def __init__(self, pdb_hierarchy, params=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
xrs = pdb_hierarchy.extract_xray_structure()
asc = pdb_hierarchy.atom_selection_cache()
self.resulting_pdb_h = pdb_hierarchy.deep_copy()
self.params = self.process_params(params)
self.log = log
self.verbose = verbose
self.r = ramachandran_eval.RamachandranEval()
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
self.ref_exclusion_selection = ""
for chain in pdb_hierarchy.only_model().chains():
print >>self.log, "Idealizing chain %s" % chain.id
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.original_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(self.resulting_pdb_h, ch_h)
for resnum in exclusions:
selection += " and not resseq %s" % resnum
self.ref_exclusion_selection += "(%s) or " % selection
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_minization.pdb" % self.params.output_prefix)
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_before_minimization_rama_outliers = ram.out_percent
if self.params.minimize_whole:
print >>self.log, "minimizing whole thing..."
print >>self.log, "self.ref_exclusion_selection", self.ref_exclusion_selection
minimize_hierarchy(self.resulting_pdb_h, xrs, self.original_pdb_h, self.ref_exclusion_selection, log=None)
# 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
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 fix_rama_outlier(self, pdb_hierarchy, out_res_num, prefix="", minimize=True):
original_pdb_h = pdb_hierarchy.deep_copy()
rotamer_manager = RotamerEval()
all_results = []
for ccd_radius, change_all, change_radius in [
(1, False, 0),
(2, False, 0),
# (3, False, 0),
(2, True, 1),
# (3, True, 1),
]:
# while ccd_radius <= 3:
print >>self.log, " Starting optimization with radius, change_all:", ccd_radius, change_all
moving_h, moving_ref_atoms_iseqs, fixed_ref_atoms = get_fixed_moving_parts(
pdb_hierarchy=pdb_hierarchy, out_res_num=out_res_num, n_following=ccd_radius, n_previous=ccd_radius
)
moving_h_set = None
if change_all:
moving_h_set = starting_conformations.get_all_starting_conformations(
moving_h, change_radius, log=self.log
)
else:
moving_h_set = starting_conformations.get_starting_conformations(moving_h, log=self.log)
if len(moving_h_set) == 0:
# outlier was fixed before somehow...
return original_pdb_h
rotamer_manager = RotamerEval()
for i, h in enumerate(moving_h_set):
ccd_obj = ccd_python(fixed_ref_atoms, h, moving_ref_atoms_iseqs)
ccd_obj.run()
resulting_rmsd = ccd_obj.resulting_rmsd
states = ccd_obj.states
n_iter = ccd_obj.n_iter
# resulting_rmsd, states, n_iter = ccd(
# fixed_ref_atoms, h, moving_ref_atoms_iseqs, moving_h)
mc_rmsd = get_main_chain_rmsd_range(moving_h, h, all_atoms=True)
print >>self.log, "Resulting anchor and backbone RMSDs, n_iter for model %d:" % i,
print >>self.log, resulting_rmsd, ",", mc_rmsd, ",", n_iter
all_results.append((h.deep_copy(), mc_rmsd, resulting_rmsd, n_iter))
#
# setting new coordinates
#
moved_with_side_chains_h = pdb_hierarchy.deep_copy()
set_xyz_smart(moved_with_side_chains_h, h)
#
# placing side-chains
#
# moved_with_side_chains_h.write_pdb_file(
# file_name="%s_before_sc_placement_%d.pdb" % (prefix, i))
placing_range = get_res_nums_around(
moved_with_side_chains_h,
center_resnum=out_res_num,
n_following=ccd_radius,
n_previous=ccd_radius,
include_intermediate=True,
)
place_side_chains(moved_with_side_chains_h, original_pdb_h, rotamer_manager, placing_range)
# moved_with_side_chains_h.write_pdb_file(
# file_name="%s_after_sc_placement_%d.pdb" % (prefix, i))
#
# finalizing with geometry_minimization
#
# !!! This is the condition of acceptance of transformation!
# if mc_rmsd < adaptive_mc_rmsd[ccd_radius]:
if self.ccd_solution_is_ok(
anchor_rmsd=resulting_rmsd,
mc_rmsd=mc_rmsd,
ccd_radius=ccd_radius,
change_all_angles=change_all,
change_radius=change_radius,
):
if minimize:
print >>self.log, "minimizing..."
moved_with_side_chains_h.write_pdb_file(file_name="%s_result_before_min_%d.pdb" % (prefix, i))
minimize_hierarchy(moved_with_side_chains_h, xrs, original_pdb_h, self.log)
moved_with_side_chains_h.write_pdb_file(file_name="%s_result_minimized_%d.pdb" % (prefix, i))
final_rmsd = get_main_chain_rmsd_range(moved_with_side_chains_h, original_pdb_h, placing_range)
print >>self.log, "FINAL RMSD after minimization:", final_rmsd
return moved_with_side_chains_h
ccd_radius += 1
print >>self.log, "Epic FAIL: failed to fix rama outlier"
all_results.sort(key=lambda tup: tup[1])
print >>self.log, " Options were: (mc_rmsd, resultign_rmsd, n_iter)"
for i in all_results:
print >>self.log, i[1:]
# STOP()
return original_pdb_h
def run(self):
t_0 = time()
ncs_obj = iotbx.ncs.input(
hierarchy=self.whole_pdb_h,
chain_max_rmsd=4.0,
chain_similarity_threshold=0.99,
residue_match_radius=999.0)
print >> self.log, "Found NCS groups:"
ncs_obj.show(format='phil', log=self.log)
ncs_restr_group_list = ncs_obj.get_ncs_restraints_group_list(
raise_sorry=False)
self.using_ncs = False
total_ncs_selected_atoms = 0
master_sel = flex.size_t([])
filtered_ncs_restr_group_list = self.filter_ncs_restraints_group_list(
self.whole_pdb_h, ncs_restr_group_list)
if len(filtered_ncs_restr_group_list) > 0:
self.using_ncs = True
master_sel = flex.bool(self.whole_pdb_h.atoms_size(), True)
for ncs_gr in filtered_ncs_restr_group_list:
for copy in ncs_gr.copies:
master_sel.set_selected(copy.iselection, False)
self.master_pdb_h = self.whole_pdb_h.select(master_sel)
self.master_sel=master_sel
self.master_pdb_h.reset_atom_i_seqs()
if self.using_ncs:
self.master_pdb_h.write_pdb_file("%s_master_h.pdb" % self.params.output_prefix)
self.working_pdb_h = self.master_pdb_h
else:
self.working_pdb_h = self.whole_pdb_h
self.working_pdb_h.reset_atom_i_seqs()
self.ann = ioss.annotation.from_phil(
phil_helices=self.params.secondary_structure.protein.helix,
phil_sheets=self.params.secondary_structure.protein.sheet,
pdb_hierarchy=self.whole_pdb_h)
self.working_xrs = self.working_pdb_h.extract_xray_structure(crystal_symmetry=self.cs)
if self.using_ncs:
self.whole_xrs = self.whole_pdb_h.extract_xray_structure(crystal_symmetry=self.cs)
else:
self.whole_xrs = self.working_xrs
if self.params.use_map_for_reference:
# self.prepare_reference_map(xrs=self.whole_xrs, pdb_h=self.whole_pdb_h)
# self.prepare_reference_map_2(xrs=self.whole_xrs, pdb_h=self.whole_pdb_h)
self.prepare_reference_map_3(xrs=self.whole_xrs, pdb_h=self.whole_pdb_h)
# STOP()
if self.ann.get_n_helices() + self.ann.get_n_sheets() == 0:
self.ann = self.pdb_input.extract_secondary_structure()
self.original_ann = None
self.filtered_whole_ann = None
if self.ann is not None:
self.original_ann = self.ann.deep_copy()
print >> self.log, "Original SS annotation"
print >> self.log, self.original_ann.as_pdb_str()
self.ann.remove_short_annotations()
self.filtered_whole_ann = self.ann.deep_copy()
self.ann.remove_empty_annotations(
hierarchy=self.working_pdb_h)
self.filtered_whole_ann.remove_empty_annotations(
hierarchy=self.whole_pdb_h)
# self.ann.concatenate_consecutive_helices()
self.ann.split_helices_with_prolines(
hierarchy=self.working_pdb_h,
asc=None)
self.filtered_whole_ann.split_helices_with_prolines(
hierarchy=self.whole_pdb_h,
asc=None)
# print >> self.log, "Splitted SS annotation"
# print >> self.log, ann.as_pdb_str()
print >> self.log, "Filtered SS annotation"
print >> self.log, self.ann.as_pdb_str()
# getting grm with SS restraints
self.get_grm()
if (self.ann is None or
self.ann.get_n_helices() + self.ann.get_n_sheets() == 0 or
not self.params.ss_idealization.enabled):
print >> self.log, "No secondary structure annotations found or SS idealization is disabled."
print >> self.log, "Secondary structure substitution step will be skipped"
self.log.flush()
# here we want to do geometry minimization anyway!
negate_selection = None
if self.reference_map is None:
outlier_selection_txt = mmtbx.building.loop_closure.utils. \
rama_outliers_selection(self.working_pdb_h, self.rama_manager, 1)
print >> self.log, "outlier_selection_txt", outlier_selection_txt
negate_selection = "all"
if outlier_selection_txt != "" and outlier_selection_txt is not None:
negate_selection = "not (%s)" % outlier_selection_txt
self.minimize(
hierarchy=self.whole_pdb_h,
xrs=self.whole_xrs,
original_pdb_h=self.whole_pdb_h,
grm=self.whole_grm,
ncs_restraints_group_list=filtered_ncs_restr_group_list,
excl_string_selection=negate_selection,
ss_annotation=self.ann,
reference_map=self.reference_map)
# self.original_boxed_hierarchy.write_pdb_file(file_name="original_boxed_h_1.pdb")
else:
self.params.ss_idealization.file_name_before_regularization = \
"%s_ss_before_reg.pdb" % self.params.output_prefix
ssb.substitute_ss(
real_h=self.working_pdb_h,
xray_structure=self.working_xrs,
ss_annotation=self.ann,
params=self.params.ss_idealization,
grm=self.working_grm,
fix_rotamer_outliers=True,
cif_objects=self.cif_objects,
verbose=True,
reference_map=self.reference_map,
rotamer_manager=self.rotamer_manager,
log=self.log)
self.log.flush()
self.after_ss_idealization = geometry_no_grm(
pdb_hierarchy=iotbx.pdb.input(
source_info=None,
lines=self.working_pdb_h.as_pdb_string()).construct_hierarchy(),
molprobity_scores=True)
# Write resulting pdb file.
self.shift_and_write_result(
hierarchy=self.working_pdb_h,
fname_suffix="ss_ideal",
grm=self.working_grm)
# STOP()
self.params.loop_idealization.minimize_whole = not self.using_ncs
# self.params.loop_idealization.enabled = False
# self.params.loop_idealization.variant_search_level = 0
loop_ideal = loop_idealization(
pdb_hierarchy=self.working_pdb_h,
params=self.params.loop_idealization,
secondary_structure_annotation=self.ann,
reference_map=self.reference_map,
crystal_symmetry=self.working_xrs.crystal_symmetry(),
grm=self.working_grm,
rama_manager=self.rama_manager,
rotamer_manager=self.rotamer_manager,
log=self.log,
verbose=True)
self.log.flush()
# STOP()
self.shift_and_write_result(
hierarchy=loop_ideal.resulting_pdb_h,
fname_suffix="rama_ideal",
grm=self.working_grm)
self.after_loop_idealization = geometry_no_grm(
pdb_hierarchy=iotbx.pdb.input(
source_info=None,
lines=loop_ideal.resulting_pdb_h.as_pdb_string()).construct_hierarchy(),
molprobity_scores=True)
# fixing remaining rotamer outliers
fixed_rot_pdb_h = loop_ideal.resulting_pdb_h.deep_copy()
fixed_rot_pdb_h.reset_atom_i_seqs()
if (self.params.additionally_fix_rotamer_outliers and
self.after_loop_idealization.rotamer_outliers > 0.004):
print >> self.log, "Processing pdb file again for fixing rotamers..."
self.log.flush()
print >> self.log, "Fixing rotamers..."
self.log.flush()
self.shift_and_write_result(
hierarchy=fixed_rot_pdb_h,
fname_suffix="just_before_rota")
fixed_rot_pdb_h = fix_rotamer_outliers(
pdb_hierarchy=fixed_rot_pdb_h,
grm=self.working_grm.geometry,
xrs=self.working_xrs,
map_data=self.reference_map,
mon_lib_srv=self.mon_lib_srv,
rotamer_manager=self.rotamer_manager,
verbose=True)
self.shift_and_write_result(
hierarchy=fixed_rot_pdb_h,
fname_suffix="rota_ideal",
grm=self.working_grm)
cs_to_write = self.cs if self.shift_vector is None else None
self.after_rotamer_fixing = geometry_no_grm(
pdb_hierarchy=iotbx.pdb.input(
source_info=None,
lines=fixed_rot_pdb_h.as_pdb_string()).construct_hierarchy(),
molprobity_scores=True)
ref_hierarchy_for_final_gm = self.original_boxed_hierarchy
if not self.params.use_starting_model_for_final_gm:
ref_hierarchy_for_final_gm = self.whole_pdb_h
ref_hierarchy_for_final_gm.reset_atom_i_seqs()
if self.params.additionally_fix_rotamer_outliers:
ssb.set_xyz_smart(self.working_pdb_h, fixed_rot_pdb_h)
if self.using_ncs:
print >> self.log, "Using ncs"
# multiply back and do geometry_minimization for the whole molecule
for ncs_gr in ncs_restr_group_list:
master_h = self.whole_pdb_h.select(ncs_gr.master_iselection)
for c in ncs_gr.copies:
new_sites = master_h.atoms().extract_xyz()
new_c_sites = c.r.elems * new_sites + c.t
self.whole_pdb_h.select(c.iselection).atoms().set_xyz(new_c_sites)
self.log.flush()
else:
# still need to run gm if rotamers were fixed
print >> self.log, "Not using ncs"
# need to update SS manager for the whole model here.
if self.params.use_ss_restraints:
ss_manager = manager(
pdb_hierarchy=self.whole_pdb_h,
geometry_restraints_manager=self.whole_grm.geometry,
sec_str_from_pdb_file=self.filtered_whole_ann,
params=None,
mon_lib_srv=self.mon_lib_srv,
verbose=-1,
log=self.log)
self.whole_grm.geometry.set_secondary_structure_restraints(
ss_manager=ss_manager,
hierarchy=self.whole_pdb_h,
log=self.log)
print >> self.log, "loop_ideal.ref_exclusion_selection", loop_ideal.ref_exclusion_selection
print >> self.log, "Minimizing whole model"
self.minimize(
hierarchy=self.whole_pdb_h,
xrs=self.whole_xrs,
grm=self.whole_grm,
ncs_restraints_group_list=filtered_ncs_restr_group_list,
original_pdb_h=ref_hierarchy_for_final_gm,
excl_string_selection=loop_ideal.ref_exclusion_selection,
ss_annotation=self.ann,
reference_map = self.reference_map)
self.shift_and_write_result(
hierarchy=self.whole_pdb_h,
fname_suffix="all_idealized",
grm=self.whole_grm)
self.final_model_statistics = geometry_no_grm(
pdb_hierarchy=iotbx.pdb.input(
source_info=None,
lines=self.whole_pdb_h.as_pdb_string()).construct_hierarchy(),
molprobity_scores=True)
# self.original_boxed_hierarchy.write_pdb_file(file_name="original_boxed_end.pdb")
self.time_for_run = time() - t_0
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