def test_lengths():
num = 3
inptrajs = ['PROJ9761/RUN3/CLONE9/frame{}.xtc'.format(i)
for i in range(num)]
stride = 8
subprocess.check_call(
['gmx', 'trjcat', '-f'] + inptrajs + ['-o', 'catty.xtc'],
stderr=subprocess.STDOUT, stdout=subprocess.DEVNULL)
with mdtraj.open("catty.xtc") as xtc:
stridelen = len(xtc) // stride
remain = len(xtc) % stride
assert stridelen == num * PROJ61_LENGTH_PER_GEN, (stridelen, remain)
top = mdtraj.load_prmtop("tops-p9712/4bw5.prmtop")
traj1 = mdtraj.load("catty.xtc", top=top)[::stride]
# blarg! the last frame is duplicatey
traj2 = mdtraj.load(inptrajs[0], top=top)[::stride][:-1]
traj2 += mdtraj.load(inptrajs[1], top=top)[::stride][:-1]
traj2 += mdtraj.load(inptrajs[2], top=top)[::stride]
traj3 = mdtraj.load(inptrajs, top=top,
discard_overlapping_frames=True)[::stride]
np.testing.assert_array_equal(traj1.xyz, traj3.xyz)
np.testing.assert_array_equal(traj1.xyz, traj2.xyz)
def generate_traj_from_stateinds(inds, meta, atom_selection='all'):
"""
Concatenate several frames from different trajectories to create a new one.
Parameters
----------
inds: list of tuples, Each element of the list has to be a 2D tuple of ints
(traj_index, frame_index)
meta: a metadata object
atom_selection: str, Which atoms to load
Returns
-------
traj: mdtraj.Trajectory
"""
frame_list = []
for traj_i, frame_i in inds:
top = mdtraj.load_prmtop(meta.loc[traj_i]['top_fn'])
atoms = top.select(atom_selection)
frame_list.append(
mdtraj.load_frame(meta.loc[traj_i]['traj_fn'], atom_indices=atoms,
index=frame_i, top=meta.loc[traj_i]['top_fn'])
)
traj = mdtraj.join(frame_list, check_topology=False)
traj.center_coordinates()
traj.superpose(traj, 0)
return traj
def cmap_Cheng(traj_generator, mask1, mask2, pairs, topology):
"""
Calculate contact maps between mask1 and mask2 as described in [1].
Parameters
----------
traj_generator: generator
MD trajectory as obtained through the load_Trajs_generator() function
mask1, mask2, pairs:
Two masks and the corresponding residue-residue list of tuples
as obtained from the get_residuepairs() function
Returns
-------
contact_frequency: np.array of shape (len(mask1), len(mask2))
Contact map between mask1 and mask2. Can be used directly as
input by the sns.heatmap() function.
References
----------
[1] Cheng, Y. et al., 2014. Biophysical Journal, 107(7), pp.1675–1685.
"""
print("Starting the cmap_Cheng calculation...")
top = md.load_prmtop(topology)
frequency = np.zeros(len(pairs))
frame_count = 0
for traj_chunk in traj_generator:
frame_count += traj_chunk.n_frames
index = 0 # To iterate through the residue-residue pair list
for residue_pair in pairs:
# Atom selection for each residue in the pair
c_atoms_residue1 = top.select("resid %d and (type C)" %
residue_pair[0])
c_atoms_residue2 = top.select("resid %d and type C" %
residue_pair[1])
not_c_atoms_residue1 = top.select("resid %d and not type C" %
residue_pair[0])
not_c_atoms_residue2 = top.select("resid %d and not type C" %
residue_pair[1])
# Calculate all the possible distances between the C-C atoms and
# the non C-C atoms. Results are stored in two np.arrays of shape:
# (traj_chunk.n_frames, c_atoms_residue1*c_atoms_residue2)
# (traj_chunk.n_frames, non_c_atoms_residue1*non_c_atoms_residue2)
c_atoms_dist = md.compute_distances(traj_chunk,
cartesianProduct(c_atoms_residue1,
c_atoms_residue2))
not_c_atoms_dist = md.compute_distances(traj_chunk,
cartesianProduct(not_c_atoms_residue1,
not_c_atoms_residue2))
# Implementation of the contact condition
if (((c_atoms_dist <= 5.4).sum(1).any() > 0) and
((not_c_atoms_dist <= 4.6).sum(1).any() > 0)):
frequency[index] += 1
index += 1
contact_frequency = (
frequency / frame_count).reshape(len(mask1), len(mask2))
print('Number of analyzed frames: %d\n' % frame_count)
print('Aggregate simulation time: %2.f ns\n' %
(frame_count * 0.02 * args.stride))
return(contact_frequency)
def get_pca_array(list_chunks, topology):
"""
Takes a list of mdtraj.Trajectory objects and featurize them to backbone -
Alpha Carbons pairwise distances. Perform 2 component Incremental
PCA on the featurized trajectory.
Parameters
----------
list_chunks: list of mdTraj.Trajectory objects
topology: str
Name of the Topology file
Returns
-------
Y: np.array shape(frames, features)
"""
pca = IncrementalPCA(n_components=2)
top = md.load_prmtop(topology)
ca_backbone = top.select("name CA")
pairs = top.select_pairs(ca_backbone, ca_backbone)
pair_distances = []
for chunk in list_chunks:
X = md.compute_distances(chunk, pairs)
pair_distances.append(X)
distance_array = np.concatenate(pair_distances)
print("No. of data points: %d" % distance_array.shape[0])
print("No. of features (pairwise distances): %d" % distance_array.shape[1])
Y = pca.fit_transform(distance_array)
return Y
def get_pca_array(list_chunks, topology):
"""
Takes a list of mdtraj.Trajectory objects and featurize them to backbone -
Alpha Carbons pairwise distances. Perform 2 component Incremental
PCA on the featurized trajectory.
Parameters
----------
list_chunks: list of mdTraj.Trajectory objects
topology: str
Name of the Topology file
Returns
-------
Y: np.array shape(frames, features)
"""
pca = IncrementalPCA(n_components=2)
top = md.load_prmtop(topology)
ca_backbone = top.select("name CA")
pairs = top.select_pairs(ca_backbone, ca_backbone)
pair_distances = []
for chunk in list_chunks:
X = md.compute_distances(chunk, pairs)
pair_distances.append(X)
distance_array = np.concatenate(pair_distances)
print("No. of data points: %d" % distance_array.shape[0])
print("No. of features (pairwise distances): %d" % distance_array.shape[1])
Y = pca.fit_transform(distance_array)
return Y
def test_0(self):
try:
import mdtraj as md
has_mdtraj = True
except ImportError:
has_mdtraj = False
if has_mdtraj:
mtop = md.load_prmtop("./data/Tc5b.top")
print mtop
pseudotop = Topology()
for mdatom in mtop.atoms:
atom = Atom(mdatom.name, mdatom.name)
mdres = mdatom.residue
pseudotop.add_atom(atom=atom,
resid=mdres.index,
resname=mdres.name)
print pseudotop
pseudotop.summary()
pseudotop.angle_info()
print pseudotop("@CA").selected_indices()
print pseudotop[4]
# try loading traj with pseudotop
traj = FrameArray()
traj.top = pseudotop.copy()
print traj.top
def renumber_mask(mask, top_fn=None):
"""
Renumbers the mask to match the correct sequence of each
subunit. Also takes into account the 0-indexed lists of
Python.
"""
if top_fn is not None:
top = md.load_prmtop(top_fn)
else:
top = None
if max(mask) > 160 and max(mask) < 249:
# It's in cTnT
if top is None:
new_mask = [x + 51 for x in mask]
else:
new_mask = ['{} {}'.format(
top.residue(x).name, x + 51) for x in mask]
elif max(mask) >= 249:
# It's in cTnI
if top is None:
new_mask = [x - 247 for x in mask]
else:
new_mask = ['{} {}'.format(
top.residue(x).name, x - 247) for x in mask]
else:
# It's in cTnC
if top is None:
new_mask = [x + 1 for x in mask]
else:
new_mask = ['{} {}'.format(
top.residue(x).name, x + 1) for x in mask]
return(new_mask)
def test_lengths():
num = 3
inptrajs = [
'PROJ9761/RUN3/CLONE9/frame{}.xtc'.format(i) for i in range(num)
]
stride = 8
subprocess.check_call(['gmx', 'trjcat', '-f'] + inptrajs +
['-o', 'catty.xtc'],
stderr=subprocess.STDOUT,
stdout=subprocess.DEVNULL)
with mdtraj.open("catty.xtc") as xtc:
stridelen = len(xtc) // stride
remain = len(xtc) % stride
assert stridelen == num * PROJ61_LENGTH_PER_GEN, (stridelen, remain)
top = mdtraj.load_prmtop("tops-p9712/4bw5.prmtop")
traj1 = mdtraj.load("catty.xtc", top=top)[::stride]
# blarg! the last frame is duplicatey
traj2 = mdtraj.load(inptrajs[0], top=top)[::stride][:-1]
traj2 += mdtraj.load(inptrajs[1], top=top)[::stride][:-1]
traj2 += mdtraj.load(inptrajs[2], top=top)[::stride]
traj3 = mdtraj.load(inptrajs, top=top,
discard_overlapping_frames=True)[::stride]
np.testing.assert_array_equal(traj1.xyz, traj3.xyz)
np.testing.assert_array_equal(traj1.xyz, traj2.xyz)
def test_ComparetoMDtraj(self):
import mdtraj as md
traj = pt.load(filename="./data/Tc5b.x",
top="./data/Tc5b.top")
m_top = md.load_prmtop("./data/Tc5b.top")
m_traj = md.load_mdcrd("./data/Tc5b.x", m_top)
m_traj.xyz = m_traj.xyz * 10 # convert `nm` to `Angstrom` unit
arr0 = pt.rmsd(traj, ref=0)
arr1 = pt.rmsd(traj, ref=0)
arr2 = pt.rmsd(traj, )
a_md0 = md.rmsd(m_traj, m_traj, 0)
aa_eq(arr0, arr1)
aa_eq(arr0, arr2)
aa_eq(arr0, a_md0)
arr0 = pt.rmsd(traj, ref=-1)
arr1 = pt.rmsd(traj, ref=-1)
a_md = md.rmsd(m_traj, m_traj, -1)
aa_eq(arr0, arr1)
aa_eq(arr0, a_md)
mask = ":3-18@CA,C"
atm = traj.top(mask)
arr0 = pt.rmsd(traj, ref=-1, mask=mask)
arr1 = pt.rmsd(traj, mask=atm.indices, ref=-1)
arr2 = pt.rmsd(traj, mask=list(atm.indices), ref=-1)
arr3 = pt.rmsd(traj, mask=tuple(atm.indices), ref=-1)
a_md = md.rmsd(m_traj, m_traj, -1, atm.indices)
aa_eq(arr0, a_md)
aa_eq(arr1, a_md)
aa_eq(arr2, a_md)
aa_eq(arr3, a_md)
fa = Trajectory(traj)
arr0 = pt.rmsd(fa, ref=-1, mask=mask)
arr1 = pt.rmsd(fa, mask=atm.indices, ref=-1)
arr2 = pt.rmsd(fa, mask=list(atm.indices), ref=-1)
arr3 = pt.rmsd(fa, mask=tuple(atm.indices), ref=-1)
a_md = md.rmsd(m_traj, m_traj, -1, atm.indices)
aa_eq(arr0, a_md)
aa_eq(arr1, a_md)
aa_eq(arr2, a_md)
aa_eq(arr3, a_md)
fa = Trajectory(traj)
mask = "!@H="
atm = fa.top(mask)
arr0 = pt.rmsd(fa, ref=4, mask=mask)
a_md = md.rmsd(m_traj, m_traj, 4, atm.indices)
# exclude 0-th frame for ref
aa_eq(arr0, a_md)
def test_ComparetoMDtraj(self):
import mdtraj as md
traj = pt.load(filename=tc5b_trajin, top=tc5b_top)
m_top = md.load_prmtop(tc5b_top)
m_traj = md.load_mdcrd(tc5b_trajin, m_top)
m_traj.xyz = m_traj.xyz * 10 # convert `nm` to `Angstrom` unit
arr0 = pt.rmsd(traj, ref=0)
arr1 = pt.rmsd(traj, ref=0)
arr2 = pt.rmsd(traj, )
a_md0 = md.rmsd(m_traj, m_traj, 0)
aa_eq(arr0, arr1)
aa_eq(arr0, arr2)
aa_eq(arr0, a_md0)
arr0 = pt.rmsd(traj, ref=-1)
arr1 = pt.rmsd(traj, ref=-1)
a_md = md.rmsd(m_traj, m_traj, -1)
aa_eq(arr0, arr1)
aa_eq(arr0, a_md)
mask = ":3-18@CA,C"
atm = traj.top(mask)
arr0 = pt.rmsd(traj, ref=-1, mask=mask)
arr1 = pt.rmsd(traj, mask=atm.indices, ref=-1)
arr2 = pt.rmsd(traj, mask=list(atm.indices), ref=-1)
arr3 = pt.rmsd(traj, mask=tuple(atm.indices), ref=-1)
a_md = md.rmsd(m_traj, m_traj, -1, atm.indices)
aa_eq(arr0, a_md)
aa_eq(arr1, a_md)
aa_eq(arr2, a_md)
aa_eq(arr3, a_md)
fa = Trajectory(traj)
arr0 = pt.rmsd(fa, ref=-1, mask=mask)
arr1 = pt.rmsd(fa, mask=atm.indices, ref=-1)
arr2 = pt.rmsd(fa, mask=list(atm.indices), ref=-1)
arr3 = pt.rmsd(fa, mask=tuple(atm.indices), ref=-1)
a_md = md.rmsd(m_traj, m_traj, -1, atm.indices)
aa_eq(arr0, a_md)
aa_eq(arr1, a_md)
aa_eq(arr2, a_md)
aa_eq(arr3, a_md)
fa = Trajectory(traj)
mask = "!@H="
atm = fa.top(mask)
arr0 = pt.rmsd(fa, ref=4, mask=mask)
a_md = md.rmsd(m_traj, m_traj, 4, atm.indices)
# exclude 0-th frame for ref
aa_eq(arr0, a_md)
def to_mdtraj_Topology(item, atom_indices='all', check=True):
if check:
digest_item(item, 'file:prmtop')
atom_indices = digest_atom_indices(atom_indices)
try:
from mdtraj import load_prmtop
except:
raise LibreryNotFoundError()
tmp_item = load_prmtop(item)
return tmp_item
def makeHMM(Trajectories, topology):
top = md.load_prmtop(topology)
alpha_carbons = [a.index for a in top.atoms if a.name == 'CA']
filenames = sorted(glob(Trajectories))
first_frame = md.load_frame(filenames[0], 0, top=top)
f = SuperposeFeaturizer(alpha_carbons, first_frame)
dataset = []
for fragment in filenames:
for chunk in md.iterload(fragment, chunk=100, top=top):
dataset.append(f.partial_transform(chunk))
hmm = GaussianHMM(n_states=8)
hmm.fit(dataset)
print(hmm.timescales_)
return hmm
def makeHMM(Trajectories, topology):
top = md.load_prmtop(topology)
alpha_carbons = [a.index for a in top.atoms if a.name == 'CA']
filenames = sorted(glob(Trajectories))
first_frame = md.load_frame(filenames[0], 0, top=top)
f = SuperposeFeaturizer(alpha_carbons, first_frame)
dataset = []
for fragment in filenames:
for chunk in md.iterload(fragment, chunk=100, top=top):
dataset.append(f.partial_transform(chunk))
hmm = GaussianHMM(n_states=8)
hmm.fit(dataset)
print(hmm.timescales_)
return hmm
def _nav_asserts1(I):
out = Path("processed.v2")
assert out.exists()
with (out / "p9704/9/19/info.json").open('r') as file:
info = json.load(file)
top = mdtraj.load_prmtop(info['stp']['outtop'])
for step in ['raw', 'cnv2', 'stp', 'ctr']:
assert len(info[step]['gens']) == I, (step, I)
for i in range(I):
assert Path(info['ctr']['gens'][i]).exists(), i
# Load all gens
traj = mdtraj.load(info['ctr']['gens'], top=top)
assert len(traj) == traj.n_frames
assert traj.n_atoms == N_TRIM_ATOMS, traj.n_atoms
assert traj.n_frames == PROJ9704_FRAMES_PER_GEN * I, traj.n_frames
def _trek_asserts1(I):
out = Path("processed.v2")
assert out.exists()
with (out / "p9712/5/32/info.json").open('r') as file:
info = json.load(file)
top = mdtraj.load_prmtop(info['stp']['outtop'])
for step in ['raw', 'cnv2', 'stp', 'ctr']:
assert len(info[step]['gens']) == I, (step, I)
for i in range(I):
assert Path(info['ctr']['gens'][i]).exists(), i
# Load all gens
traj = mdtraj.load(info['ctr']['gens'], top=top)
assert len(traj) == traj.n_frames
assert traj.n_atoms == 30962, (traj.n_atoms)
assert traj.n_frames == 16 * I, traj.n_frames
def _trek_asserts1(I):
out = Path("processed.v2")
assert out.exists()
with (out / "p9712/5/32/info.json").open('r') as file:
info = json.load(file)
top = mdtraj.load_prmtop(info['stp']['outtop'])
for step in ['raw', 'cnv2', 'stp', 'ctr']:
assert len(info[step]['gens']) == I, (step, I)
for i in range(I):
assert Path(info['ctr']['gens'][i]).exists(), i
# Load all gens
traj = mdtraj.load(info['ctr']['gens'], top=top)
assert len(traj) == traj.n_frames
assert traj.n_atoms == 30962, (traj.n_atoms)
assert traj.n_frames == 16 * I, traj.n_frames
def _nav_asserts1(I):
out = Path("processed.v2")
assert out.exists()
with (out / "p9704/9/19/info.json").open('r') as file:
info = json.load(file)
top = mdtraj.load_prmtop(info['stp']['outtop'])
for step in ['raw', 'cnv2', 'stp', 'ctr']:
assert len(info[step]['gens']) == I, (step, I)
for i in range(I):
assert Path(info['ctr']['gens'][i]).exists(), i
# Load all gens
traj = mdtraj.load(info['ctr']['gens'], top=top)
assert len(traj) == traj.n_frames
assert traj.n_atoms == N_TRIM_ATOMS, traj.n_atoms
assert traj.n_frames == PROJ9704_FRAMES_PER_GEN * I, traj.n_frames
def _trek_asserts2(I):
out = Path("processed.v2")
assert out.exists()
with (out / "p9761/3/9/info.json").open('r') as file:
info = json.load(file)
assert info['cnv2']['had_overlapping_frames']
top = mdtraj.load_prmtop(info['stp']['outtop'])
for step in ['raw', 'cnv1', 'cnv2', 'stp', 'ctr']:
assert len(info[step]['gens']) == I, (step, I)
for i in range(I):
assert Path(info['ctr']['gens'][i]).exists(), i
# Load all gens
traj = mdtraj.load(info['ctr']['gens'], top=top)
assert len(traj) == traj.n_frames
assert traj.n_atoms == 30962, traj.n_atoms
assert traj.n_frames == PROJ61_LENGTH_PER_GEN * I, traj.n_frames
def _trek_asserts2(I):
out = Path("processed.v2")
assert out.exists()
with (out / "p9761/3/9/info.json").open('r') as file:
info = json.load(file)
assert info['cnv2']['had_overlapping_frames']
top = mdtraj.load_prmtop(info['stp']['outtop'])
for step in ['raw', 'cnv1', 'cnv2', 'stp', 'ctr']:
assert len(info[step]['gens']) == I, (step, I)
for i in range(I):
assert Path(info['ctr']['gens'][i]).exists(), i
# Load all gens
traj = mdtraj.load(info['ctr']['gens'], top=top)
assert len(traj) == traj.n_frames
assert traj.n_atoms == 30962, traj.n_atoms
assert traj.n_frames == PROJ61_LENGTH_PER_GEN * I, traj.n_frames
ax.set_zlabel("z (nm)")
p = ax.scatter(com_matrix[:, 0],
com_matrix[:, 1],
com_matrix[:, 2],
c=time,
cmap='viridis')
cbar = fig.colorbar(p)
cbar.set_label('Time (ns)')
return fig, ax
if __name__ == '__main__':
plt.style.use(['seaborn-talk', 'seaborn-whitegrid'])
args = parser.parse_args()
print(args)
top = mdtraj.load_prmtop(args.prmtop)
traj = mdtraj.load([t for t in args.Trajectories],
top=args.prmtop,
stride=args.stride)
print('{} frames have been loaded'.format(traj.n_frames))
# Center all coordinates, makes center of geometry of the system (0, 0, 0)
traj.center_coordinates()
# Superpose trajectory onto first frame
# This is exactly like the 'hold selection steady' command in Chimera
traj.superpose(traj, 0)
# Create separate trajectory for the ligand
ligand_indices = top.select(args.ligand_selection)
lig_traj = traj.atom_slice(ligand_indices)
center_mass_ligand = mdtraj.compute_center_of_mass(lig_traj)
# Plot projections
def SMD(system, prmtop, platform, platformProperties, temperature, positions, velocities, keyInteraction, spring_k, dist_in, dist_fin, SMD_num, save_step, move_force_step):
# See page 456 of http://ambermd.org/doc12/Amber17.pdf
pullforce = mm.CustomExternalForce('k_sp*0.5*(dx^2+dy^2+dz^2); \
dx=x-(x0+displace_x); \
dy=y-(y0+displace_y); \
dz=z-(z0+displace_z);')
pullforce.addPerParticleParameter('k_sp')
pullforce.addPerParticleParameter('x0')
pullforce.addPerParticleParameter('y0')
pullforce.addPerParticleParameter('z0')
pullforce.addGlobalParameter("displace_x", 0.0 * u.nanometer)
pullforce.addGlobalParameter("displace_y", 0.0 * u.nanometer)
pullforce.addGlobalParameter("displace_z", 0.0 * u.nanometer)
keyInteraction_pos = [positions[keyInteraction[0]], positions[keyInteraction[1]]]
keyInteraction_dist = np.linalg.norm(keyInteraction_pos[0] - keyInteraction_pos[1])
keyInteraction_vect = (keyInteraction_pos[1] - keyInteraction_pos[0]) / keyInteraction_dist
keyInteraction_vect = u.Quantity(value=keyInteraction_vect, unit=u.nanometers)
pullto = keyInteraction_pos[0] + 0.25 * keyInteraction_vect
pullto_old = pullto
pullforce.addParticle(keyInteraction[1], [spring_k, pullto[0], pullto[1], pullto[2] ])
system.addForce(pullforce)
integrator = mm.LangevinIntegrator(temperature, 4/u.picosecond, 0.002*u.picosecond)
simulation = app.Simulation(prmtop.topology, system, integrator, platform,
platformProperties)
simulation.context.setPositions(positions)
simulation.context.setVelocities(velocities)
force_val_old = -spring_k*(keyInteraction_dist - dist_in)
energy_val_old = u.Quantity(value=0, unit=u.kilocalorie/u.mole)
f=open('duck_'+str(temperature).split()[0].replace('.0','K')+'_'+str(SMD_num)+'.dat','w')
steps = int((dist_fin.value_in_unit(u.nanometer) / 0.000001 - dist_in.value_in_unit(u.nanometer) / 0.000001)) / move_force_step
pull_distance = 0.000001 * move_force_step
#write trajectory
top = md.load_prmtop('system_solv.prmtop')
atom_subset = top.select('not water')
simulation.reporters.append(app.StateDataReporter("smd_"+str(temperature).split()[0].replace('.0','K')+"_"+str(SMD_num)+".csv", move_force_step, step=True, time=True, totalEnergy=True, kineticEnergy=True, potentialEnergy=True,
temperature=True, density=True, progress=True, totalSteps=move_force_step*steps, speed=True))
simulation.reporters.append(HDF5Reporter("smd_"+str(temperature).split()[0].replace('.0','K')+"_"+str(SMD_num)+".h5", move_force_step*20, atomSubset=atom_subset))
for i in range(steps):
state = simulation.context.getState(getPositions=True)
pos_keyInt = state.getPositions()
keyInteraction_pos = [pos_keyInt[keyInteraction[0]], pos_keyInt[keyInteraction[1]]]
keyInteraction_dist = np.linalg.norm(keyInteraction_pos[0]-keyInteraction_pos[1])
keyInteraction_vect = (keyInteraction_pos[1] - keyInteraction_pos[0]) / keyInteraction_dist
keyInteraction_vect = u.Quantity(value=keyInteraction_vect, unit=u.nanometers)
pullto = keyInteraction_pos[0] + (0.25 + float(i) * pull_distance) * keyInteraction_vect
displace = pullto - pullto_old
simulation.context.setParameter('displace_x', displace[0])
simulation.context.setParameter('displace_y', displace[1])
simulation.context.setParameter('displace_z', displace[2])
if i == 0:
distance = 0.0
else:
distance = pull_distance
dist_spring = (0.25 + float(i) * pull_distance) * u.nanometer
force_val = -spring_k * (keyInteraction_dist - dist_spring)
energy_val = energy_val_old + (distance * u.nanometer) * 0.5 * (force_val+force_val_old)
force_val_old = force_val
energy_val_old = energy_val
if (i%int(save_step/move_force_step)) == 0:
f.write(str(i)+' '+str(dist_spring)+' '+str(keyInteraction_dist)+' '+str(force_val)+' '+str(energy_val)+'\n')
simulation.step(move_force_step)
#f.write(str(i)+' '+str(dist_spring)+' '+str(keyInteraction_dist)+' '+str(force_val)+' '+str(energy_val)+'\n')
f.close()
import matplotlib.pyplot as plt
plt.style.use("ggplot")
from msmbuilder.featurizer import SuperposeFeaturizer
from msmbuilder.example_datasets import AlanineDipeptide
from msmbuilder.hmm import GaussianHMM
from msmbuilder.cluster import KCenters
from msmbuilder.msm import MarkovStateModel
from msmbuilder.dataset import dataset
import mdtraj as md
from glob import glob
filenames = sorted(glob("05_Prod_*.nc"))
topology = md.load_prmtop(glob("*nowat.prmtop")[0])
first_frame = md.load_frame(filenames[0], 0, top=topology)
indices = [atom.index for atom in topology.atoms if atom.element.symbol in ['C', 'O', 'N']]
featurizer = SuperposeFeaturizer(indices, first_frame)
sequences = []
for fragment in filenames:
for chunk in md.iterload(fragment, chunk = 100, top = topology):
sequences.append(featurizer.partial_transform(chunk))
lag_times = [1, 20, 50, 100, 200, 400]
hmm_ts0 = {}
hmm_ts1 = {}
n_states = [n for n in range(2,11,2)]
# coding: utf-8
import scipy as sp
from scipy import stats
import numpy as np
import matplotlib.pyplot as plt
import mdtraj as md
import sys
from sys import argv
import math
script, parmfile, trajfile = argv
top = md.load_prmtop(parmfile)
traj = md.load(trajfile, top=top)
print(traj)
psi_list = md.compute_psi(traj)
#phi_list = md.compute_phi(traj)
#chi1_list = md.compute_chi1(traj)
#chi2_list = md.compute_chi2(traj)
for i in xrange(psi_list[1].shape[1]):
dihedral_traj = psi_list[1][:, i]
dihedral_traj_deg = (dihedral_traj * 180) / math.pi
index = np.linspace(1, len(dihedral_traj),
len(dihedral_traj)).astype('int')
(n, bins, patches) = plt.hist(dihedral_traj_deg,
bins=300,
range=(-180.00, 180.00),
normed=True)
# coding: utf-8
# In[4]:
get_ipython().magic(u'pylab inline')
import mdtraj as md
# In[5]:
# Load parameters
top = md.load_prmtop("SYSTEM.top")
# In[12]:
# Select residues of ATP phosphate and Tyr
atp_phos = top.select("resSeq 288 and name PG")
tyr = top.select("resSeq 53 and name OH")
# In[13]:
# Check correct atoms selected
atom = top.atom(int(atp_phos))
print('''This is the %sth atom, atom type is %s.
Part of a %s residue.''' % (atom.index, atom.name, atom.residue.name))
atom = top.atom(int(tyr))
print('''This is the %sth atom, atom type is %s.
Part of a %s residue.''' % (atom.index, atom.name, atom.residue.name))
# In[15]:
# Make an array containing each atom pair involved in the interaction
import os
import matplotlib
from matplotlib.pyplot import *
import matplotlib.pyplot as plt
plt.style.use("ggplot")
from msmbuilder.featurizer import SuperposeFeaturizer
from msmbuilder.example_datasets import AlanineDipeptide
from msmbuilder.hmm import GaussianHMM
from msmbuilder.cluster import KCenters
from msmbuilder.msm import MarkovStateModel
from msmbuilder.dataset import dataset
import mdtraj as md
from glob import glob
filenames = sorted(glob("05_Prod_*.nc"))
topology = md.load_prmtop(glob("*nowat.prmtop")[0])
first_frame = md.load_frame(filenames[0], 0, top=topology)
indices = [
atom.index for atom in topology.atoms
if atom.element.symbol in ['C', 'O', 'N']
]
featurizer = SuperposeFeaturizer(indices, first_frame)
sequences = []
for fragment in filenames:
for chunk in md.iterload(fragment, chunk=100, top=topology):
sequences.append(featurizer.partial_transform(chunk))
lag_times = [1, 20, 50, 100, 200, 400]
hmm_ts0 = {}
# Run from a folder with directory structure *current folder*/ATP_OUTPUT/
# coding: utf-8
# get_ipython().magic(u'pylab inline')
import scipy as sp
from scipy import stats
import numpy as np
import matplotlib.pyplot as plt
import mdtraj as md
import sys
from sys import argv
#use pylab inline with ipython, but switch to import when converting to a script
import mdtraj as md
# Load parameters
top = md.load_prmtop("complex_solv.parm7")
# Load trajectory
traj = md.load('traj000000002.dcd', top=top)
print traj
# Select residues of ATP phosphate and Peptide Thr
atp_phos = top.select("resSeq 288 and name PG")
pep_thr = top.select("resSeq 293 and name OG1")
# Check correct atoms selected
atom = top.atom(int(atp_phos))
print('''This is the %sth atom, atom type is %s.
Part of a %s residue.''' % (atom.index, atom.name, atom.residue.name))
atom = top.atom(int(pep_thr))
print('''This is the %sth atom, atom type is %s.
