def add_hydrogens_by_openmm(self):
from simtk.openmm.app import ForceField, Modeller, PDBFile
from pdbfixer import PDBFixer
fixer = PDBFixer(self.name)
field = ForceField('amber99sb.xml', 'tip3p.xml')
fixer.findMissingResidues()
fixer.findMissingAtoms()
fixer.addMissingAtoms()
fixer.addMissingHydrogens(7.0)
modeller = Modeller(fixer.topology, fixer.positions)
modeller.addHydrogens(forcefield=field)
modeller.deleteWater()
PDBFile.writeModel(modeller.topology, modeller.positions, open(self.shotname+'_h.pdb', 'w'))
class MoleculeUtil(object):
"""
A class for managing a molecule defined by a PDB file
"""
np.random.seed(20)
def __init__(self, pdb_path, offset_size=2):
# OpenMM init
self.pdb_path = pdb_path
self.pdb = PDBFile(self.pdb_path)
self.forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml')
self.modeller = Modeller(self.pdb.topology, self.pdb.positions)
# Remove any water that might be present in the PDB file
self.modeller.deleteWater()
# Add any hydrogens not present
self.modeller.addHydrogens(self.forcefield)
self.system = self.forcefield.createSystem(self.modeller.topology,
nonbondedMethod=PME,
nonbondedCutoff=1 *
u.nanometer,
constraints=HBonds)
self.integrator = LangevinIntegrator(300 * u.kelvin, 1 / u.picosecond,
0.002 * u.picoseconds)
self.simulation = Simulation(self.modeller.topology, self.system,
self.integrator)
self.pdb_positions = self.modeller.getPositions()
# Initialize bond dictionary and positions for chemcoord
self.cc_bonds = {}
self.offset_size = offset_size
self._init_pdb_bonds()
self.set_cc_positions(self.pdb_positions)
# Perform initial minimization, which updates self.pdb_positions
min_energy, min_positions = self.run_simulation()
# Reset the positions after the minimization
self.set_cc_positions(self.pdb_positions)
self.torsion_indices = self._get_torsion_indices()
self.starting_positions = min_positions
self.starting_torsions = np.array([
self.zmat.loc[self.torsion_indices[:, 0], 'dihedral'],
self.zmat.loc[self.torsion_indices[:, 1], 'dihedral']
]).T
self.seed_offsets()
def _add_backbone_restraint(self):
# https://github.com/ParmEd/ParmEd/wiki/OpenMM-Tricks-and-Recipes#positional-restraints
positions = self.modeller.getPositions()
force = CustomExternalForce('k*((x-x0)^2+(y-y0)^2+(z-z0)^2)')
force.addGlobalParameter(
'k', 5.0 * u.kilocalories_per_mole / u.angstroms**2)
force.addPerParticleParameter('x0')
force.addPerParticleParameter('y0')
force.addPerParticleParameter('z0')
for index, atom in enumerate(self.modeller.topology.atoms()):
if atom.name in ('CA', 'C', 'N'):
coord = positions[index]
force.addParticle(index, coord.value_in_unit(u.nanometers))
self.restraint_force_id = self.system.addForce(force)
def _remove_backbone_restraint(self):
self.system.removeForce(self.restraint_force_id)
def _fix_backbone(self):
for index, atom in enumerate(self.modeller.topology.atoms()):
if atom.name in ('CA', 'C', 'N'):
self.system.setParticleMass(index, 0)
def seed_offsets(self):
self.offsets = np.random.choice([0, 0, -1, 1],
self.starting_torsions.shape)
def get_torsions(self):
return np.array([
self.zmat.loc[self.torsion_indices[:, 0], 'dihedral'],
self.zmat.loc[self.torsion_indices[:, 1], 'dihedral']
]).T
def set_torsions(self, new_torsions):
self.zmat.safe_loc[self.torsion_indices[:, 0],
'dihedral'] = new_torsions[:, 0]
self.zmat.safe_loc[self.torsion_indices[:, 1],
'dihedral'] = new_torsions[:, 1]
def get_offset_torsions(self, scale_factor):
"""
Calculates and returns new torsion angles based on randomly generated
offsets.
Args:
scale_factor: the relative scale of the offset relative to
self.offset_size
Returns:
The new torsion angles
"""
total_offset = self.offset_size * scale_factor
new_torsions = np.zeros(shape=self.starting_torsions.shape)
new_torsions[:, 0] = self.starting_torsions[:, 0] + \
(self.offsets[:, 0] * total_offset)
new_torsions[:, 1] = self.starting_torsions[:, 1] + \
(self.offsets[:, 1] * total_offset)
return new_torsions
def run_simulation(self):
"""
Run a simulation to calculate the current configuration's energy level.
Note that the atoms will likely move somewhat during the calculation,
since energy minimization is used.
Returns:
A tuple of the form (potential_energy, updated_positions)
"""
# Delete solvent that's based on previous positions
cartesian = self.zmat.get_cartesian().sort_index()
self.simulation.context.setPositions([
Vec3(x, y, z)
for x, y, z in zip(cartesian['x'], cartesian['y'], cartesian['z'])
])
# self._add_backbone_restraint()
# self._fix_backbone()
self.modeller.addSolvent(self.forcefield, padding=1.0 * u.nanometer)
self.simulation.minimizeEnergy(maxIterations=200)
state = self.simulation.context.getState(getEnergy=True,
getPositions=True)
p_energy = state.getPotentialEnergy()
positions = state.getPositions(asNumpy=True)
# Clean up - remove solvent and backbone restraint (for next iteration)
self.modeller.deleteWater()
# self._remove_backbone_restraint()
return p_energy, positions
def _init_pdb_bonds(self):
"""Construct a dictionary describing the PDB's bonds for chemcoord use"""
for index in range(self.modeller.topology.getNumAtoms()):
self.cc_bonds[index] = set()
for bond in self.modeller.topology.bonds():
self.cc_bonds[bond[0].index].add(bond[1].index)
self.cc_bonds[bond[1].index].add(bond[0].index)
def set_cc_positions(self, positions):
"""
Calculates the zmat from an OpenMM modeller
Args:
positions (list): A list
"""
cc_df = self._get_cartesian_df(positions)
self.cartesian = cc.Cartesian(cc_df)
self.cartesian.set_bonds(self.cc_bonds)
self.cartesian._give_val_sorted_bond_dict(use_lookup=True)
self.zmat = self.cartesian.get_zmat(use_lookup=True)
def _get_cartesian_df(self, positions):
cc_positions = np.zeros((3, self.modeller.topology.getNumAtoms()))
atom_names = []
for index, atom in enumerate(self.modeller.topology.atoms()):
pos = positions[index] / u.nanometer
atom_names.append(atom.name)
cc_positions[:, index] = pos
cc_df = pd.DataFrame({
'atom': atom_names,
'x': cc_positions[0, :],
'y': cc_positions[1, :],
'z': cc_positions[2, :]
})
return cc_df
def _get_torsion_indices(self):
"""
Calculates indices into the zmatrix which correspond to phi
and psi angles.
Args:
zmat: the zmatrix specifying the molecule
Returns:
a numpy.array, with first column as phi_indices, second column
as psi_indices
"""
phi_indices = []
psi_indices = []
for i in range(len(self.zmat.index)):
b_index = self.zmat.loc[i, 'b']
a_index = self.zmat.loc[i, 'a']
d_index = self.zmat.loc[i, 'd']
# If this molecule references a magic string (origin, e_x, e_y, e_z, etc)
if isinstance(b_index, str) or isinstance(
a_index, str) or isinstance(d_index, str):
continue
# Psi angles
if (self.zmat.loc[i, 'atom'] == 'N') & \
(self.zmat.loc[b_index, 'atom'] == 'CA') & \
(self.zmat.loc[a_index, 'atom'] == 'C') & \
(self.zmat.loc[d_index, 'atom'] == 'N'):
psi_indices.append(i)
elif (self.zmat.loc[i, 'atom'] == 'N') & \
(self.zmat.loc[b_index, 'atom'] == 'C') & \
(self.zmat.loc[a_index, 'atom'] == 'CA') & \
(self.zmat.loc[d_index, 'atom'] == 'N'):
psi_indices.append(i)
elif (self.zmat.loc[i, 'atom'] == 'C') & \
(self.zmat.loc[b_index, 'atom'] == 'N') & \
(self.zmat.loc[a_index, 'atom'] == 'CA') & \
(self.zmat.loc[d_index, 'atom'] == 'C'):
phi_indices.append(i)
elif (self.zmat.loc[i, 'atom'] == 'C') & \
(self.zmat.loc[b_index, 'atom'] == 'CA') & \
(self.zmat.loc[a_index, 'atom'] == 'N') & \
(self.zmat.loc[d_index, 'atom'] == 'C'):
phi_indices.append(i)
return np.array([phi_indices, psi_indices]).T