def exercise():
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
ann = pdb_inp.extract_secondary_structure()
pdb_int_params = mmtbx.model.manager.get_default_pdb_interpretation_params()
pdb_int_params.pdb_interpretation.ncs_search.enabled=True
model = mmtbx.model.manager(
model_input = pdb_inp,
pdb_interpretation_params=pdb_int_params,
build_grm = True)
model.setup_ncs_constraints_groups()
log = StringIO()
rm = ssb.substitute_ss(
model,
log=log)
rm.run()
# with open("res.pdb", 'w') as f:
# f.write(model.model_as_pdb())
# check that strict NCS was implemented throughout
dif = get_difference_chainsAB(strings=model.model_as_pdb())
for d in dif:
assert approx_equal(d, (0,0,0), eps=0.005)
log_cont = log.getvalue()
# print("===========================")
# print (log_cont)
# print("===========================")
assert log_cont.find("1 element(s) were skipped because they are in NCS copies") > 0
print("OK")
def exercise_some_atoms_are_absent():
""" CD1, CD2 from 101 residue are absent"""
tst_pdb_2 = """\
HELIX 2 2 GLY A 100 TRP A 101 1 2
ATOM 866 N GLY A 100 -128.903-139.939-104.460 1.00 27.58 N
ATOM 867 CA GLY A 100 -128.223-140.890-103.603 1.00 27.58 C
ATOM 868 C GLY A 100 -128.381-142.356-103.959 1.00 27.58 C
ATOM 869 O GLY A 100 -129.283-142.755-104.686 1.00 27.58 O
ATOM 871 N TRP A 101 -127.491-143.163-103.397 1.00 25.83 N
ATOM 872 CA TRP A 101 -127.455-144.601-103.619 1.00 25.83 C
ATOM 873 C TRP A 101 -128.824-145.274-103.752 1.00 25.83 C
ATOM 874 O TRP A 101 -129.091-145.962-104.734 1.00 25.83 O
ATOM 875 CB TRP A 101 -126.659-145.240-102.480 1.00 42.04 C
ATOM 876 CG TRP A 101 -126.207-146.635-102.729 1.00 42.04 C
ATOM 879 NE1 TRP A 101 -125.663-148.557-103.751 1.00 42.04 N
ATOM 880 CE2 TRP A 101 -125.347-148.723-102.429 1.00 42.04 C
ATOM 881 CE3 TRP A 101 -125.446-147.437-100.384 1.00 42.04 C
"""
pdb_inp = iotbx.pdb.input(source_info=None, lines=tst_pdb_2)
ann = pdb_inp.extract_secondary_structure()
model = mmtbx.model.manager(
model_input = pdb_inp,
build_grm = True)
model.set_ss_annotation(ann)
rm = ssb.substitute_ss(
model)
rm.run()
def exercise_presence_of_h():
tst_pdb_1 = """\
HELIX 2 2 GLY A 100 TRP A 101 1 2
ATOM 866 N GLY A 100 -128.903-139.939-104.460 1.00 27.58 N
ATOM 867 CA GLY A 100 -128.223-140.890-103.603 1.00 27.58 C
ATOM 868 C GLY A 100 -128.381-142.356-103.959 1.00 27.58 C
ATOM 869 O GLY A 100 -129.283-142.755-104.686 1.00 27.58 O
ATOM 870 H GLY A 100 -129.862-139.777-104.337 1.00 36.09 H
ATOM 871 N TRP A 101 -127.491-143.163-103.397 1.00 25.83 N
ATOM 872 CA TRP A 101 -127.455-144.601-103.619 1.00 25.83 C
ATOM 873 C TRP A 101 -128.824-145.274-103.752 1.00 25.83 C
ATOM 874 O TRP A 101 -129.091-145.962-104.734 1.00 25.83 O
ATOM 875 CB TRP A 101 -126.659-145.240-102.480 1.00 42.04 C
ATOM 876 CG TRP A 101 -126.207-146.635-102.729 1.00 42.04 C
ATOM 877 CD1 TRP A 101 -126.180-147.300-103.922 1.00 42.04 C
ATOM 878 CD2 TRP A 101 -125.677-147.528-101.760 1.00 42.04 C
ATOM 879 NE1 TRP A 101 -125.663-148.557-103.751 1.00 42.04 N
ATOM 880 CE2 TRP A 101 -125.347-148.723-102.429 1.00 42.04 C
ATOM 881 CE3 TRP A 101 -125.446-147.437-100.384 1.00 42.04 C
ATOM 882 CZ2 TRP A 101 -124.799-149.820-101.767 1.00 42.04 C
ATOM 883 CZ3 TRP A 101 -124.899-148.528 -99.725 1.00 42.04 C
ATOM 884 CH2 TRP A 101 -124.581-149.703-100.415 1.00 42.04 C
ATOM 885 H TRP A 101 -126.863-142.797-102.745 1.00 42.04 H
"""
pdb_inp = iotbx.pdb.input(source_info=None, lines=tst_pdb_1)
ann = pdb_inp.extract_secondary_structure()
model = mmtbx.model.manager(
model_input = pdb_inp,
build_grm = True)
model.set_ss_annotation(ann)
rm = ssb.substitute_ss(
model,
)
def exercise_ca_absent():
# should raise Sorry
""" CA from 101 residue are absent. We don't handle it at the moment."""
tst_pdb_1 = """\
HELIX 2 2 GLY A 100 TRP A 101 1 2
ATOM 866 N GLY A 100 -128.903-139.939-104.460 1.00 27.58 N
ATOM 867 CA GLY A 100 -128.223-140.890-103.603 1.00 27.58 C
ATOM 868 C GLY A 100 -128.381-142.356-103.959 1.00 27.58 C
ATOM 869 O GLY A 100 -129.283-142.755-104.686 1.00 27.58 O
ATOM 870 H GLY A 100 -129.862-139.777-104.337 1.00 36.09 H
ATOM 871 N TRP A 101 -127.491-143.163-103.397 1.00 25.83 N
ATOM 873 C TRP A 101 -128.824-145.274-103.752 1.00 25.83 C
ATOM 874 O TRP A 101 -129.091-145.962-104.734 1.00 25.83 O
ATOM 875 CB TRP A 101 -126.659-145.240-102.480 1.00 42.04 C
ATOM 876 CG TRP A 101 -126.207-146.635-102.729 1.00 42.04 C
ATOM 877 CD1 TRP A 101 -126.180-147.300-103.922 1.00 42.04 C
ATOM 878 CD2 TRP A 101 -125.677-147.528-101.760 1.00 42.04 C
ATOM 879 NE1 TRP A 101 -125.663-148.557-103.751 1.00 42.04 N
ATOM 880 CE2 TRP A 101 -125.347-148.723-102.429 1.00 42.04 C
ATOM 881 CE3 TRP A 101 -125.446-147.437-100.384 1.00 42.04 C
ATOM 882 CZ2 TRP A 101 -124.799-149.820-101.767 1.00 42.04 C
ATOM 883 CZ3 TRP A 101 -124.899-148.528 -99.725 1.00 42.04 C
ATOM 884 CH2 TRP A 101 -124.581-149.703-100.415 1.00 42.04 C
ATOM 885 H TRP A 101 -126.863-142.797-102.745 1.00 42.04 H
"""
pdb_inp = iotbx.pdb.input(source_info=None, lines=tst_pdb_1)
ann = pdb_inp.extract_secondary_structure()
model = mmtbx.model.manager(
model_input = pdb_inp,
build_grm = True)
model.set_ss_annotation(ann)
try:
rm = ssb.substitute_ss(
model)
rm.run()
except Sorry as e:
assert e.args[0].startswith("C, CA or N")
except Exception:
assert 0, "Error: This test failed"
else:
assert 0, "Error: This test failed"
def run(self):
# I'm guessing self.data_manager, self.params and self.logger
# are already defined here...
print('Using model: %s' % self.data_manager.get_default_model_name(), file=self.logger)
# this must be mmtbx.model.manager?
self.model = self.data_manager.get_model()
# Done in place!
self.params.ss_idealization.enabled=True
sss = ss_idealization.substitute_ss(
model = self.model,
params = self.params.ss_idealization,
log=self.logger)
sss.run()
inp_fn = os.path.basename(self.data_manager.get_default_model_name())[:-4]
fn = "%s" % self.get_default_output_filename(
prefix='%s_' % inp_fn,
suffix='ss_idealized',
serial=Auto)
self.data_manager.write_model_file(self.model, filename=fn)
def exercise_process_params():
pdb_inp = iotbx.pdb.input(lines=model_1yjp, source_info='model_1yjp')
model = mmtbx.model.manager(model_input=pdb_inp)
ssb_obj = ssb.substitute_ss(model)
ppars = ssb_obj._process_params(params=None)
assert ppars.enabled
assert not ppars.restrain_torsion_angles
#
params = ssb.master_phil.fetch().extract()
ppars = ssb_obj._process_params(params=params)
assert not ppars.enabled
assert not ppars.restrain_torsion_angles
params.ss_idealization.enabled = True
ppars = ssb_obj._process_params(params=params)
assert ppars.enabled
ppars = ssb_obj._process_params(params=params.ss_idealization)
assert ppars.enabled
params.ss_idealization.sigma_on_reference_non_ss = -1
try:
ppars = ssb_obj._process_params(params=params.ss_idealization)
except AssertionError as e:
assert e.args[0] == 'Bad sigma_on_reference_non_ss parameter'
def run(self):
t_0 = time()
self.ann = self.model.get_ss_annotation()
self._setup_model_h()
self.model.set_restraint_objects(self.model_h.get_restraint_objects())
self.model.process_input_model(make_restraints=True)
# set SS restratins
self.set_ss_restraints(self.ann)
self.model.setup_ncs_constraints_groups()
self.init_model_statistics = self.get_statistics(self.model)
#
# Cablam idealization
#
if self.params.debug:
self.shift_and_write_result(model=self.model, fname_suffix="start")
self.shift_and_write_result(model=self.model_h,
fname_suffix="start_h")
self.params.cablam_idealization.find_ss_after_fixes = False
ci_results = cablam_idealization(
model=self.model,
params=self.params.cablam_idealization,
log=self.log).get_results()
self.model = ci_results.model
self.after_cablam_statistics = self.get_statistics(self.model)
if self.params.debug:
self.shift_and_write_result(model=self.model,
fname_suffix="cablam_id")
# Here we are preparing maps if needed.
if self.user_supplied_map is not None:
self.prepare_user_map()
if self.reference_map is None and self.params.use_map_for_reference:
self.prepare_reference_map_3()
if self.params.run_minimization_first:
# running simple minimization and updating all
# self.master, self.working, etc...
# self.whole_pdb_h.reset_atom_i_seqs()
if self.init_ref_map is None:
self.prepare_init_reference_map()
print("Minimization first", file=self.log)
self.minimize(
model=self.model,
original_pdb_h=self.original_hierarchy,
excl_string_selection=None, # don't need if we have map
reference_map=self.init_ref_map,
)
self.init_gm_model_statistics = self.get_statistics(self.model)
if self.params.debug:
self.shift_and_write_result(model=self.model,
fname_suffix="init_gm")
if (self.init_gm_model_statistics is not None
and self.init_gm_model_statistics.ramachandran.outliers == 0
and self.init_gm_model_statistics.omega.twisted_general <= 0.01
and self.init_gm_model_statistics.omega.twisted_proline <= 0.01
and self.init_gm_model_statistics.omega.cis_general <= 0.01
and self.init_gm_model_statistics.omega.cis_proline <= 0.01
and self.init_gm_model_statistics.rotamer.outliers <= 0.01):
print("Simple minimization was enough", file=self.log)
# Early exit!!!
self.shift_and_write_result(model=self.model,
fname_suffix="all_idealized")
if self.params.output_model_h:
self.shift_and_write_result(model=self.model_h,
fname_suffix="all_idealized_h")
self.final_model_statistics = self.get_statistics(self.model)
# self.original_boxed_hierarchy.write_pdb_file(file_name="original_boxed_end.pdb")
self.time_for_run = time() - t_0
if self.params.output_pkl:
easy_pickle.dump(file_name="%s.pkl" %
self.params.output_prefix,
obj=self.get_stats_obj())
return
self.filtered_whole_ann = None
if self.ann is not None:
self.filtered_whole_ann = self.ann.deep_copy()
print("Original SS annotation", file=self.log)
print(self.ann.as_pdb_str(), file=self.log)
if self.params.filter_input_ss:
self.filtered_whole_ann = self.ann.filter_annotation(
hierarchy=self.model.get_hierarchy(),
asc=self.model.get_atom_selection_cache())
print("Filtered SS annotation", file=self.log)
print(self.filtered_whole_ann.as_pdb_str(), file=self.log)
self.model.set_ss_annotation(self.filtered_whole_ann)
# getting grm with SS restraints
self.update_ss_in_grm(self.filtered_whole_ann)
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(
"No secondary structure annotations found or SS idealization is disabled.",
file=self.log)
print("Secondary structure substitution step will be skipped",
file=self.log)
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_score_selection(self.model.get_hierarchy(), self.model.get_ramachandran_manager(), "outlier",1)
print("outlier_selection_txt",
outlier_selection_txt,
file=self.log)
negate_selection = "all"
if outlier_selection_txt != "" and outlier_selection_txt is not None:
negate_selection = "not (%s)" % outlier_selection_txt
# if self.params.run_minimization_first:
# self.minimize(
# model=self.model,
# original_pdb_h=self.whole_pdb_h,
# ncs_restraints_group_list=self.filtered_ncs_restr_group_list,
# excl_string_selection=negate_selection,
# reference_map=self.reference_map)
# self.original_boxed_hierarchy.write_pdb_file(file_name="original_boxed_h_1.pdb")
else:
if self.params.debug:
self.params.ss_idealization.file_name_before_regularization = \
"%s_ss_before_reg.pdb" % self.params.output_prefix
self.params.ss_idealization.skip_good_ss_elements = True
ssb.substitute_ss(model=self.model,
params=self.params.ss_idealization,
reference_map=self.master_map,
log=self.log)
self.log.flush()
self.after_ss_idealization = self.get_statistics(self.model)
self.shift_and_write_result(model=self.model,
fname_suffix="ss_ideal_stat")
# Write resulting pdb file.
if self.params.debug:
self.shift_and_write_result(
model=self.model,
fname_suffix="ss_ideal",
)
# self.params.loop_idealization.minimize_whole = not self.model.ncs_constraints_present() and self.params.loop_idealization.minimize_whole
self.params.loop_idealization.debug = self.params.debug or self.params.loop_idealization.debug
# self.params.loop_idealization.enabled = False
# self.params.loop_idealization.variant_search_level = 0
print("Starting loop idealization", file=self.log)
loop_ideal = loop_idealization(self.model,
params=self.params.loop_idealization,
reference_map=self.master_map,
log=self.log,
verbose=True)
self.log.flush()
if self.params.debug:
self.shift_and_write_result(model=self.model,
fname_suffix="rama_ideal")
self.after_loop_idealization = self.get_statistics(self.model)
# fixing remaining rotamer outliers
if (self.params.additionally_fix_rotamer_outliers
and self.after_loop_idealization.rotamer.outliers > 0.004):
self.idealize_rotamers()
self.after_rotamer_fixing = self.get_statistics(self.model)
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.model.get_hierarchy().deep_copy()
ref_hierarchy_for_final_gm.reset_atom_i_seqs()
if self.model.ncs_constraints_present():
print("Using ncs", file=self.log)
# assert 0
else:
print("Not using ncs", file=self.log)
# assert 0
# need to update SS manager for the whole model here.
if self.params.use_ss_restraints:
ss_params = sec_str_master_phil.fetch().extract()
ss_params.secondary_structure.protein.remove_outliers = not self.params.ss_idealization.enabled
self.set_ss_restraints(ss_annotation=self.filtered_whole_ann,
params=ss_params.secondary_structure)
if self.params.run_minimization_last:
print("loop_ideal.ref_exclusion_selection",
loop_ideal.ref_exclusion_selection,
file=self.log)
print("Minimizing whole model", file=self.log)
self.minimize(
model=self.model,
original_pdb_h=ref_hierarchy_for_final_gm,
excl_string_selection=loop_ideal.ref_exclusion_selection,
reference_map=self.reference_map)
self.shift_and_write_result(model=self.model,
fname_suffix="all_idealized")
if self.params.output_model_h:
self.shift_and_write_result(model=self.model_h,
fname_suffix="all_idealized_h")
self.final_model_statistics = self.get_statistics(self.model)
self.time_for_run = time() - t_0
if self.params.output_pkl or self.params.debug:
easy_pickle.dump(file_name="%s.pkl" % self.params.output_prefix,
obj=self.get_stats_obj())
