def metarun(self):
results = adfaccurateembeddingresults()
# run supermolecular calculation
self.settings.set_lmo(True)
self.settings.set_save_tapes([21, 10])
self.settings.set_exactdensity(True)
results.super = adfsinglepointjob(self.supermol, self.basis, settings=self.settings).run()
# orbital localization is turned off for the other calculations
self.settings.set_lmo(False)
# fragment density is obtained from frag1 and frag2
results.frag1 = adfsinglepointjob(self.frag1, self.basis, settings=self.settings).run()
results.frag2 = adfsinglepointjob(self.frag2, self.basis, settings=self.settings).run()
# get localized orbital densities for subsystems 1 and 2
self.assign_locorbs_to_fragments(results)
adfGrid = adfgrid(results.super)
sys1_dens = results.super.get_locorb_density(grid=adfGrid, orbs=results.locorb_set1)
sys1_dens.prop = 'density scf'
# results.sys2_dens = results.super.get_locorb_density(grid=adfGrid, orbs=results.locorb_set2)
# results.sys2_dens.prop = 'density scf'
print 'reference calculations finished'
# Now comes the potential reconstruction:
self.settings.set_ncycles(self.cyc)
job = adffragmentsjob([fragment(results.frag1, [self.frag1])], self.basis, settings=self.settings)
potjob = adfpotentialjob(job, sys1_dens, potoptions=self.potoptions)
print 'Running potential reconstruction job ... '
results.pot = potjob.run()
# Calculate the error in the density
# the reconstructed density
rec_dens = results.pot.get_density(grid=adfGrid)
# the difference density
diff_dens = sys1_dens - rec_dens
# calculate integral over the difference density
results.dens_error = diff_dens.integral(func=lambda x: abs(x))
return results
def prepare_frags(self):
"""
Prepare fragments (make single point runs for molecules).
@note:
we cannot use symmetry in the fragments if we are interested in the
difference density -> otherwise they will be rotated
@returns: The results of single point runs for a list of molecules
@rtype: tuple of list of adfsinglepointresults and fragments
"""
r_frags = []
frags = []
self.adfsettings.set_occupations(None)
for mol in self.molecules:
mol.set_symmetry('NOSYM')
r = adfsinglepointjob(mol=mol,
basis=self.basis,
core=self.core,
settings=self.adfsettings,
options=['NOSYMFIT']).run()
r_frags.append(r)
if self.settings.usebasis == 'True':
frags.append(
fragment(r,
mol,
fdeoptions={
"USEBASIS": "",
"RELAX": "",
"XC": " GGA PBE"
}))
else:
frags.append(fragment(r, mol, fdeoptions={"XC": "GGA PBE"}))
return frags, r_frags
def metarun(self):
import copy
print "-" * 50
print "Beginning WFT-in-DFT embedding calculation "
print
print "Performing %i RELAX-cycles" % self._cycles
print
# first we do a normal DFT-in-DFT run
initial_frags = copy.deepcopy(self._frags)
for f in initial_frags:
# rename RELAX option to RELAXsave so that we can handle it, instead of ADF
if f.has_fdeoption("RELAX"):
f.delete_fdeoption("RELAX")
f.add_fdeoption("RELAXsave", "")
# pylint: disable-msg=W0142
dftindft_res = adffragmentsjob(initial_frags, **self._adfoptions).run()
# now construct a new list of fragments
frags = copy.deepcopy(fragmentlist(initial_frags.get_frozen_frags()))
for f in frags:
if f.has_fdeoption("RELAXsave"):
f.isfrozen = False
dirac_frag = fragment(dftindft_res, dftindft_res.get_nonfrozen_molecule(), subfrag="active", isfrozen=True)
dirac_mol = dirac_frag.get_total_molecule()
frags.append(dirac_frag)
# now run a DFT-in-DFT calculation with the Dirac-fragment frozen,
# and all DFT-relax fragments nonfrozen in order to get a grid that
# can be used for all of them
# (the Dirac step is expensive, so we want to do it only for one common grid)
#
# FIXME: Alternatively, Dirac could use several grids for exporting
#
# FIXME: we do the full DFT-in-DFT calculation, but only extracting
# the grid would be enough; maybe use some STOPAFTER option?
# pylint: disable-msg=W0142
outgrid_res = adffragmentsjob(frags, **self._adfoptions).run()
for i in range(self._cycles):
print "-" * 50
print "Performing cycle ", i + 1
print
# do the Dirac calculation
# pylint: disable-msg=W0142
dirac_res = diracsinglepointjob(dirac_mol, fdein=dftindft_res, fdeout=outgrid_res, **self._diracoptions).run()
# construct new list of fragments, update the Dirac fragment,
frags = fragmentlist(initial_frags.get_frozen_frags())
dirac_frag = diracfragment(dirac_res, dirac_mol, isfrozen=True)
frags.append(dirac_frag)
# loop over all DFT-relax fragments and update them
for f in frags:
if f.has_fdeoption("RELAXsave"):
f.isfrozen = False
# pylint: disable-msg=W0142
f.results = adffragmentsjob(frags, **self._adfoptions).run()
f.isfrozen = True
frozenfrags = frags.get_frozen_frags()
frozenfrags = [f for f in frozenfrags if not isinstance(f, diracfragment)]
initial_frags = copy.deepcopy(fragmentlist(frozenfrags))
dirac_frag = fragment(dftindft_res, dirac_mol, subfrag="active", isfrozen=False)
initial_frags.append(dirac_frag)
# run ADF DFT-in-DFT again to get a new embedding potential
#
# FIXME: rho1 is calculated by ADF in this step again.
# Instead, the rho1 calculated by Dirac should be used
dftindft_res = adffragmentsjob(initial_frags, **self._adfoptions).run()
print "-" * 50
print "Performing final DIRAC calculation "
print
dirac_res = diracsinglepointjob(dirac_mol, fdein=dftindft_res, fdeout=outgrid_res, **self._diracoptions).run()
return dirac_res
def partition_protein(self,
mol,
special_reslists=None,
fragsize=None,
caps=None):
"""
Partition a protein into the individual amino acids and caps.
@param mol: the protein
@type mol: L{molecule}
@param special_reslists:
lists of residue numbers that should be treated within one 3-FDE fragment
@type special_reslists: list of lists of integers
@param fragsize:
number of residues in one 3-FDE fragment (not for residues in special_reslist)
@type fragsize: int
@param caps:
type of cap molecule, standard is mfcc caps, option is hydrogen caps
@type caps: str
"""
# generate fragment list
res_of_atoms = mol.get_residx_of_atoms()
capped_bonds = []
# list of bonds between residues within fragment
non_capped_res = []
# to which fragment belongs the residue
frag_indices = []
# clear the fragment list
self._frags = []
self._caps = []
if fragsize is None:
fragsize = 1
if special_reslists is None:
special_reslists = {}
# ----------------------------------------------------------------------------------
# simplest fragmentation: each residue is one fragment
if special_reslists == {} and fragsize == 1:
# get the individual residues
for res in mol.get_residues():
self.append(cappedfragment(None, res))
frag_indices.append(len(self._frags) - 1)
# ----------------------------------------------------------------------------------
# more complicated fragmentation
else:
# careful with residue numbers (internal openbabel index idx starts at 0)
fragmentation_list = []
residuelist = mol.get_residues()
frag_indices = [-1] * len(residuelist)
# get list of residues connected by covalent bonds (tuples)
connected_residues = []
for b in mol.get_all_bonds():
if not (res_of_atoms[b[0] - 1] == res_of_atoms[b[1] - 1]):
# -1 is not totally clear to me, but works
connected_residues.append(
{res_of_atoms[b[0] - 1], res_of_atoms[b[1] - 1]})
# ------------------------------------------------------------------------------
# first get fragmentation list: which residues belong to which fragment
# something like [ [3,4,5], [11,12,14], [1, 2], [6, 7], [8, 9], [10], [13]]
# ugly
# dictionary that connects pdb resnums with internal idx
# problem: resnum as key is not unique, use combination of residue name and chain as key
res_idx = {}
for reschain, resname, resnum in mol.residue_iter():
reskey = 'c' + str(reschain) + str(resnum)
res_idx[reskey] = mol.get_residx_from_resinfo(
reschain, resname, resnum)
# convert residue numbers in special_reslists to internal numbers
for i in range(len(special_reslists)):
for j in range(len(special_reslists[i])):
special_reslists[i][j] = res_idx[special_reslists[i][j]]
# flatten list of lists so that you can check whether a residue is a special residue
specialresidxs = []
for slist in special_reslists:
specialresidxs.extend(slist)
# first add special residues to fragmentation list
for slist in special_reslists:
fragmentation_list.append(slist)
fragl = [] # templist for fragments
# add all other residues
# use res.GetIdx() instead of enumerate
for res, (reschain, resname,
resnum) in zip(residuelist, mol.residue_iter()):
residx = mol.get_residx_from_resinfo(reschain, resname, resnum)
if residx not in specialresidxs:
# take care of fragsize
if fragsize == 1:
fragmentation_list.append([residx])
else:
# check whether there has to be a new fragment
if len(fragl) != 0:
if not {residx, fragl[-1]} in connected_residues or \
len(fragl) == fragsize:
# no covalent connection? new fragment! fragsize reached?
fragmentation_list.append(fragl)
fragl = []
# new fragment
if len(fragl) == 0:
fragl.append(residx)
# add to fragment
else:
fragl.append(residx)
# last fragment
if len(fragl) != 0:
fragmentation_list.append(fragl)
# ------------------------------------------------------------------------------
# now add fragments to _frags using fragmentation list
# take care of uncapped bonds and fragindices etc.
frag_reslist = []
for rlist in fragmentation_list:
for rnum in range(len(rlist)):
res = mol.get_residues(idx=rlist[rnum])[0]
frag_reslist.append(res)
# if residue is added to an existing fragment: add tuple of residues to
# list of connected (non-capped) residues (only if they are connected)
# take also care of disulfide bonds within one fragment
# check whether new residue is connected to another residue within this fragment
if len(frag_reslist) > 1:
for rd in range(len(frag_reslist)):
if {rlist[rnum], rlist[rd]} in connected_residues:
non_capped_res.append({rlist[rnum], rlist[rd]})
# take care of residue information here
frag_indices[rlist[rnum]] = len(self._frags)
res_frag = fragment(None, mols=frag_reslist)
self.append(cappedfragment(None,
res_frag.get_total_molecule()))
# clear frag_reslist for next fragment
frag_reslist = []
# ------------------------------------------------------------------------------
# find all peptide bonds and create caps
if caps is None:
caps = 'mfcc'
if caps == 'mfcc':
sp = '[C;X4;H1,H2][CX3](=O)[NX3][C;X4;H1,H2][CX3](=O)'
# this SMARTS pattern matches all peptide bonds
#
# (in this comment: atom numbering starts at 1;
# in the code below the numbering of the list starts at 0)
# the peptide bond is between atoms 2 and 4
# atoms 1-5 (+ connected hydrogens) form the cap fragment
# part 1 (o-part): atoms 1-3
# part 2 (n-part): atoms 4-5
elif caps == 'hydrogen':
sp = '[C;X4;H1,H2][CX3](=O)[NX3]([*])[C;X4;H1,H2][CX3](=O)'
# same as above, but also matches any atom bound to the nitrogen
# the * might not be a good idea, but [C,H] was not working...
# (this is needed for the proper calculation of the hydrogen coordinates)
else:
raise PyAdfError(
'unsupported cap type, please specify one of the following: mfcc, hydrogen'
)
maplist = mol.get_smarts_matches(sp)
for mp in maplist:
if {res_of_atoms[mp[3] - 1],
res_of_atoms[mp[1] - 1]} not in non_capped_res:
capped_bonds.append({mp[1], mp[3]})
if caps == 'mfcc':
cap_o = mp[0:3] + mol.find_adjacent_hydrogens(mp[0:3])
cap_n = mp[3:5] + mol.find_adjacent_hydrogens(mp[3:5])
m_cap = mol.get_fragment(cap_o + cap_n)
m_cap.set_spin(0)
m_cap.set_residue('CAP', 1, atoms=range(1, len(cap_o) + 1))
m_cap.set_residue('CAP',
2,
atoms=range(
len(cap_o) + 1,
len(cap_o + cap_n) + 1))
m_cap.add_hydrogens_to_sp3_c(1)
m_cap.add_hydrogens_to_sp2_n(len(cap_o) + 1)
m_cap.add_hydrogens_to_sp3_c(len(cap_o) + 2)
elif caps == 'hydrogen':
ccap = mol.get_fragment([mp[1], mp[2], mp[0]])
ccap.add_hydrogens_to_sp2_c(1)
ncap = mol.get_fragment([mp[3], mp[4], mp[5]])
ncap.add_hydrogens_to_sp3_n(1)
m_cap = capmolecule()
m_cap.add_atoms(['H'], ncap.get_coordinates([4]))
m_cap.add_atoms(['H'],
ccap.get_coordinates([4]),
bond_to=1)
m_cap.set_spin(0)
m_cap.set_residue('CAP', 1, atoms=[1])
m_cap.set_residue('CAP', 2, atoms=[2])
m_cap.set_symmetry('NOSYM')
# find the capped fragments
# frag1: fragment that contains the N atom (mp[3])
# frag2: fragment that contains the C=O atom (mp[1])
fi1 = frag_indices[res_of_atoms[mp[3] - 1]]
fi2 = frag_indices[res_of_atoms[mp[1] - 1]]
frag1 = self._frags[fi1]
frag2 = self._frags[fi2]
self.append_cap(cappedfragment(None, capmolecule(m_cap)),
frag1, frag2)
# ------------------------------------------------------------------------------
# check here for disulfide bonds and create disulfide bond caps
maplist_sulfide = mol.get_smarts_matches('[C;X4;H1,H2]SS[C;X4;H1,H2]')
# SMARTS pattern for disulfde bonds
for mps in maplist_sulfide:
if {res_of_atoms[mps[2] - 1],
res_of_atoms[mps[1] - 1]} not in non_capped_res:
capped_bonds.append({mps[1], mps[2]})
if caps == 'mfcc':
cap_s1 = mps[0:2] + mol.find_adjacent_hydrogens(mps[0:2])
cap_s2 = mps[2:] + mol.find_adjacent_hydrogens(mps[2:])
m_cap_s = mol.get_fragment(cap_s1 + cap_s2)
m_cap_s.set_spin(0)
m_cap_s.set_residue('SCP',
1,
atoms=range(1,
len(cap_s1) + 1))
m_cap_s.set_residue('SCP',
2,
atoms=range(
len(cap_s1) + 1,
len(cap_s1 + cap_s2) + 1))
m_cap_s.add_hydrogens_to_sp3_c(1)
m_cap_s.add_hydrogens_to_sp3_c(len(cap_s1) + 2)
elif caps == 'hydrogen':
import numpy
import math
s1_coord = numpy.array(mol.get_coordinates([mps[1]]))
s2_coord = numpy.array(mol.get_coordinates([mps[2]]))
h1_coord = s1_coord + 1.34 * (
(s2_coord - s1_coord) /
numpy.linalg.norm(s2_coord - s1_coord))
h2_coord = s2_coord + 1.34 * (
(s1_coord - s2_coord) /
numpy.linalg.norm(s1_coord - s2_coord))
m_cap_s = capmolecule()
m_cap_s.add_atoms(['H'], h2_coord)
m_cap_s.add_atoms(['H'], h1_coord, bond_to=1)
m_cap_s.set_spin(0)
m_cap_s.set_residue('SCP', 1, atoms=[1])
m_cap_s.set_residue('SCP', 2, atoms=[2])
m_cap_s.set_symmetry('NOSYM')
fi1 = frag_indices[res_of_atoms[mps[2] - 1]]
fi2 = frag_indices[res_of_atoms[mps[1] - 1]]
frag_s1 = self._frags[fi1]
frag_s2 = self._frags[fi2]
self.append_scap(cappedfragment(None, capmolecule(m_cap_s)),
frag_s1, frag_s2)
# ------------------------------------------------------------------------------
# find bonds between different residues and check if they have been capped
num_uncapped_bonds = 0
for b in mol.get_all_bonds():
if not (res_of_atoms[b[0] - 1] == res_of_atoms[b[1] - 1]):
if not {b[0], b[1]} in capped_bonds \
and not {res_of_atoms[b[0] - 1], res_of_atoms[b[1] - 1]} in non_capped_res \
and not {res_of_atoms[b[0] - 1], res_of_atoms[b[1] - 1]}.issubset(special_reslists):
num_uncapped_bonds += 1
print "Bond between atoms ", b[0], " and ", b[
1], "not capped."
print "Bond between res ", res_of_atoms[
b[0] - 1], " and ", res_of_atoms[b[1] -
1], "not capped."
if num_uncapped_bonds > 0:
print num_uncapped_bonds, " bonds not capped. "
raise PyAdfError('Not all bonds capped in partition_protein')
def partition_protein(self, mol, special_reslists=None, fragsize=None):
"""
Partition a protein into the individual amino acids and caps.
@param mol: the protein
@type mol: L{molecule}
@param special_reslists:
lists of residue numbers that should be treated within one 3-FDE fragment
@type special_reslists: list of lists of integers
@param fragsize:
number of residues in one 3-FDE fragment (not for residues in special_reslist)
@type fragsize: int
"""
import openbabel
# generate fragment list
res_of_atoms = mol.get_residue_numbers_of_atoms()
capped_bonds = []
# list of bonds between residues within fragment
non_capped_res = []
# to which fragment belongs the residue
frag_indices = []
# clear the fragment list
self._frags = []
self._caps = []
if fragsize == None:
fragsize = 1
if special_reslists == None:
special_reslists = {}
#----------------------------------------------------------------------------------
# simplest fragmentation: each residue is one fragment
if special_reslists == {} and fragsize == 1:
# get the individual residues
for res in mol.get_residues():
self.append(cappedfragment(None, res))
frag_indices.append(len(self._frags) - 1)
#----------------------------------------------------------------------------------
# more complicated fragmentation
else:
# careful with residue numbers (internal openbabel index idx starts at 0)
fragmentation_list = []
residuelist = mol.get_residues()
frag_indices = [-1 for i in range(len(residuelist))]
# get list of residues connected by covalent bonds (tuples)
connected_residues = []
for b in mol.get_all_bonds():
if not (res_of_atoms[b[0] - 1] == res_of_atoms[b[1] - 1]):
# -1 is not totally clear to me, but works
connected_residues.append(
set([res_of_atoms[b[0] - 1], res_of_atoms[b[1] - 1]]))
#------------------------------------------------------------------------------
# first get fragmentation list: which residues belong to which fragment
# something like [ [3,4,5], [11,12,14], [1, 2], [6, 7], [8, 9], [10], [13]]
# ugly
# dictionary that connects pdb resnums with internal idx
# problem: resnum as key is not unique, use combination of residue name and chain as key
res_idx = {}
for res in residuelist:
resnum = res.mol.GetResidue(0).GetNum()
reschain = res.mol.GetResidue(0).GetChain()
reskey = 'c' + str(reschain) + str(resnum)
intresnum = res.mol.GetResidue(0).GetIdx()
res_idx[reskey] = intresnum
# convert residue numbers in special_reslists to internal numbers
for i in range(len(special_reslists)):
for j in range(len(special_reslists[i])):
special_reslists[i][j] = res_idx[special_reslists[i][j]]
# flatten list of lists so that you can check whether a residue is a special residue
specialresnums = []
for slist in special_reslists:
if isinstance(slist, (list, tuple)):
specialresnums.extend(slist)
# else should not occur, but maybe it is needed later
else:
specialresnums.append(slist)
# first add special residues to fragmentation list
for slist in special_reslists:
fragmentation_list.append(slist)
fragl = [] # templist for fragments
# add all other residues
# use res.GetIdx() instead of enumerate
for res in residuelist:
resnum = res.mol.GetResidue(0).GetIdx()
if resnum not in specialresnums:
# take care of fragsize
if fragsize == 1:
fragmentation_list.append([resnum])
else:
# check whether there has to be a new fragment
if len(fragl) != 0:
if not set([resnum, fragl[-1]]) in connected_residues or \
len(fragl) == fragsize:
# no covalent connection? new fragment! fragsize reached?
fragmentation_list.append(fragl)
fragl = []
# new fragment
if len(fragl) == 0:
fragl.append(resnum)
# add to fragment
else:
fragl.append(resnum)
# last fragment
if len(fragl) != 0:
fragmentation_list.append(fragl)
#------------------------------------------------------------------------------
# now add fragments to _frags using fragmentation list
# take care of uncapped bonds and fragindices etc.
frag_reslist = []
for rlist in fragmentation_list:
for rnum in range(len(rlist)):
res = mol.get_residues(idx=rlist[rnum])[0]
frag_reslist.append(res)
# if residue is added to an existing fragment: add tuple of residues to
# list of connected (non-capped) residues (only if they are connected)
# take also care of disulfide bonds within one fragment
# check whether new residue is connected to another residue within this fragment
if len(frag_reslist) > 1:
for rd in range(len(frag_reslist)):
if set([rlist[rnum],
rlist[rd]]) in connected_residues:
non_capped_res.append(
set([rlist[rnum], rlist[rd]]))
# take care of residue information here
frag_indices[rlist[rnum]] = len(self._frags)
res_frag = fragment(None, mols=frag_reslist)
self.append(cappedfragment(None,
res_frag.get_total_molecule()))
# clear frag_reslist for next fragment
frag_reslist = []
#------------------------------------------------------------------------------
# find all peptide bonds and create caps
sp = openbabel.OBSmartsPattern()
sp.Init('[C;X4;H1,H2][CX3](=O)[NX3][C;X4;H1,H2][CX3](=O)')
# this SMARTS pattern matches all peptide bonds
#
# (in this comment: atom numbering starts at 1;
# in the code below the numbering of the list starts at 0)
# the peptide bond is between atoms 2 and 4
# atoms 1-5 (+ connected hydrogens) form the cap fragment
# part 1 (o-part): atoms 1-3
# part 2 (n-part): atoms 4-5
sp.Match(mol.mol)
maplist = sp.GetUMapList()
for mp in maplist:
mp = list(mp)
if set([res_of_atoms[mp[3] - 1],
res_of_atoms[mp[1] - 1]]) not in non_capped_res:
capped_bonds.append(set([mp[1], mp[3]]))
cap_o = mp[0:3] + mol.find_adjacent_hydrogens(mp[0:3])
cap_n = mp[3:5] + mol.find_adjacent_hydrogens(mp[3:5])
m_cap = mol.get_fragment(cap_o + cap_n)
m_cap.set_spin(0)
m_cap.set_residue('CAP', 1, atoms=range(1, len(cap_o) + 1))
m_cap.set_residue('CAP',
2,
atoms=range(
len(cap_o) + 1,
len(cap_o + cap_n) + 1))
m_cap.add_hydrogens()
# find the capped fragments
# frag1: fragment that contains the N atom (mp[3])
# frag2: fragment that contains the C=O atom (mp[1])
# why minus 1???
fi1 = frag_indices[res_of_atoms[mp[3] - 1]]
fi2 = frag_indices[res_of_atoms[mp[1] - 1]]
frag1 = self._frags[fi1]
frag2 = self._frags[fi2]
self.append_cap(cappedfragment(None, capmolecule(m_cap)),
frag1, frag2)
#------------------------------------------------------------------------------
# check here for disulfide bonds and create disulfide bond caps
sp_sulfide = openbabel.OBSmartsPattern()
sp_sulfide.Init('[C;X4;H1,H2]SS[C;X4;H1,H2]')
# SMARTS pattern for disulfde bonds
sp_sulfide.Match(mol.mol)
maplist_sulfide = sp_sulfide.GetUMapList()
for mps in maplist_sulfide:
mps = list(mps)
if set([res_of_atoms[mps[2] - 1],
res_of_atoms[mps[1] - 1]]) not in non_capped_res:
capped_bonds.append(set([mps[1], mps[2]]))
cap_s1 = mps[0:2] + mol.find_adjacent_hydrogens(mps[0:2])
cap_s2 = mps[2:] + mol.find_adjacent_hydrogens(mps[2:])
m_cap_s = mol.get_fragment(cap_s1 + cap_s2)
m_cap_s.set_spin(0)
m_cap_s.set_residue('SCP', 1, atoms=range(1, len(cap_s1) + 1))
m_cap_s.set_residue('SCP',
2,
atoms=range(
len(cap_s1) + 1,
len(cap_s1 + cap_s2) + 1))
m_cap_s.add_hydrogens()
fi1 = frag_indices[res_of_atoms[mps[2] - 1]]
fi2 = frag_indices[res_of_atoms[mps[1] - 1]]
frag_s1 = self._frags[fi1]
frag_s2 = self._frags[fi2]
self.append_scap(cappedfragment(None, capmolecule(m_cap_s)),
frag_s1, frag_s2)
#------------------------------------------------------------------------------
# find bonds between different residues and check if they have been capped
num_uncapped_bonds = 0
for b in mol.get_all_bonds():
if not (res_of_atoms[b[0] - 1] == res_of_atoms[b[1] - 1]):
if not set([b[0], b[1]]) in capped_bonds \
and not set([res_of_atoms[b[0] - 1], res_of_atoms[b[1] - 1]]) in non_capped_res \
and not set([res_of_atoms[b[0] - 1], res_of_atoms[b[1] - 1]]).issubset(special_reslists):
num_uncapped_bonds += 1
print "Bond between atoms ", b[0], " and ", b[
1], "not capped."
print "Bond between res ", res_of_atoms[
b[0] - 1], " and ", res_of_atoms[b[1] -
1], "not capped."
if num_uncapped_bonds > 0:
print num_uncapped_bonds, " bonds not capped. "
raise PyAdfError('Not all bonds capped in partition_protein')
