['MOVIE/movie.000000000.pdb','testout.xtc']
perms = [ABC,ACB,BCA,BAC,CAB,CBA]
todos= []
for eps in epsilons:
for stat in state:
coords = md.Universe(os.path.join(eps, stat, file_coords[0]),os.path.join(eps,stat,file_coords[1]))
for perm in perms:
rmsd = rms.RMSD(coords,perm)
rmsd_A = rms.RMSD(coords,perm,select='bynum 1-70')
rmsd_B = rms.RMSD(coords,perm,select='bynum 71-140')
rmsd_C = rms.RMSD(coords,perm,select='bynum 141-210')
for R in [rmsd,rmsd_A,rmsd_B,rmsd_C]:
R.run()
full_arr = np.vstack((rmsd.rmsd[:,1],rmsd.rmsd[:,2],rmsd_A.rmsd[:,2],rmsd_B.rmsd[:,2],rmsd_C.rmsd[:,2]))
todos.append(full_arr)
np.save('todos',todos)
#ok so todos has shape (96,5,10001). it goes bound, unbound by increments of six.
#bound_25 = np.min(todos[0:6], axis=0)
#unbound_25 = np.min(todos[6:12], axis=0)
#bound_27 = np.min(todos[12:18], axis=0)
#unbound_27 = np.min(todos[18:24], axis=0)
#bound_29 = np.min(todos[24:30], axis=0)
#unbound_29 = np.min(todos[30:36], axis=0)
min_rmsd_dcd_path = os.path.join(models_directory, target,
'min_rmsd_pairs.dcd')
dcdw = DCD.DCDWriter(min_rmsd_dcd_path, u.atoms.numberOfAtoms())
# Each template will be added to the list uniques if it is further than
# 0.2 Angstroms (RMSD) from the nearest template.
uniques = []
min_rmsd = []
for (t, template) in enumerate(valid_templates):
# Add the first template to the list of uniques
if t == 0:
uniques.append(template)
continue
# Cluster using CA atoms
rmsd_obj = rms.RMSD(u, select='name CA')
rmsd_obj.run(start=0, stop=t, ref_frame=t)
rmsd = rmsd_obj.rmsd.swapaxes(0, 1)[2]
min_rmsd.append(min(rmsd))
if min_rmsd[-1] < cutoff:
if min_rmsd_option:
dcdw.write(u.trajectory[t]) # write the current template
# find the index of the nearest neighbor
min_rmsd_index = list(rmsd).index(min_rmsd[-1])
dcdw.write(
u.trajectory[min_rmsd_index]) # write the nearest neighbor
u.trajectory[t] # go back to the current template
continue
else:
uniques.append(template)
#!/usr/bin/env python
#Corinn 17.5.25
#this should convert output data to numpy array, calculate RMSDs six times, take the minimum.
import MDAnalysis as md
import MDAnalysis.analysis.rms as rms
import numpy as np
import matplotlib.pyplot as plt
import os
file_coords = ['MOVIE/movie.000000000.pdb', 'testout.xtc']
todos = []
ref = md.Universe(os.path.join(file_coords[0]))
coords = md.Universe(os.path.join(file_coords[0]),
os.path.join(file_coords[1]))
rmsd = rms.RMSD(coords, ref)
rmsd_A = rms.RMSD(coords, ref, select='bynum 1-70')
rmsd_B = rms.RMSD(coords, ref, select='bynum 91-161')
rmsd_C = rms.RMSD(coords, ref, select='bynum 182-252')
for R in [rmsd, rmsd_A, rmsd_B, rmsd_C]:
R.run()
full_arr = np.vstack((rmsd.rmsd[:, 1], rmsd.rmsd[:, 2], rmsd_A.rmsd[:, 2],
rmsd_B.rmsd[:, 2], rmsd_C.rmsd[:, 2]))
print(full_arr.shape)
#todos.append(full_arr)
#todoss = np.array(todos)
np.save('todos.npy', full_arr)
#ok so todos has shape (96,5,10001). it goes bound, unbound by increments of six.
#bound_25 = np.min(todos[0:6], axis=0)
#unbound_25 = np.min(todos[6:12], axis=0)
#bound_27 = np.min(todos[12:18], axis=0)
#fig = plt.figure()
ax = plt.subplot(111)
ax.plot(ts.frame, rmsf, 'r--', lw=2, label="all")
#plt.plot(ts, rmsf)
ax.legend(loc="best")
ax.set_xlabel("time (ps)")
ax.set_ylabel(r"rmsf ($\AA$)")
ax.figure.savefig("rmsf_all_t300i50.png")
plt.draw()
print(ts)
########################################
############# RMSD ##################
rd = rms.RMSD(al, filename=sys.argv[3])
rd.run()
rd.save()
rmsd = rd.rmsd.T
time = rmsd[1]
import seaborn.apionly as sns
plt.style.use('ggplot')
sns.set_style('ticks')
fig = plt.figure(figuresize=(6, 6))
ax = fig.add_subplot(111)
ax.plot(time, rmsd[2], 'b--', linewidth=2, label="All")
ax.fill_between(protein.atoms.ids, R.rmsf, color="red", alpha=0.1)
references = []
for name in structure_names:
u = md.Universe('isolated_289/new_' + name + '_fixed.pdb')
references.append(u)
## Do smth
for k, ref in enumerate(references):
## change folder name here
folder = 'data/charmm36m/2us/'
for i, j in enumerate(range(ind_start, ind_end)):
struct = folder + 'chain.pdb'
traj = folder + 'chain_{}.xtc'.format(j + 1)
print(k, traj)
sys.stdout.flush()
system = md.Universe(struct, traj)
R = rms.RMSD(system, reference=ref, select='not name H*')
#R = rms.RMSD(system, reference=ref, select='backbone and not type H', groupselections=[state_selection])
R.run()
rmsd_ = R.rmsd.T # transpose makes it easier for plotting
local_rmsd[i, k] = rmsd_[2]
#local_rmsd[i, k] = rmsd_[3]
comm.barrier()
## Gather data
comm.Gather(local_rmsd, rmsd, root=0)
## save or print
if rank == 0:
print(rmsd.shape)
npy_file = '1_rmsd/charmm36m/rmsd_2us'
if symmetry:
r = rmsd.symmrmsd(selection.atoms.positions, ref_positions, atomicmap,
atomicmap, A, A)
else:
r = rmsd.rmsd(selection.atoms.positions, ref_positions, atomicmap,
atomicmap)
time.append(ts.time)
rmsds.append(r)
rmsd_nosymm.append(
rmsd.rmsd(selection.atoms.positions, ref_positions, atomicmap,
atomicmap))
# Check rmsd_nosymm is the same as rms.RMDS
rmsd_mda = rms.RMSD(traj,
ref,
select="backbone",
groupselections=["resname LIG"],
ref_frame=iframe)
R = rmsd_mda.run(verbose=True, step=step)
assert np.allclose(rmsd_nosymm, R.results.rmsd[:, -1])
plt.plot(time, rmsds, label="symmrmsd")
plt.plot(time, rmsd_nosymm, label="rmsd")
plt.plot(time, R.results.rmsd[:, -1], label="mda")
plt.xlabel("Time")
plt.ylabel("RMSD")
plt.legend()
plt.savefig("rmsd.png")
import MDAnalysis.analysis.rms as rms
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
u = md.Universe('MOVIE/movie.000000000.pdb','testout.xtc')
ref = md.Universe('/home/cod33/foldon/templates/references/1rfo_beads.pdb')
#A = u.A.atoms
#B = u.B.atoms
#C = u.C.atoms
#A_nterm = A[0:10]
#A_cterm = A[-10:]
#B_nterm = B[0:10]
#B_cterm = B[-10:]
#C_nterm = C[0:10]
#C_cterm = C[-10:]
A_nterm = rms.RMSD(u,ref,select='bynum 0-5')
A_cterm = rms.RMSD(u,ref,select='bynum 65-70')
B_nterm = rms.RMSD(u,ref,select='bynum 70-75')
B_cterm = rms.RMSD(u,ref,select='bynum 135-140')
C_nterm = rms.RMSD(u,ref,select='bynum 140-145')
C_cterm = rms.RMSD(u,ref,select='bynum 205-210')
for obj in [A_nterm,A_cterm,B_nterm,B_cterm,C_nterm,C_cterm]:
obj.run()
full_arr=[A_nterm.rmsd[:,0],A_nterm.rmsd[:,2],A_cterm.rmsd[:,2],B_nterm.rmsd[:,2],B_cterm.rmsd[:,2],C_nterm.rmsd[:,2],C_cterm.rmsd[:,2]]
np.save('terms.npy',full_arr)
