def run(self):
# Transform paths in absolute paths since we'll change the working directory
input_files = {local + os.path.splitext(path)[1]: os.path.abspath(path)
for local, path in self._file_paths.items() if 'moli' in local}
output_files = {local + os.path.splitext(path)[1]: os.path.abspath(path)
for local, path in self._file_paths.items() if 'molo' in local}
# Resolve all the names in the script
local_files = {local: local + os.path.splitext(path)[1]
for local, path in self._file_paths.items()}
script = self._script.format(**local_files) + 'quit\n'
with enter_temp_directory():
# Copy input files
for local_file, file_path in input_files.items():
shutil.copy(file_path, local_file)
# Save script and run tleap
with open('leap.in', 'w') as f:
f.write(script)
subprocess.check_output(['tleap', '-f', 'leap.in'])
#Copy back output files
for local_file, file_path in output_files.items():
shutil.copy(local_file, file_path)
def test_delete():
tica_mdl = load(os.path.join(base_dir,"dihedral_mdl/tica_mdl.pkl"))
tica_data = load(os.path.join(base_dir,"dihedral_mdl/tica_features.pkl"))
df = pd.read_pickle(os.path.join(base_dir,"./dihedral_mdl/feature_descriptor.pkl"))
with enter_temp_directory():
cur_dir = os.path.abspath(os.path.curdir)
TicaMetadSim(base_dir=cur_dir, tica_data=tica_data,tica_mdl=tica_mdl,
data_frame=df, grid=False, interval=False,render_scripts=True,
delete_existing=True)
f = open("tic_0/rand.txt",'w')
f.writelines("t")
f.close()
TicaMetadSim(base_dir=cur_dir, tica_mdl=tica_mdl,tica_data=tica_data,
data_frame=df, grid=False, interval=False,
delete_existing=False)
assert os.path.isfile("tic_0/rand.txt")
TicaMetadSim(base_dir=cur_dir, tica_mdl=tica_mdl,tica_data=tica_data,
data_frame=df, grid=False, interval=False,
delete_existing=True)
assert not os.path.isfile("tic_0/rand.txt")
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 test_iterload():
t_ref = md.load(get_fn('frame0.h5'))[:20]
with enter_temp_directory():
for ext in t_ref._savers().keys():
# only a 1 frame per file format
if ext in ('.ncrst', '.rst7'):
continue
if ext in ('.lh5') and (on_win and on_py3):
continue
fn = 'temp%s' % ext
t_ref.save(fn)
def test():
for stride in [1, 2, 3]:
loaded = md.load(fn, top=t_ref, stride=stride)
iterloaded = functools.reduce(lambda a, b: a.join(b), md.iterload(fn, top=t_ref, stride=stride, chunk=6))
eq(loaded.xyz, iterloaded.xyz)
eq(loaded.time, iterloaded.time)
eq(loaded.unitcell_angles, iterloaded.unitcell_angles)
eq(loaded.unitcell_lengths, iterloaded.unitcell_lengths)
test.description = 'test_iterload: %s' % ext
yield test
def chemical_shifts_ppm(trj):
"""Predict chemical shifts of a trajectory using ppm.
Parameters
----------
trj : Trajectory
Trajectory to predict shifts for.
Returns
-------
results : pandas.DataFrame
Dataframe containing results, with index consisting of
(resSeq, atom_name) pairs and columns for each frame in trj.
Notes
-----
You must have ppm available on your path; see
(http://spin.ccic.ohio-state.edu/index.php/download/index).
Chemical shift prediction is for PROTEIN atoms; trajectory objects
with ligands, solvent, ions, or other non-protein components may give
UNKNOWN RESULTS.
Please cite the appropriate reference below.
References
----------
.. [1] Li, DW, and Bruschweiler, R. "PPM: a side-chain and backbone chemical
shift predictor for the assessment of protein conformational ensembles."
J Biomol NMR. 2012 Nov;54(3):257-65.
"""
pd = import_('pandas')
binary = find_executable(PPM)
first_resSeq = trj.top.residue(0).resSeq
if binary is None:
raise OSError('External command not found. Looked for %s in PATH. `chemical_shifts_ppm` requires the external program PPM, available at http://spin.ccic.ohio-state.edu/index.php/download/index' % ', '.join(PPM))
with enter_temp_directory():
trj.save("./trj.pdb")
cmd = "%s -pdb trj.pdb -mode detail" % binary
return_flag = os.system(cmd)
if return_flag != 0:
raise(IOError("Could not successfully execute command '%s', check your PPM installation or your input trajectory." % cmd))
d = pd.read_csv("./bb_details.dat", delim_whitespace=True)
columns = ["resSeq", "resName", "name", "expt", "other"]
d = pd.read_csv("./bb_details.dat", delim_whitespace=True, header=None).drop([0, 4], axis=1)
d = d.rename(columns={1: "resSeq", 2: "resName", 3: "name"})
d["resSeq"] += first_resSeq - 1 # Fix bug in PPM that reindexes to 1
d = d.drop("resName", axis=1)
d = d.set_index(["resSeq", "name"])
d.columns = np.arange(trj.n_frames)
d.columns.name = "frame"
return d
def _test_against_vmd(pdb):
# this is probably not cross-platform compatible. I assume that the exact
# path to this CHARMM topology that is included with VMD depends on
# the install mechanism, especially for bundled mac or windows installers
VMD_ROOT = os.path.join(os.path.dirname(os.path.realpath(VMD)), '..')
top_paths = [os.path.join(r, f) for (r, _, fs) in os.walk(VMD_ROOT) for f in fs
if 'top_all27_prot_lipid_na.inp' in f]
assert len(top_paths) >= 0
top = os.path.abspath(top_paths[0]).replace(" ", "\\ ")
TEMPLATE = '''
package require psfgen
topology %(top)s
pdbalias residue HIS HSE
pdbalias atom ILE CD1 CD
segment U {pdb %(pdb)s}
coordpdb %(pdb)s U
guesscoord
writepdb out.pdb
writepsf out.psf
exit
''' % {'top': top, 'pdb' : pdb}
with enter_temp_directory():
with open('script.tcl', 'w') as f:
f.write(TEMPLATE)
os.system(' '.join([VMD, '-e', 'script.tcl', '-dispdev', 'none']))
out_pdb = md.load('out.pdb')
out_psf = md.load_psf('out.psf')
# make sure the two topologies are equal
eq(out_pdb.top, out_psf)
def _test_plumed_run():
tica_mdl = load(os.path.join(base_dir, "dihedral_mdl/tica_mdl.pkl"))
df = pd.read_pickle(
os.path.join(base_dir, "./dihedral_mdl/feature_descriptor.pkl"))
starting_coordinates_folder = os.path.join(base_dir,
"starting_coordinates")
with enter_temp_directory():
cur_dir = os.path.abspath(os.path.curdir)
TicaMetadSim(base_dir=cur_dir,
starting_coordinates_folder=starting_coordinates_folder,
tica_mdl=tica_mdl,
data_frame=df,
grid=False,
interval=False,
wall=False,
platform='CPU',
n_iterations=1,
swap_rate=5,
sim_save_rate=10,
pace=1,
stride=1)
meta_sim = load("./metad_sim.pkl")
run_meta_sim("./metad_sim.pkl")
for i in range(1):
for j in [meta_sim.bias_file, meta_sim.hills_file,\
"speed_report.txt","trajectory.dcd","plumed_script.dat",\
"checkpt.chk"]:
print(i, j)
assert os.path.isfile(os.path.join(cur_dir, "tic_%d" % i, j))
assert not os.path.isfile("swap_log.txt")
def test_against_vmd(pdb, get_fn):
pdb = get_fn(pdb)
# this is probably not cross-platform compatible. I assume that the exact
# path to this CHARMM topology that is included with VMD depends on
# the install mechanism, especially for bundled mac or windows installers
VMD_ROOT = os.path.join(os.path.dirname(os.path.realpath(VMD)), '..')
top_paths = [os.path.join(r, f) for (r, _, fs) in os.walk(VMD_ROOT) for f in fs
if 'top_all27_prot_lipid_na.inp' in f]
assert len(top_paths) >= 0
top = os.path.abspath(top_paths[0]).replace(" ", "\\ ")
TEMPLATE = '''
package require psfgen
topology %(top)s
pdbalias residue HIS HSE
pdbalias atom ILE CD1 CD
segment U {pdb %(pdb)s}
coordpdb %(pdb)s U
guesscoord
writepdb out.pdb
writepsf out.psf
exit
''' % {'top': top, 'pdb': pdb}
with enter_temp_directory():
with open('script.tcl', 'w') as f:
f.write(TEMPLATE)
subprocess.check_call([VMD, '-startup', 'script.tcl', '-dispdev', 'none'])
out_pdb = md.load('out.pdb')
out_psf = md.load_psf('out.psf')
# make sure the two topologies are equal
eq(out_pdb.top, out_psf)
def test_phase_creation():
"""Phases are initialized correctly by Yank.create()."""
phase_name = 'my-solvent-phase'
toluene = testsystems.TolueneImplicit()
protocol = AbsoluteAlchemicalFactory.defaultSolventProtocolImplicit()
atom_indices = find_components(toluene.system, toluene.topology, 'resname TOL')
phase = AlchemicalPhase(phase_name, toluene.system, toluene.topology,
toluene.positions, atom_indices, protocol)
thermodynamic_state = ThermodynamicState(temperature=300.0*unit.kelvin)
# Create new simulation.
with enter_temp_directory():
output_dir = 'output'
utils.config_root_logger(verbose=False)
yank = Yank(store_directory=output_dir)
yank.create(thermodynamic_state, phase)
# Netcdf dataset has been created
nc_path = os.path.join(output_dir, phase_name + '.nc')
assert os.path.isfile(nc_path)
# Read data
try:
nc_file = netcdf.Dataset(nc_path, mode='r')
metadata_group = nc_file.groups['metadata']
serialized_topology = metadata_group.variables['topology'][0]
finally:
nc_file.close()
# Topology has been stored correctly
deserialized_topology = utils.deserialize_topology(serialized_topology)
assert deserialized_topology == mdtraj.Topology.from_openmm(toluene.topology)
def chemical_shifts_shiftx2(trj, pH=5.0, temperature=298.00):
"""Predict chemical shifts of a trajectory using ShiftX2.
Parameters
----------
trj : Trajectory
Trajectory to predict shifts for.
pH : float, optional, default=5.0
pH value which gets passed to the ShiftX2 predictor.
temperature : float, optional, default=298.00
Temperature which gets passed to the ShiftX2 predictor.
Returns
-------
results : pandas DataFrame
Dataframe containing results, with index consisting of
(resSeq, atom_name) pairs and columns for each frame in trj.
Notes
-----
You must have ShiftX2 available on your path; see (http://www.shiftx2.ca/).
Chemical shift prediction is for PROTEIN atoms; trajectory objects
with ligands, solvent, ions, or other non-protein components may give
UNKNOWN RESULTS.
Please cite the appropriate reference below.
References
----------
.. [1] Beomsoo Han, Yifeng Liu, Simon Ginzinger, and David Wishart.
"SHIFTX2: significantly improved protein chemical shift
prediction." J. Biomol. NMR, 50, 1 43-57 (2011)
"""
pd = import_('pandas')
binary = find_executable(SHIFTX2)
if binary is None:
raise OSError('External command not found. Looked for %s in PATH. `chemical_shifts_shiftx2` requires the external program SHIFTX2, available at http://www.shiftx2.ca/' % ', '.join(SHIFTX2))
results = []
with enter_temp_directory():
for i in range(trj.n_frames):
trj[i].save("./trj%d.pdb" % i)
cmd = "%s -b 'trj*.pdb' -p %.1f -t %.2f" % (binary, pH, temperature)
return_flag = os.system(cmd)
if return_flag != 0:
raise(IOError("Could not successfully execute command '%s', check your ShiftX2 installation or your input trajectory." % cmd))
for i in range(trj.n_frames):
d = pd.read_csv("./trj%d.pdb.cs" % i)
d.rename(columns={"NUM": "resSeq", "RES": "resName", "ATOMNAME": "name"}, inplace=True)
d["frame"] = i
results.append(d)
results = pd.concat(results)
results = results.pivot_table(rows=["resSeq", "name"], cols="frame", values="SHIFT")
return results
def test_load_freesolv_gaffmol2_vs_sybylmol2_vs_obabelpdb():
with enter_temp_directory():
tar_filename = "freesolve_v0.3.tar.bz2"
tar = tarfile.open(get_fn(tar_filename), mode="r:bz2")
tar.extractall()
tar.close()
with open("./v0.3/database.pickle", 'rb') as f:
database = pickle.load(f)
for key in database:
gaff_filename = "./v0.3/mol2files_gaff/%s.mol2" % key
pdb_filename = "./v0.3/mol2files_gaff/%s.pdb" % key
sybyl_filename = "./v0.3/mol2files_sybyl/%s.mol2" % key
cmd = "obabel -imol2 %s -opdb > %s 2>/dev/null" % (sybyl_filename,
pdb_filename)
assert os.system(cmd) == 0
t_pdb = md.load(pdb_filename)
t_gaff = md.load(gaff_filename)
t_sybyl = md.load(sybyl_filename)
eq(t_pdb.n_atoms, t_gaff.n_atoms)
eq(t_pdb.n_atoms, t_sybyl.n_atoms)
eq(t_pdb.n_frames, t_gaff.n_frames)
eq(t_pdb.n_frames, t_gaff.n_frames)
eq(t_pdb.xyz, t_gaff.xyz, decimal=4)
eq(t_pdb.xyz, t_sybyl.xyz, decimal=4)
top_pdb, bonds_pdb = t_pdb.top.to_dataframe()
top_gaff, bonds_gaff = t_gaff.top.to_dataframe()
top_sybyl, bonds_sybyl = t_sybyl.top.to_dataframe()
eq(top_sybyl.name.values, top_pdb.name.values)
# eq(top_gaff.name.values, top_sybyl.name.values) # THEY CAN HAVE DIFFERENT NAMES, so this isn't TRUE!
def make_bonds_comparable(bond_array):
"""Create a bond connectivity matrix from a numpy array of atom pairs. Avoids having to compare the order in which bonds are listed."""
n_bonds = len(bond_array)
data = np.ones(n_bonds)
i = bond_array[:, 0]
j = bond_array[:, 1]
matrix = scipy.sparse.coo_matrix(
(data, (i, j)),
shape=(t_pdb.n_atoms, t_pdb.n_atoms)).toarray()
return matrix + matrix.T # Symmetrize to account for (a ~ b) versus (b ~ a)
bond_matrix_pdb = make_bonds_comparable(bonds_pdb)
bond_matrix_gaff = make_bonds_comparable(bonds_gaff)
bond_matrix_sybyl = make_bonds_comparable(bonds_sybyl)
eq(bond_matrix_pdb, bond_matrix_gaff)
eq(bond_matrix_pdb, bond_matrix_sybyl)
def chemical_shifts_ppm(trj):
"""Predict chemical shifts of a trajectory using ppm.
Parameters
----------
trj : Trajectory
Trajectory to predict shifts for.
Returns
-------
results : pandas.DataFrame
Dataframe containing results, with index consisting of
(resSeq, atom_name) pairs and columns for each frame in trj.
Notes
-----
You must have ppm available on your path; see
(http://spin.ccic.ohio-state.edu/index.php/download/index).
Chemical shift prediction is for PROTEIN atoms; trajectory objects
with ligands, solvent, ions, or other non-protein components may give
UNKNOWN RESULTS.
Please cite the appropriate reference below.
References
----------
.. [1] Li, DW, and Bruschweiler, R. "PPM: a side-chain and backbone chemical
shift predictor for the assessment of protein conformational ensembles."
J Biomol NMR. 2012 Nov;54(3):257-65.
"""
pd = import_('pandas')
binary = find_executable(PPM)
first_resSeq = trj.top.residue(0).resSeq
if binary is None:
raise OSError('External command not found. Looked for %s in PATH. `chemical_shifts_ppm` requires the external program PPM, available at http://spin.ccic.ohio-state.edu/index.php/download/index' % ', '.join(PPM))
with enter_temp_directory():
trj.save("./trj.pdb")
cmd = "%s -pdb trj.pdb -mode detail" % binary
return_flag = os.system(cmd)
if return_flag != 0:
raise(IOError("Could not successfully execute command '%s', check your PPM installation or your input trajectory." % cmd))
d = pd.read_table("./bb_details.dat", index_col=False, header=None, sep="\s*").drop([3], axis=1)
d = d.rename(columns={0: "resSeq", 1: "resName", 2: "name"})
d["resSeq"] += first_resSeq - 1 # Fix bug in PPM that reindexes to 1
d = d.drop("resName", axis=1)
d = d.set_index(["resSeq", "name"])
d.columns = np.arange(trj.n_frames)
d.columns.name = "frame"
return d
def chemical_shifts_shiftx2(trj):
"""Predict chemical shifts of a trajectory using ShiftX2.
Parameters
----------
trj : Trajectory
Trajectory to predict shifts for.
Returns
-------
results : pandas DataFrame
Dataframe containing results, with index consisting of
(resSeq, atom_name) pairs and columns for each frame in trj.
Notes
-----
You must have ShiftX2 available on your path; see (http://www.shiftx2.ca/).
Chemical shift prediction is for PROTEIN atoms; trajectory objects
with ligands, solvent, ions, or other non-protein components may give
UNKNOWN RESULTS.
Please cite the appropriate reference below.
References
----------
.. [1] Beomsoo Han, Yifeng Liu, Simon Ginzinger, and David Wishart.
"SHIFTX2: significantly improved protein chemical shift
prediction." J. Biomol. NMR, 50, 1 43-57 (2011)
"""
pd = import_('pandas')
binary = find_executable(SHIFTX2)
if binary is None:
raise OSError('External command not found. Looked for %s in PATH. `chemical_shifts_shiftx2` requires the external program SHIFTX2, available at http://www.shiftx2.ca/' % ', '.join(SHIFTX2))
results = []
with enter_temp_directory():
for i in range(trj.n_frames):
trj[i].save("./trj%d.pdb" % i)
cmd = "%s -b 'trj*.pdb'" % binary
return_flag = os.system(cmd)
if return_flag != 0:
raise(IOError("Could not successfully execute command '%s', check your ShiftX2 installation or your input trajectory." % cmd))
for i in range(trj.n_frames):
d = pd.read_csv("./trj%d.pdb.cs" % i)
d.rename(columns={"NUM": "resSeq", "RES": "resName", "ATOMNAME": "name"}, inplace=True)
d["frame"] = i
results.append(d)
results = pd.concat(results)
results = results.pivot_table(rows=["resSeq", "name"], cols="frame", values="SHIFT")
return results
def test_load_freesolv_gaffmol2_vs_sybylmol2_vs_obabelpdb():
with enter_temp_directory():
tar_filename = "freesolve_v0.3.tar.bz2"
tar = tarfile.open(get_fn(tar_filename), mode="r:bz2")
tar.extractall()
tar.close()
with open("./v0.3/database.pickle", 'rb') as f:
database = pickle.load(f)
for key in database:
gaff_filename = "./v0.3/mol2files_gaff/%s.mol2" % key
pdb_filename = "./v0.3/mol2files_gaff/%s.pdb" % key
sybyl_filename = "./v0.3/mol2files_sybyl/%s.mol2" % key
cmd = "obabel -imol2 %s -opdb > %s 2>/dev/null" % (sybyl_filename, pdb_filename)
assert os.system(cmd) == 0
t_pdb = md.load(pdb_filename)
t_gaff = md.load(gaff_filename)
t_sybyl = md.load(sybyl_filename)
eq(t_pdb.n_atoms, t_gaff.n_atoms)
eq(t_pdb.n_atoms, t_sybyl.n_atoms)
eq(t_pdb.n_frames, t_gaff.n_frames)
eq(t_pdb.n_frames, t_gaff.n_frames)
eq(t_pdb.xyz, t_gaff.xyz, decimal=4)
eq(t_pdb.xyz, t_sybyl.xyz, decimal=4)
top_pdb, bonds_pdb = t_pdb.top.to_dataframe()
top_gaff, bonds_gaff = t_gaff.top.to_dataframe()
top_sybyl, bonds_sybyl = t_sybyl.top.to_dataframe()
eq(top_sybyl.name.values, top_pdb.name.values)
# eq(top_gaff.name.values, top_sybyl.name.values) # THEY CAN HAVE DIFFERENT NAMES, so this isn't TRUE!
def make_bonds_comparable(bond_array):
"""Create a bond connectivity matrix from a numpy array of atom pairs. Avoids having to compare the order in which bonds are listed."""
n_bonds = len(bond_array)
data = np.ones(n_bonds)
i = bond_array[:, 0]
j = bond_array[:, 1]
matrix = scipy.sparse.coo_matrix((data, (i, j)), shape=(t_pdb.n_atoms, t_pdb.n_atoms)).toarray()
return matrix + matrix.T # Symmetrize to account for (a ~ b) versus (b ~ a)
bond_matrix_pdb = make_bonds_comparable(bonds_pdb)
bond_matrix_gaff = make_bonds_comparable(bonds_gaff)
bond_matrix_sybyl = make_bonds_comparable(bonds_sybyl)
eq(bond_matrix_pdb, bond_matrix_gaff)
eq(bond_matrix_pdb, bond_matrix_sybyl)
def smiles_to_antechamber(
smiles_string,
gaff_mol2_filename,
frcmod_filename,
residue_name="MOL",
strictStereo=False,
):
"""Build a molecule from a smiles string and run antechamber,
generating GAFF mol2 and frcmod files from a smiles string. Charges
will be generated using the OpenEye QuacPac AM1-BCC implementation.
Parameters
----------
smiles_string : str
Smiles string of molecule to construct and charge
gaff_mol2_filename : str
Filename of mol2 file output of antechamber, with charges
created from openeye
frcmod_filename : str
Filename of frcmod file output of antechamber. Most likely
this file will be almost empty, at least for typical molecules.
residue_name : str, optional, default="MOL"
OpenEye writes mol2 files with <0> as the residue / ligand name.
This chokes many mol2 parsers, so we replace it with a string of
your choosing. This might be useful for downstream applications
if the residue names are required to be unique.
strictStereo : bool, optional, default=False
If False, permits smiles strings with unspecified stereochemistry.
See https://docs.eyesopen.com/omega/usage.html
"""
oechem = import_("openeye.oechem")
if not oechem.OEChemIsLicensed():
raise (ImportError("Need License for oechem!"))
# Get the absolute path so we can find these filenames from inside a temporary directory.
gaff_mol2_filename = os.path.abspath(gaff_mol2_filename)
frcmod_filename = os.path.abspath(frcmod_filename)
m = smiles_to_oemol(smiles_string)
m = get_charges(m, strictStereo=strictStereo, keep_confs=1)
with enter_temp_directory(
): # Avoid dumping 50 antechamber files in local directory.
_unused = molecule_to_mol2(m, "./tmp.mol2", residue_name=residue_name)
net_charge = oechem.OENetCharge(m)
tmp_gaff_mol2_filename, tmp_frcmod_filename = run_antechamber(
"tmp", "./tmp.mol2", charge_method=None,
net_charge=net_charge) # USE OE AM1BCC charges!
shutil.copy(tmp_gaff_mol2_filename, gaff_mol2_filename)
shutil.copy(tmp_frcmod_filename, frcmod_filename)
def smiles_to_antechamber(
smiles_string,
gaff_mol2_filename,
frcmod_filename,
residue_name="MOL",
strictStereo=False,
):
"""Build a molecule from a smiles string and run antechamber,
generating GAFF mol2 and frcmod files from a smiles string. Charges
will be generated using the OpenEye QuacPac AM1-BCC implementation.
Parameters
----------
smiles_string : str
Smiles string of molecule to construct and charge
gaff_mol2_filename : str
Filename of mol2 file output of antechamber, with charges
created from openeye
frcmod_filename : str
Filename of frcmod file output of antechamber. Most likely
this file will be almost empty, at least for typical molecules.
residue_name : str, optional, default="MOL"
OpenEye writes mol2 files with <0> as the residue / ligand name.
This chokes many mol2 parsers, so we replace it with a string of
your choosing. This might be useful for downstream applications
if the residue names are required to be unique.
strictStereo : bool, optional, default=False
If False, permits smiles strings with unspecified stereochemistry.
See https://docs.eyesopen.com/omega/usage.html
"""
oechem = import_("openeye.oechem")
if not oechem.OEChemIsLicensed():
raise (ImportError("Need License for oechem!"))
# Get the absolute path so we can find these filenames from inside a temporary directory.
gaff_mol2_filename = os.path.abspath(gaff_mol2_filename)
frcmod_filename = os.path.abspath(frcmod_filename)
m = smiles_to_oemol(smiles_string)
m = get_charges(m, strictStereo=strictStereo, keep_confs=1)
with enter_temp_directory(): # Avoid dumping 50 antechamber files in local directory.
_unused = molecule_to_mol2(m, "./tmp.mol2", residue_name=residue_name)
net_charge = oechem.OENetCharge(m)
tmp_gaff_mol2_filename, tmp_frcmod_filename = run_antechamber(
"tmp", "./tmp.mol2", charge_method=None, net_charge=net_charge
) # USE OE AM1BCC charges!
shutil.copy(tmp_gaff_mol2_filename, gaff_mol2_filename)
shutil.copy(tmp_frcmod_filename, frcmod_filename)
def test_against_gmx():
t1 = md.load(get_fn('frame0.pdb'))
# generated by converting frame0.pdb to gro with g_trajconv
t2 = md.load(get_fn('frame0.gro'))
with enter_temp_directory():
t1.save('temp.gro')
t3 = md.load('temp.gro')
eq(t1.xyz, t2.xyz)
eq(t1.xyz, t3.xyz)
eq(t1.time, t2.time)
eq(t1.time, t3.time)
eq(t1.unitcell_vectors, t2.unitcell_vectors)
eq(t1.unitcell_vectors, t3.unitcell_vectors)
def test_no_alchemical_atoms():
"""Test whether Yank raises exception when no alchemical atoms are specified."""
toluene = testsystems.TolueneImplicit()
# Create parameters. With the exception of atom_indices, all other
# parameters must be legal, we don't want to catch an exception
# different than the one we are testing.
phase = AlchemicalPhase(name='solvent-implicit', reference_system=toluene.system,
reference_topology=toluene.topology,
positions=toluene.positions, atom_indices={'ligand': []},
protocol=AbsoluteAlchemicalFactory.defaultSolventProtocolImplicit())
thermodynamic_state = ThermodynamicState(temperature=300.0*unit.kelvin)
# Create new simulation.
with enter_temp_directory():
yank = Yank(store_directory='output')
yank.create(thermodynamic_state, phase)
def test_force_overwrite():
t_ref = md.load(get_fn('native2.pdb'), no_boxchk=True)
for ext in t_ref._savers().keys():
with enter_temp_directory():
def test_1():
fn = 'temp-1%s' % ext
open(fn, 'w').close()
t_ref.save(fn, force_overwrite=True)
def test_2():
fn = 'temp-2%s' % ext
open(fn, 'w').close()
assert_raises(IOError,
lambda: t_ref.save(fn, force_overwrite=False))
test_1.description = 'test_force_overwrite (1): %s' % ext
test_2.description = 'test_force_overwrite (2): %s' % ext
yield test_1
yield test_2
def test_setup():
tica_mdl = load(os.path.join(base_dir,"dihedral_mdl/tica_mdl.pkl"))
tica_data = load(os.path.join(base_dir,"dihedral_mdl/tica_features.pkl"))
df = pd.read_pickle(os.path.join(base_dir,"./dihedral_mdl/feature_descriptor.pkl"))
with enter_temp_directory():
cur_dir = os.path.abspath(os.path.curdir)
TicaMetadSim(base_dir=cur_dir,tica_data=tica_data, tica_mdl=tica_mdl,
data_frame=df, grid=False, interval=False,wall=False,
render_scripts=True,
delete_existing=True)
metad_sim = load("./metad_sim.pkl")
assert eq(tica_mdl.components_, metad_sim.tica_mdl.components_)
for i in range(metad_sim.n_tics):
assert os.path.isdir("tic_%d"%i)
assert os.path.isfile(("tic_%d/plumed.dat"%i))
assert os.path.isfile("sub.sh")
def test_resuming():
"""Test that sampling correctly resumes."""
# Prepare ModifiedHamiltonianExchange arguments
toluene_test = testsystems.TolueneImplicit()
ligand_atoms = range(15)
alchemical_factory = AbsoluteAlchemicalFactory(toluene_test.system,
ligand_atoms=ligand_atoms)
base_state = ThermodynamicState(temperature=300.0 * unit.kelvin)
base_state.system = alchemical_factory.alchemically_modified_system
alchemical_state1 = AlchemicalState(lambda_electrostatics=1.0,
lambda_sterics=1.0)
alchemical_state0 = AlchemicalState(lambda_electrostatics=0.0,
lambda_sterics=0.0)
alchemical_states = [alchemical_state1, alchemical_state0]
positions = toluene_test.positions
# We pass as fully_interacting_expanded_state and noninteracting_expanded_state the normal
# reference state as we just want to check that they are correctly
# set on resume
fully_interacting_expanded_state = ThermodynamicState(temperature=300.0 *
unit.kelvin)
fully_interacting_expanded_state.system = toluene_test.system
noninteracting_expanded_state = copy.deepcopy(base_state)
with enter_temp_directory():
store_file_name = 'simulation.nc'
simulation = ModifiedHamiltonianExchange(store_file_name)
simulation.create(
base_state,
alchemical_states,
positions,
mc_atoms=ligand_atoms,
fully_interacting_expanded_state=fully_interacting_expanded_state,
noninteracting_expanded_state=noninteracting_expanded_state)
# Clean up simulation and resume
del simulation
simulation = ModifiedHamiltonianExchange(store_file_name)
simulation.resume()
assert simulation.fully_interacting_expanded_state is not None
assert simulation.noninteracting_expanded_state is not None
def test_force_overwrite():
t_ref = md.load(get_fn('native2.pdb'), no_boxchk=True)
for ext in t_ref._savers().keys():
with enter_temp_directory():
def test_1():
fn = 'temp-1%s' % ext
open(fn, 'w').close()
t_ref.save(fn, force_overwrite=True)
def test_2():
fn = 'temp-2%s' % ext
open(fn, 'w').close()
assert_raises(IOError,
lambda: t_ref.save(fn, force_overwrite=False))
test_1.description = 'test_force_overwrite (1): %s' % ext
test_2.description = 'test_force_overwrite (2): %s' % ext
yield test_1
yield test_2
def test_save_load():
# this cycles all the known formats you can save to, and then tries
# to reload, using just a single-frame file.
t_ref = md.load(get_fn('native.pdb'))
t_ref.unitcell_vectors = np.array([[[1,0,0], [0,1,0], [0,0,1]]])
with enter_temp_directory():
for ext in t_ref._savers().keys():
def test():
fn = 'temp%s' % ext
t_ref.save(fn)
t = md.load(fn, top=t_ref.topology)
eq(t.xyz, t_ref.xyz)
eq(t.time, t_ref.time)
if t._have_unitcell:
eq(t.unitcell_angles, t_ref.unitcell_angles)
eq(t.unitcell_lengths, t_ref.unitcell_lengths)
test.description = 'test_save_load: %s' % ext
yield test
def test_1():
fn = get_fn("frame0.h5")
with enter_temp_directory():
assert os.system("mixtape DRIDFeaturizer --trjs {} --out a.pkl".format(fn)) == 0
assert os.system("mixtape DihedralFeaturizer --types phi psi --trjs {} --out b.pkl".format(fn)) == 0
assert os.system("mixtape tICA --inp a.pkl --out ticamodel.pkl --transformed tics.pkl") == 0
assert (
os.system(
"mixtape KMeans --random_state 0 --n_init 1 --inp b.pkl --out kmeans.pkl --transformed labels.pkl"
)
== 0
)
kmeans0 = verboseload("labels.pkl")
kmeans1 = KMeans(random_state=0, n_init=1).fit_predict(verboseload("b.pkl"))
tica0 = verboseload("tics.pkl")
tica1 = tICA().fit_transform(verboseload("a.pkl"))
eq(kmeans0[0], kmeans1[0])
eq(tica0[0], tica1[0])
def chemical_shifts_spartaplus(trj, rename_HN=True):
"""Predict chemical shifts of a trajectory using SPARTA+.
Parameters
----------
trj : Trajectory
Trajectory to predict shifts for.
rename_HN : bool, optional, default=True
SPARTA+ calls the amide proton "HN" instead of the standard "H".
When True, this option renames the output as "H" to match the PDB
and BMRB nomenclature.
Returns
-------
results : pandas.DataFrame
Dataframe containing results, with index consisting of
(resSeq, atom_name) pairs and columns for each frame in trj.
Notes
-----
You must have SPARTA+ available on your path; see
(http://spin.niddk.nih.gov/bax/software/SPARTA+/). Also, the SPARTAP_DIR
environment variable must be set so that SPARTA+ knows where to find
its database files.
Chemical shift prediction is for PROTEIN atoms; trajectory objects
with ligands, solvent, ions, or other non-protein components may give
UNKNOWN RESULTS.
Please cite the appropriate reference below.
References
----------
.. [1] Shen, Y., and Bax, Ad. "SPARTA+: a modest improvement in empirical
NMR chemical shift prediction by means of an artificial neural network."
J. Biomol. NMR, 48, 13-22 (2010)
"""
pd = import_('pandas')
binary = find_executable(SPARTA_PLUS)
if binary is None:
raise OSError(
'External command not found. Looked for %s in PATH. `chemical_shifts_spartaplus` requires the external program SPARTA+, available at http://spin.niddk.nih.gov/bax/software/SPARTA+/'
% ', '.join(SPARTA_PLUS))
names = [
"resSeq", "resName", "name", "SS_SHIFT", "SHIFT", "RC_SHIFT",
"HM_SHIFT", "EF_SHIFT", "SIGMA"
]
with enter_temp_directory():
for i in range(trj.n_frames):
trj[i].save("./trj%d.pdb" % i)
subprocess.check_call(
[binary, '-in'] +
["trj{}.pdb".format(i)
for i in range(trj.n_frames)] + ['-out', 'trj0_pred.tab'])
lines_to_skip = _get_lines_to_skip("trj0_pred.tab")
results = []
for i in range(trj.n_frames):
d = pd.read_table("./trj%d_pred.tab" % i,
names=names,
header=None,
sep="\s+",
skiprows=lines_to_skip)
d["frame"] = i
results.append(d)
results = pd.concat(results)
if rename_HN:
results.name[results.name == "HN"] = "H"
if LooseVersion(pd.__version__) < LooseVersion('0.14.0'):
results = results.pivot_table(rows=["resSeq", "name"],
cols="frame",
values="SHIFT")
else:
results = results.pivot_table(index=["resSeq", "name"],
columns="frame",
values="SHIFT")
return results
def chemical_shifts_spartaplus(trj):
"""Predict chemical shifts of a trajectory using SPARTA+.
Parameters
----------
trj : Trajectory
Trajectory to predict shifts for.
Returns
-------
results : pandas.DataFrame
Dataframe containing results, with index consisting of
(resSeq, atom_name) pairs and columns for each frame in trj.
Notes
-----
You must have SPARTA+ available on your path; see
(http://spin.niddk.nih.gov/bax/software/SPARTA+/). Also, the SPARTAP_DIR
environment variable must be set so that SPARTA+ knows where to find
its database files.
Chemical shift prediction is for PROTEIN atoms; trajectory objects
with ligands, solvent, ions, or other non-protein components may give
UNKNOWN RESULTS.
Please cite the appropriate reference below.
References
----------
.. [1] Shen, Y., and Bax, Ad. "SPARTA+: a modest improvement in empirical
NMR chemical shift prediction by means of an artificial neural network."
J. Biomol. NMR, 48, 13-22 (2010)
"""
pd = import_('pandas')
binary = find_executable(SPARTA_PLUS)
if binary is None:
raise OSError('External command not found. Looked for %s in PATH. `chemical_shifts_spartaplus` requires the external program SPARTA+, available at http://spin.niddk.nih.gov/bax/software/SPARTA+/' % ', '.join(SPARTA_PLUS))
names = ["VARS", "resSeq", "resName", "name", "SS_SHIFT", "SHIFT", "RC_SHIFT", "HM_SHIFT", "EF_SHIFT", "SIGMA"]
with enter_temp_directory():
for i in range(trj.n_frames):
trj[i].save("./trj%d.pdb" % i)
cmd = "%s -in %s" % (binary, ' '.join("trj%d.pdb" % i for i in range(trj.n_frames)))
return_flag = os.system(cmd)
if return_flag != 0:
raise(IOError("Could not successfully execute command '%s', check your SPARTA+ installation or your input trajectory." % cmd))
lines_to_skip = _get_lines_to_skip("trj0_pred.tab")
results = []
for i in range(trj.n_frames):
d = pd.read_csv("./trj%d_pred.tab" % i, skiprows=lines_to_skip, delim_whitespace=True, header=None, names=names)
d["frame"] = i
results.append(d)
results = pd.concat(results)
results = results.pivot_table(rows=["resSeq", "name"], cols="frame", values="SHIFT")
return results
def chemical_shifts_spartaplus(trj, rename_HN=True):
"""Predict chemical shifts of a trajectory using SPARTA+.
Parameters
----------
trj : Trajectory
Trajectory to predict shifts for.
rename_HN : bool, optional, default=True
SPARTA+ calls the amide proton "HN" instead of the standard "H".
When True, this option renames the output as "H" to match the PDB
and BMRB nomenclature.
Returns
-------
results : pandas.DataFrame
Dataframe containing results, with index consisting of
(resSeq, atom_name) pairs and columns for each frame in trj.
Notes
-----
You must have SPARTA+ available on your path; see
(http://spin.niddk.nih.gov/bax/software/SPARTA+/). Also, the SPARTAP_DIR
environment variable must be set so that SPARTA+ knows where to find
its database files.
Chemical shift prediction is for PROTEIN atoms; trajectory objects
with ligands, solvent, ions, or other non-protein components may give
UNKNOWN RESULTS.
Please cite the appropriate reference below.
References
----------
.. [1] Shen, Y., and Bax, Ad. "SPARTA+: a modest improvement in empirical
NMR chemical shift prediction by means of an artificial neural network."
J. Biomol. NMR, 48, 13-22 (2010)
"""
pd = import_('pandas')
binary = find_executable(SPARTA_PLUS)
if binary is None:
raise OSError('External command not found. Looked for %s in PATH. `chemical_shifts_spartaplus` requires the external program SPARTA+, available at http://spin.niddk.nih.gov/bax/software/SPARTA+/' % ', '.join(SPARTA_PLUS))
names = ["resSeq", "resName", "name", "SS_SHIFT", "SHIFT", "RC_SHIFT", "HM_SHIFT", "EF_SHIFT", "SIGMA"]
with enter_temp_directory():
for i in range(trj.n_frames):
trj[i].save("./trj%d.pdb" % i)
cmd = "%s -in %s" % (binary, ' '.join("trj%d.pdb" % i for i in range(trj.n_frames)))
return_flag = os.system(cmd)
if return_flag != 0:
raise(IOError("Could not successfully execute command '%s', check your SPARTA+ installation or your input trajectory." % cmd))
lines_to_skip = _get_lines_to_skip("trj0_pred.tab")
results = []
for i in range(trj.n_frames):
d = pd.read_table("./trj%d_pred.tab" % i, names=names, header=None, sep="\s*", skiprows=lines_to_skip)
d["frame"] = i
results.append(d)
results = pd.concat(results)
if rename_HN:
results.name[results.name == "HN"] = "H"
results = results.pivot_table(rows=["resSeq", "name"], cols="frame", values="SHIFT")
return results
def chemical_shifts_shiftx2(trj, pH=5.0, temperature=298.00):
"""Predict chemical shifts of a trajectory using ShiftX2.
Parameters
----------
trj : Trajectory
Trajectory to predict shifts for.
pH : float, optional, default=5.0
pH value which gets passed to the ShiftX2 predictor.
temperature : float, optional, default=298.00
Temperature which gets passed to the ShiftX2 predictor.
Returns
-------
results : pandas DataFrame
Dataframe containing results, with index consisting of
(resSeq, atom_name) pairs and columns for each frame in trj.
Notes
-----
You must have ShiftX2 available on your path; see (http://www.shiftx2.ca/).
Chemical shift prediction is for PROTEIN atoms; trajectory objects
with ligands, solvent, ions, or other non-protein components may give
UNKNOWN RESULTS.
Please cite the appropriate reference below.
References
----------
.. [1] Beomsoo Han, Yifeng Liu, Simon Ginzinger, and David Wishart.
"SHIFTX2: significantly improved protein chemical shift
prediction." J. Biomol. NMR, 50, 1 43-57 (2011)
"""
pd = import_('pandas')
binary = find_executable(SHIFTX2)
if binary is None:
raise OSError(
'External command not found. Looked for {} in PATH. '
'`chemical_shifts_shiftx2` requires the external program SHIFTX2, '
'available at http://www.shiftx2.ca/'.format(', '.join(SHIFTX2)))
results = []
with enter_temp_directory():
for i in range(trj.n_frames):
fn = './trj%d.pdb' % i
trj[i].save(fn)
subprocess.check_call([
binary,
'-b',
fn,
'-p',
"{:.1f}".format(pH),
'-t',
"{:.2f}".format(temperature),
])
d = pd.read_csv("./trj%d.pdb.cs" % i)
d.rename(columns={
"NUM": "resSeq",
"RES": "resName",
"ATOMNAME": "name"
},
inplace=True)
d["frame"] = i
results.append(d)
results = pd.concat(results)
if LooseVersion(pd.__version__) < LooseVersion('0.14.0'):
results = results.pivot_table(rows=["resSeq", "name"],
cols="frame",
values="SHIFT")
else:
results = results.pivot_table(index=["resSeq", "name"],
columns="frame",
values="SHIFT")
return results
def chemical_shifts_shiftx2(trj, pH=5.0, temperature=298.00):
"""Predict chemical shifts of a trajectory using ShiftX2.
Parameters
----------
trj : Trajectory
Trajectory to predict shifts for.
pH : float, optional, default=5.0
pH value which gets passed to the ShiftX2 predictor.
temperature : float, optional, default=298.00
Temperature which gets passed to the ShiftX2 predictor.
Returns
-------
results : pandas DataFrame
Dataframe containing results, with index consisting of
(resSeq, atom_name) pairs and columns for each frame in trj.
Notes
-----
You must have ShiftX2 available on your path; see (http://www.shiftx2.ca/).
Chemical shift prediction is for PROTEIN atoms; trajectory objects
with ligands, solvent, ions, or other non-protein components may give
UNKNOWN RESULTS.
Please cite the appropriate reference below.
References
----------
.. [1] Beomsoo Han, Yifeng Liu, Simon Ginzinger, and David Wishart.
"SHIFTX2: significantly improved protein chemical shift
prediction." J. Biomol. NMR, 50, 1 43-57 (2011)
"""
binary = find_executable(SHIFTX2)
if binary is None:
raise OSError('External command not found. Looked for {} in PATH. '
'`chemical_shifts_shiftx2` requires the external program SHIFTX2, '
'available at http://www.shiftx2.ca/'.format(', '.join(SHIFTX2)))
results = []
with enter_temp_directory():
for i in range(trj.n_frames):
fn = './trj%d.pdb' % i
trj[i].save(fn)
subprocess.check_call([binary,
'-b', fn,
'-p', "{:.1f}".format(pH),
'-t', "{:.2f}".format(temperature),
])
d = pd.read_csv("./trj%d.pdb.cs" % i)
d.rename(columns={"NUM": "resSeq", "RES": "resName", "ATOMNAME": "name"}, inplace=True)
d["frame"] = i
results.append(d)
results = pd.concat(results)
if LooseVersion(pd.__version__) < LooseVersion('0.14.0'):
results = results.pivot_table(rows=["resSeq", "name"], cols="frame", values="SHIFT")
else:
results = results.pivot_table(index=["resSeq", "name"], columns="frame", values="SHIFT")
return results
