class FFMolecule(Molecule):
"""
filename -- a mol2 format input geometry
rtf, prm -- rtf, prm files
method -- if rtf, prm == None, guess atom types according to this method ( of enum FFTypeMethod )
"""
def __init__(self, filename=None, name=None, rtf=None, prm=None, netcharge=None, method=FFTypeMethod.CGenFF_2b6,
qm=None, outdir="./", mol=None, acCharges=None):
if filename is not None and not filename.endswith('.mol2'):
raise ValueError('Input file must be mol2 format')
if mol is None:
super().__init__(filename=filename, name=name)
else:
for v in mol.__dict__:
self.__dict__[v] = deepcopy(mol.__dict__[v])
# Guess bonds
if len(self.bonds) == 0:
logger.warning('No bonds found! Guessing them...')
self.bonds = self._guessBonds()
# Guess angles and dihedrals
self.angles, self.dihedrals = guessAnglesAndDihedrals(self.bonds, cyclicdih=True)
# Detect equivalent atoms
equivalents = detectEquivalents(self)
self._equivalent_atom_groups = equivalents[0] # List of groups of equivalent atoms
self._equivalent_atoms = equivalents[1] # List of equivalent atoms, indexed by atom
self._equivalent_group_by_atom = equivalents[2] # Mapping from atom index to equivalent atom group
# Detect rotatable dihedrals
self._rotatable_dihedrals = detectSoftDihedrals(self, equivalents)
# Set total charge
if netcharge is None:
self.netcharge = int(round(np.sum(self.charge)))
else:
self.netcharge = int(round(netcharge))
# Canonicalise the names
self._rename()
# Assign atom types, charges, and initial parameters
self.method = method
if rtf and prm:
# If the user has specified explicit RTF and PRM files go ahead and load those
self._rtf = RTF(rtf)
self._prm = PRM(prm)
logger.info('Reading FF parameters from %s and %s' % (rtf, prm))
elif method == FFTypeMethod.NONE:
pass # Don't assign any atom types
else:
# Otherwise make atom types using the specified method
fftype = FFType(self, method=self.method, acCharges=acCharges)
logger.info('Assigned atom types with %s' % self.method.name)
self._rtf = fftype._rtf
self._prm = fftype._prm
if hasattr(self, '_rtf'):
self.atomtype[:] = [self._rtf.type_by_name[name] for name in self.name]
self.charge[:] = [self._rtf.charge_by_name[name] for name in self.name]
self.impropers = np.array(self._rtf.impropers)
# Set atom masses
# TODO: maybe move to molecule
if self.masses.size == 0:
if hasattr(self, '_rtf'):
self.masses[:] = [self._rtf.mass_by_type[self._rtf.type_by_index[i]] for i in range(self.numAtoms)]
else:
self.masses[:] = [vdw.massByElement(element) for element in self.element]
self.qm = qm if qm else Psi4()
self.outdir = outdir
def copy(self):
# HACK! Circumvent 'qm' coping problem
qm, self.qm = self.qm, None
copy = super().copy()
self.qm = copy.qm = qm
return copy
def printReport(self):
print('\n == Molecule report ==\n')
print('Total number of atoms: %d' % self.numAtoms)
print('Total charge: %d' % self.netcharge)
print('Equivalent atom groups:')
for atom_group in self._equivalent_atom_groups:
print(' ' + ', '.join(self.name[atom_group]))
print('Rotatable dihedral angles:')
for dihedral in self._rotatable_dihedrals:
print(' ' + '-'.join(self.name[dihedral.atoms]))
if dihedral.equivalents:
print(' Equivalents:')
for equivalent_dihedral in dihedral.equivalents:
print(' ' + '-'.join(self.name[equivalent_dihedral]))
def _rename(self):
"""
This fixes up the atom naming and reside name to be consistent.
NB this scheme matches what MATCH does.
Don't change it or the naming will be inconsistent with the RTF.
"""
self.segid[:] = 'L'
logger.info('Rename segment to %s' % self.segid[0])
self.resname[:] = 'MOL'
logger.info('Rename residue to %s' % self.resname[0])
sufices = dict()
for i in range(self.numAtoms):
name = self.name[i].upper()
# This fixes the specific case where a name is 3 or 4 characters, as X-TOOL seems to make
if re.match('^[A-Z]{3,4}$', name):
name = name[:-2] # Remove the last 2 characters
# Remove any character that isn't alpha
name = re.sub('[^A-Z]*', '', name)
sufices[name] = sufices.get(name, 0) + 1
name += str(sufices[name])
logger.info('Rename atom %d: %-4s --> %-4s' % (i, self.name[i], name))
self.name[i] = name
def output_directory_name(self):
basis = self.qm.basis
basis = re.sub('\+', 'plus', basis) # Replace '+' with 'plus'
basis = re.sub('\*', 'star', basis) # Replace '*' with 'star'
name = self.qm.theory + '-' + basis + '-' + self.qm.solvent
return name
def minimize(self):
assert self.numFrames == 1
mindir = os.path.join(self.outdir, "minimize", self.output_directory_name())
os.makedirs(mindir, exist_ok=True)
self.qm.molecule = self
self.qm.esp_points = None
self.qm.optimize = True
self.qm.restrained_dihedrals = None
self.qm.directory = mindir
results = self.qm.run()
if results[0].errored:
raise RuntimeError('\nQM minimization failed! Check logs at %s\n' % mindir)
# Replace coordinates with the minimized set
self.coords = results[0].coords
@property
def centreOfMass(self):
return np.dot(self.masses, self.coords[:, :, self.frame])/np.sum(self.masses)
def removeCOM(self):
"""Relocate centre of mass to the origin"""
for frame in range(self.numFrames):
self.frame = frame
self.coords[:, :, frame] -= self.centreOfMass
def fitCharges(self, fixed=[]):
# Cereate an ESP directory
espDir = os.path.join(self.outdir, "esp", self.output_directory_name() )
os.makedirs(espDir, exist_ok=True)
# Get ESP points
point_file = os.path.join(espDir, "00000", "grid.dat")
if os.path.exists(point_file):
# Load a point file if one exists from a previous job
esp_points = np.loadtxt(point_file)
logger.info('Reusing ESP grid from %s' % point_file)
else:
# Generate ESP points
esp_points = ESP.generate_points(self)[0]
# Run QM simulation
self.qm.molecule = self
self.qm.esp_points = esp_points
self.qm.optimize = False
self.qm.restrained_dihedrals = None
self.qm.directory = espDir
qm_results = self.qm.run()
if qm_results[0].errored:
raise RuntimeError('\nQM calculation failed! Check logs at %s\n' % espDir)
# Safeguard QM code from changing coordinates :)
assert np.all(np.isclose(self.coords, qm_results[0].coords, atol=1e-6))
# Fit ESP charges
self.esp = ESP()
self.esp.molecule = self
self.esp.qm_results = qm_results
self.esp.fixed = fixed
esp_result = self.esp.run()
esp_charges, esp_loss = esp_result['charges'], esp_result['loss']
# Update the charges
self.charge[:] = esp_charges
self._rtf.updateCharges(esp_charges)
return esp_loss, qm_results[0].dipole
def getDipole(self):
"""Calculate the dipole moment (in Debyes) of the molecule"""
coords = self.coords[:, :, self.frame] - self.centreOfMass
dipole = np.zeros(4)
dipole[:3] = np.dot(self.charge, coords)
dipole[3] = np.linalg.norm(dipole[:3]) # Total dipole moment
dipole *= 1e11*const.elementary_charge*const.speed_of_light # e * Ang --> Debye (https://en.wikipedia.org/wiki/Debye)
return dipole
def getRotatableDihedrals(self):
return [dihedral.atoms.copy() for dihedral in self._rotatable_dihedrals]
def fitDihedrals(self, dihedrals, geomopt=True):
"""
Dihedrals passed as 4 atom indices
"""
# Create molecules with rotamers
molecules = []
for dihedral in dihedrals:
nrotamers = 36 # Number of rotamers for each dihedral to compute
# Create a copy of molecule with "nrotamers" frames
mol = self.copy()
while mol.numFrames < nrotamers:
mol.appendFrames(self)
assert mol.numFrames == nrotamers
# Set rotamer coordinates
angles = np.linspace(-np.pi, np.pi, num=nrotamers, endpoint=False)
for frame, angle in enumerate(angles):
mol.frame = frame
mol.setDihedral(dihedral, angle, bonds=mol.bonds)
molecules.append(mol)
# Run QM calculation of the rotamers
dirname = 'dihedral-opt' if geomopt else 'dihedral-single-point'
qm_results = []
for dihedral, molecule in zip(dihedrals, molecules):
name = "%s-%s-%s-%s" % tuple(self.name[dihedral])
fitdir = os.path.join(self.outdir, dirname, name, self.output_directory_name())
os.makedirs(fitdir, exist_ok=True)
self.qm.molecule = molecule
self.qm.esp_points = None
self.qm.optimize = geomopt
self.qm.restrained_dihedrals = np.array([dihedral])
self.qm.directory = fitdir
qm_results.append(self.qm.run()) # TODO submit all jobs at once
# Fit the dihedral parameters
df = DihedralFitting()
df.molecule = self
df.dihedrals = dihedrals
df.qm_results = qm_results
df.result_directory = os.path.join(self.outdir, 'parameters', self.method.name,
self.output_directory_name(), 'plots')
# In case of FakeQM, the initial parameters are set to zeros.
# It prevents DihedralFitting class from cheating :D
if isinstance(self.qm, FakeQM):
df.zeroed_parameters = True
# Fit dihedral parameters
df.run()
# Update atom types
self.atomtype[:] = [self._rtf.type_by_name[name] for name in self.name]
def _duplicateAtomType(self, atom_index):
"""Duplicate the type of the specified atom"""
# Get the type name. If the type is already dubplicated, remove the suffix
type_ = self._rtf.type_by_index[atom_index]
type_ = re.sub('x\d+$', '', type_)
# Create a new atom type name
i = 0
while ('%sx%d' % (type_, i)) in self._rtf.types:
i += 1
newtype = '%sx%d' % (type_, i)
logger.info('Create a new atom type %s from %s' % (newtype, type_))
# Duplicate the type in RTF
# TODO: move to RTF class
self._rtf.type_by_index[atom_index] = newtype
self._rtf.mass_by_type[newtype] = self._rtf.mass_by_type[type_]
self._rtf.types.append(newtype)
self._rtf.type_by_name[self._rtf.names[atom_index]] = newtype
self._rtf.type_by_index[atom_index] = newtype
self._rtf.typeindex_by_type[newtype] = self._rtf.typeindex_by_type[type_] + 1000
self._rtf.element_by_type[newtype] = self._rtf.element_by_type[type_]
# Rename the atom types of the equivalent atoms
for index in self._equivalent_atoms[atom_index]:
if atom_index != index:
assert 'x' not in self._rtf.type_by_index[index]
self._rtf.type_by_index[index] = newtype
self._rtf.type_by_name[self._rtf.names[index]] = newtype
# PRM parameters are duplicated during FF evaluation
# TODO: move to PRM class
FFEvaluate(self).run(self.coords[:, :, 0])
def write(self, filename, sel=None, type=None, typemap=None):
# TODO: remove type mapping
if typemap:
mol = self.copy()
mol.atomtype[:] = [typemap[atomtype] for atomtype in self.atomtype]
mol.write(filename, sel=sel, type=type)
else:
if filename.endswith('.rtf'):
self._rtf.write(filename)
elif filename.endswith('.prm'):
self._prm.write(filename)
else:
super().write(filename, sel=sel, type=type)
def writeParameters(self, original_molecule=None):
paramDir = os.path.join(self.outdir, 'parameters', self.method.name, self.output_directory_name())
os.makedirs(paramDir, exist_ok=True)
typemap = None
extensions = ('mol2', 'pdb', 'coor')
if self.method == FFTypeMethod.CGenFF_2b6:
extensions += ('psf', 'rtf', 'prm')
# TODO: remove?
f = open(os.path.join(paramDir, "input.namd"), "w")
tmp = '''parameters mol.prm
paraTypeCharmm on
coordinates mol.pdb
bincoordinates mol.coor
temperature 0
timestep 0
1-4scaling 1.0
exclude scaled1-4
outputname .out
outputenergies 1
structure mol.psf
cutoff 20.
switching off
stepsPerCycle 1
rigidbonds none
cellBasisVector1 50. 0. 0.
cellBasisVector2 0. 50. 0.
cellBasisVector3 0. 0. 50.
run 0'''
print(tmp, file=f)
f.close()
elif self.method in (FFTypeMethod.GAFF, FFTypeMethod.GAFF2):
# types need to be remapped because Amber FRCMOD format limits the type to characters
# writeFrcmod does this on the fly and returns a mapping that needs to be applied to the mol
# TODO: get rid of this mapping
frcFile = os.path.join(paramDir, 'mol.frcmod')
typemap = self._prm.writeFrcmod(self._rtf, frcFile) # TODO move to FFMolecule.write
logger.info('Write FRCMOD file: %s' % frcFile)
tleapFile = os.path.join(paramDir, 'tleap.in')
with open(tleapFile, 'w') as file_:
file_.write('loadAmberParams mol.frcmod\n')
file_.write('A = loadMol2 mol.mol2\n')
file_.write('saveAmberParm A structure.prmtop mol.crd\n')
file_.write('quit\n')
logger.info('Write tleap input file: %s' % tleapFile)
# TODO: remove?
f = open(os.path.join(paramDir, "input.namd"), "w")
tmp = '''parmfile structure.prmtop
amber on
coordinates mol.pdb
bincoordinates mol.coor
temperature 0
timestep 0
1-4scaling 0.83333333
exclude scaled1-4
outputname .out
outputenergies 1
cutoff 20.
switching off
stepsPerCycle 1
rigidbonds none
cellBasisVector1 50. 0. 0.
cellBasisVector2 0. 50. 0.
cellBasisVector3 0. 0. 50.
run 0'''
print(tmp, file=f)
f.close()
else:
raise NotImplementedError
for ext in extensions:
file_ = os.path.join(paramDir, "mol." + ext)
self.write(file_, typemap=typemap)
logger.info('Write %s file: %s' % (ext.upper(), file_))
if original_molecule:
molFile = os.path.join(paramDir, 'mol-orig.mol2')
original_molecule.write(molFile, typemap=typemap)
logger.info('Write MOL2 file (with original coordinates): %s' % molFile)
def __init__(self, filename=None, name=None, rtf=None, prm=None, netcharge=None, method=FFTypeMethod.CGenFF_2b6,
qm=None, outdir="./", mol=None, acCharges=None):
if filename is not None and not filename.endswith('.mol2'):
raise ValueError('Input file must be mol2 format')
if mol is None:
super().__init__(filename=filename, name=name)
else:
for v in mol.__dict__:
self.__dict__[v] = deepcopy(mol.__dict__[v])
# Guess bonds
if len(self.bonds) == 0:
logger.warning('No bonds found! Guessing them...')
self.bonds = self._guessBonds()
# Guess angles and dihedrals
self.angles, self.dihedrals = guessAnglesAndDihedrals(self.bonds, cyclicdih=True)
# Detect equivalent atoms
equivalents = detectEquivalents(self)
self._equivalent_atom_groups = equivalents[0] # List of groups of equivalent atoms
self._equivalent_atoms = equivalents[1] # List of equivalent atoms, indexed by atom
self._equivalent_group_by_atom = equivalents[2] # Mapping from atom index to equivalent atom group
# Detect rotatable dihedrals
self._rotatable_dihedrals = detectSoftDihedrals(self, equivalents)
# Set total charge
if netcharge is None:
self.netcharge = int(round(np.sum(self.charge)))
else:
self.netcharge = int(round(netcharge))
# Canonicalise the names
self._rename()
# Assign atom types, charges, and initial parameters
self.method = method
if rtf and prm:
# If the user has specified explicit RTF and PRM files go ahead and load those
self._rtf = RTF(rtf)
self._prm = PRM(prm)
logger.info('Reading FF parameters from %s and %s' % (rtf, prm))
elif method == FFTypeMethod.NONE:
pass # Don't assign any atom types
else:
# Otherwise make atom types using the specified method
fftype = FFType(self, method=self.method, acCharges=acCharges)
logger.info('Assigned atom types with %s' % self.method.name)
self._rtf = fftype._rtf
self._prm = fftype._prm
if hasattr(self, '_rtf'):
self.atomtype[:] = [self._rtf.type_by_name[name] for name in self.name]
self.charge[:] = [self._rtf.charge_by_name[name] for name in self.name]
self.impropers = np.array(self._rtf.impropers)
# Set atom masses
# TODO: maybe move to molecule
if self.masses.size == 0:
if hasattr(self, '_rtf'):
self.masses[:] = [self._rtf.mass_by_type[self._rtf.type_by_index[i]] for i in range(self.numAtoms)]
else:
self.masses[:] = [vdw.massByElement(element) for element in self.element]
self.qm = qm if qm else Psi4()
self.outdir = outdir
class FFMolecule(Molecule):
def __init__(self, filename=None, name=None, rtf=None, prm=None, netcharge=None, method=FFTypeMethod.CGenFF_2b6,
basis=BasisSet._6_31G_star, solvent=True, theory=Theory.B3LYP, execution=Execution.Inline,
qmcode=Code.PSI4, outdir="./"):
# filename -- a mol2 format input geometry
# rtf, prm -- rtf, prm files
# method -- if rtf, prm == None, guess atom types according to this method ( of enum FFTypeMethod )
self.basis = basis
self.theory = theory
self.solvent = solvent
self.solvent_name = "vacuum"
if solvent:
self.solvent_name = "water"
if theory == Theory.RHF:
self.theory_name = "rhf"
if theory == Theory.B3LYP:
self.theory_name = "b3lyp"
if basis == BasisSet._6_31G_star:
self.basis_name = "6-31g-star"
elif basis == BasisSet._cc_pVDZ:
self.basis_name = "cc-pVDZ"
else:
raise ValueError("Unknown Basis Set")
self.execution = execution
self.qmcode = qmcode
self.method = method
self.outdir = outdir
if not (filename.endswith(".mol2")):
raise ValueError("Input file must be mol2 format")
super().__init__(filename=filename, name=name)
if(len(self.bonds)==0):
print("No bounds found. Guessing them")
self.bonds = self._guessBonds()
(a, b) = guessAnglesAndDihedrals(self.bonds)
self.natoms = self.serial.shape[0]
self.angles = a
self.dihedrals = b
ee = detectEquivalents(self)
self._soft_dihedrals = detectSoftDihedrals(self, ee)
self._equivalent_atom_groups = ee[0] # list of groups of equivalent atoms
self._equivalent_atoms = ee[1] # list of equivalent atoms, indexed by atom
self._equivalent_group_by_atom = ee[2] # mapping from atom index to equivalent atom group
if netcharge is None:
self.netcharge = int(round(np.sum(self.charge)))
else:
self.netcharge = int(round(netcharge))
# Canonicalise the atom naming.
self._rename_mol()
if rtf and prm:
# If the user has specified explicit RTF and PRM files go ahead and load those
self._rtf = RTF(rtf)
self._prm = PRM(prm)
else:
# Otherwise make atom types using the specified method
# (Right now only MATCH)
fftype = FFType(self, method=self.method)
self._rtf = fftype._rtf
self._prm = fftype._prm
if not self._rtf or not self._prm:
raise ValueError("RTF and PRM not defined")
self.report()
def report(self):
print("Net Charge: {}".format(self.netcharge))
print("Equivalent atom groups:")
for i in self._equivalent_atom_groups:
for j in i:
print(" {}".format(self.name[j]), end="")
print("")
print("Soft torsions:")
for i in self._soft_dihedrals:
for j in i.atoms:
print(" {}".format(self.name[j]), end="")
print("")
def _rename_mol(self):
# This fixes up the atom naming and reside name to be consistent
# NB this scheme matches what MATCH does. Don't change it
# Or the naming will be inconsistent with the RTF
import re
hh = dict()
for i in range(len(self.name)):
# Remove any character that isn't alpha
t = re.sub('[^A-Z]*', "", self.name[i].upper())
# print("RENAMED %s to %s" %(self.name[i], t ) )
idx = 0
if not t in hh:
hh[t] = idx
idx = hh[t] + 1
hh[t] = idx
t += str(idx)
self.name[i] = t
self.resname[i] = "MOL"
def output_directory_name(self):
return self.theory_name + "-" + self.basis_name + "-" + self.solvent_name
def minimize(self):
mindir = os.path.join(self.outdir, "minimize", self.output_directory_name())
try:
os.makedirs(mindir, exist_ok=True)
except:
raise OSError('Directory {} could not be created. Check if you have permissions.'.format(mindir))
# Kick off a QM calculation -- unconstrained geometry optimization
qm = QMCalculation(self, charge=self.netcharge, optimize=True,
directory=mindir, basis=self.basis, theory=self.theory, solvent=self.solvent,
execution=self.execution, code=self.qmcode)
results = qm.results()
if results[0].errored:
raise RuntimeError("QM Optimization failed")
# Replace coordinates with the minimized set
self.coords = np.atleast_3d(results[0].coords)
def _fitCharges_map_back_to_charges(self, x):
charges = np.zeros((self.natoms))
qsum = 0.
for i in range(len(x)):
charges[self._equivalent_atom_groups[i]] = x[i]
qsum += x[i] * len(self._equivalent_atom_groups[i])
# diff = self.netcharge - qsum;
# diff = diff / len(self._equivalent_atom_groups[ len(x) ])
# print( self._equivalent_atom_groups[ len(x) ] )
# print( diff )
# charges[ self._equivalent_atom_groups[ len(x) ] ] = diff
return charges
def _fitCharges_con(self, x):
charges = self._fitCharges_map_back_to_charges(x)
s = np.sum(charges) - self.netcharge
return s
def _fitCharges_objective(self, x):
# Map the fit variables back to per-atom charges
chisq = 0.
charges = self._fitCharges_map_back_to_charges(x)
# if( range_penalty == 1 ): chisq = 1000.
for i in range(self._fitCharges_grid.shape[0]):
ee = np.sum(charges * self._fitCharges_distances[i, :])
delta_ee = self._fitCharges_esp[i] - ee
chisq = chisq + (delta_ee * delta_ee)
return chisq
def _fitDihedral_objective(self, x):
inv = math.pi / 180.
# evaluate the torsion with the input params
# for each of the phi's poses
chisq = 0.
for t in range(self._fitDihedral_results.N):
e = .0 # FFEvaluate.evaluateTorsion( self._fitDihedral_results["phi_coords"][t], phi )
for s in range(len(self._fitDihedral_results.phis[t])):
for j in range(6):
e += x[j] * (1. + cos((j + 1) * (self._fitDihedral_results.phis[t][s] * inv) - x[6 + j] * inv))
e = e + x[12]
diff = self._fitDihedral_results.mm_delta[t] - e
chisq += diff * diff
return chisq
def _removeCOM(self):
# Relocate centre of mass to the origin
for f in range(self.coords.shape[2]):
com = np.zeros(3)
mass = 0.
for i in range(self.coords.shape[0]):
m = self._rtf.mass_by_type[self._rtf.type_by_index[i]]
mass = mass + m
com = com + self.coords[i, :, f] * m
com /= mass
self.coords[:, :, f] = self.coords[:, :, f] - com
def _try_load_pointfile(self):
# Load a point file if one exists from a previous job
pointfile = os.path.join(self.outdir, "esp", self.output_directory_name(), "00000", "grid.dat")
if os.path.exists(pointfile):
f = open(pointfile, "r")
fl = f.readlines()
f.close()
ret = np.zeros((len(fl), 3))
for i in range(len(fl)):
s = fl[i].split()
ret[i, 0] = float(s[0])
ret[i, 1] = float(s[1])
ret[i, 2] = float(s[2])
print("Reusing previously-generated point cloud")
return ret
return True
def fitCharges(self, fixed=[]):
# Remove the COM from the coords, or PSI4 does it and then the grid is incorrectly centred
self._removeCOM()
# Kick off a QM calculation -- unconstrained single point with grid
points = self._try_load_pointfile()
espdir = os.path.join(self.outdir, "esp", self.output_directory_name() )
try:
os.makedirs(espdir, exist_ok=True)
except:
raise OSError('Directory {} could not be created. Check if you have permissions.'.format(espdir))
qmcode = self.qmcode
if self.qmcode == Code.TeraChem:
print("Charge-fitting requires a feature TeraChem doesn't have yet. Using PSI4 instead")
qmcode = Code.PSI4
qm = QMCalculation(self, charge=self.netcharge, optimize=False, esp=points, theory=self.theory, solvent=self.solvent,
directory=espdir, basis=self.basis, execution=self.execution,
code=qmcode)
results = qm.results()
if results[0].errored:
raise RuntimeError("QM Calculation failed")
esp_grid = results[0].esp_points
esp = results[0].esp_scalar
self.coords = results[0].coords
# print(results[0].dipole )
# print(results[0].quadrupole )
# print(results[0].mulliken )
self._fitCharges_grid = esp_grid
self._fitCharges_esp = esp
# set up the restraints to fit
N = len(self._equivalent_atom_groups) # - 1
lb = np.ones((N)) * -1.25
ub = np.ones((N)) * +1.25
# Fix the charges of the specified atoms to those already set in the
# charge array. Note this also fixes the charges of the atoms in the
# same equivalency group.
#
for atom in fixed:
group = self._equivalent_group_by_atom[ atom ]
lb[group] = self.charge[atom]
ub[group] = self.charge[atom]
# If the restraint relates to an H, set the lower bound to 0
for i in range(N):
if "H" == self.element[self._equivalent_atom_groups[i][0]]:
lb[i] = 0.001
bounds = []
for a in range(len(lb)):
bounds.append((lb[a], ub[a]))
# Start off by equally distributing the mol's charge
start = np.zeros(N)
# Precompute the 1/r distances
self._fitCharges_distances = np.zeros((self._fitCharges_grid.shape[0], self.coords.shape[0]))
for i in range(self._fitCharges_grid.shape[0]):
p1 = self._fitCharges_grid[i, :]
for j in range(self.coords.shape[0]):
p2 = self.coords[j, :, 0]
r = np.linalg.norm(p1 - p2)
self._fitCharges_distances[i, j] = 1. / r
# initial_chisq = self._fitCharges_objective( start )
xopt = optimize.minimize(self._fitCharges_objective, start, method="SLSQP", bounds=bounds,
options={"disp": False}, constraints={'type': 'eq', 'fun': self._fitCharges_con})
# xopt = optimize.minimize( self._fitCharges_objective, start, method="L-BFGS-B",
# bounds = bounds, options={"disp":False} )
charges = self._fitCharges_map_back_to_charges(xopt.x)
# Calculate the dipole from the fitted charges
dpx = dpy = dpz = 0.
nc = 0.
for i in range(len(charges)):
dpx = dpx + charges[i] * self.coords[i, 0, 0]
dpy = dpy + charges[i] * self.coords[i, 1, 0]
dpz = dpz + charges[i] * self.coords[i, 2, 0]
nc = nc + charges[i]
fac = (2.541766 / 0.529177249)
dpx *= fac
dpy *= fac
dpz *= fac
dp = math.sqrt(dpx * dpx + dpy * dpy + dpz * dpz)
fit_chisq = self._fitCharges_objective(xopt.x)
self.charges = charges
self._rtf.updateCharges(charges)
return fit_chisq, results[0].dipole, [dpx, dpy, dpz, dp]
def getSoftTorsions(self):
dd = []
for d in self._soft_dihedrals:
dd.append(d.atoms.copy())
return dd
# def scanSoftDihedral(self, phi, directory = "dihedral", step=10):
# found=False
# phi_to_fit = None
# frozens=[]
# dih_index=0
# i=0
# for d in self._soft_dihedrals:
# if (d.atoms == phi).all():
# phi_to_fit = d
# dih_index=i
# frozens.append(d.atoms)
# else:
# pass
# i=i+1
# if not phi_to_fit: raise ValueError( "specified phi is not a recognised soft dihedral" )
#
# atoms = phi_to_fit.atoms
# left = phi_to_fit.left
# right = phi_to_fit.right
# equivs= phi_to_fit.equivalents
#
## step = 10 # degrees
# nstep = (int)(360/step)
# cset = np.zeros( ( self.natoms, 3, nstep ) )
#
# i=0
# for phi in range( -180, 180, step ):
# cset[:,:,i] = setPhi( self.coords[:,:,0], atoms, left, right, phi )
# i=i+1
#
# mol = self.copy()
# mol.coords = cset
# try:
# os.mkdir( directory )
# except:
# pass
# dih_name = "%s-%s-%s-%s" % ( self.name[atoms[0]], self.name[atoms[1]], self.name[atoms[2]], self.name[atoms[3]] )
# qmset = QMCalculation( mol, charge=self.netcharge, directory="%s/%s" % (directory, dih_name), frozen=frozens, optimized=True )
# r = qmset.results()
# x=0
# ret=[]
# for phi in range( -180, 180, step ):
# r[x].phi = phi
# if r[x].errored == False:
# ret.append(r[x])
# x=x+1
# return ret
def fitSoftTorsion(self, phi, geomopt=True):
found = False
phi_to_fit = None
frozens = []
dih_index = 0
i = 0
bkp_coords = self.coords.copy()
for d in self._soft_dihedrals:
if (d.atoms == phi).all():
phi_to_fit = d
dih_index = i
frozens.append(d.atoms)
else:
if not geomopt:
frozens.append(d.atoms)
i += 1
if not phi_to_fit:
raise ValueError("specified phi is not a recognised soft dihedral")
self._makeDihedralUnique(phi_to_fit)
atoms = phi_to_fit.atoms
left = phi_to_fit.left
right = phi_to_fit.right
equivs = phi_to_fit.equivalents
step = 10 # degrees
nstep = int(360 / step)
cset = np.zeros((self.natoms, 3, nstep))
i = 0
for phi in range(-180, 180, step):
cset[:, :, i] = setPhi(self.coords[:, :, 0], atoms, left, right, phi)
i += 1
mol = self.copy()
mol.coords = cset
dirname = "dihedral-single-point"
if geomopt:
dirname = "dihedral-opt"
dih_name = "%s-%s-%s-%s" % (self.name[atoms[0]], self.name[atoms[1]], self.name[atoms[2]], self.name[atoms[3]])
fitdir = os.path.join(self.outdir, dirname, dih_name, self.output_directory_name())
try:
os.makedirs(fitdir, exist_ok=True)
except:
raise OSError('Directory {} could not be created. Check if you have permissions.'.format(fitdir))
qmset = QMCalculation(mol, charge=self.netcharge, directory=fitdir, frozen=frozens, optimize=geomopt, theory=self.theory, solvent=self.solvent,
basis=self.basis, execution=self.execution, code=self.qmcode)
ret = self._makeDihedralFittingSetFromQMResults(atoms, qmset.results())
# Get the initial parameters of the dihedral we are going to fit
param = self._prm.dihedralParam(self._rtf.type_by_index[atoms[0]],
self._rtf.type_by_index[atoms[1]],
self._rtf.type_by_index[atoms[2]],
self._rtf.type_by_index[atoms[3]])
# Save these parameters as the best fit (fit to beat)
best_param = np.zeros((13))
for t in range(6):
best_param[t] = param[t].k0
best_param[t + 6] = param[t].phi0
best_param[12] = 0.
# print(param)
# Evalaute the mm potential with this dihedral zeroed out
# The objective function will try to fit to the delta between
# The QM potential and the this modified mm potential
for t in param:
t.k0 = t.phi0 = 0.
t.e14 = 1. # Always fit with e14 scaling of 1. per CHARMM
self._prm.updateDihedral(param)
ffe = FFEvaluate(self)
# print(ffe.evaluate( ret.coords[0] ) )
# input
# Now evaluate the ff without the dihedral being fitted
for t in range(ret.N):
mm_zeroed = (ffe.evaluate(ret.coords[t])["total"])
ret.mm_delta.append(ret.qm[t] - mm_zeroed)
ret.mm_zeroed.append(mm_zeroed)
mmin1 = min(ret.mm_zeroed)
mmin2 = min(ret.mm_delta)
for t in range(ret.N):
ret.mm_zeroed[t] = ret.mm_zeroed[t] - mmin1
ret.mm_delta[t] = ret.mm_delta[t] - mmin2
self._fitDihedral_results = ret
self._fitDihedral_phi = param
# Now measure all of the soft dihedrals phis that are mapped to this dihedral
ret.phis = []
for i in range(ret.N):
ret.phis.append([ret.phi[i]])
for e in equivs:
ret.phis[i].append(getPhi(ret.coords[i], e))
# print ("EQUIVALENT DIHEDRALS FOR THIS DIHEDRAL" )
# print(equivs)
# print ("PHI VALUES TO FIT")
# print (ret.phis)
# Set up the NOLOPT fit
# There are 13 parameters, k,phi for n=1,2,3,4,5,6 and a shift
N = 13
# initial guess,
st = np.zeros(13)
# bounds
best_chisq = self._fitDihedral_objective(best_param)
# print("CHISQ of initial = %f" % ( best_chisq ) )
# Now zero out the terms of the dihedral we are going to fit
bar = ProgressBar(64, description="Fitting")
for i in range(64):
(bounds, start) = self._fitDihedral_make_bounds(i)
xopt = optimize.minimize(self._fitDihedral_objective, start, method="L-BFGS-B", bounds=bounds,
options={'disp': False})
chisq = self._fitDihedral_objective(xopt.x)
# print( "CHISQ of fit = %f " % (chisq) )
if (chisq < best_chisq):
best_chisq = chisq
best_param = xopt.x
bar.progress()
bar.stop()
# print("Best ChiSQ = %f" %(best_chisq) )
# Update the target dihedral with the optimized parameters
# print(param)
# print(best_param )
for i in range(6):
param[i].k0 = best_param[0 + i]
param[i].phi0 = best_param[6 + i]
self._prm.updateDihedral(param)
# print(param)
param = self._prm.dihedralParam(self._rtf.type_by_index[atoms[0]],
self._rtf.type_by_index[atoms[1]],
self._rtf.type_by_index[atoms[2]],
self._rtf.type_by_index[atoms[3]])
# print(param)
# Finally evaluate the fitted potential
ffe = FFEvaluate(self)
for t in range(ret.N):
ret.mm_fitted.append(ffe.evaluate(ret.coords[t])["total"])
mmin = min(ret.mm_fitted)
chisq = 0.
# print( "QM energies" )
# print( ret.qm )
for t in range(ret.N):
ret.mm_fitted[t] = ret.mm_fitted[t] - mmin
delta = ret.mm_fitted[t] - ret.qm[t]
chisq = chisq + (delta * delta)
ret.chisq = chisq
# TODO Score it
self.coords = bkp_coords
return ret
def _fitDihedral_make_bounds(self, i):
lb = np.zeros(13)
ub = np.zeros(13)
start = np.zeros(13)
bounds = []
for j in range(6):
start[j] = 0.
bounds.append((-20., 20.))
for j in range(6):
if i & (2 ** j):
bounds.append((180., 180.))
start[6 + j] = 180.
else:
bounds.append((0., 0.))
start[6 + j] = 0.
bounds.append((-10., 10.))
return bounds, start
def _makeDihedralFittingSetFromQMResults(self, atoms, results):
# Extract the valid QM poses and energies from the QM result set
# Evaluate the MM on those poses
ffe = FFEvaluate(self)
ret = QMFittingSet()
ret.name = "%s-%s-%s-%s" % (
self._rtf.names[atoms[0]], self._rtf.names[atoms[1]], self._rtf.names[atoms[2]], self._rtf.names[atoms[3]])
completed = 0
qmin = 1.e100
for q in results:
if q.completed and not q.errored:
if q.energy < qmin:
qmin = q.energy
completed = 0
for q in results:
if q.completed and not q.errored:
if (q.energy - qmin) < 20.: # Only fit against QM points < 20 kcal above the minimum
mmeval = ffe.evaluate(q.coords)
if mmeval["vdw"] < 200:
completed += 1
phi = getPhi(q.coords, atoms)
ret.qm.append(q.energy - qmin)
ret.mm_original.append(mmeval['total'])
ret.coords.append(q.coords)
if phi > 180.:
phi -= 360.
ret.phi.append(phi)
else:
print("Omitting optimised pose for phi=%f (MM VDW too high)" % phi)
else:
print("Omitting optimised pose for phi=%f (QM energy too high)" % phi)
mmin = min(ret.mm_original)
# roughly align the qm with the mm
for q in range(completed):
ret.mm_original[q] = ret.mm_original[q] - mmin
ret.N = completed
if completed < 5:
raise RuntimeError("Fewer than 5 valid QM points. Not enough to fit!")
return ret
def _makeDihedralUnique(self, phi_to_fit):
# (number_of_uses, uses) = self._countUsesOfDihedral( phi_to_fit.atoms )
# if( number_of_uses > 1 ):
# Create a new type for (arbitrarily) a middle atom of the dihedral
# So that the dihedral we are going to modify is unique
# TODO -- check symmetry
# print( "Dihedral term is not unique. Copying type.." ) # Used %d times, by:" % ( number_of_uses ) )
# print( uses )
# Duplicate the dihedrals types so this modified term is unique
# print("Duplicating types..")
for i in range(4):
if not ("x" in self._rtf.type_by_index[phi_to_fit.atoms[i]]):
self._duplicateTypeOfAtom(phi_to_fit.atoms[i])
(number_of_uses, uses) = self._countUsesOfDihedral(phi_to_fit.atoms)
if number_of_uses > 1:
print(phi_to_fit.atoms)
print(number_of_uses)
print(uses)
raise ValueError("Dihedral term still not unique after duplication")
def _countUsesOfDihedral(self, aidx):
# Return the number of uses of the dihedral
# specified by the types of the 4 atom indices in the aidx list
#
# print( "countUsesOfDihedral in " )
t1 = self._rtf.type_by_index[aidx[0]]
t2 = self._rtf.type_by_index[aidx[1]]
t3 = self._rtf.type_by_index[aidx[2]]
t4 = self._rtf.type_by_index[aidx[3]]
count = 0
uses = []
for d in self.dihedrals:
s1 = self._rtf.type_by_index[d[0]]
s2 = self._rtf.type_by_index[d[1]]
s3 = self._rtf.type_by_index[d[2]]
s4 = self._rtf.type_by_index[d[3]]
if s1 == t1 and s2 == t2 and s3 == t3 and s4 == t4:
count += 1
uses.append(d)
elif s1 == t4 and s2 == t3 and s3 == t2 and s4 == t1:
count += 1
uses.append(d)
# return(count, uses )
# print(uses)
# Now for each of the uses, remove any which are equivalent
c = 1
unique_uses = [aidx]
g1 = self._equivalent_group_by_atom[aidx[0]]
g2 = self._equivalent_group_by_atom[aidx[1]]
g3 = self._equivalent_group_by_atom[aidx[2]]
g4 = self._equivalent_group_by_atom[aidx[3]]
for u in uses:
h1 = self._equivalent_group_by_atom[u[0]]
h2 = self._equivalent_group_by_atom[u[1]]
h3 = self._equivalent_group_by_atom[u[2]]
h4 = self._equivalent_group_by_atom[u[3]]
equiv = False
if g1 == h1 and g2 == h2 and g3 == h3 and g4 == h4:
equiv = True
if g1 == h4 and g2 == h3 and g3 == h2 and g4 == h1:
equiv = True
if equiv is False:
c += 1
unique_uses.append(u)
else:
# print(" Dih %s-%s-%s-%s and %s-%s-%s-%s are equivalent " % (
# self._rtf.names[aidx[0]], self._rtf.names[aidx[1]], self._rtf.names[aidx[2]],
# self._rtf.names[aidx[3]], self._rtf.names[u[0]], self._rtf.names[u[1]], self._rtf.names[u[2]],
# self._rtf.names[u[3]]))
pass
# return(count, uses )
# print( c )
# print( unique_uses )
return c, unique_uses
def _duplicateTypeOfAtom(self, aidx):
# This duplicates the type of the specified atom
# First get the type
atype = self._rtf.type_by_index[aidx]
# perhaps the type is already a duplicate? if so
# remove the duplicated suffix
atype = re.sub("x[0123456789]+$", "", atype)
i = 0
# make the new type name
while ("%sx%d" % (atype, i)) in self._rtf.types:
i += 1
newtype = "%sx%d" % (atype, i)
print("Creating new type %s from %s for atom %s" % (newtype, atype, self._rtf.names[aidx]))
# duplicate the type in the fields RTF -- todo: move to a method in the RTF
self._rtf.type_by_index[aidx] = newtype
self._rtf.mass_by_type[newtype] = self._rtf.mass_by_type[atype]
self._rtf.types.append(newtype)
self._rtf.type_by_name[self._rtf.names[aidx]] = newtype
self._rtf.type_by_index[aidx] = newtype
self._rtf.typeindex_by_type[newtype] = self._rtf.typeindex_by_type[atype] + 1000
self._rtf.element_by_type[newtype] = self._rtf.element_by_type[atype]
# # Now also reset the type of any atoms that share equivalency
for bidx in self._equivalent_atoms[aidx]:
if aidx != bidx:
if "x" in self._rtf.type_by_index[bidx]:
raise RuntimeError(
"Equivalent atom already has a duplicated type: {} {}".format(bidx, self._rtf.type_by_index[bidx]))
self._rtf.type_by_index[bidx] = newtype
self._rtf.type_by_name[self._rtf.names[bidx]] = newtype
# the PRM parameters will be automatically duplicated by forcing an ff evaluation
ffe = FFEvaluate(self)
ffe.evaluate(self.coords)
def plotConformerEnergies( self, fits, show=True ):
import matplotlib as mpl
if not show:
mpl.use('Agg')
import matplotlib.pyplot as plt
fh = plt.figure()
ax1 = fh.gca()
mm_energy = []
qm_energy = []
for r in fits:
mm_energy.extend(r.mm_fitted)
qm_energy.extend(r.qm)
qm_energy = np.array(qm_energy)
mm_energy = np.array(mm_energy)
qm_energy = qm_energy - min(qm_energy)
mm_energy = mm_energy - min(mm_energy)
qm_energy = qm_energy.reshape(qm_energy.shape[0], 1)
mm_energy = mm_energy.reshape(mm_energy.shape[0], 1)
# print(qm_energy)
# print(qm_energy.shape)
# print(mm_energy)
# print(mm_energy.shape)
from sklearn import linear_model
regr = linear_model.LinearRegression(fit_intercept=False)
regr.fit(qm_energy, mm_energy)
ax1.set_xlabel("QM Energy kcal/mol")
ax1.set_xlabel("MM Energy kcal/mol")
ax1.set_title("Conformer Energies MM vs QM")
ax1.plot(qm_energy, mm_energy, color="black", marker="o", linestyle="None")
ax1.plot(qm_energy, regr.predict(qm_energy), color="red", linewidth=2)
if show:
plt.show()
else:
plotdir = os.path.join(self.outdir, "parameters", self.method.name, self.output_directory_name(), "plots")
try:
os.makedirs(plotdir, exist_ok=True)
except:
raise OSError('Directory {} could not be created. Check if you have permissions.'.format(plotdir))
tf = os.path.join(plotdir, "conformer-energies.svg" )
plt.savefig(tf, format="svg")
# Return RMS error, variance and fit coeffients
return (
np.mean((regr.predict(qm_energy) - mm_energy)**2),
regr.score(qm_energy, mm_energy),
regr.coef_
)
def plotTorsionFit(self, fit, show=True):
import matplotlib as mpl
if not show:
mpl.use('Agg')
import matplotlib.pyplot as plt
fh = plt.figure()
ax1 = fh.gca()
ax1.set_xlim(-180., 180.)
ax1.set_xticks([-180, -135, -90, -45, 0, 45, 90, 135, 180])
ax1.set_xlabel("Phi")
ax1.set_ylabel("kcal/mol")
ax1.set_title(fit.name)
x = sorted(fit.phi)
plotdata = []
for i in range(len(fit.phi)):
plotdata.append((fit.phi[i], fit.qm[i]))
plotdata = sorted(plotdata)
plotdatax = [float(i[0]) for i in plotdata]
plotdatay = [float(i[1]) for i in plotdata]
ax1.plot(plotdatax, plotdatay, label="QM", color="r", marker="o")
plotdata=[]
for i in range(len(fit.phi)):
plotdata.append((fit.phi[i], fit.mm_original[i]))
plotdata = sorted(plotdata)
plotdatax = [float(i[0]) for i in plotdata]
plotdatay = [float(i[1]) for i in plotdata]
ax1.plot(plotdatax, plotdatay, label="MM Original", color="b", marker="d")
plotdata=[]
for i in range(len(fit.phi)):
plotdata.append((fit.phi[i], fit.mm_fitted[i]))
plotdata = sorted(plotdata)
plotdatax = [float(i[0]) for i in plotdata]
plotdatay = [float(i[1]) for i in plotdata]
ax1.plot(plotdatax, plotdatay, label="MM Fitted", color="g", marker="s")
#ax1.plot(fit.phi, fit.qm, label="QM", color="r", marker="o")
#ax1.plot(fit.phi, fit.mm_original, label="MM Original", color="b", marker="d")
#ax1.plot(fit.phi, fit.mm_fitted, label="MM Fitted", color="g", marker="s")
## ax1.plot( fit.phi , fit.mm_zeroed , label="MM With phi zeroed", color="black", marker="x" )
## ax1.plot( fit.phi , fit.mm_delta , label="QM-MM target", color="magenta", marker="x" )
ax1.legend(prop={'size': 8})
if show:
plt.show()
else:
plotdir = os.path.join(self.outdir, "parameters", self.method.name, self.output_directory_name(), "plots")
try:
os.makedirs(plotdir, exist_ok=True)
except:
raise OSError('Directory {} could not be created. Check if you have permissions.'.format(plotdir))
tf = os.path.join(plotdir, fit.name) + ".svg"
plt.savefig(tf, format="svg")
plt.clf()
return tf
def write(self, filename, sel=None, type=None, typemap=None):
if hasattr(self, "_rtf"): # Update base Molecule's attributes so write() works correctly
for i in range(self.charge.shape[0]):
self.segid[i] = "L"
self.charge[i] = self._rtf.charge_by_name[self.name[i]]
self.atomtype[i] = self._rtf.type_by_name[self.name[i]]
ref_atomtype = deepcopy(self.atomtype)
if typemap:
for i in range(self.charge.shape[0]):
self.atomtype[i] = typemap[self.atomtype[i]]
super().write(filename, sel=sel, type=type)
self.atomtype = ref_atomtype
def __init__(self, filename=None, name=None, rtf=None, prm=None, netcharge=None, method=FFTypeMethod.CGenFF_2b6,
basis=BasisSet._6_31G_star, solvent=True, theory=Theory.B3LYP, execution=Execution.Inline,
qmcode=Code.PSI4, outdir="./"):
# filename -- a mol2 format input geometry
# rtf, prm -- rtf, prm files
# method -- if rtf, prm == None, guess atom types according to this method ( of enum FFTypeMethod )
self.basis = basis
self.theory = theory
self.solvent = solvent
self.solvent_name = "vacuum"
if solvent:
self.solvent_name = "water"
if theory == Theory.RHF:
self.theory_name = "rhf"
if theory == Theory.B3LYP:
self.theory_name = "b3lyp"
if basis == BasisSet._6_31G_star:
self.basis_name = "6-31g-star"
elif basis == BasisSet._cc_pVDZ:
self.basis_name = "cc-pVDZ"
else:
raise ValueError("Unknown Basis Set")
self.execution = execution
self.qmcode = qmcode
self.method = method
self.outdir = outdir
if not (filename.endswith(".mol2")):
raise ValueError("Input file must be mol2 format")
super().__init__(filename=filename, name=name)
if(len(self.bonds)==0):
print("No bounds found. Guessing them")
self.bonds = self._guessBonds()
(a, b) = guessAnglesAndDihedrals(self.bonds)
self.natoms = self.serial.shape[0]
self.angles = a
self.dihedrals = b
ee = detectEquivalents(self)
self._soft_dihedrals = detectSoftDihedrals(self, ee)
self._equivalent_atom_groups = ee[0] # list of groups of equivalent atoms
self._equivalent_atoms = ee[1] # list of equivalent atoms, indexed by atom
self._equivalent_group_by_atom = ee[2] # mapping from atom index to equivalent atom group
if netcharge is None:
self.netcharge = int(round(np.sum(self.charge)))
else:
self.netcharge = int(round(netcharge))
# Canonicalise the atom naming.
self._rename_mol()
if rtf and prm:
# If the user has specified explicit RTF and PRM files go ahead and load those
self._rtf = RTF(rtf)
self._prm = PRM(prm)
else:
# Otherwise make atom types using the specified method
# (Right now only MATCH)
fftype = FFType(self, method=self.method)
self._rtf = fftype._rtf
self._prm = fftype._prm
if not self._rtf or not self._prm:
raise ValueError("RTF and PRM not defined")
self.report()
def __init__(self,
mol,
method=FFTypeMethod.CGenFF_2b6,
acCharges=None,
tmpDir=None):
# Find the executables
if method == FFTypeMethod.GAFF or method == FFTypeMethod.GAFF2:
antechamber_binary = shutil.which("antechamber")
if not antechamber_binary:
raise RuntimeError("antechamber executable not found")
parmchk2_binary = shutil.which("parmchk2")
if not parmchk2_binary:
raise RuntimeError("parmchk2 executable not found")
elif method == FFTypeMethod.CGenFF_2b6:
match_binary = shutil.which("match-typer")
if not match_binary:
raise RuntimeError("match-typer executable not found")
else:
raise ValueError('method')
# Create a temporary directory
with TemporaryDirectory() as tmpdir:
# HACK to keep the files
tmpdir = tmpdir if tmpDir is None else tmpDir
if method == FFTypeMethod.GAFF or method == FFTypeMethod.GAFF2:
# Write the molecule to a file
mol.write(os.path.join(tmpdir, 'mol.mol2'))
# Run antechamber
if method == FFTypeMethod.GAFF:
atomtype = "gaff"
elif method == FFTypeMethod.GAFF2:
atomtype = "gaff2"
else:
raise ValueError('method')
cmd = [
antechamber_binary, '-at', atomtype, '-nc',
str(mol.netcharge), '-fi', 'mol2', '-i', 'mol.mol2', '-fo',
'prepi', '-o', 'mol.prepi'
]
if acCharges is not None:
cmd += ['-c', acCharges]
returncode = subprocess.call(cmd, cwd=tmpdir)
if returncode != 0:
raise RuntimeError('"antechamber" failed')
# Run parmchk2
returncode = subprocess.call([
parmchk2_binary, '-f', 'prepi', '-i', 'mol.prepi', '-o',
'mol.frcmod', '-a', 'Y'
],
cwd=tmpdir)
if returncode != 0:
raise RuntimeError('"parmchk2" failed')
# Read the results
self._rtf = AmberRTF(mol, os.path.join(tmpdir, 'mol.prepi'),
os.path.join(tmpdir, 'mol.frcmod'))
self._prm = AmberPRM(os.path.join(tmpdir, 'mol.prepi'),
os.path.join(tmpdir, 'mol.frcmod'))
elif method == FFTypeMethod.CGenFF_2b6:
# Write the molecule to a file
mol.write(os.path.join(tmpdir, 'mol.pdb'))
# Run match-type
returncode = subprocess.call([
match_binary, '-charge',
str(mol.netcharge), '-forcefield', 'top_all36_cgenff_new',
'mol.pdb'
],
cwd=tmpdir)
if returncode != 0:
raise RuntimeError('"match-typer" failed')
# Read the results
self._rtf = RTF(os.path.join(tmpdir, 'mol.rtf'))
self._prm = PRM(os.path.join(tmpdir, 'mol.prm'))
else:
raise ValueError('method')
def __init__(self,
filename=None,
name=None,
rtf=None,
prm=None,
netcharge=None,
method=FFTypeMethod.CGenFF_2b6,
basis=BasisSet._6_31G_star,
solvent=True,
theory=Theory.B3LYP,
execution=Execution.Inline,
qmcode=Code.PSI4,
outdir="./"):
# filename -- a mol2 format input geometry
# rtf, prm -- rtf, prm files
# method -- if rtf, prm == None, guess atom types according to this method ( of enum FFTypeMethod )
self.basis = basis
self.theory = theory
self.solvent = solvent
self.solvent_name = "vacuum"
if solvent:
self.solvent_name = "water"
if theory == Theory.RHF:
self.theory_name = "rhf"
if theory == Theory.B3LYP:
self.theory_name = "b3lyp"
if basis == BasisSet._6_31G_star:
self.basis_name = "6-31g-star"
elif basis == BasisSet._cc_pVDZ:
self.basis_name = "cc-pVDZ"
else:
raise ValueError("Unknown Basis Set")
self.execution = execution
self.qmcode = qmcode
self.method = method
self.outdir = outdir
if not (filename.endswith(".mol2")):
raise ValueError("Input file must be mol2 format")
super().__init__(filename=filename, name=name)
if (len(self.bonds) == 0):
print("No bonds found. Guessing them")
self.bonds = self._guessBonds()
(a, b) = guessAnglesAndDihedrals(self.bonds, cyclicdih=True)
self.natoms = self.serial.shape[0]
self.angles = a
self.dihedrals = b
ee = detectEquivalents(self)
self._soft_dihedrals = detectSoftDihedrals(self, ee)
self._equivalent_atom_groups = ee[
0] # list of groups of equivalent atoms
self._equivalent_atoms = ee[
1] # list of equivalent atoms, indexed by atom
self._equivalent_group_by_atom = ee[
2] # mapping from atom index to equivalent atom group
if netcharge is None:
self.netcharge = int(round(np.sum(self.charge)))
else:
self.netcharge = int(round(netcharge))
# Canonicalise the atom naming.
self._rename_mol()
if rtf and prm:
# If the user has specified explicit RTF and PRM files go ahead and load those
self._rtf = RTF(rtf)
self._prm = PRM(prm)
else:
# Otherwise make atom types using the specified method
# (Right now only MATCH)
fftype = FFType(self, method=self.method)
self._rtf = fftype._rtf
self._prm = fftype._prm
if not self._rtf or not self._prm:
raise ValueError("RTF and PRM not defined")
self.impropers = np.array(self._rtf.impropers)
self.report()
class FFMolecule(Molecule):
def __init__(self,
filename=None,
name=None,
rtf=None,
prm=None,
netcharge=None,
method=FFTypeMethod.CGenFF_2b6,
basis=BasisSet._6_31G_star,
solvent=True,
theory=Theory.B3LYP,
execution=Execution.Inline,
qmcode=Code.PSI4,
outdir="./"):
# filename -- a mol2 format input geometry
# rtf, prm -- rtf, prm files
# method -- if rtf, prm == None, guess atom types according to this method ( of enum FFTypeMethod )
self.basis = basis
self.theory = theory
self.solvent = solvent
self.solvent_name = "vacuum"
if solvent:
self.solvent_name = "water"
if theory == Theory.RHF:
self.theory_name = "rhf"
if theory == Theory.B3LYP:
self.theory_name = "b3lyp"
if basis == BasisSet._6_31G_star:
self.basis_name = "6-31g-star"
elif basis == BasisSet._cc_pVDZ:
self.basis_name = "cc-pVDZ"
else:
raise ValueError("Unknown Basis Set")
self.execution = execution
self.qmcode = qmcode
self.method = method
self.outdir = outdir
if not (filename.endswith(".mol2")):
raise ValueError("Input file must be mol2 format")
super().__init__(filename=filename, name=name)
if (len(self.bonds) == 0):
print("No bonds found. Guessing them")
self.bonds = self._guessBonds()
(a, b) = guessAnglesAndDihedrals(self.bonds, cyclicdih=True)
self.natoms = self.serial.shape[0]
self.angles = a
self.dihedrals = b
ee = detectEquivalents(self)
self._soft_dihedrals = detectSoftDihedrals(self, ee)
self._equivalent_atom_groups = ee[
0] # list of groups of equivalent atoms
self._equivalent_atoms = ee[
1] # list of equivalent atoms, indexed by atom
self._equivalent_group_by_atom = ee[
2] # mapping from atom index to equivalent atom group
if netcharge is None:
self.netcharge = int(round(np.sum(self.charge)))
else:
self.netcharge = int(round(netcharge))
# Canonicalise the atom naming.
self._rename_mol()
if rtf and prm:
# If the user has specified explicit RTF and PRM files go ahead and load those
self._rtf = RTF(rtf)
self._prm = PRM(prm)
else:
# Otherwise make atom types using the specified method
# (Right now only MATCH)
fftype = FFType(self, method=self.method)
self._rtf = fftype._rtf
self._prm = fftype._prm
if not self._rtf or not self._prm:
raise ValueError("RTF and PRM not defined")
self.impropers = np.array(self._rtf.impropers)
self.report()
def report(self):
print("Net Charge: {}".format(self.netcharge))
print("Equivalent atom groups:")
for i in self._equivalent_atom_groups:
for j in i:
print(" {}".format(self.name[j]), end="")
print("")
print("Soft torsions:")
for i in self._soft_dihedrals:
for j in i.atoms:
print(" {}".format(self.name[j]), end="")
print("")
def _rename_mol(self):
# This fixes up the atom naming and reside name to be consistent
# NB this scheme matches what MATCH does. Don't change it
# Or the naming will be inconsistent with the RTF
sufices = dict()
print('\nRename atoms:')
for i in range(len(self.name)):
name = self.name[i].upper()
# This fixes the specific case where a name is 3 or 4 characters, as X-TOOL seems to make
if re.match('^[A-Z]{3,4}$', name):
name = name[:-2] # Remove the last 2 characters
# Remove any character that isn't alpha
name = re.sub('[^A-Z]*', '', name)
sufices[name] = sufices.get(name, 0) + 1
name += str(sufices[name])
print(' %-4s --> %-4s' % (self.name[i], name))
self.name[i] = name
self.resname[i] = "MOL"
print()
def output_directory_name(self):
return self.theory_name + "-" + self.basis_name + "-" + self.solvent_name
def minimize(self):
mindir = os.path.join(self.outdir, "minimize",
self.output_directory_name())
try:
os.makedirs(mindir, exist_ok=True)
except:
raise OSError(
'Directory {} could not be created. Check if you have permissions.'
.format(mindir))
# Kick off a QM calculation -- unconstrained geometry optimization
qm = QMCalculation(self,
charge=self.netcharge,
optimize=True,
directory=mindir,
basis=self.basis,
theory=self.theory,
solvent=self.solvent,
execution=self.execution,
code=self.qmcode)
results = qm.results()
if results[0].errored:
raise RuntimeError("QM Optimization failed")
# Replace coordinates with the minimized set
self.coords = np.atleast_3d(results[0].coords)
def _fitCharges_map_back_to_charges(self, x):
charges = np.zeros((self.natoms))
qsum = 0.
for i in range(len(x)):
charges[self._equivalent_atom_groups[i]] = x[i]
qsum += x[i] * len(self._equivalent_atom_groups[i])
# diff = self.netcharge - qsum;
# diff = diff / len(self._equivalent_atom_groups[ len(x) ])
# print( self._equivalent_atom_groups[ len(x) ] )
# print( diff )
# charges[ self._equivalent_atom_groups[ len(x) ] ] = diff
return charges
def _fitCharges_con(self, x):
charges = self._fitCharges_map_back_to_charges(x)
s = np.sum(charges) - self.netcharge
return s
def _fitCharges_objective(self, x):
# Map the fit variables back to per-atom charges
chisq = 0.
charges = self._fitCharges_map_back_to_charges(x)
# if( range_penalty == 1 ): chisq = 1000.
for i in range(self._fitCharges_grid.shape[0]):
ee = np.sum(charges * self._fitCharges_distances[i, :])
delta_ee = self._fitCharges_esp[i] - ee
chisq = chisq + (delta_ee * delta_ee)
return chisq
def _fitDihedral_objective(self, x):
inv = math.pi / 180.
# evaluate the torsion with the input params
# for each of the phi's poses
chisq = 0.
for t in range(self._fitDihedral_results.N):
e = .0 # FFEvaluate.evaluateTorsion( self._fitDihedral_results["phi_coords"][t], phi )
for s in range(len(self._fitDihedral_results.phis[t])):
for j in range(6):
e += x[j] * (1. + cos(
(j + 1) *
(self._fitDihedral_results.phis[t][s] * inv) -
x[6 + j] * inv))
e = e + x[12]
diff = self._fitDihedral_results.mm_delta[t] - e
chisq += diff * diff
return chisq
def _removeCOM(self):
# Relocate centre of mass to the origin
for f in range(self.coords.shape[2]):
com = np.zeros(3)
mass = 0.
for i in range(self.coords.shape[0]):
m = self._rtf.mass_by_type[self._rtf.type_by_index[i]]
mass = mass + m
com = com + self.coords[i, :, f] * m
com /= mass
self.coords[:, :, f] = self.coords[:, :, f] - com
def _try_load_pointfile(self):
# Load a point file if one exists from a previous job
pointfile = os.path.join(self.outdir, "esp",
self.output_directory_name(), "00000",
"grid.dat")
if os.path.exists(pointfile):
f = open(pointfile, "r")
fl = f.readlines()
f.close()
ret = np.zeros((len(fl), 3))
for i in range(len(fl)):
s = fl[i].split()
ret[i, 0] = float(s[0])
ret[i, 1] = float(s[1])
ret[i, 2] = float(s[2])
print("Reusing previously-generated point cloud")
return ret
return True
def fitCharges(self, fixed=[]):
# Remove the COM from the coords, or PSI4 does it and then the grid is incorrectly centred
self._removeCOM()
# Kick off a QM calculation -- unconstrained single point with grid
points = self._try_load_pointfile()
espdir = os.path.join(self.outdir, "esp", self.output_directory_name())
try:
os.makedirs(espdir, exist_ok=True)
except:
raise OSError(
'Directory {} could not be created. Check if you have permissions.'
.format(espdir))
qmcode = self.qmcode
if self.qmcode == Code.TeraChem:
print(
"Charge-fitting requires a feature TeraChem doesn't have yet. Using PSI4 instead"
)
qmcode = Code.PSI4
qm = QMCalculation(self,
charge=self.netcharge,
optimize=False,
esp=points,
theory=self.theory,
solvent=self.solvent,
directory=espdir,
basis=self.basis,
execution=self.execution,
code=qmcode)
results = qm.results()
if results[0].errored:
raise RuntimeError("QM Calculation failed")
esp_grid = results[0].esp_points
esp = results[0].esp_scalar
self.coords = results[0].coords
# print(results[0].dipole )
# print(results[0].quadrupole )
# print(results[0].mulliken )
self._fitCharges_grid = esp_grid
self._fitCharges_esp = esp
# set up the restraints to fit
N = len(self._equivalent_atom_groups) # - 1
lb = np.ones((N)) * -1.25
ub = np.ones((N)) * +1.25
# Fix the charges of the specified atoms to those already set in the
# charge array. Note this also fixes the charges of the atoms in the
# same equivalency group.
#
for atom in fixed:
group = self._equivalent_group_by_atom[atom]
lb[group] = self.charge[atom]
ub[group] = self.charge[atom]
# If the restraint relates to an H, set the lower bound to 0
for i in range(N):
if "H" == self.element[self._equivalent_atom_groups[i][0]]:
lb[i] = 0.001
bounds = []
for a in range(len(lb)):
bounds.append((lb[a], ub[a]))
# Start off by equally distributing the mol's charge
start = np.zeros(N)
# Precompute the 1/r distances
self._fitCharges_distances = np.zeros(
(self._fitCharges_grid.shape[0], self.coords.shape[0]))
for i in range(self._fitCharges_grid.shape[0]):
p1 = self._fitCharges_grid[i, :]
for j in range(self.coords.shape[0]):
p2 = self.coords[j, :, 0]
r = np.linalg.norm(p1 - p2)
self._fitCharges_distances[i, j] = 1. / r
# initial_chisq = self._fitCharges_objective( start )
xopt = optimize.minimize(self._fitCharges_objective,
start,
method="SLSQP",
bounds=bounds,
options={"disp": False},
constraints={
'type': 'eq',
'fun': self._fitCharges_con
})
# xopt = optimize.minimize( self._fitCharges_objective, start, method="L-BFGS-B",
# bounds = bounds, options={"disp":False} )
charges = self._fitCharges_map_back_to_charges(xopt.x)
# Calculate the dipole from the fitted charges
dpx = dpy = dpz = 0.
nc = 0.
for i in range(len(charges)):
dpx = dpx + charges[i] * self.coords[i, 0, 0]
dpy = dpy + charges[i] * self.coords[i, 1, 0]
dpz = dpz + charges[i] * self.coords[i, 2, 0]
nc = nc + charges[i]
fac = (2.541766 / 0.529177249)
dpx *= fac
dpy *= fac
dpz *= fac
dp = math.sqrt(dpx * dpx + dpy * dpy + dpz * dpz)
fit_chisq = self._fitCharges_objective(xopt.x)
self.charges = charges
self._rtf.updateCharges(charges)
return fit_chisq, results[0].dipole, [dpx, dpy, dpz, dp]
def getSoftTorsions(self):
dd = []
for d in self._soft_dihedrals:
dd.append(d.atoms.copy())
return dd
# def scanSoftDihedral(self, phi, directory = "dihedral", step=10):
# found=False
# phi_to_fit = None
# frozens=[]
# dih_index=0
# i=0
# for d in self._soft_dihedrals:
# if (d.atoms == phi).all():
# phi_to_fit = d
# dih_index=i
# frozens.append(d.atoms)
# else:
# pass
# i=i+1
# if not phi_to_fit: raise ValueError( "specified phi is not a recognised soft dihedral" )
#
# atoms = phi_to_fit.atoms
# left = phi_to_fit.left
# right = phi_to_fit.right
# equivs= phi_to_fit.equivalents
#
## step = 10 # degrees
# nstep = (int)(360/step)
# cset = np.zeros( ( self.natoms, 3, nstep ) )
#
# i=0
# for phi in range( -180, 180, step ):
# cset[:,:,i] = setPhi( self.coords[:,:,0], atoms, left, right, phi )
# i=i+1
#
# mol = self.copy()
# mol.coords = cset
# try:
# os.mkdir( directory )
# except:
# pass
# dih_name = "%s-%s-%s-%s" % ( self.name[atoms[0]], self.name[atoms[1]], self.name[atoms[2]], self.name[atoms[3]] )
# qmset = QMCalculation( mol, charge=self.netcharge, directory="%s/%s" % (directory, dih_name), frozen=frozens, optimized=True )
# r = qmset.results()
# x=0
# ret=[]
# for phi in range( -180, 180, step ):
# r[x].phi = phi
# if r[x].errored == False:
# ret.append(r[x])
# x=x+1
# return ret
def fitSoftTorsion(self, angle, geomopt=True):
bkp_coords = self.coords.copy()
phi_to_fit = None
frozens = []
for d in self._soft_dihedrals:
if (d.atoms == angle).all():
phi_to_fit = d
frozens.append(d.atoms)
else:
if not geomopt:
frozens.append(d.atoms)
if not phi_to_fit:
raise ValueError("specified phi is not a recognised soft dihedral")
self._makeDihedralUnique(phi_to_fit)
atoms = phi_to_fit.atoms
equivs = phi_to_fit.equivalents
# Number of rotamers for each dihedral to compute
nrotamer = 36
# Create a copy of molecule with nrotamer frames
mol = self.copy()
for _ in range(nrotamer - 1):
mol.appendFrames(self)
assert mol.numFrames == nrotamer
# Set rotamer coordinates
angles = np.linspace(-np.pi, np.pi, num=nrotamer, endpoint=False)
for frame, angle in enumerate(angles):
mol.frame = frame
mol.setDihedral(atoms, angle, bonds=mol.bonds)
dirname = "dihedral-single-point"
if geomopt:
dirname = "dihedral-opt"
dih_name = "%s-%s-%s-%s" % (self.name[atoms[0]], self.name[atoms[1]],
self.name[atoms[2]], self.name[atoms[3]])
fitdir = os.path.join(self.outdir, dirname, dih_name,
self.output_directory_name())
try:
os.makedirs(fitdir, exist_ok=True)
except:
raise OSError(
'Directory {} could not be created. Check if you have permissions.'
.format(fitdir))
qmset = QMCalculation(mol,
charge=self.netcharge,
directory=fitdir,
frozen=frozens,
optimize=geomopt,
theory=self.theory,
solvent=self.solvent,
basis=self.basis,
execution=self.execution,
code=self.qmcode)
ret = self._makeDihedralFittingSetFromQMResults(atoms, qmset.results())
# Get the initial parameters of the dihedral we are going to fit
param = self._prm.dihedralParam(self._rtf.type_by_index[atoms[0]],
self._rtf.type_by_index[atoms[1]],
self._rtf.type_by_index[atoms[2]],
self._rtf.type_by_index[atoms[3]])
# Save these parameters as the best fit (fit to beat)
best_param = np.zeros((13))
for t in range(6):
best_param[t] = param[t].k0
best_param[t + 6] = param[t].phi0
best_param[12] = 0.
# Evalaute the mm potential with this dihedral zeroed out
# The objective function will try to fit to the delta between
# The QM potential and the this modified mm potential
for t in param:
t.k0 = t.phi0 = 0.
#t.e14 = 1. # Use whatever e14 has been inherited for the type
self._prm.updateDihedral(param)
ffeval = FFEvaluate(self)
# Now evaluate the ff without the dihedral being fitted
for t in range(ret.N):
mm_zeroed = ffeval.run(ret.coords[t][:, :, 0])['total']
ret.mm_delta.append(ret.qm[t] - mm_zeroed)
ret.mm_zeroed.append(mm_zeroed)
mmin1 = min(ret.mm_zeroed)
mmin2 = min(ret.mm_delta)
for t in range(ret.N):
ret.mm_zeroed[t] = ret.mm_zeroed[t] - mmin1
ret.mm_delta[t] = ret.mm_delta[t] - mmin2
self._fitDihedral_results = ret
self._fitDihedral_phi = param
# Now measure all of the soft dihedrals phis that are mapped to this dihedral
ret.phis = []
for iframe in range(ret.N):
ret.phis.append([ret.phi[iframe]])
for atoms in equivs:
angle = dihedralAngle(ret.coords[iframe][atoms, :, 0])
ret.phis[iframe].append(angle)
best_chisq = self._fitDihedral_objective(best_param)
bar = ProgressBar(64, description="Fitting")
for iframe in range(64):
(bounds, start) = self._fitDihedral_make_bounds(iframe)
xopt = optimize.minimize(self._fitDihedral_objective,
start,
method="L-BFGS-B",
bounds=bounds,
options={'disp': False})
chisq = self._fitDihedral_objective(xopt.x)
if (chisq < best_chisq):
best_chisq = chisq
best_param = xopt.x
bar.progress()
bar.stop()
# Update the target dihedral with the optimized parameters
for iframe in range(6):
param[iframe].k0 = best_param[0 + iframe]
param[iframe].phi0 = best_param[6 + iframe]
self._prm.updateDihedral(param)
param = self._prm.dihedralParam(self._rtf.type_by_index[atoms[0]],
self._rtf.type_by_index[atoms[1]],
self._rtf.type_by_index[atoms[2]],
self._rtf.type_by_index[atoms[3]])
# Finally evaluate the fitted potential
ffeval = FFEvaluate(self)
for t in range(ret.N):
ret.mm_fitted.append(ffeval.run(ret.coords[t][:, :, 0])['total'])
mmin = min(ret.mm_fitted)
chisq = 0.
for t in range(ret.N):
ret.mm_fitted[t] = ret.mm_fitted[t] - mmin
delta = ret.mm_fitted[t] - ret.qm[t]
chisq = chisq + (delta * delta)
ret.chisq = chisq
# TODO Score it
self.coords = bkp_coords
return ret
def _fitDihedral_make_bounds(self, i):
lb = np.zeros(13)
ub = np.zeros(13)
start = np.zeros(13)
bounds = []
for j in range(6):
start[j] = 0.
bounds.append((-20., 20.))
for j in range(6):
if i & (2**j):
bounds.append((180., 180.))
start[6 + j] = 180.
else:
bounds.append((0., 0.))
start[6 + j] = 0.
bounds.append((-10., 10.))
return bounds, start
def _makeDihedralFittingSetFromQMResults(self, atoms, results):
# Extract the valid QM poses and energies from the QM result set
# Evaluate the MM on those poses
ffeval = FFEvaluate(self)
ret = QMFittingSet()
ret.name = "%s-%s-%s-%s" % (self._rtf.names[atoms[0]], self._rtf.names[
atoms[1]], self._rtf.names[atoms[2]], self._rtf.names[atoms[3]])
completed = 0
qmin = 1.e100
for q in results:
if q.completed and not q.errored:
if q.energy < qmin:
qmin = q.energy
completed = 0
for q in results:
if q.completed and not q.errored:
if (
q.energy - qmin
) < 20.: # Only fit against QM points < 20 kcal above the minimum
mmeval = ffeval.run(q.coords[:, :, 0])
angle = dihedralAngle(q.coords[atoms, :, 0])
if mmeval["vdw"] < 200:
completed += 1
ret.qm.append(q.energy - qmin)
ret.mm_original.append(mmeval['total'])
ret.coords.append(q.coords)
ret.phi.append(angle)
else:
print(
"Omitting optimised pose for phi=%f (MM VDW too high)"
% angle)
else:
print(
"Omitting optimised QM pose (QM energy too high %f)" %
q.energy)
mmin = min(ret.mm_original)
# roughly align the qm with the mm
for q in range(completed):
ret.mm_original[q] = ret.mm_original[q] - mmin
ret.N = completed
if completed < 5:
raise RuntimeError(
"Fewer than 5 valid QM points. Not enough to fit!")
return ret
def _makeDihedralUnique(self, phi_to_fit):
# (number_of_uses, uses) = self._countUsesOfDihedral( phi_to_fit.atoms )
# if( number_of_uses > 1 ):
# Create a new type for (arbitrarily) a middle atom of the dihedral
# So that the dihedral we are going to modify is unique
# TODO -- check symmetry
# print( "Dihedral term is not unique. Copying type.." ) # Used %d times, by:" % ( number_of_uses ) )
# print( uses )
# Duplicate the dihedrals types so this modified term is unique
# print("Duplicating types..")
for i in range(4):
if not ("x" in self._rtf.type_by_index[phi_to_fit.atoms[i]]):
self._duplicateTypeOfAtom(phi_to_fit.atoms[i])
(number_of_uses, uses) = self._countUsesOfDihedral(phi_to_fit.atoms)
if number_of_uses > 1:
print(phi_to_fit.atoms)
print(number_of_uses)
print(uses)
raise ValueError(
"Dihedral term still not unique after duplication")
def _countUsesOfDihedral(self, aidx):
# Return the number of uses of the dihedral
# specified by the types of the 4 atom indices in the aidx list
#
# print( "countUsesOfDihedral in " )
t1 = self._rtf.type_by_index[aidx[0]]
t2 = self._rtf.type_by_index[aidx[1]]
t3 = self._rtf.type_by_index[aidx[2]]
t4 = self._rtf.type_by_index[aidx[3]]
count = 0
uses = []
for d in self.dihedrals:
s1 = self._rtf.type_by_index[d[0]]
s2 = self._rtf.type_by_index[d[1]]
s3 = self._rtf.type_by_index[d[2]]
s4 = self._rtf.type_by_index[d[3]]
if s1 == t1 and s2 == t2 and s3 == t3 and s4 == t4:
count += 1
uses.append(d)
elif s1 == t4 and s2 == t3 and s3 == t2 and s4 == t1:
count += 1
uses.append(d)
# return(count, uses )
# print(uses)
# Now for each of the uses, remove any which are equivalent
c = 1
unique_uses = [aidx]
g1 = self._equivalent_group_by_atom[aidx[0]]
g2 = self._equivalent_group_by_atom[aidx[1]]
g3 = self._equivalent_group_by_atom[aidx[2]]
g4 = self._equivalent_group_by_atom[aidx[3]]
for u in uses:
h1 = self._equivalent_group_by_atom[u[0]]
h2 = self._equivalent_group_by_atom[u[1]]
h3 = self._equivalent_group_by_atom[u[2]]
h4 = self._equivalent_group_by_atom[u[3]]
equiv = False
if g1 == h1 and g2 == h2 and g3 == h3 and g4 == h4:
equiv = True
if g1 == h4 and g2 == h3 and g3 == h2 and g4 == h1:
equiv = True
if equiv is False:
c += 1
unique_uses.append(u)
else:
# print(" Dih %s-%s-%s-%s and %s-%s-%s-%s are equivalent " % (
# self._rtf.names[aidx[0]], self._rtf.names[aidx[1]], self._rtf.names[aidx[2]],
# self._rtf.names[aidx[3]], self._rtf.names[u[0]], self._rtf.names[u[1]], self._rtf.names[u[2]],
# self._rtf.names[u[3]]))
pass
# return(count, uses )
# print( c )
# print( unique_uses )
return c, unique_uses
def _duplicateTypeOfAtom(self, aidx):
# This duplicates the type of the specified atom
# First get the type
atype = self._rtf.type_by_index[aidx]
# perhaps the type is already a duplicate? if so
# remove the duplicated suffix
atype = re.sub("x[0123456789]+$", "", atype)
i = 0
# make the new type name
while ("%sx%d" % (atype, i)) in self._rtf.types:
i += 1
newtype = "%sx%d" % (atype, i)
print("Creating new type %s from %s for atom %s" %
(newtype, atype, self._rtf.names[aidx]))
# duplicate the type in the fields RTF -- todo: move to a method in the RTF
self._rtf.type_by_index[aidx] = newtype
self._rtf.mass_by_type[newtype] = self._rtf.mass_by_type[atype]
self._rtf.types.append(newtype)
self._rtf.type_by_name[self._rtf.names[aidx]] = newtype
self._rtf.type_by_index[aidx] = newtype
self._rtf.typeindex_by_type[
newtype] = self._rtf.typeindex_by_type[atype] + 1000
self._rtf.element_by_type[newtype] = self._rtf.element_by_type[atype]
# # Now also reset the type of any atoms that share equivalency
for bidx in self._equivalent_atoms[aidx]:
if aidx != bidx:
if "x" in self._rtf.type_by_index[bidx]:
raise RuntimeError(
"Equivalent atom already has a duplicated type: {} {}".
format(bidx, self._rtf.type_by_index[bidx]))
self._rtf.type_by_index[bidx] = newtype
self._rtf.type_by_name[self._rtf.names[bidx]] = newtype
# the PRM parameters will be automatically duplicated by forcing an ff evaluation
FFEvaluate(self).run(self.coords[:, :, 0])
def plotConformerEnergies(self, fits, show=True):
import matplotlib as mpl
if not show:
mpl.use('Agg')
import matplotlib.pyplot as plt
fh = plt.figure()
ax1 = fh.gca()
if (len(fits) == 0): return
mm_energy = []
qm_energy = []
for r in fits:
mm_energy.extend(r.mm_fitted)
qm_energy.extend(r.qm)
qm_energy = np.array(qm_energy)
mm_energy = np.array(mm_energy)
qm_energy = qm_energy - min(qm_energy)
mm_energy = mm_energy - min(mm_energy)
qm_energy = qm_energy.reshape(qm_energy.shape[0], 1)
mm_energy = mm_energy.reshape(mm_energy.shape[0], 1)
# print(qm_energy)
# print(qm_energy.shape)
# print(mm_energy)
# print(mm_energy.shape)
from sklearn import linear_model
regr = linear_model.LinearRegression(fit_intercept=False)
regr.fit(qm_energy, mm_energy)
ax1.set_xlabel("QM Energy kcal/mol")
ax1.set_xlabel("MM Energy kcal/mol")
ax1.set_title("Conformer Energies MM vs QM")
ax1.plot(qm_energy,
mm_energy,
color="black",
marker="o",
linestyle="None")
ax1.plot(qm_energy, regr.predict(qm_energy), color="red", linewidth=2)
if show:
plt.show()
else:
plotdir = os.path.join(self.outdir, "parameters", self.method.name,
self.output_directory_name(), "plots")
try:
os.makedirs(plotdir, exist_ok=True)
except:
raise OSError(
'Directory {} could not be created. Check if you have permissions.'
.format(plotdir))
tf = os.path.join(plotdir, "conformer-energies.svg")
plt.savefig(tf, format="svg")
# Return RMS error, variance and fit coeffients
return (np.mean((regr.predict(qm_energy) - mm_energy)**2),
regr.score(qm_energy, mm_energy), regr.coef_)
def plotTorsionFit(self, fit, phi_original, show=True):
import matplotlib as mpl
if not show:
mpl.use('Agg')
import matplotlib.pyplot as plt
fh = plt.figure()
ax1 = fh.gca()
ax1.set_xlim(-180., 180.)
ax1.set_xticks([-180, -135, -90, -45, 0, 45, 90, 135, 180])
ax1.set_xlabel("Phi")
ax1.set_ylabel("kcal/mol")
ax1.set_title(fit.name)
x = sorted(fit.phi)
plotdata = []
for i in range(len(fit.phi)):
plotdata.append((fit.phi[i], fit.qm[i]))
plotdata = sorted(plotdata)
plotdatax = [float(i[0]) for i in plotdata]
plotdatay = [float(i[1]) for i in plotdata]
ax1.plot(plotdatax, plotdatay, label="QM", color="r", marker="o")
plotdata = []
for i in range(len(phi_original)):
plotdata.append((phi_original[i], fit.mm_original[i]))
plotdata = sorted(plotdata)
plotdatax = [float(i[0]) for i in plotdata]
plotdatay = [float(i[1]) for i in plotdata]
ax1.plot(plotdatax,
plotdatay,
label="MM Original",
color="b",
marker="d")
plotdata = []
for i in range(len(fit.phi)):
plotdata.append((fit.phi[i], fit.mm_fitted[i]))
plotdata = sorted(plotdata)
plotdatax = [float(i[0]) for i in plotdata]
plotdatay = [float(i[1]) for i in plotdata]
ax1.plot(plotdatax,
plotdatay,
label="MM Fitted",
color="g",
marker="s")
#ax1.plot(fit.phi, fit.qm, label="QM", color="r", marker="o")
#ax1.plot(fit.phi, fit.mm_original, label="MM Original", color="b", marker="d")
#ax1.plot(fit.phi, fit.mm_fitted, label="MM Fitted", color="g", marker="s")
## ax1.plot( fit.phi , fit.mm_zeroed , label="MM With phi zeroed", color="black", marker="x" )
## ax1.plot( fit.phi , fit.mm_delta , label="QM-MM target", color="magenta", marker="x" )
ax1.legend(prop={'size': 8})
if show:
plt.show()
else:
plotdir = os.path.join(self.outdir, "parameters", self.method.name,
self.output_directory_name(), "plots")
try:
os.makedirs(plotdir, exist_ok=True)
except:
raise OSError(
'Directory {} could not be created. Check if you have permissions.'
.format(plotdir))
tf = os.path.join(plotdir, fit.name) + ".svg"
plt.savefig(tf, format="svg")
plt.clf()
return tf
def write(self, filename, sel=None, type=None, typemap=None):
if hasattr(
self, "_rtf"
): # Update base Molecule's attributes so write() works correctly
for i in range(self.charge.shape[0]):
self.segid[i] = "L"
self.charge[i] = self._rtf.charge_by_name[self.name[i]]
self.atomtype[i] = self._rtf.type_by_name[self.name[i]]
ref_atomtype = deepcopy(self.atomtype)
if typemap:
for i in range(self.charge.shape[0]):
self.atomtype[i] = typemap[self.atomtype[i]]
super().write(filename, sel=sel, type=type)
self.atomtype = ref_atomtype
def __init__(self,
filename=None,
name=None,
rtf=None,
prm=None,
netcharge=None,
method=FFTypeMethod.CGenFF_2b6,
qm=None,
outdir="./",
mol=None,
acCharges=None):
if filename is not None and not filename.endswith('.mol2'):
raise ValueError('Input file must be mol2 format')
if mol is None:
super().__init__(filename=filename, name=name)
else:
for v in mol.__dict__:
self.__dict__[v] = deepcopy(mol.__dict__[v])
# Guess bonds
if len(self.bonds) == 0:
logger.warning('No bonds found! Guessing them...')
self.bonds = self._guessBonds()
# Guess angles and dihedrals
self.angles, self.dihedrals = guessAnglesAndDihedrals(self.bonds,
cyclicdih=True)
# Detect equivalent atoms
equivalents = detectEquivalents(self)
self._equivalent_atom_groups = equivalents[
0] # List of groups of equivalent atoms
self._equivalent_atoms = equivalents[
1] # List of equivalent atoms, indexed by atom
self._equivalent_group_by_atom = equivalents[
2] # Mapping from atom index to equivalent atom group
# Detect rotatable dihedrals
self._rotatable_dihedrals = detectSoftDihedrals(self, equivalents)
# Set total charge
if netcharge is None:
self.netcharge = int(round(np.sum(self.charge)))
else:
self.netcharge = int(round(netcharge))
# Canonicalise the names
self._rename()
# Assign atom types, charges, and initial parameters
self.method = method
if rtf and prm:
# If the user has specified explicit RTF and PRM files go ahead and load those
self._rtf = RTF(rtf)
self._prm = PRM(prm)
logger.info('Reading FF parameters from %s and %s' % (rtf, prm))
elif method == FFTypeMethod.NONE:
pass # Don't assign any atom types
else:
# Otherwise make atom types using the specified method
fftype = FFType(self, method=self.method, acCharges=acCharges)
logger.info('Assigned atom types with %s' % self.method.name)
self._rtf = fftype._rtf
self._prm = fftype._prm
if hasattr(self, '_rtf'):
self.atomtype[:] = [
self._rtf.type_by_name[name] for name in self.name
]
self.charge[:] = [
self._rtf.charge_by_name[name] for name in self.name
]
self.impropers = np.array(self._rtf.impropers)
# Check if atom type names are compatible
for type_ in self._rtf.types:
if re.match(FFMolecule._ATOM_TYPE_REG_EX, type_):
raise ValueError(
'Atom type %s is incompatable. It cannot finish with "x" + number!'
% type_)
# Set atom masses
# TODO: maybe move to molecule
if self.masses.size == 0:
if hasattr(self, '_rtf'):
self.masses[:] = [
self._rtf.mass_by_type[self._rtf.type_by_index[i]]
for i in range(self.numAtoms)
]
else:
self.masses[:] = [
vdw.massByElement(element) for element in self.element
]
self.qm = qm if qm else Psi4()
self.outdir = outdir
class FFMolecule(Molecule):
"""
filename -- a mol2 format input geometry
rtf, prm -- rtf, prm files
method -- if rtf, prm == None, guess atom types according to this method ( of enum FFTypeMethod )
"""
_ATOM_TYPE_REG_EX = re.compile('^\S+x\d+$')
def __init__(self,
filename=None,
name=None,
rtf=None,
prm=None,
netcharge=None,
method=FFTypeMethod.CGenFF_2b6,
qm=None,
outdir="./",
mol=None,
acCharges=None):
if filename is not None and not filename.endswith('.mol2'):
raise ValueError('Input file must be mol2 format')
if mol is None:
super().__init__(filename=filename, name=name)
else:
for v in mol.__dict__:
self.__dict__[v] = deepcopy(mol.__dict__[v])
# Guess bonds
if len(self.bonds) == 0:
logger.warning('No bonds found! Guessing them...')
self.bonds = self._guessBonds()
# Guess angles and dihedrals
self.angles, self.dihedrals = guessAnglesAndDihedrals(self.bonds,
cyclicdih=True)
# Detect equivalent atoms
equivalents = detectEquivalents(self)
self._equivalent_atom_groups = equivalents[
0] # List of groups of equivalent atoms
self._equivalent_atoms = equivalents[
1] # List of equivalent atoms, indexed by atom
self._equivalent_group_by_atom = equivalents[
2] # Mapping from atom index to equivalent atom group
# Detect rotatable dihedrals
self._rotatable_dihedrals = detectSoftDihedrals(self, equivalents)
# Set total charge
if netcharge is None:
self.netcharge = int(round(np.sum(self.charge)))
else:
self.netcharge = int(round(netcharge))
# Canonicalise the names
self._rename()
# Assign atom types, charges, and initial parameters
self.method = method
if rtf and prm:
# If the user has specified explicit RTF and PRM files go ahead and load those
self._rtf = RTF(rtf)
self._prm = PRM(prm)
logger.info('Reading FF parameters from %s and %s' % (rtf, prm))
elif method == FFTypeMethod.NONE:
pass # Don't assign any atom types
else:
# Otherwise make atom types using the specified method
fftype = FFType(self, method=self.method, acCharges=acCharges)
logger.info('Assigned atom types with %s' % self.method.name)
self._rtf = fftype._rtf
self._prm = fftype._prm
if hasattr(self, '_rtf'):
self.atomtype[:] = [
self._rtf.type_by_name[name] for name in self.name
]
self.charge[:] = [
self._rtf.charge_by_name[name] for name in self.name
]
self.impropers = np.array(self._rtf.impropers)
# Check if atom type names are compatible
for type_ in self._rtf.types:
if re.match(FFMolecule._ATOM_TYPE_REG_EX, type_):
raise ValueError(
'Atom type %s is incompatable. It cannot finish with "x" + number!'
% type_)
# Set atom masses
# TODO: maybe move to molecule
if self.masses.size == 0:
if hasattr(self, '_rtf'):
self.masses[:] = [
self._rtf.mass_by_type[self._rtf.type_by_index[i]]
for i in range(self.numAtoms)
]
else:
self.masses[:] = [
vdw.massByElement(element) for element in self.element
]
self.qm = qm if qm else Psi4()
self.outdir = outdir
def copy(self):
# HACK! Circumvent 'qm' coping problem
qm, self.qm = self.qm, None
copy = super().copy()
self.qm = copy.qm = qm
return copy
def printReport(self):
print('\n == Molecule report ==\n')
print('Total number of atoms: %d' % self.numAtoms)
print('Total charge: %d' % self.netcharge)
print('Equivalent atom groups:')
for atom_group in self._equivalent_atom_groups:
print(' ' + ', '.join(self.name[atom_group]))
print('Rotatable dihedral angles:')
for dihedral in self._rotatable_dihedrals:
print(' ' + '-'.join(self.name[dihedral.atoms]))
if dihedral.equivalents:
print(' Equivalents:')
for equivalent_dihedral in dihedral.equivalents:
print(' ' + '-'.join(self.name[equivalent_dihedral]))
def _rename(self):
"""
This fixes up the atom naming and reside name to be consistent.
NB this scheme matches what MATCH does.
Don't change it or the naming will be inconsistent with the RTF.
"""
self.segid[:] = 'L'
logger.info('Rename segment to %s' % self.segid[0])
self.resname[:] = 'MOL'
logger.info('Rename residue to %s' % self.resname[0])
sufices = dict()
for i in range(self.numAtoms):
name = self.name[i].upper()
# This fixes the specific case where a name is 3 or 4 characters, as X-TOOL seems to make
if re.match('^[A-Z]{3,4}$', name):
name = name[:-2] # Remove the last 2 characters
# Remove any character that isn't alpha
name = re.sub('[^A-Z]*', '', name)
sufices[name] = sufices.get(name, 0) + 1
name += str(sufices[name])
logger.info('Rename atom %d: %-4s --> %-4s' %
(i, self.name[i], name))
self.name[i] = name
def output_directory_name(self):
basis = self.qm.basis
basis = re.sub('\+', 'plus', basis) # Replace '+' with 'plus'
basis = re.sub('\*', 'star', basis) # Replace '*' with 'star'
name = self.qm.theory + '-' + basis + '-' + self.qm.solvent
return name
def minimize(self):
assert self.numFrames == 1
mindir = os.path.join(self.outdir, "minimize",
self.output_directory_name())
os.makedirs(mindir, exist_ok=True)
self.qm.molecule = self
self.qm.esp_points = None
self.qm.optimize = True
self.qm.restrained_dihedrals = None
self.qm.directory = mindir
results = self.qm.run()
if results[0].errored:
raise RuntimeError('\nQM minimization failed! Check logs at %s\n' %
mindir)
# Replace coordinates with the minimized set
self.coords = results[0].coords
@property
def centreOfMass(self):
return np.dot(self.masses, self.coords[:, :, self.frame]) / np.sum(
self.masses)
def removeCOM(self):
"""Relocate centre of mass to the origin"""
for frame in range(self.numFrames):
self.frame = frame
self.coords[:, :, frame] -= self.centreOfMass
def fitCharges(self, fixed=[]):
# Cereate an ESP directory
espDir = os.path.join(self.outdir, "esp", self.output_directory_name())
os.makedirs(espDir, exist_ok=True)
# Get ESP points
point_file = os.path.join(espDir, "00000", "grid.dat")
if os.path.exists(point_file):
# Load a point file if one exists from a previous job
esp_points = np.loadtxt(point_file)
logger.info('Reusing ESP grid from %s' % point_file)
else:
# Generate ESP points
esp_points = ESP.generate_points(self)[0]
# Run QM simulation
self.qm.molecule = self
self.qm.esp_points = esp_points
self.qm.optimize = False
self.qm.restrained_dihedrals = None
self.qm.directory = espDir
qm_results = self.qm.run()
if qm_results[0].errored:
raise RuntimeError('\nQM calculation failed! Check logs at %s\n' %
espDir)
# Safeguard QM code from changing coordinates :)
assert np.all(np.isclose(self.coords, qm_results[0].coords, atol=1e-6))
# Fit ESP charges
self.esp = ESP()
self.esp.molecule = self
self.esp.qm_results = qm_results
self.esp.fixed = fixed
esp_result = self.esp.run()
esp_charges, esp_loss = esp_result['charges'], esp_result['loss']
# Update the charges
self.charge[:] = esp_charges
self._rtf.updateCharges(esp_charges)
for name, charge in zip(self.name, self.charge):
logger.info('Set charge %4s: %6.3f' % (name, charge))
return esp_loss, qm_results[0].dipole
def getDipole(self):
"""Calculate the dipole moment (in Debyes) of the molecule"""
coords = self.coords[:, :, self.frame] - self.centreOfMass
dipole = np.zeros(4)
dipole[:3] = np.dot(self.charge, coords)
dipole[3] = np.linalg.norm(dipole[:3]) # Total dipole moment
dipole *= 1e11 * const.elementary_charge * const.speed_of_light # e * Ang --> Debye (https://en.wikipedia.org/wiki/Debye)
return dipole
def getRotatableDihedrals(self):
return [
dihedral.atoms.copy() for dihedral in self._rotatable_dihedrals
]
def fitDihedrals(self, dihedrals, geomopt=True):
"""
Dihedrals passed as 4 atom indices
"""
# Create molecules with rotamers
molecules = []
for dihedral in dihedrals:
nrotamers = 36 # Number of rotamers for each dihedral to compute
# Create a copy of molecule with "nrotamers" frames
mol = self.copy()
while mol.numFrames < nrotamers:
mol.appendFrames(self)
assert mol.numFrames == nrotamers
# Set rotamer coordinates
angles = np.linspace(-np.pi, np.pi, num=nrotamers, endpoint=False)
for frame, angle in enumerate(angles):
mol.frame = frame
mol.setDihedral(dihedral, angle, bonds=mol.bonds)
molecules.append(mol)
# Run QM calculation of the rotamers
dirname = 'dihedral-opt' if geomopt else 'dihedral-single-point'
qm_results = []
for dihedral, molecule in zip(dihedrals, molecules):
name = "%s-%s-%s-%s" % tuple(self.name[dihedral])
fitdir = os.path.join(self.outdir, dirname, name,
self.output_directory_name())
os.makedirs(fitdir, exist_ok=True)
self.qm.molecule = molecule
self.qm.esp_points = None
self.qm.optimize = geomopt
self.qm.restrained_dihedrals = np.array([dihedral])
self.qm.directory = fitdir
qm_results.append(self.qm.run()) # TODO submit all jobs at once
# Fit the dihedral parameters
df = DihedralFitting()
df.molecule = self
df.dihedrals = dihedrals
df.qm_results = qm_results
df.result_directory = os.path.join(self.outdir, 'parameters',
self.method.name,
self.output_directory_name(),
'plots')
# In case of FakeQM, the initial parameters are set to zeros.
# It prevents DihedralFitting class from cheating :D
if isinstance(self.qm, FakeQM2):
df.zeroed_parameters = True
# Fit dihedral parameters
df.run()
# Update atom types
self.atomtype[:] = [self._rtf.type_by_name[name] for name in self.name]
def _duplicateAtomType(self, atom_index):
"""Duplicate the type of the specified atom
Duplicated types are named: original_name + "x" + number, e.g. ca --> cax0
"""
# Get a type name
type_ = self._rtf.type_by_index[atom_index]
# if the type is already duplicated
if re.match(FFMolecule._ATOM_TYPE_REG_EX, type_):
return
# Create a new atom type name
i = 0
while ('%sx%d' % (type_, i)) in self._rtf.types:
i += 1
newtype = '%sx%d' % (type_, i)
logger.info('Create a new atom type %s from %s' % (newtype, type_))
# Duplicate the type in RTF
# TODO: move to RTF class
self._rtf.type_by_index[atom_index] = newtype
self._rtf.mass_by_type[newtype] = self._rtf.mass_by_type[type_]
self._rtf.types.append(newtype)
self._rtf.type_by_name[self._rtf.names[atom_index]] = newtype
self._rtf.type_by_index[atom_index] = newtype
self._rtf.typeindex_by_type[
newtype] = self._rtf.typeindex_by_type[type_] + 1000
self._rtf.element_by_type[newtype] = self._rtf.element_by_type[type_]
# Rename the atom types of the equivalent atoms
for index in self._equivalent_atoms[atom_index]:
if atom_index != index:
assert not re.match(FFMolecule._ATOM_TYPE_REG_EX,
self._rtf.type_by_index[index])
self._rtf.type_by_index[index] = newtype
self._rtf.type_by_name[self._rtf.names[index]] = newtype
# PRM parameters are duplicated during FF evaluation
# TODO: move to PRM class
FFEvaluate(self).run(self.coords[:, :, 0])
def write(self, filename, sel=None, type=None, typemap=None):
# TODO: remove type mapping
if typemap:
mol = self.copy()
mol.atomtype[:] = [typemap[atomtype] for atomtype in self.atomtype]
mol.write(filename, sel=sel, type=type)
else:
if filename.endswith('.rtf'):
self._rtf.write(filename)
elif filename.endswith('.prm'):
self._prm.write(filename)
else:
super().write(filename, sel=sel, type=type)
def writeParameters(self, original_molecule=None):
paramDir = os.path.join(self.outdir, 'parameters', self.method.name,
self.output_directory_name())
os.makedirs(paramDir, exist_ok=True)
typemap = None
extensions = ('mol2', 'pdb', 'coor')
if self.method == FFTypeMethod.CGenFF_2b6:
extensions += ('psf', 'rtf', 'prm')
# TODO: remove?
f = open(os.path.join(paramDir, "input.namd"), "w")
tmp = '''parameters mol.prm
paraTypeCharmm on
coordinates mol.pdb
bincoordinates mol.coor
temperature 0
timestep 0
1-4scaling 1.0
exclude scaled1-4
outputname .out
outputenergies 1
structure mol.psf
cutoff 20.
switching off
stepsPerCycle 1
rigidbonds none
cellBasisVector1 50. 0. 0.
cellBasisVector2 0. 50. 0.
cellBasisVector3 0. 0. 50.
run 0'''
print(tmp, file=f)
f.close()
elif self.method in (FFTypeMethod.GAFF, FFTypeMethod.GAFF2):
# types need to be remapped because Amber FRCMOD format limits the type to characters
# writeFrcmod does this on the fly and returns a mapping that needs to be applied to the mol
# TODO: get rid of this mapping
frcFile = os.path.join(paramDir, 'mol.frcmod')
typemap = self._prm.writeFrcmod(
self._rtf, frcFile) # TODO move to FFMolecule.write
logger.info('Write FRCMOD file: %s' % frcFile)
tleapFile = os.path.join(paramDir, 'tleap.in')
with open(tleapFile, 'w') as file_:
file_.write('loadAmberParams mol.frcmod\n')
file_.write('A = loadMol2 mol.mol2\n')
file_.write('saveAmberParm A structure.prmtop mol.crd\n')
file_.write('quit\n')
logger.info('Write tleap input file: %s' % tleapFile)
# TODO: remove?
f = open(os.path.join(paramDir, "input.namd"), "w")
tmp = '''parmfile structure.prmtop
amber on
coordinates mol.pdb
bincoordinates mol.coor
temperature 0
timestep 0
1-4scaling 0.83333333
exclude scaled1-4
outputname .out
outputenergies 1
cutoff 20.
switching off
stepsPerCycle 1
rigidbonds none
cellBasisVector1 50. 0. 0.
cellBasisVector2 0. 50. 0.
cellBasisVector3 0. 0. 50.
run 0'''
print(tmp, file=f)
f.close()
else:
raise NotImplementedError
for ext in extensions:
file_ = os.path.join(paramDir, "mol." + ext)
self.write(file_, typemap=typemap)
logger.info('Write %s file: %s' % (ext.upper(), file_))
if original_molecule:
molFile = os.path.join(paramDir, 'mol-orig.mol2')
original_molecule.write(molFile, typemap=typemap)
logger.info('Write MOL2 file (with original coordinates): %s' %
molFile)
