def get_resnums_of_chain_rama_outliers(self, pdb_hierarchy):
phi_psi_atoms = utils.get_phi_psi_atoms(pdb_hierarchy, omega=True)
# pdb_hierarchy.write_pdb_file(file_name="phi_psi_atoms.pdb")
# print "len phi psi atoms", len(phi_psi_atoms)
result = []
rama_results = []
ranges_for_idealization = []
# print >> self.log, "rama outliers for input hierarchy:"
list_of_reference_exclusion = []
outp = utils.list_rama_outliers_h(pdb_hierarchy, self.r)
print >> self.log, outp
for phi_psi_pair, rama_key, omega in phi_psi_atoms:
if phi_psi_pair[0] is not None and phi_psi_pair[1] is not None:
# print "resseq:", phi_psi_pair[0][2].parent().parent().resseq
ev = utils.rama_evaluate(phi_psi_pair, self.r, rama_key)
# print " ev", ev
rama_results.append(ev)
# print phi_psi_pair[0][0].id_str()
resnum = phi_psi_pair[0][2].parent().parent().resseq
if ev == ramalyze.RAMALYZE_OUTLIER:
result.append(resnum)
if omega is not None and abs(abs(omega)-180) > 30:
print >> self.log, "Spotted twisted/cis peptide:", resnum, omega
result.append(resnum)
# STOP()
return result
def idealize_chain(self, hierarchy):
# check no ac:
for c in hierarchy.chains():
if len(c.conformers()) > 1:
raise Sorry("Alternative conformations are not supported.")
if "UNK" in c.get_residue_names_padded():
pass
# raise Sorry("UNK residues are not supported.")
working_h = hierarchy.deep_copy()
working_h.reset_atom_i_seqs()
rama_results = []
ranges_for_idealization = []
print >> self.log, "rama outliers for input hierarchy:"
rama_out_resnums = self.get_resnums_of_chain_rama_outliers(working_h)
if len(rama_out_resnums) == 0:
return None, None
# get list of residue numbers that should be excluded from reference
list_of_reference_exclusion = []
for resnum in rama_out_resnums:
excl_res = get_res_nums_around(hierarchy,
resnum,
2,
2,
include_intermediate=True)
list_of_reference_exclusion += excl_res
out_i = 0
chain_ss_annot = self.secondary_structure_annotation
if chain_ss_annot is not None:
chain_ss_annot = self.secondary_structure_annotation.deep_copy()
chain_ss_annot.remove_empty_annotations(hierarchy=working_h)
for rama_out_resnum in rama_out_resnums:
print >> self.log
print >> self.log, "Fixing outlier:", rama_out_resnum
self.log.flush()
new_h = self.fix_rama_outlier(pdb_hierarchy=working_h,
out_res_num=rama_out_resnum,
prefix=self.params.output_prefix,
minimize=False,
ss_annotation=chain_ss_annot)
print >> self.log, "listing outliers after loop minimization"
outp = utils.list_rama_outliers_h(new_h, self.r)
print >> self.log, outp
self.log.flush()
# fn = "%s_after_loop_%d.pdb" % (self.params.output_prefix, out_i)
# print >> self.log, " writing file %s" % fn
# new_h.write_pdb_file(file_name=fn)
working_h = new_h
out_i += 1
return list_of_reference_exclusion, new_h
def get_resnums_of_chain_rama_outliers(self, pdb_hierarchy):
phi_psi_atoms = utils.get_phi_psi_atoms(pdb_hierarchy)
# print "len phi psi atoms", len(phi_psi_atoms)
result = []
rama_results = []
ranges_for_idealization = []
# print >> self.log, "rama outliers for input hierarchy:"
list_of_reference_exclusion = []
outp = utils.list_rama_outliers_h(pdb_hierarchy, self.r)
print >>self.log, outp
for phi_psi_pair, rama_key in phi_psi_atoms:
# print "resseq:", phi_psi_pair[0][2].parent().parent().resseq
ev = utils.rama_evaluate(phi_psi_pair, self.r, rama_key)
# print " ev", ev
rama_results.append(ev)
if ev == ramalyze.RAMALYZE_OUTLIER:
resnum = phi_psi_pair[0][2].parent().parent().resseq
result.append(resnum)
return result
def get_resnums_of_chain_rama_outliers(self, pdb_hierarchy):
phi_psi_atoms = utils.get_phi_psi_atoms(pdb_hierarchy)
# print "len phi psi atoms", len(phi_psi_atoms)
result = []
rama_results = []
ranges_for_idealization = []
# print >> self.log, "rama outliers for input hierarchy:"
list_of_reference_exclusion = []
outp = utils.list_rama_outliers_h(pdb_hierarchy, self.r)
print >> self.log, outp
for phi_psi_pair, rama_key in phi_psi_atoms:
# print "resseq:", phi_psi_pair[0][2].parent().parent().resseq
ev = utils.rama_evaluate(phi_psi_pair, self.r, rama_key)
# print " ev", ev
rama_results.append(ev)
if ev == ramalyze.RAMALYZE_OUTLIER:
resnum = phi_psi_pair[0][2].parent().parent().resseq
result.append(resnum)
return result
def idealize_chain(self, hierarchy):
# check no ac:
for c in hierarchy.chains():
if len(c.conformers()) > 1:
raise Sorry("Alternative conformations are not supported.")
if "UNK" in c.get_residue_names_padded():
pass
# raise Sorry("UNK residues are not supported.")
working_h = hierarchy.deep_copy()
working_h.reset_atom_i_seqs()
rama_results = []
ranges_for_idealization = []
print >> self.log, "rama outliers for input hierarchy:"
rama_out_resnums = self.get_resnums_of_chain_rama_outliers(
working_h)
if len(rama_out_resnums) == 0:
return None, None
# get list of residue numbers that should be excluded from reference
list_of_reference_exclusion = []
for resnum in rama_out_resnums:
excl_res = get_res_nums_around(
hierarchy, resnum, 2, 2, include_intermediate=True)
list_of_reference_exclusion += excl_res
out_i = 0
for rama_out_resnum in rama_out_resnums:
print >> self.log
print >> self.log, "Fixing outlier:", rama_out_resnum
self.log.flush()
new_h = self.fix_rama_outlier(
pdb_hierarchy=working_h,
out_res_num=rama_out_resnum,
prefix=self.params.output_prefix,
minimize=False)
print >> self.log, "listing outliers after loop minimization"
outp = utils.list_rama_outliers_h(new_h, self.r)
print >> self.log, outp
self.log.flush()
fn = "%s_after_loop_%d.pdb" % (self.params.output_prefix, out_i)
# print >> self.log, " writing file %s" % fn
# new_h.write_pdb_file(file_name=fn)
working_h = new_h
out_i += 1
return list_of_reference_exclusion, new_h
def __init__(self,
pdb_hierarchy,
params=None,
secondary_structure_annotation=None,
reference_map=None,
crystal_symmetry=None,
grm=None,
rama_manager=None,
rotamer_manager=None,
log=null_out(),
verbose=False,
tried_rama_angles={},
tried_final_rama_angles={},
n_run=0):
if len(pdb_hierarchy.models()) > 1:
raise Sorry("Multi-model files are not supported")
self.original_pdb_h = pdb_hierarchy
self.secondary_structure_annotation=secondary_structure_annotation
asc = pdb_hierarchy.atom_selection_cache()
self.xrs = pdb_hierarchy.extract_xray_structure(crystal_symmetry=crystal_symmetry)
self.reference_map = reference_map
self.resulting_pdb_h = pdb_hierarchy.deep_copy()
self.resulting_pdb_h.reset_atom_i_seqs()
self.params = self.process_params(params)
self.log = log
self.verbose = verbose
self.grm = grm
self.r = rama_manager
self.ideal_res_dict = idealized_aa.residue_dict()
self.n_run = n_run
if self.r is None:
self.r = rama_eval()
self.rotamer_manager = rotamer_manager
if self.rotamer_manager is None:
self.rotamer_manager = RotamerEval()
ram = ramalyze.ramalyze(pdb_hierarchy=pdb_hierarchy)
self.p_initial_rama_outliers = ram.out_percent
self.p_before_minimization_rama_outliers = None
self.p_after_minimiaztion_rama_outliers = None
n_inputs = [reference_map, crystal_symmetry].count(None)
if not (n_inputs == 0 or n_inputs == 2):
print >> log, "Need to have both map and symmetry info. Not using map."
self.reference_map = None
# here we are recording what CCD solutions were used to fix particular
# outliers to not use the same in the next CCD try.
# Nested dict. First level:
# key: chain id, value: dict
# key: resid (string), value: list of tried variants.
self.tried_rama_angles = tried_rama_angles
self.tried_final_rama_angles = tried_final_rama_angles
berkeley_count = utils.list_rama_outliers_h(self.resulting_pdb_h).count("\n")
self.berkeley_p_before_minimization_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
n_bad_omegas = utils.n_bad_omegas(self.resulting_pdb_h)
self.berkeley_p_after_minimiaztion_rama_outliers = self.berkeley_p_before_minimization_rama_outliers
self.ref_exclusion_selection = ""
self.number_of_ccd_trials = 0
# print "logic expr outcome:", (self.number_of_ccd_trials < 10 and self.berkeley_p_before_minimization_rama_outliers > 0.001)
# print self.number_of_ccd_trials < 10
# print "berkeley before rama out:", self.berkeley_p_before_minimization_rama_outliers
if (self.berkeley_p_before_minimization_rama_outliers <= 0.001 and
(n_bad_omegas<1 and self.params.make_all_trans)):
print >> self.log, "No ramachandran outliers, skipping CCD step."
print "n_bad_omegas", n_bad_omegas
print "self.params.make_all_trans",self.params.make_all_trans
if not self.params.enabled:
print >> self.log, "Loop idealization is not enabled, use 'enabled=True'."
while (self.number_of_ccd_trials < self.params.number_of_ccd_trials
and (self.berkeley_p_after_minimiaztion_rama_outliers > 0.001 or
(n_bad_omegas>=1 and self.params.make_all_trans))
and self.params.enabled):
print >> self.log, "CCD try number, outliers:", self.number_of_ccd_trials, self.berkeley_p_before_minimization_rama_outliers
processed_chain_ids = []
for chain in self.resulting_pdb_h.only_model().chains():
if chain.id not in self.tried_rama_angles.keys():
self.tried_rama_angles[chain.id] = {}
if chain.id not in self.tried_final_rama_angles.keys():
self.tried_final_rama_angles[chain.id] = {}
print >> self.log, "Idealizing chain %s" % chain.id
if chain.id not in processed_chain_ids:
processed_chain_ids.append(chain.id)
else:
continue
selection = "protein and chain %s and (name N or name CA or name C or name O)" % chain.id
sel = asc.selection("chain %s" % chain.id)
chain_h = self.resulting_pdb_h.select(sel)
m = chain_h.only_model()
i = 0
cutted_chain_h = None
for c in m.chains():
if i == 0:
cutted_chain_h = iotbx.pdb.hierarchy.new_hierarchy_from_chain(c)
else:
print >> self.log, "WARNING!!! Duplicating chain ids! Only the first chain will be processed."
print >> self.log, " Removing chain %s with %d residues" % (c.id, len(c.residues()))
m.remove_chain(c)
i += 1
exclusions, ch_h = self.idealize_chain(
hierarchy=(cutted_chain_h if cutted_chain_h else chain_h),
tried_rama_angles_for_chain=self.tried_rama_angles[chain.id],
tried_final_rama_angles_for_chain=self.tried_final_rama_angles[chain.id])
if ch_h is not None:
set_xyz_smart(
# dest_h=self.resulting_pdb_h,
dest_h=chain,
source_h=ch_h)
for resnum in exclusions:
selection += " and not resseq %s" % resnum
self.ref_exclusion_selection += "(%s) or " % selection
print "self.tried_rama_angles", self.tried_rama_angles
print "self.tried_final_rama_angles", self.tried_final_rama_angles
#
# dumping and reloading hierarchy to do proper rounding of coordinates
self.resulting_pdb_h = iotbx.pdb.input(
source_info=None,
lines=self.resulting_pdb_h.as_pdb_string()).construct_hierarchy()
berkeley_count = utils.list_rama_outliers_h(self.resulting_pdb_h).count("\n")
self.berkeley_p_before_minimization_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
if len(self.ref_exclusion_selection) > 0:
self.ref_exclusion_selection = self.ref_exclusion_selection[:-3]
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_before_minimization_rama_outliers = ram.out_percent
duke_count = ram.get_outliers_count_and_fraction()[0]
if berkeley_count != duke_count:
print >> self.log, "Discrepancy between berkeley and duke after ccd:", berkeley_count, duke_count
self.resulting_pdb_h.write_pdb_file(file_name="%d%s_discrepancy.pdb" % (self.number_of_ccd_trials, self.params.output_prefix))
if self.params.debug:
self.resulting_pdb_h.write_pdb_file(
file_name="%d%s_all_not_minized.pdb" % (self.number_of_ccd_trials,
self.params.output_prefix))
if self.params.minimize_whole:
print >> self.log, "minimizing whole chain..."
print >> self.log, "self.ref_exclusion_selection", self.ref_exclusion_selection
# print >> sel
# XXX but first let's check and fix rotamers...
print >> self.log, "Fixing/checking rotamers in loop idealization..."
excl_sel = self.ref_exclusion_selection
if len(excl_sel) == 0:
excl_sel = None
non_outliers_for_check = asc.selection("(%s)" % self.ref_exclusion_selection)
pre_result_h = mmtbx.utils.fix_rotamer_outliers(
pdb_hierarchy=self.resulting_pdb_h,
grm=self.grm.geometry,
xrs=self.xrs,
map_data=self.reference_map,
radius=5,
mon_lib_srv=None,
rotamer_manager=self.rotamer_manager,
backrub_range=None, # don't sample backrub at this point
non_outliers_to_check=non_outliers_for_check, # bool selection
asc=asc,
verbose=True,
log=self.log)
if self.reference_map is None:
minimize_wrapper_for_ramachandran(
hierarchy=self.resulting_pdb_h,
xrs=self.xrs,
original_pdb_h=self.original_pdb_h,
excl_string_selection=self.ref_exclusion_selection,
grm=self.grm,
log=None,
ss_annotation=self.secondary_structure_annotation)
else:
mwwm = minimize_wrapper_with_map(
pdb_h=self.resulting_pdb_h,
xrs=self.xrs,
target_map=self.reference_map,
grm=self.grm,
ss_annotation=self.secondary_structure_annotation,
number_of_cycles=Auto,
log=self.log)
if self.params.debug:
self.resulting_pdb_h.write_pdb_file(
file_name="%d%s_all_minized.pdb" % (self.number_of_ccd_trials,
self.params.output_prefix))
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_after_minimiaztion_rama_outliers = ram.out_percent
berkeley_count = utils.list_rama_outliers_h(self.resulting_pdb_h).count("\n")
duke_count = ram.get_outliers_count_and_fraction()[0]
n_bad_omegas = utils.n_bad_omegas(self.resulting_pdb_h)
self.berkeley_p_after_minimiaztion_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
if berkeley_count != duke_count:
print >> self.log, "Discrepancy between berkeley and duke after min:", berkeley_count, duke_count
else:
print >> self.log, "Number of Rama outliers after min:", berkeley_count
print >> self.log, "Number of bad omegas:", n_bad_omegas
self.number_of_ccd_trials += 1
def idealize_chain(self, hierarchy, tried_rama_angles_for_chain={},
tried_final_rama_angles_for_chain={}):
# check no ac:
for c in hierarchy.chains():
if len(c.conformers()) > 1:
raise Sorry("Alternative conformations are not supported.")
if "UNK" in c.get_residue_names_padded():
pass
# raise Sorry("UNK residues are not supported.")
working_h = hierarchy.deep_copy()
working_h.reset_atom_i_seqs()
rama_results = []
ranges_for_idealization = []
print >> self.log, "rama outliers for input hierarchy:"
rama_out_resnums = self.get_resnums_of_chain_rama_outliers(
working_h)
if len(rama_out_resnums) == 0:
return None, None
# get list of residue numbers that should be excluded from reference
list_of_reference_exclusion = []
for resnum in rama_out_resnums:
excl_res = get_res_nums_around(
hierarchy, [resnum], 2, 2, include_intermediate=True)
list_of_reference_exclusion += excl_res
out_i = 0
chain_ss_annot = self.secondary_structure_annotation
if chain_ss_annot is not None:
chain_ss_annot = self.secondary_structure_annotation.deep_copy()
chain_ss_annot.remove_empty_annotations(hierarchy=working_h)
# combine outliers next to each other
comb_rama_out_resnums = [[rama_out_resnums[0]]]
for r_out_resnum in rama_out_resnums[1:]:
if abs(hy36decode(4, r_out_resnum)-hy36decode(4, comb_rama_out_resnums[-1][-1])) < 2:
# combine
comb_rama_out_resnums[-1].append(r_out_resnum)
else:
# separate
comb_rama_out_resnums.append([r_out_resnum])
print >> self.log, "Combined outliers for fixing:", comb_rama_out_resnums
for rama_out_resnum in comb_rama_out_resnums:
print >> self.log
print >> self.log, "Fixing outlier:", rama_out_resnum
self.log.flush()
new_h = self.fix_rama_outlier(
pdb_hierarchy=working_h,
out_res_num_list=rama_out_resnum,
prefix=self.params.output_prefix,
minimize=False,
ss_annotation=chain_ss_annot,
tried_rama_angles_for_chain=tried_rama_angles_for_chain,
tried_final_rama_angles_for_chain=tried_final_rama_angles_for_chain)
print >> self.log, "listing outliers after loop minimization"
outp = utils.list_rama_outliers_h(new_h, self.r)
print >> self.log, outp
utils.list_omega_outliers(new_h, self.log)
self.log.flush()
working_h = new_h
out_i += 1
return list_of_reference_exclusion, new_h
def __init__(self,
pdb_hierarchy,
params=None,
secondary_structure_annotation=None,
reference_map=None,
crystal_symmetry=None,
grm=None,
rama_manager=None,
rotamer_manager=None,
log=null_out(),
verbose=False):
if len(pdb_hierarchy.models()) > 1:
raise Sorry("Multi-model files are not supported")
self.original_pdb_h = pdb_hierarchy
self.secondary_structure_annotation = secondary_structure_annotation
asc = pdb_hierarchy.atom_selection_cache()
self.xrs = pdb_hierarchy.extract_xray_structure(
crystal_symmetry=crystal_symmetry)
self.reference_map = reference_map
self.resulting_pdb_h = pdb_hierarchy.deep_copy()
self.resulting_pdb_h.reset_atom_i_seqs()
self.params = self.process_params(params)
self.log = log
self.verbose = verbose
self.grm = grm
self.r = rama_manager
if self.r is None:
self.r = rama_eval()
self.rotamer_manager = rotamer_manager
if self.rotamer_manager is None:
self.rotamer_manager = RotamerEval()
ram = ramalyze.ramalyze(pdb_hierarchy=pdb_hierarchy)
self.p_initial_rama_outliers = ram.out_percent
self.p_before_minimization_rama_outliers = None
self.p_after_minimiaztion_rama_outliers = None
n_inputs = [reference_map, crystal_symmetry].count(None)
if not (n_inputs == 0 or n_inputs == 2):
print >> log, "Need to have both map and symmetry info. Not using map."
self.reference_map = None
berkeley_count = utils.list_rama_outliers_h(
self.resulting_pdb_h).count("\n")
self.berkeley_p_before_minimization_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
# self.berkeley_p_before_minimization_rama_outliers = None
self.berkeley_p_after_minimiaztion_rama_outliers = self.berkeley_p_before_minimization_rama_outliers
self.ref_exclusion_selection = ""
number_of_ccd_trials = 0
# print "logic expr outcome:", (number_of_ccd_trials < 10 and self.berkeley_p_before_minimization_rama_outliers > 0.001)
# print number_of_ccd_trials < 10
# print "berkeley before rama out:", self.berkeley_p_before_minimization_rama_outliers
if self.berkeley_p_before_minimization_rama_outliers <= 0.001:
print >> self.log, "No ramachandran outliers, skipping CCD step."
if not self.params.enabled:
print >> self.log, "Loop idealization is not enabled, use 'enabled=True'."
while (number_of_ccd_trials < self.params.number_of_ccd_trials
and self.berkeley_p_after_minimiaztion_rama_outliers > 0.001
and self.params.enabled):
print "CCD try number, outliers:", number_of_ccd_trials, self.berkeley_p_before_minimization_rama_outliers
number_of_ccd_trials += 1
processed_chain_ids = []
for chain in self.resulting_pdb_h.only_model().chains():
print >> self.log, "Idealizing chain %s" % chain.id
if chain.id not in processed_chain_ids:
processed_chain_ids.append(chain.id)
else:
continue
selection = "protein and chain %s and (name N or name CA or name C or name O)" % chain.id
sel = asc.selection("chain %s" % chain.id)
chain_h = self.resulting_pdb_h.select(sel)
m = chain_h.only_model()
i = 0
cutted_chain_h = None
for c in m.chains():
if i == 0:
cutted_chain_h = iotbx.pdb.hierarchy.new_hierarchy_from_chain(
c)
else:
print >> self.log, "WARNING!!! Duplicating chain ids! Only the first chain will be processed."
print >> self.log, " Removing chain %s with %d residues" % (
c.id, len(c.residues()))
m.remove_chain(c)
i += 1
exclusions, ch_h = self.idealize_chain(
hierarchy=(cutted_chain_h if cutted_chain_h else chain_h))
if ch_h is not None:
set_xyz_smart(
# dest_h=self.resulting_pdb_h,
dest_h=chain,
source_h=ch_h)
for resnum in exclusions:
selection += " and not resseq %s" % resnum
self.ref_exclusion_selection += "(%s) or " % selection
#
# dumping and reloading hierarchy to do proper rounding of coordinates
self.resulting_pdb_h = iotbx.pdb.input(
source_info=None, lines=self.resulting_pdb_h.as_pdb_string(
)).construct_hierarchy()
berkeley_count = utils.list_rama_outliers_h(
self.resulting_pdb_h).count("\n")
self.berkeley_p_before_minimization_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
if len(self.ref_exclusion_selection) > 0:
self.ref_exclusion_selection = self.ref_exclusion_selection[:
-3]
# self.resulting_pdb_h.write_pdb_file(file_name="%s_before_minimization.pdb" % self.params.output_prefix)
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_before_minimization_rama_outliers = ram.out_percent
duke_count = ram.get_outliers_count_and_fraction()[0]
if berkeley_count != duke_count:
print >> self.log, "Discrepancy between berkeley and duke after ccd:", berkeley_count, duke_count
if self.params.minimize_whole:
print >> self.log, "minimizing whole thing..."
print >> self.log, "self.ref_exclusion_selection", self.ref_exclusion_selection
# print >> sel
if self.reference_map is None:
minimize_wrapper_for_ramachandran(
hierarchy=self.resulting_pdb_h,
xrs=self.xrs,
original_pdb_h=self.original_pdb_h,
excl_string_selection=self.ref_exclusion_selection,
grm=self.grm,
log=None,
ss_annotation=self.secondary_structure_annotation)
else:
mwwm = minimize_wrapper_with_map(
pdb_h=self.resulting_pdb_h,
xrs=self.xrs,
target_map=self.reference_map,
grm=self.grm,
ss_annotation=self.secondary_structure_annotation,
log=self.log)
# self.resulting_pdb_h.write_pdb_file(file_name="%s_all_minized.pdb" % self.params.output_prefix)
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_after_minimiaztion_rama_outliers = ram.out_percent
berkeley_count = utils.list_rama_outliers_h(
self.resulting_pdb_h).count("\n")
duke_count = ram.get_outliers_count_and_fraction()[0]
self.berkeley_p_after_minimiaztion_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
if berkeley_count != duke_count:
print >> self.log, "Discrepancy between berkeley and duke after min:", berkeley_count, duke_count
else:
print >> self.log, "Number of Rama outliers after min:", berkeley_count
def __init__(self,
pdb_hierarchy,
params=None,
secondary_structure_annotation=None,
log=null_out(),
verbose=True):
if len(pdb_hierarchy.models()) > 1:
raise Sorry("Multi-model files are not supported")
self.original_pdb_h = pdb_hierarchy
self.secondary_structure_annotation=secondary_structure_annotation
xrs = pdb_hierarchy.extract_xray_structure()
asc = pdb_hierarchy.atom_selection_cache()
self.resulting_pdb_h = pdb_hierarchy.deep_copy()
self.resulting_pdb_h.reset_atom_i_seqs()
self.params = self.process_params(params)
self.log = log
self.verbose = verbose
self.r = rama_eval()
self.rotamer_manager = RotamerEval()
ram = ramalyze.ramalyze(pdb_hierarchy=pdb_hierarchy)
self.p_initial_rama_outliers = ram.out_percent
self.p_before_minimization_rama_outliers = None
self.p_after_minimiaztion_rama_outliers = None
berkeley_count = utils.list_rama_outliers_h(self.resulting_pdb_h).count("\n")
self.berkeley_p_before_minimization_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
# self.berkeley_p_before_minimization_rama_outliers = None
self.berkeley_p_after_minimiaztion_rama_outliers = None
self.ref_exclusion_selection = ""
number_of_ccd_trials = 0
# print "logic expr outcome:", (number_of_ccd_trials < 10 and self.berkeley_p_before_minimization_rama_outliers > 0.001)
# print number_of_ccd_trials < 10
# print "berkeley before rama out:", self.berkeley_p_before_minimization_rama_outliers
if self.berkeley_p_before_minimization_rama_outliers <= 0.001:
print >> self.log, "No ramachandran outliers, skipping CCD step."
if not self.params.enabled:
print >> self.log, "Loop idealization is not enabled, use 'enabled=True'."
while (number_of_ccd_trials < self.params.number_of_ccd_trials
and self.berkeley_p_before_minimization_rama_outliers > 0.001
and self.params.enabled):
print "CCD try number, outliers:", number_of_ccd_trials, self.berkeley_p_before_minimization_rama_outliers
number_of_ccd_trials += 1
processed_chain_ids = []
for chain in self.resulting_pdb_h.only_model().chains():
print >> self.log, "Idealizing chain %s" % chain.id
if chain.id not in processed_chain_ids:
processed_chain_ids.append(chain.id)
else:
continue
selection = "protein and chain %s and (name N or name CA or name C or name O)" % chain.id
sel = asc.selection("chain %s" % chain.id)
chain_h = self.resulting_pdb_h.select(sel)
m = chain_h.only_model()
i = 0
cutted_chain_h = None
for c in m.chains():
if i == 0:
cutted_chain_h = iotbx.pdb.hierarchy.new_hierarchy_from_chain(c)
else:
print >> self.log, "WARNING!!! Duplicating chain ids! Only the first chain will be processed."
print >> self.log, " Removing chain %s with %d residues" % (c.id, len(c.residues()))
m.remove_chain(c)
i += 1
exclusions, ch_h = self.idealize_chain(
hierarchy=(cutted_chain_h if cutted_chain_h else chain_h))
if ch_h is not None:
set_xyz_smart(
# dest_h=self.resulting_pdb_h,
dest_h=chain,
source_h=ch_h)
for resnum in exclusions:
selection += " and not resseq %s" % resnum
self.ref_exclusion_selection += "(%s) or " % selection
#
# dumping and reloading hierarchy to do proper rounding of coordinates
self.resulting_pdb_h = iotbx.pdb.input(
source_info=None,
lines=self.resulting_pdb_h.as_pdb_string()).construct_hierarchy()
berkeley_count = utils.list_rama_outliers_h(self.resulting_pdb_h).count("\n")
self.berkeley_p_before_minimization_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
if len(self.ref_exclusion_selection) > 0:
self.ref_exclusion_selection = self.ref_exclusion_selection[:-3]
self.resulting_pdb_h.write_pdb_file(file_name="%s_before_minimization.pdb" % self.params.output_prefix)
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_before_minimization_rama_outliers = ram.out_percent
duke_count = ram.get_outliers_count_and_fraction()[0]
if berkeley_count != duke_count:
print >> self.log, "Discrepancy between berkeley and duke after ccd:", berkeley_count, duke_count
if self.params.minimize_whole:
print >> self.log, "minimizing whole thing..."
print >> self.log, "self.ref_exclusion_selection", self.ref_exclusion_selection
# print >> sel
minimize_wrapper_for_ramachandran(
hierarchy=self.resulting_pdb_h,
xrs=xrs,
original_pdb_h=self.original_pdb_h,
excl_string_selection=self.ref_exclusion_selection,
log=None,
ss_annotation=self.secondary_structure_annotation)
# self.resulting_pdb_h.write_pdb_file(file_name="%s_all_minized.pdb" % self.params.output_prefix)
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_after_minimiaztion_rama_outliers = ram.out_percent
berkeley_count = utils.list_rama_outliers_h(self.resulting_pdb_h).count("\n")
duke_count = ram.get_outliers_count_and_fraction()[0]
self.berkeley_p_after_minimiaztion_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
if berkeley_count != duke_count:
print >> self.log, "Discrepancy between berkeley and duke after min:", berkeley_count, duke_count
else:
print >> self.log, "Number of Rama outliers after min:", berkeley_count
