def _add_sbm_dihedrals(self, Model):
structure = Model.mapping
if hasattr(Model, "ref_traj"):
kd = self._default_parameters["kd"]
code = self._default_potentials["dihedral"]
for atm1, atm2, atm3, atm4 in structure._dihedrals:
idxs = np.array(
[[atm1.index, atm2.index, atm3.index, atm4.index]])
phi0 = md.compute_dihedrals(Model.ref_traj, idxs)[0][0]
#if temp_phi > 0:
# phi0 = 2.*np.pi - temp_phi
#else:
# phi0 = -temp_phi
self._add_dihedral(code, atm1, atm2, atm3, atm4, kd, phi0, 1)
self._add_dihedral(code, atm1, atm2, atm3, atm4, 0.5 * kd,
phi0, 3)
kd = self._default_parameters["ka"]
code = self._default_potentials["improper_dihedral"]
for atm1, atm2, atm3, atm4 in structure._improper_dihedrals:
# How does mdtraj angle correspond to gromacs?
idxs = np.array(
[[atm1.index, atm2.index, atm3.index, atm4.index]])
phi0 = md.compute_dihedrals(Model.ref_traj, idxs)[0][0]
self._add_dihedral(code, atm1, atm2, atm3, atm4, kd, phi0)
else:
util.missing_reference_warning()
def map_angles(self, trj):
"""
trj:
mdtraj pbject
output:
n_ec x n_frames
"""
# map coordinate space to reaction coorinates space
import mdtraj as md
import numpy as np
phi = md.compute_phi(trj)[1]
z_phi = np.array([phi[i][0] for i in range(len(phi))]) # in rad
psi = md.compute_psi(trj)[1]
z_psi = np.array([psi[i][0] for i in range(len(psi))]) # in rad
# (name CA or name N and resname ALA) or ((name C or name O) and resname ACE)
#atom_indix_theta = trj.topology.select('name O or name C or name N or name CA')
atom_indix_theta = trj.topology.select('(name O and resname ACE) or (name C and resname ACE) or (name N and resname ALA) or (name CA and resname ALA)')
theta = md.compute_dihedrals(trj, [atom_indix_theta])
z_theta = np.array([theta[i][0] for i in range(len(theta))])
# (name CA) or ((name C and resname ALA) or ((name N or name H) and resname NME))
#atom_indix_ksi = trj.topology.select('name CA or name C or name N or name H')
atom_indix_ksi = trj.topology.select('(name CA and resname ALA) or (name C and resname ALA) or (name N and resname NME) or (name H and resname NME)')
ksi = md.compute_dihedrals(trj, [atom_indix_ksi])
z_ksi = np.array([ksi[i][0] for i in range(len(ksi))])
trj_theta2 = []
trj_theta2.append(z_phi)
trj_theta2.append(z_psi)
trj_theta2.append(z_theta)
trj_theta2.append(z_ksi)
return trj_theta2
def main():
parser = argparse.ArgumentParser()
parser.add_argument('assignments', default='Macro4/MacroAssignments.h5', help='Path to an assignments file. (default=Macro4/MacroAssignments.h5)')
parser.add_argument('--project', default='ProjectInfo.yaml', help='Path to ProjectInfo.yaml file. (default=ProjectInfo.yaml)')
args = parser.parse_args()
project = Project.load_from(args.project)
t = reduce(operator.add, (project.load_traj(i) for i in range(project.n_trajs)))
phi_angles = md.compute_dihedrals(t, [PHI_INDICES]) * 180.0 / np.pi
psi_angles = md.compute_dihedrals(t, [PSI_INDICES]) * 180.0 / np.pi
state_index = np.hstack(io.loadh(args.assignments)['arr_0'])
for i in np.unique(state_index):
pp.plot(phi_angles[np.where(state_index == i)],
psi_angles[np.where(state_index == i)],
'x', label='State %d' % i)
pp.title("Alanine Dipeptide Macrostates")
pp.xlabel(r"$\phi$")
pp.ylabel(r"$\psi$")
annotate()
pp.legend(loc=1, labelspacing=0.075, prop={'size': 8.0}, scatterpoints=1,
markerscale=0.5, numpoints=1)
pp.xlim([-180, 180])
pp.ylim([-180, 180])
pp.show()
def test_generator():
N_FRAMES = 2
N_ATOMS = 5
xyz = np.asarray(np.random.randn(N_FRAMES, N_ATOMS, 3), dtype=np.float32)
ptraj = md.Trajectory(xyz=xyz, topology=None)
quartets = np.array(list(itertools.combinations(range(N_ATOMS), 4)), dtype=np.int32)
quartets2 = itertools.combinations(range(N_ATOMS), 4)
a = md.compute_dihedrals(ptraj, quartets)
b = md.compute_dihedrals(ptraj, quartets2)
eq(a, b)
def test_generator():
N_FRAMES = 2
N_ATOMS = 5
xyz = np.asarray(np.random.randn(N_FRAMES, N_ATOMS, 3), dtype=np.float32)
ptraj = md.Trajectory(xyz=xyz, topology=None)
quartets = np.array(list(itertools.combinations(range(N_ATOMS), 4)), dtype=np.int32)
quartets2 = itertools.combinations(range(N_ATOMS), 4)
a = md.compute_dihedrals(ptraj, quartets)
b = md.compute_dihedrals(ptraj, quartets2)
eq(a, b)
def test_dihedral_1(pymol, tmpdir):
xyz = '''MODEL 0
ATOM 1 A ACE 1 4.300 13.100 8.600 1.00 0.00
ATOM 2 B ACE 1 5.200 13.600 8.800 1.00 0.00
ATOM 3 C ACE 1 4.900 14.300 9.600 1.00 0.00
ATOM 4 D ACE 1 5.600 14.200 7.900 1.00 0.00
'''
script = '''
from pymol import cmd
with open('output.txt', 'w') as f:
f.write('%f' % cmd.get_dihedral('1/A', '1/B', '1/C', '1/D'))
'''
prevdir = os.path.abspath('.')
try:
os.chdir(tmpdir)
with open('xyz.pdb', 'w') as f:
f.write(xyz)
with open('pymolscript.py', 'w') as f:
f.write(script)
os.system('%s %s -cr %s' % (pymol, 'xyz.pdb', 'pymolscript.py'))
with open('output.txt') as f:
pymol_value = np.deg2rad(float(f.read()))
t = md.load('xyz.pdb')
finally:
os.chdir(prevdir)
mdtraj_value = md.compute_dihedrals(t, [[0, 1, 2, 3]])[0, 0]
np.testing.assert_array_almost_equal(pymol_value, mdtraj_value)
def test_VonMisesFeaturizer_describe_features():
feat = VonMisesFeaturizer()
rnd_traj = np.random.randint(len(trajectories))
features = feat.transform([trajectories[rnd_traj]])
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
for f in range(25):
f_index = np.random.choice(len(df))
atom_inds = df.iloc[f_index].atominds
bin_index = int(df.iloc[f_index].otherinfo.strip('bin-'))
dihedral_value = md.compute_dihedrals(trajectories[rnd_traj],
[atom_inds])
feature_value = [
vm.pdf(i, loc=feat.loc, kappa=feat.kappa)[bin_index]
for i in dihedral_value
]
assert (features[0][:, f_index] == feature_value).all()
def fix_dihedral(molecule, system, dihedrals):
"""
Author: Simon Boothroyd
impose a dihedral angle constraint so that bonds and angles can change during optimization
"""
mdtraj_trajectory = mdtraj.Trajectory(
xyz=molecule.conformers[0],
topology=mdtraj.Topology.from_openmm(molecule.topology.to_openmm()),
)
dihedral_angle = mdtraj.compute_dihedrals(mdtraj_trajectory, np.array([dihedrals]))[
0
][0].item()
dihedral_restraint = openmm.CustomTorsionForce(
f"k * min(min(abs(theta - theta_0), abs(theta - theta_0 + 2 * "
f"{np.pi})), abs(theta - theta_0 - 2 * {np.pi}))^2"
)
dihedral_restraint.addPerTorsionParameter("k")
dihedral_restraint.addPerTorsionParameter("theta_0")
theta_0 = dihedral_angle
k = 1.0 * unit.kilocalories_per_mole / unit.radian ** 2
dihedral_restraint.addTorsion(
dihedrals[0],
dihedrals[1],
dihedrals[2],
dihedrals[3],
[k, theta_0],
)
system.addForce(dihedral_restraint)
def calc_dihedral_energy(self, traj, improper=False, sum=True):
"""Energy for dihedral interactions
Parameters
----------
traj : mdtraj.Trajectory
sum : bool (opt.)
If sum=True return the total energy.
"""
phi = md.compute_dihedrals(traj, self._dihedral_idxs)
#if improper:
# phi = np.pi + md.compute_dihedrals(traj, self._dihedral_idxs) # ?
#else:
# phi = -temp_phi.copy()
# phi[temp_phi > 0] = 2.*np.pi - temp_phi[temp_phi > 0]
if sum:
Edihedral = np.zeros(traj.n_frames, float)
else:
Edihedral = np.zeros((traj.n_frames, self.n_dihedrals), float)
for i in range(self.n_dihedrals):
if sum:
Edihedral += self._dihedrals[i].V(phi[:,i])
else:
Edihedral[:,i] = self._dihedrals[i].V(phi[:,i])
return Edihedral
def get_phipsi(self, trajs, phi, psi):
#phi = [6, 8, 14, 16]
#psi = [4, 6, 8, 14]
PHI_INDICES = []
PSI_INDICES = []
for i in range(len(phi)):
PHI_INDICES.append(self.atom_indices.index(phi[i]))
PSI_INDICES.append(self.atom_indices.index(psi[i]))
#len_trajs = len(trajs)
print("PSI:", PSI_INDICES)
print("PHI:", PHI_INDICES)
phi_angles = md.compute_dihedrals(trajs, [PHI_INDICES]) * 180.0 / np.pi
psi_angles = md.compute_dihedrals(trajs, [PSI_INDICES]) * 180.0 / np.pi
#phi_psi=np.column_stack((phi_angles, psi_angles))
#return phi_psi
return phi_angles, psi_angles
def partial_transform(self, traj):
"""Featurize an MD trajectory into a vector space via calculation
of dihedral (torsion) angles of alpha carbon backbone
Parameters
----------
traj : mdtraj.Trajectory
A molecular dynamics trajectory to featurize.
Returns
-------
features : np.ndarray, dtype=float, shape=(n_samples, n_features)
A featurized trajectory is a 2D array of shape
`(length_of_trajectory x n_features)` where each `features[i]`
vector is computed by applying the featurization function
to the `i`th snapshot of the input trajectory.
"""
ca = [a.index for a in traj.top.atoms if a.name == 'CA']
if len(ca) < 4:
return np.zeros((len(traj), 0), dtype=np.float32)
alpha_indices = np.array(
[(ca[i - 1], ca[i], ca[i+1], ca[i + 2]) for i in range(1, len(ca) - 2)])
result = md.compute_dihedrals(traj, alpha_indices)
x = []
if self.atom_indices is None:
self.atom_indices = np.vstack(alpha_indices)
if self.sincos:
x.extend([np.cos(result), np.sin(result)])
else:
x.append(result)
return np.hstack(x)
def test_no_indices():
for fn in ['2EQQ.pdb', '1bpi.pdb']:
for opt in [True, False]:
t = md.load(get_fn(fn))
assert md.compute_distances(t, np.zeros((0,2), dtype=int), opt=opt).shape == (t.n_frames, 0)
assert md.compute_angles(t, np.zeros((0,3), dtype=int), opt=opt).shape == (t.n_frames, 0)
assert md.compute_dihedrals(t, np.zeros((0,4), dtype=int), opt=opt).shape == (t.n_frames, 0)
def DFG_dihedral_byrun(project, runs, def_DFG):
dihedral = []
dihedral_combinetrajs = []
print "Working on project %s." % project
for run in range(runs):
trajectories = dataset.MDTrajDataset(
"/cbio/jclab/projects/fah/fah-data/munged2/no-solvent/%d/run%d-clone*.h5"
% (project, run))
print "Run %s has %s trajectories." % (run, len(trajectories))
for traj in trajectories:
dihedral_combinetrajs.append(md.compute_dihedrals(traj, [def_DFG]))
# flatten
dihedral_combinetrajs = [
val for sublist in dihedral_combinetrajs for val in sublist
]
dihedral.append(dihedral_combinetrajs)
dihedral_combinetrajs = []
dihedral = np.asarray([dihedral])
return [dihedral]
def Get_Dihedral(self, atom_indices, periodic=True, newchunk=100, unit='degrees'):
'''
Uses MDTraj compute_dihedrals to compute the dihedrals formed by input atom indices in each frame.
Input: atom_indices-> An array of shape (num_dihedrals,4) with each row giving indices of four atoms to compute dihedrals for.
The angle is between the planes spanned by the first 3 atoms and the last 3 atoms, a torsion around the
bond between the middle two atoms.
periodic-> If periodic is True and the trajectory contains unitcell information, minimum image convention will be used
for dihedrls crossing periodic boundary.
unit -> MDTraj default unit is radian. Here by default, angles are returned in degrees.
Returns: an array of shape (num_frames, num_dihedrals).
'''
dihed_angles = numpy.empty((0, numpy.shape(atom_indices)[0])) #Return array
#Reload Trajectory in case user already worked with few/all chunks, working with newchunk frames at a time
self.Reload(newchunk=newchunk)
s_time = time.clock()
for partial_traj in self.traj_iter:
partial_dihed = md.compute_dihedrals(partial_traj, atom_indices, periodic=periodic)
dihed_angles = numpy.vstack((dihed_angles,partial_dihed))
e_time = time.clock()
print "Dihedral Calculation Finished! \nTotal time spent: \n\t%0.2f seconds process time\
\n\tOn average, %0.4f seconds per trajectory frame" % (e_time-s_time, (e_time-s_time)/numpy.array(dihed_angles).shape[0])
if unit.lower() == 'degrees':
return numpy.degrees(dihed_angles)
else:
return dihed_angles
def get_phipsi(self, trajs, phi, psi):
#phi = [6, 8, 14, 16]
#psi = [4, 6, 8, 14]
PHI_INDICES = []
PSI_INDICES = []
for i in xrange(len(phi)):
PHI_INDICES.append(self.atom_indices.index(phi[i]))
PSI_INDICES.append(self.atom_indices.index(psi[i]))
len_trajs = len(trajs)
print "PSI:", PSI_INDICES
print "PHI:", PHI_INDICES
phi_angles = md.compute_dihedrals(trajs, [PHI_INDICES]) * 180.0 / np.pi
psi_angles = md.compute_dihedrals(trajs, [PSI_INDICES]) * 180.0 / np.pi
#phi_psi=np.column_stack((phi_angles, psi_angles))
#return phi_psi
return phi_angles, psi_angles
def test_dihedral_1():
pymol = find_executable('pymol')
if pymol is None:
raise SkipTest("pymol executable not found")
xyz = '''MODEL 0
ATOM 1 A ACE 1 4.300 13.100 8.600 1.00 0.00
ATOM 2 B ACE 1 5.200 13.600 8.800 1.00 0.00
ATOM 3 C ACE 1 4.900 14.300 9.600 1.00 0.00
ATOM 4 D ACE 1 5.600 14.200 7.900 1.00 0.00
'''
script = '''
with open('output.txt', 'w') as f:
f.write('%f' % cmd.get_dihedral('1/A', '1/B', '1/C', '1/D'))
'''
with enter_temp_directory():
with open('xyz.pdb', 'w') as f:
f.write(xyz)
with open('pymolscript.py', 'w') as f:
f.write(script)
os.system('%s %s -cr %s' % (pymol, 'xyz.pdb', 'pymolscript.py'))
with open('output.txt') as f:
pymol_value = np.deg2rad(float(f.read()))
t = md.load('xyz.pdb')
mdtraj_value = md.compute_dihedrals(t, [[0,1,2,3]])[0,0]
np.testing.assert_array_almost_equal(pymol_value, mdtraj_value)
def check_chirality(traj, threshold=0):
"""Check whether each chiral amino acid is in the L-configuration
at each frame in the trajectory.
Parameters
----------
traj : mdtraj.trajectory
threshold : angle (in degrees)
angles below this threshold will be taken as errors
Returns
-------
chiral_atoms : list of 4-tuples
errors : numpy.ndarray, shape=(len(traj), len(chiral_atoms)), dtype=bool
errors[i,j] means there is a chirality error in frame i, quartet j
References
----------
[1] Stereochemical errors and their implications for molecular dynamics simulations.
Schriener et al., 2011. BMC Bioinformatics. DOI: 10.1186/1471-2105-12-190
http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-190
"""
chiral_atoms = get_chiral_atoms(traj.top)
dihedrals = md.compute_dihedrals(traj, chiral_atoms)
print(dihedrals)
in_degrees = dihedrals * 180 / np.pi
errors = in_degrees < threshold
return chiral_atoms, errors
def chi1_feat(traj, res):
chi1 = traj.topology.select('resid %i and (name C or name CA or name CB or name CG or name SG or name CG1 or name OG or name OG1)' %res)
if chi1.shape[0] != 4:
return None
chi1 = chi1.reshape([1,4])
traj_chi1 = md.compute_dihedrals(traj, chi1)
return traj_chi1
def check_cispeptide_bond(traj, dihedral_cutoff=85):
"""Given a trajectory, check every peptide bond in every frame.
Return a boolean array of shape=(len(traj), n_peptide_bonds)
Print a warning if any cis-peptide bonds are found
Parameters
----------
traj : mdtraj.Trajectory
dihedral_cutoff : default 85
Dihedral angle cutoff, in degrees
Returns
-------
problems : boolean array
problems[i,j] means that, in frame i, peptide bond j is cis
References
----------
[1] Stereochemical errors and their implications for molecular dynamics simulations.
Schriener et al., 2011. BMC Bioinformatics. DOI: 10.1186/1471-2105-12-190
http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-190
"""
indices, residues = find_peptide_bonds(traj.top)
angles = md.compute_dihedrals(traj, indices)
in_degrees = angles * 180 / np.pi
problems = in_degrees > dihedral_cutoff
if np.sum(problems) > 0:
print('Problems in {0} frames!'.format(sum(problems.sum(1) != 0)))
print('Problems in {0} bonds!'.format(sum(problems.sum(0) != 0)))
return problems
def sugar_angles_traj(traj, residues=None, angles=None):
top = traj.topology
# initialize nucleic class
nn = nucleic.Nucleic(top)
all_idx, rr = nn.get_sugar_torsion_idx(residues)
if (angles == None):
idx_angles = np.arange(all_idx.shape[1])
else:
# find indeces corresponding to angles
idx_angles = []
for i in range(len(angles)):
if (angles[i] in definitions.sugar_angles):
idx_angles.append(definitions.sugar_angles.index(angles[i]))
else:
msg = "# Fatal error. requested angle \"%s\" not available.\n" % angles[
i]
msg += "# Choose from: %s \n" % (definitions.sugar_angles)
sys.stderr.write(msg)
sys.exit(1)
idxs = (all_idx[:, idx_angles, :]).reshape(-1, 4)
missing = np.where(np.sum(idxs, axis=1) == 0)
torsions = md.compute_dihedrals(traj, idxs, opt=True)
# set to NaN where atoms are missing
torsions[:, missing[0]] = np.nan
torsions = torsions.reshape(
(traj.n_frames, all_idx.shape[0], len(idx_angles)))
return torsions, rr
def sidechain_example(yaml_file):
# Parse a YAML configuration, return as Dict
cfg = Settings(yaml_file).asDict()
structure = cfg['Structure']
#Select move type
sidechain = SideChainMove(structure, [1])
#Iniitialize object that selects movestep
sidechain_mover = MoveEngine(sidechain)
#Generate the openmm.Systems outside SimulationFactory to allow modifications
systems = SystemFactory(structure, sidechain.atom_indices, cfg['system'])
#Generate the OpenMM Simulations
simulations = SimulationFactory(systems, sidechain_mover, cfg['simulation'], cfg['md_reporters'],
cfg['ncmc_reporters'])
# Run BLUES Simulation
blues = BLUESSimulation(simulations, cfg['simulation'])
blues.run()
#Analysis
import mdtraj as md
import numpy as np
traj = md.load_netcdf('vacDivaline-test/vacDivaline.nc', top='tests/data/vacDivaline.prmtop')
indicies = np.array([[0, 4, 6, 8]])
dihedraldata = md.compute_dihedrals(traj, indicies)
with open("vacDivaline-test/dihedrals.txt", 'w') as output:
for value in dihedraldata:
output.write("%s\n" % str(value)[1:-1])
def DFG_dihedral_states(trajectories,def_DFG):
dihedral = []
for traj in trajectories:
dihedral.append(md.compute_dihedrals(traj,[def_DFG]))
return [dihedral]
def DFG_dihedral_states(trajectories, def_DFG):
dihedral = []
for traj in trajectories:
dihedral.append(md.compute_dihedrals(traj, [def_DFG]))
return [dihedral]
def load_data(self):
load_time_start = time.time()
data = []
for tfn in self.filenames:
kwargs = {} if tfn.endswith('h5') else {'top': self.top}
for t in md.iterload(tfn, chunk=self.args.split, **kwargs):
item = np.asarray(md.compute_dihedrals(t, self.indices), np.double)
data.append(item)
return data
def test_dihedral_op(self):
""" Create a dihedral order parameter """
psi_atoms = [6,8,14,16]
dihedral_op = op.CV_MDTraj_Function("psi", md.compute_dihedrals, indices= [psi_atoms])
md_dihed = md.compute_dihedrals(self.mdtraj, indices=[psi_atoms])
my_dihed = dihedral_op(self.traj)
np.testing.assert_allclose(md_dihed.reshape(md_dihed.shape[:-1]), my_dihed, rtol=10**-6, atol=10**-10)
def compute_dihedrals(self, indices):
"""
:param indices: Each row gives the indices of four atoms which together make a dihedral angle (np.ndarray, shape=(n_dihedrals, 4), dtype=int)
:return: dihedrals : np.ndarray, shape=(n_frames, n_dihedrals), dtype=float. The output array gives,
in each frame from the trajectory, each of the n_dihedrals torsion angles. The angles are measured in radians
"""
return md.compute_dihedrals(self.traj, indices)
def calculate_dihedrals():
print("Calculating dihedrals...")
traj_files = sorted(glob.glob("traj*xtc"))
traj = [ md.load(filename, top='structure.gro') for filename in traj_files ]
indices = list(traj[0].topology.select('backbone'))
dihedral_quartets = np.array([indices[i:i+4] for i in range(len(indices)-4)])
for i in range(len(traj)):
thetas = md.compute_dihedrals(traj[i], dihedral_quartets)
cos_sin_dihedrals = np.hstack([np.cos(thetas), np.sin(thetas)])
np.save('out_' + str(i) + '.npy', thetas)
def calculate_rama_energy(self, traj, total=True):
"""Calculate the one-body burial potential
Parameters
----------
traj : mdtraj.Trajectory
Trajectory to calculate energy over.
sum : opt, bool
If true (default) return the sum of the burial potentials. If
false, return the burial energy of each individual residue.
"""
rama = self.potential_forms["RAMA"]
pro_rama = self.potential_forms["RAMA_PROLINE"]
bb_traj = self.backbone_mapping.map_traj(traj)
# where does AWSEM define 0.
phi = md.compute_dihedrals(bb_traj, self._phi_idxs)
psi = md.compute_dihedrals(bb_traj, self._psi_idxs)
if total:
Vrama = np.zeros(bb_traj.n_frames, float)
else:
Vrama = np.zeros((bb_traj.n_frames, self.n_phi + self.n_pro_phi),
float)
for i in range(self.n_phi):
if total:
Vrama += rama.V(phi[:, i], psi[:, i])
else:
Vrama[:, i] = rama.V(phi[:, i], psi[:, i])
if self.n_pro_phi > 0:
pro_phi = md.compute_dihedrals(bb_traj, self._pro_phi_idxs)
pro_psi = md.compute_dihedrals(bb_traj, self._pro_psi_idxs)
for i in range(self.n_pro_phi):
if total:
Vrama += pro_rama.V(pro_phi[:, i], pro_psi[:, i])
else:
Vrama[:,
self.n_phi + i] = pro_rama.V(pro_phi[:, i],
pro_psi[:, i])
return Vrama
def transform(self, traj):
rad = mdtraj.compute_dihedrals(traj, self.angle_indexes, self.periodic)
if self.cossin:
rad = np.dstack((np.cos(rad), np.sin(rad)))
rad = rad.reshape(rad.shape[0], rad.shape[1] * rad.shape[2])
# convert to degrees
if self.deg and not self.cossin:
rad = np.rad2deg(rad)
return rad
def load_data(self):
load_time_start = time.time()
data = []
for tfn in self.filenames:
kwargs = {} if tfn.endswith('h5') else {'top': self.top}
for t in md.iterload(tfn, chunk=self.args.split, **kwargs):
item = np.asarray(md.compute_dihedrals(t, self.indices),
np.double)
data.append(item)
return data
def DFG_dihedral(trajectories,def_DFG):
dihedral = []
for traj in trajectories:
dihedral.append(md.compute_dihedrals(traj,[def_DFG]))
flattened_dihedral = np.asarray([val for sublist in dihedral for val in sublist])
return [flattened_dihedral]
def transform(self, traj):
# compute dihedral energy
phi = md.compute_dihedrals(traj, self.dihedrals)
Edih = np.array(map(lambda x,y: x(y), self.Vdih, phi.T)).T
# compute pair energy
r = md.compute_distances(traj, self.pairs)
Epair = np.array(map(lambda x,y: x(y), self.Vpair, r.T)).T
return np.hstack((Edih, Epair))
def DFG_dihedral(trajectories, def_DFG):
dihedral = []
for traj in trajectories:
dihedral.append(md.compute_dihedrals(traj, [def_DFG]))
flattened_dihedral = np.asarray([val for sublist in dihedral for val in sublist])
return [flattened_dihedral]
def compute_torsion(traj, *args):
"""
Compute the specified torsion.
"""
indices = [get_atom_index(traj, selection) for selection in args]
min_frame = 400
end_frame = len(traj)
short_traj = traj.slice(range(min_frame, end_frame), copy=False)
# Compute torsion in degrees
torsions = md.compute_dihedrals(short_traj, [indices]).squeeze() * (180.0 / np.pi)
return torsions
def calculate_rama_energy(self, traj, total=True):
"""Calculate the one-body burial potential
Parameters
----------
traj : mdtraj.Trajectory
Trajectory to calculate energy over.
sum : opt, bool
If true (default) return the sum of the burial potentials. If
false, return the burial energy of each individual residue.
"""
rama = self.potential_forms["RAMA"]
pro_rama = self.potential_forms["RAMA_PROLINE"]
bb_traj = self.backbone_mapping.map_traj(traj)
# where does AWSEM define 0.
phi = md.compute_dihedrals(bb_traj, self._phi_idxs)
psi = md.compute_dihedrals(bb_traj, self._psi_idxs)
if total:
Vrama = np.zeros(bb_traj.n_frames, float)
else:
Vrama = np.zeros((bb_traj.n_frames, self.n_phi + self.n_pro_phi), float)
for i in range(self.n_phi):
if total:
Vrama += rama.V(phi[:,i], psi[:,i])
else:
Vrama[:, i] = rama.V(phi[:,i], psi[:,i])
if self.n_pro_phi > 0:
pro_phi = md.compute_dihedrals(bb_traj, self._pro_phi_idxs)
pro_psi = md.compute_dihedrals(bb_traj, self._pro_psi_idxs)
for i in range(self.n_pro_phi):
if total:
Vrama += pro_rama.V(pro_phi[:,i], pro_psi[:,i])
else:
Vrama[:,self.n_phi + i] = pro_rama.V(pro_phi[:,i], pro_psi[:,i])
return Vrama
def test_dihedral_op(self):
""" Create a dihedral order parameter """
psi_atoms = [6,8,14,16]
dihedral_op = op.CV_MD_Function("psi", md.compute_dihedrals,
indices=[psi_atoms])
mdtraj_version = self.storage.trajectories.load(0).md()
md_dihed = md.compute_dihedrals(mdtraj_version, indices=[psi_atoms])
traj = self.storage.trajectories.load(0)
my_dihed = dihedral_op( traj )
np.testing.assert_allclose(md_dihed, my_dihed)
def test_build_mdtraj_function_cv(self):
if not HAS_OPENMM:
pytest.skip("Requires OpenMM for ops_load_trajectory")
yml = self.yml.format(kwargs=self.kwargs, func="compute_dihedrals")
dct = yaml.load(yml, Loader=yaml.FullLoader)
cv = MDTRAJ_CV_PLUGIN(dct)
assert isinstance(cv, MDTrajFunctionCV)
assert cv.func == md.compute_dihedrals
md_trj = md.load(self.ad_pdb)
ops_trj = paths.engines.openmm.tools.ops_load_trajectory(self.ad_pdb)
expected = md.compute_dihedrals(md_trj, indices=[[4, 6, 8, 14]])
npt.assert_array_almost_equal(
cv(ops_trj).reshape(expected.shape), expected)
def test_dihedral_op(self):
""" Create a dihedral order parameter """
psi_atoms = [6, 8, 14, 16]
dihedral_op = op.MDTrajFunctionCV(
"psi",
md.compute_dihedrals,
topology=self.topology,
indices=[psi_atoms])
md_dihed = md.compute_dihedrals(self.mdtraj, indices=[psi_atoms])
my_dihed = dihedral_op(self.traj_topology)
np.testing.assert_allclose(
md_dihed.reshape(md_dihed.shape[:-1]),
my_dihed, rtol=10 ** -6, atol=10 ** -10)
def calc_coord(traj, idxs):
vals = np.zeros(traj.n_frames)
idxs = np.array(idxs)
NM_IN_ANG = 10.0
RAD_IN_DEG = 180.0/np.pi
if len(idxs) == 2:
vals = md.compute_distances(traj, idxs.reshape(1, -1))
vals *= NM_IN_ANG
elif len(idxs) == 3:
vals = md.compute_angles(traj, idxs.reshape(1, -1))
vals *= RAD_IN_DEG
elif len(idxs) == 4:
vals = md.compute_dihedrals(traj, idxs.reshape(1, -1))
vals *= RAD_IN_DEG
return vals[:, 0]
def parseDihedFile(file, traj):
# Get topology
top = traj.topology
diheds = []
with open(file) as f:
for line in f:
# for each line from the input file select atoms and
# calculate dihed for the trajectory
ai = top.select("name " + line)
ai = np.array(np.split(ai, ai.shape[0] // 4))
dihed = np.concatenate(md.compute_dihedrals(traj, ai), axis=None)
# Append calculated dihedrals to the diheds
diheds.append(dihed)
diheds = np.array(diheds)
np.savetxt("diheds.txt", diheds)
def gln_array(*args):
if len(args) == 4:
dihedral = np.array([[args[0], args[1], args[2], args[3]]])
gln_dihedral_array = md.compute_dihedrals(traj, dihedral)
return gln_dihedral_array
elif len(args) == 3:
angle = np.array([[args[0], args[1], args[2]]])
gln_angle_array = md.compute_angles(traj, angle)
return gln_angle_array
else:
print(
'The amount of arguments (number of atoms) is not compatible with the selected light state'
)
def load_data(self):
load_time_start = time.time()
data = []
for tfn in self.filenames:
kwargs = {} if tfn.endswith('h5') else {'top': self.top}
for t in md.iterload(tfn, chunk=self.args.split, **kwargs):
item = np.asarray(md.compute_dihedrals(t, self.indices), np.double)
data.append(item)
print('Loading data into memory + vectorization: %f s' %
(time.time() - load_time_start))
print('''Fitting with %s timeseries from %d trajectories with %d
total observations''' % (len(data), len(self.filenames),
sum(len(e) for e in data)))
return data
def label_alanine(traj):
''' use dihedral angles to cluster and label alanine dipeptide
'''
# calculating psi and phi angles
psi_atoms = [6,8,14,16]
phi_atoms = [4,6,8,14]
indices = np.asarray([phi_atoms,psi_atoms])
dihedrals = md.compute_dihedrals(traj,indices)
# print(dihedrals)
trans_di = np.transpose(dihedrals)
# print(trans_di)
plt.scatter(trans_di[0],trans_di[1])
plt.xlabel('phi')
plt.ylabel('psi')
plt.savefig("/output/tempplot")
# deal with the periodical condition: manually put same cluster together
def shift_phi(x):
if x>2.2:
return -2*np.pi+x
else:
return x
def shift_psi(x):
if x<-2.2:
return 2*np.pi+x
else:
return x
dihedrals_shift = np.asarray(
[np.vectorize(shift_phi)(trans_di[0]),
np.vectorize(shift_psi)(trans_di[1])])
# print(trans_di)
plt.figure()
plt.scatter(dihedrals_shift[0],dihedrals_shift[1])
plt.xlabel('phi')
plt.ylabel('psi')
plt.savefig("/output/tempplot1")
print(dihedrals.shape)
print(dihedrals_shift.shape)
# do clustering with given initial centers
centers = np.array([[55,48],[-77,138],[-77, -39],[60, -72]])*np.pi/180.0
clu = kmeans(n_clusters=4,init=centers)
# labels = clu.fit_predict(dihedrals)
labels = clu.fit_predict(np.transpose(dihedrals_shift))
labels =
print("centers:")
print(clu.cluster_centers_*180.0/np.pi)
return labels
def dihedral_making(trajectories, dihedral_indices):
dihedral = []
for traj in trajectories:
dihedral.append(md.compute_dihedrals(traj, [dihedral_indices]))
flattened_dihedral = np.asarray([val for sublist in dihedral for val in sublist])
# import math
# flattened_dihedral_rotate = [A-(2*math.pi) if A >= 1.9 else A for A in flattened_dihedral]
flattened_dihedral = [A for A in flattened_dihedral]
# return flattened_dihedral_rotate
return flattened_dihedral
def test_DihedralFeaturizer_describe_features_nosincos():
feat = DihedralFeaturizer(sincos=False)
rnd_traj = np.random.randint(len(trajectories))
features = feat.transform([trajectories[rnd_traj]])
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
for f in range(25):
f_index = np.random.choice(len(df))
atom_inds = df.iloc[f_index].atominds
feature_value = md.compute_dihedrals(trajectories[rnd_traj],
[atom_inds])
if feat.sincos:
func = getattr(np, '%s' % df.iloc[f_index].otherinfo)
feature_value = func(feature_value)
assert (features[0][:, f_index] == feature_value.flatten()).all()
def test_DihedralFeaturizer_describe_features_nosincos():
feat = DihedralFeaturizer(sincos=False)
rnd_traj = np.random.randint(len(trajectories))
features = feat.transform([trajectories[rnd_traj]])
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
for f in range(25):
f_index = np.random.choice(len(df))
atom_inds = df.iloc[f_index].atominds
feature_value = md.compute_dihedrals(trajectories[rnd_traj],
[atom_inds])
if feat.sincos:
func = getattr(np, '%s' % df.iloc[f_index].otherinfo)
feature_value = func(feature_value)
assert (features[0][:, f_index] == feature_value.flatten()).all()
def test_VonMisesFeaturizer_describe_features():
feat = VonMisesFeaturizer()
rnd_traj = np.random.randint(len(trajectories))
features = feat.transform([trajectories[rnd_traj]])
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
for f in range(25):
f_index = np.random.choice(len(df))
atom_inds = df.iloc[f_index].atominds
bin_index = int(df.iloc[f_index].otherinfo.strip('bin-'))
dihedral_value = md.compute_dihedrals(trajectories[rnd_traj],
[atom_inds])
feature_value = [vm.pdf(i, loc=feat.loc, kappa=feat.kappa)[bin_index]
for i in dihedral_value]
assert (features[0][:, f_index] == feature_value).all()
def DFG_dihedral_byrun(files,def_DFG):
# Since we are going to sort files by where they are in first frame of clone0
# we can only analyze trajectories with a clone0 present.
path_base = files.split('*')[0]
clone0_files = "%s/*clone0.h5" % path_base
globfiles = glob(clone0_files)
runs_list = []
for filename in globfiles:
run_string = re.search('run([^-]+)',filename).group(1)
run = int(run_string)
if run not in runs_list:
runs_list.append(run)
runs_list.sort()
dihedral = []
dihedral_combinetrajs = []
for run in runs_list:
trajectories = dataset.MDTrajDataset("%s/run%d-*.h5" % (path_base,run))
print "Run %s has %s trajectories." % (run,len(trajectories))
for traj in trajectories:
dihedral_combinetrajs.append(md.compute_dihedrals(traj,[def_DFG]))
# flatten
dihedral_combinetrajs = [val for sublist in dihedral_combinetrajs for val in sublist]
dihedral.append(dihedral_combinetrajs)
dihedral_combinetrajs = []
dihedral = np.asarray([dihedral])
return [dihedral]
def DFG_dihedral_byrun(project,runs,def_DFG):
dihedral = []
dihedral_combinetrajs = []
print "Working on project %s." % project
for run in range(runs):
trajectories = dataset.MDTrajDataset("/cbio/jclab/projects/fah/fah-data/munged2/no-solvent/%d/run%d-clone*.h5" % (project,run))
print "Run %s has %s trajectories." % (run,len(trajectories))
for traj in trajectories:
dihedral_combinetrajs.append(md.compute_dihedrals(traj,[def_DFG]))
# flatten
dihedral_combinetrajs = [val for sublist in dihedral_combinetrajs for val in sublist]
dihedral.append(dihedral_combinetrajs)
dihedral_combinetrajs = []
dihedral = np.asarray([dihedral])
return [dihedral]
x = [eps[i][2] for i in range(len(eps))]
plt.scatter(x , np.absolute(deltaDist))
plt.savefig('fig1.png')
plt.show()
####################################################################################################################
# calculating dihedrals
####################################################################################################################
top1 = md.load('3SN6-R.pdb').topology
top2 = md.load('2RH1.pdb').topology
dhdrls101 = []
dhdrls102 = []
dhdrls201 = []
dhdrls202 = []
for i in range(len(eps)):
atoms101 = top1.select("type == C and resid " + str(eps[i][0]))
atoms102 = top1.select("type == C and resid " + str(eps[i][1]))
atoms201 = top2.select("type == C and resid " + str(eps[i][0]))
atoms202 = top2.select("type == C and resid " + str(eps[i][1]))
dhdrl101 = md.compute_dihedrals(t1, atoms101)
dhdrl102 = md.compute_dihedrals(t1, atoms102)
dhdrl201 = md.compute_dihedrals(t2, atoms201)
dhdrl202 = md.compute_dihedrals(t2, atoms202)
dhdrls101.append(dhdrl101)
dhdrls102.append(dhdrl102)
dhdrls201.append(dhdrl201)
dhdrls202.append(dhdrl202)
def psi_feat(traj, res):
psi = traj.topology.select('(resid %i and (name N or name CA or name C)) or (resid %i and name N)' %(res, res + 1))
psi = psi.reshape([1,4])
traj_psi = md.compute_dihedrals(traj, psi)
return traj_psi
with open('reweight.txt') as file:
lines = file.readlines()
for i in range(len(lines)):
lines[i] = lines[i].strip()
lines[i] = lines[i].split()
lines[i][0] = int(lines[i][0])
lines[i][1] = float(lines[i][1])
x_ticks = [''.join(choices[i][0]) for i in range(len(choices))]
dihedrals = []
for i in range(len(traj)):
dihedrals.append(md.compute_dihedrals(traj[i], np.asarray(indices)))
best_dihedrals = []
results = [0]*len(choices)
for i in range(len(dihedrals)): # each trajectory
for j in range(len(dihedrals[i])): # each frame
best_dihedrals = []
for k in range(len(dihedrals[i][j])): # each angle
if dihedrals[i][j][k] >= -1.57 and dihedrals[i][j][k] < 1.57:
best_dihedrals.append("c")
elif dihedrals[i][j][k] < -1.57 or dihedrals[i][j][k] >= 1.57:
best_dihedrals.append("t")
for k in range(len(choices)):
if best_dihedrals in choices[k]:
results[k] += 1000**lines[i][1]
# Parse a YAML configuration, return as Dict
cfg = Settings('sidechain_cuda.yaml').asDict()
structure = cfg['Structure']
#Select move type
sidechain = SideChainMove(structure, [1])
#Iniitialize object that selects movestep
sidechain_mover = MoveEngine(sidechain)
#Generate the openmm.Systems outside SimulationFactory to allow modifications
systems = SystemFactory(structure, sidechain.atom_indices, cfg['system'])
#Generate the OpenMM Simulations
simulations = SimulationFactory(systems, sidechain_mover, cfg['simulation'], cfg['md_reporters'],
cfg['ncmc_reporters'])
# Run BLUES Simulation
blues = BLUESSimulation(simulations, cfg['simulation'])
blues.run()
#Analysis
import mdtraj as md
import numpy as np
traj = md.load_netcdf('vacDivaline-test/vacDivaline.nc', top='tests/data/vacDivaline.prmtop')
indicies = np.array([[0, 4, 6, 8]])
dihedraldata = md.compute_dihedrals(traj, indicies)
with open("vacDivaline-test/dihedrals.txt", 'w') as output:
for value in dihedraldata:
output.write("%s\n" % str(value)[1:-1])
def map(self, traj):
rad = mdtraj.compute_dihedrals(traj, self.dih_indexes)
if self.deg:
return np.rad2deg(rad)
else:
return rad
