def merge_models(self, pdb_hierarchy):
t0=time.time()
assert pdb_hierarchy.models_size() == self.number_of_trials, \
pdb_hierarchy.models_size()
from mmtbx.building.merge_models import run as merge_models
pdb_hierarchy_merged = merge_models(
map_data = self.map_data,
pdb_hierarchy = pdb_hierarchy,
crystal_symmetry = self.xray_structure.crystal_symmetry(),
out = self.log)
if(self.show):
pdb_hierarchy_merged.write_pdb_file(
file_name="SA_ensemble_refined_merged.pdb")
self.xray_structure = self.xray_structure.replace_sites_cart(
new_sites=pdb_hierarchy_merged.atoms().extract_xyz())
self.pdb_hierarchy.adopt_xray_structure(self.xray_structure)
print >> self.log, "Time (merge): %10.3f"%(time.time()-t0)
def tst_01():
print "\nTesting merge_model\n"
prefix='tst_01'
# Full good answer model
pdb_file_name_answer_full = "%s_answer_full.pdb"%prefix
pdb_inp = iotbx.pdb.input(source_info=None, lines = pdb_str_answer_AE)
pdb_inp.write_pdb_file(file_name="%s_answer.pdb"%prefix)
ph_answer_full = pdb_inp.construct_hierarchy()
ph_answer_full.atoms().reset_i_seq()
xrs_answer_full = pdb_inp.xray_structure_simple()
# Two models we want to merge
pdb_file_name_two_models = "%s_poor_two_models.pdb"%prefix
pdb_inp = iotbx.pdb.input(source_info=None, lines = pdb_str_two_models)
pdb_inp.write_pdb_file(file_name="%s_two_models.pdb"%prefix)
xrs_two_models_full = pdb_inp.xray_structure_simple()
# Compute target map
fc = xrs_answer_full.structure_factors(d_min=3.5).f_calc()
fft_map = fc.fft_map(resolution_factor = 0.25)
fft_map.apply_sigma_scaling()
target_map_data = fft_map.real_map_unpadded()
ccp4_map(crystal_symmetry=fc.crystal_symmetry(),
file_name="%s_map.ccp4" %prefix,
map_data=target_map_data)
# Output map coefficients
mtz_dataset = fc.as_mtz_dataset(column_root_label="FC")
mtz_object = mtz_dataset.mtz_object()
mtz_object.write(file_name = "%s_map.mtz"%prefix)
from mmtbx.building.merge_models import run as merge_models
hierarchy=merge_models(
map_data=target_map_data,
pdb_inp=pdb_inp,
dist_max=100,
verbose=True)
pdb_inp=hierarchy.as_pdb_input(crystal_symmetry=fc.crystal_symmetry())
xrs=pdb_inp.xray_structure_simple()
hierarchy.write_pdb_file(file_name="%s_refined.pdb"%prefix)
rmsd=xrs.sites_cart().rms_difference(xrs_answer_full.sites_cart())
print "RMSD from TARGET allowing any CROSSOVERS: %8.2f " %(rmsd)
return rmsd
def tst_01():
print "\nTesting merge_model\n"
prefix = 'tst_01'
# Full good answer model
pdb_file_name_answer_full = "%s_answer_full.pdb" % prefix
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str_answer_AE)
pdb_inp.write_pdb_file(file_name="%s_answer.pdb" % prefix)
ph_answer_full = pdb_inp.construct_hierarchy()
ph_answer_full.atoms().reset_i_seq()
xrs_answer_full = pdb_inp.xray_structure_simple()
# Two models we want to merge
pdb_file_name_two_models = "%s_poor_two_models.pdb" % prefix
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str_two_models)
pdb_inp.write_pdb_file(file_name="%s_two_models.pdb" % prefix)
xrs_two_models_full = pdb_inp.xray_structure_simple()
# Compute target map
fc = xrs_answer_full.structure_factors(d_min=3.5).f_calc()
fft_map = fc.fft_map(resolution_factor=0.25)
fft_map.apply_sigma_scaling()
target_map_data = fft_map.real_map_unpadded()
ccp4_map(crystal_symmetry=fc.crystal_symmetry(),
file_name="%s_map.ccp4" % prefix,
map_data=target_map_data)
# Output map coefficients
mtz_dataset = fc.as_mtz_dataset(column_root_label="FC")
mtz_object = mtz_dataset.mtz_object()
mtz_object.write(file_name="%s_map.mtz" % prefix)
from mmtbx.building.merge_models import run as merge_models
hierarchy = merge_models(map_data=target_map_data,
pdb_inp=pdb_inp,
dist_max=100,
verbose=True)
pdb_inp = hierarchy.as_pdb_input(crystal_symmetry=fc.crystal_symmetry())
xrs = pdb_inp.xray_structure_simple()
hierarchy.write_pdb_file(file_name="%s_refined.pdb" % prefix)
rmsd = xrs.sites_cart().rms_difference(xrs_answer_full.sites_cart())
print "RMSD from TARGET allowing any CROSSOVERS: %8.2f " % (rmsd)
return rmsd
def merge_models(self, pdb_hierarchy):
#
t0 = time.time()
sites_cart = []
if ("geometry" in self.score_method or "cc" in self.score_method):
if ("geometry" in self.score_method):
sites_cart.append(
self._score_by_geometry(pdb_hierarchy=pdb_hierarchy))
if ("cc" in self.score_method):
sites_cart.append(
self._score_by_cc(pdb_hierarchy=pdb_hierarchy))
sites_cart_best = None
cc_best = -1
for sc in sites_cart:
cc = self.get_cc(sites_cart=sc)
print("CC:", cc)
if (cc > cc_best):
cc_best = cc
sites_cart_best = sc.deep_copy()
self.xray_structure.set_sites_cart(sites_cart=sites_cart_best)
self.pdb_hierarchy.adopt_xray_structure(self.xray_structure)
self.pdb_hierarchy.write_pdb_file(file_name="merged.pdb")
#
if ("merge_models" in self.score_method):
assert pdb_hierarchy.models_size() == self.number_of_trials, \
pdb_hierarchy.models_size()
from mmtbx.building.merge_models import run as merge_models
pdb_hierarchy_merged = merge_models(
map_data=self.map_data,
resolution=self.resolution,
pdb_hierarchy=pdb_hierarchy,
crystal_symmetry=self.xray_structure.crystal_symmetry(),
out=self.log)
if (self.show):
pdb_hierarchy_merged.write_pdb_file(
file_name="SA_ensemble_refined_merged.pdb")
self.xray_structure = self.xray_structure.replace_sites_cart(
new_sites=pdb_hierarchy_merged.atoms().extract_xyz())
self.pdb_hierarchy.adopt_xray_structure(self.xray_structure)
print("Time (merge): %10.3f" % (time.time() - t0), file=self.log)