def tst_02(args, prefix=None):
if prefix is None:
prefix = 'tst_02'
# 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_full)
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()
# Poor full model that we want to refine so it matches the answer
pdb_file_name_poor = "%s_poor.pdb" % prefix
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str_poor_full)
pdb_inp.write_pdb_file(file_name="%s_poor.pdb" % prefix)
ph_poor_full = pdb_inp.construct_hierarchy()
ph_poor_full.atoms().reset_i_seq()
xrs_poor_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)
hierarchy, multiple_model_hierarchy = run(
args=args,
map_data=target_map_data,
crystal_symmetry=fc.crystal_symmetry(),
pdb_string=pdb_str_poor_full)
pdb_inp = hierarchy.as_pdb_input(crystal_symmetry=fc.crystal_symmetry())
xrs_refined = pdb_inp.xray_structure_simple()
hierarchy.write_pdb_file(file_name="%s_refined.pdb" % prefix)
multiple_model_hierarchy.write_pdb_file(
file_name="%s_refined_all_states.pdb" % prefix)
rmsd = xrs_refined.sites_cart().rms_difference(
xrs_answer_full.sites_cart())
print "RMSD from TARGET for FULL-model refinement: %8.2f " % (rmsd)
return rmsd
def tst_02(args,prefix=None):
if prefix is None:
prefix='tst_02'
# 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_full)
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()
# Poor full model that we want to refine so it matches the answer
pdb_file_name_poor = "%s_poor.pdb"%prefix
pdb_inp = iotbx.pdb.input(source_info=None, lines = pdb_str_poor_full)
pdb_inp.write_pdb_file(file_name="%s_poor.pdb"%prefix)
ph_poor_full = pdb_inp.construct_hierarchy()
ph_poor_full.atoms().reset_i_seq()
xrs_poor_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)
hierarchy,multiple_model_hierarchy=run(
args=args,map_data=target_map_data,
crystal_symmetry=fc.crystal_symmetry(),
pdb_string=pdb_str_poor_full)
pdb_inp=hierarchy.as_pdb_input(crystal_symmetry=fc.crystal_symmetry())
xrs_refined=pdb_inp.xray_structure_simple()
hierarchy.write_pdb_file(file_name="%s_refined.pdb"%prefix)
multiple_model_hierarchy.write_pdb_file(
file_name="%s_refined_all_states.pdb"%prefix)
rmsd=xrs_refined.sites_cart().rms_difference(xrs_answer_full.sites_cart())
print "RMSD from TARGET for FULL-model refinement: %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 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
