def split_models (hierarchy,
crystal_symmetry,
output_base,
original_file=None,
log=None) :
if (log is None) : log = null_out()
import iotbx.pdb.hierarchy
n_models = len(hierarchy.models())
file_names = []
for k, model in enumerate(hierarchy.models()) :
k += 1
new_hierarchy = iotbx.pdb.hierarchy.root()
new_hierarchy.append_model(model.detached_copy())
if (model.id == "") :
model_id = str(k)
else :
model_id = model.id.strip()
output_file = "%s_%s.pdb" % (output_base, model_id)
f = open(output_file, "w")
if (crystal_symmetry is not None) :
print >> f, iotbx.pdb.format_cryst1_and_scale_records(
crystal_symmetry=crystal_symmetry,
write_scale_records=True)
print >> f, "REMARK Model %d of %d" % (k, n_models)
if (original_file is not None) :
print >> f, "REMARK Original file:"
print >> f, "REMARK %s" % original_file
f.write(new_hierarchy.as_pdb_string())
f.close()
file_names.append(output_file)
print >> log, "Wrote %s" % output_file
return file_names
def split_models(hierarchy,
crystal_symmetry,
output_base,
original_file=None,
log=None):
if (log is None): log = null_out()
import iotbx.pdb.hierarchy
n_models = len(hierarchy.models())
file_names = []
for k, model in enumerate(hierarchy.models()):
k += 1
new_hierarchy = iotbx.pdb.hierarchy.root()
new_hierarchy.append_model(model.detached_copy())
if (model.id == ""):
model_id = str(k)
else:
model_id = model.id.strip()
output_file = "%s_%s.pdb" % (output_base, model_id)
f = open(output_file, "w")
if (crystal_symmetry is not None):
print >> f, iotbx.pdb.format_cryst1_and_scale_records(
crystal_symmetry=crystal_symmetry, write_scale_records=True)
print >> f, "REMARK Model %d of %d" % (k, n_models)
if (original_file is not None):
print >> f, "REMARK Original file:"
print >> f, "REMARK %s" % original_file
f.write(new_hierarchy.as_pdb_string())
f.close()
file_names.append(output_file)
print >> log, "Wrote %s" % output_file
return file_names
def add_structure(self, filename):
pdb = iotbx.pdb.hierarchy.input(file_name=filename)
hierarchy = pdb.hierarchy
structure_name = os.path.basename(filename).strip(".pdb").strip()
# We're using the "third view" of the PDB hierarchy here;
# see cctbx_project/iotbx/examples/pdb_hierarchy.py for what that means.
# We're doing this basically because we want a complete conformation for
# every residue, which most pressingly means we want e.g. ' '+'A' for cases
# where the backbone is alt ' ' but the sidechain has alts 'A' and 'B'.
# The "first view" is bad here because it gives separate pieces
# of each residue in ' ', 'A', and 'B'.
# The "second view" is bad because it gives an entire structure for each
# alt conf, so even single-conformer residues get multiple appearances.
for m, model in enumerate(hierarchy.models()):
for chain in model.chains():
for residue_group in chain.residue_groups():
resseq = residue_group.resseq_as_int()
icode = residue_group.icode
for residue_conformer in residue_group.conformers():
residue = residue_conformer.only_residue()
resname = residue.resname
if resname not in aa_resnames: continue
cnit = (chain.id, resseq, icode, resname)
if cnit in self.conf_ensems:
conf_ensem = self.conf_ensems[cnit]
else:
conf_ensem = ConformerEnsemble(chain.id, resseq, icode, resname)
self.conf_ensems[cnit] = conf_ensem
assert conf_ensem is not None
if resname in ["GLY", "ALA"]:
chis = None
value = None
rotamer = "n/a"
else:
chis = sc_angles_measurer.measureChiAngles(residue)
if None in chis: # probably some missing atoms because disordered
continue # go to next atom_group
else:
value = rotamer_scorer.evaluate(resname, chis)
rotamer = rotamer_scorer.evaluate_residue(residue)
if rotamer is None:
rotamer = "(disordered?)"
altloc = None
if len(residue_group.conformers()) > 1:
altloc = residue_conformer.altloc
modelnum=None
if len(hierarchy.models()) > 1:
modelnum = m+1
conformer = Conformer(structure_name, chain.id, resseq, icode,
resname, chis, value, rotamer, a=altloc, m=modelnum)
conf_ensem.add(conformer)
def print_hier_atoms(hierarchy):
"""
Basic printing of an iotbx.pdb.hierarchy
:param hierarchy:
:return:
"""
for model in hierarchy.models():
print("Model: {}".format(model.id))
for chain in model.chains():
print("Chain: {}".format(chain.id))
for residue_group in chain.residue_groups():
print("Residue: {}".format(residue_group.resseq))
for atom_group in residue_group.atom_groups():
print("Altloc: {}".format(atom_group.altloc))
for atom in atom_group.atoms():
print("Atom Name: {}".format(atom.name))
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
def exercise_synthetic () :
from mmtbx.regression import tst_build_alt_confs
pdb_in = iotbx.pdb.hierarchy.input(pdb_string=tst_build_alt_confs.pdb_raw)
xrs = pdb_in.input.xray_structure_simple()
fc = abs(xrs.structure_factors(d_min=1.5).f_calc())
flags = fc.resolution_filter(d_min=1.6).generate_r_free_flags()
ls = fc.lone_set(other=flags)
# case 1: no work set in high-res shell
flags2 = ls.array(data=flex.bool(ls.size(), True))
flags_all = flags.concatenate(other=flags2)
mtz_out = fc.as_mtz_dataset(column_root_label="F")
mtz_out.add_miller_array(flags_all, column_root_label="FreeR_flag")
mtz_out.mtz_object().write("tst_molprobity_1.mtz")
open("tst_molprobity_1.pdb", "w").write(tst_build_alt_confs.pdb_raw)
args = [
"tst_molprobity_1.pdb",
"tst_molprobity_1.mtz",
"--kinemage",
"--maps",
"flags.clashscore=False",
"flags.xtriage=True",
]
result = molprobity.run(args=args,
ignore_missing_modules=True,
out=null_out()).validation
out = StringIO()
result.show(out=out)
# case 2: no test set in high-res shell
flags2 = ls.array(data=flex.bool(ls.size(), False))
flags_all = flags.concatenate(other=flags2)
mtz_out = fc.as_mtz_dataset(column_root_label="F")
mtz_out.add_miller_array(flags_all, column_root_label="FreeR_flag")
result = molprobity.run(args=args,
ignore_missing_modules=True,
out=null_out()).validation
out = StringIO()
result.show(out=out)
# case 3: multi-MODEL structure
# XXX This is not a very sophisticated test - it only ensures that the
# program does not crash. We need a test for expected output...
hierarchy = pdb_in.hierarchy
model2 = hierarchy.only_model().detached_copy()
hierarchy.append_model(model2)
hierarchy.models()[0].id = "1"
hierarchy.models()[1].id = "2"
open("tst_molprobity_multi_model.pdb", "w").write(hierarchy.as_pdb_string())
args = [
"tst_molprobity_multi_model.pdb",
"tst_molprobity_1.mtz",
"--kinemage",
"--maps",
"flags.clashscore=False",
]
result = molprobity.run(args=args,
ignore_missing_modules=True,
out=null_out()).validation
out = StringIO()
result.show(out=out)
# test rotamer distributions
open("tst_molprobity_misc1.pdb", "w").write(tst_build_alt_confs.pdb_raw)
args = [
"tst_molprobity_1.pdb",
"rotamer_library=8000",
"outliers_only=False",
"flags.clashscore=False",
]
out = StringIO()
result = molprobity.run(args=args,
ignore_missing_modules=True,
out=null_out()).validation
result.show(outliers_only=False, out=out)
assert (""" A 7 TYR m-80 93.10 299.6,92.0""" in
out.getvalue()), out.getvalue()
def exercise():
pdb_str = """\
MODEL 1
ATOM 1 CA ALA A 73 40.400 8.490 10.792 0.01 13.69 C
ATOM 2 CA ARG A 74 36.672 7.864 11.359 0.01 13.55 C
ATOM 3 CA LYS A 75 37.406 4.181 10.841 0.01 13.28 C
ENDMDL
MODEL 2
ATOM 1 CA ALA A 73 40.320 8.758 11.103 0.01 13.69 C
ATOM 2 CA ARG A 74 36.630 7.923 11.568 0.01 13.55 C
ATOM 3 CA LYS A 75 36.856 4.141 11.040 0.01 13.28 C
ENDMDL
MODEL 3
ATOM 1 CA ALA A 73 40.192 9.182 11.213 0.01 13.69 C
ATOM 2 CA ARG A 74 36.644 7.871 11.649 0.01 13.55 C
ATOM 3 CA LYS A 75 37.564 4.235 11.057 0.01 13.28 C
ENDMDL
MODEL 4
ATOM 1 CA ALA A 73 39.924 9.073 10.840 0.01 13.69 C
ATOM 2 CA ARG A 74 36.434 7.623 11.282 0.01 13.55 C
ATOM 3 CA LYS A 75 38.120 4.233 11.095 0.01 13.28 C
ENDMDL
MODEL 5
ATOM 1 CA ALA A 73 39.870 8.700 11.195 0.01 13.69 C
ATOM 2 CA ARG A 74 36.328 7.379 11.083 0.01 13.55 C
ATOM 3 CA LYS A 75 37.543 3.760 10.681 0.01 13.28 C
ENDMDL
MODEL 6
ATOM 1 CA ALA A 73 40.236 9.111 11.122 0.01 13.69 C
ATOM 2 CA ARG A 74 36.653 7.850 11.378 0.01 13.55 C
ATOM 3 CA LYS A 75 37.252 4.117 10.669 0.01 13.28 C
ENDMDL
MODEL 7
ATOM 1 CA ALA A 73 40.532 8.599 10.939 0.01 13.69 C
ATOM 2 CA ARG A 74 36.830 7.868 11.208 0.01 13.55 C
ATOM 3 CA LYS A 75 37.397 4.094 11.107 0.01 13.28 C
ENDMDL
MODEL 8
ATOM 1 CA ALA A 73 40.340 9.520 11.303 0.01 13.69 C
ATOM 2 CA ARG A 74 36.701 8.474 11.515 0.01 13.55 C
ATOM 3 CA LYS A 75 37.522 4.781 11.633 0.01 13.28 C
ENDMDL
MODEL 9
ATOM 1 CA ALA A 73 40.296 8.642 10.668 0.01 13.69 C
ATOM 2 CA ARG A 74 36.832 7.269 11.324 0.01 13.55 C
ATOM 3 CA LYS A 75 38.541 3.900 11.400 0.01 13.28 C
ENDMDL
MODEL 10
ATOM 1 CA ALA A 73 39.396 8.850 11.064 0.01 13.69 C
ATOM 2 CA ARG A 74 35.915 7.291 10.970 0.01 13.55 C
ATOM 3 CA LYS A 75 37.173 3.719 10.662 0.01 13.28 C
ENDMDL
"""
pdb_in = iotbx.pdb.hierarchy.input(pdb_string=pdb_str)
hierarchy = pdb_in.hierarchy
disorder.set_ensemble_b_factors_to_xyz_displacement(
pdb_hierarchy=hierarchy, method="rmsf", log=null_out())
model = hierarchy.models()[0]
assert approx_equal(list(model.atoms().extract_b()),
[0.480574, 0.478505, 0.616683])
disorder.set_ensemble_b_factors_to_xyz_displacement(
pdb_hierarchy=hierarchy, method="mcs", log=null_out())
model = hierarchy.models()[0]
assert approx_equal(list(model.atoms().extract_b()),
[0.684434, 0.779022, 0.881886])
def exercise_synthetic () :
from mmtbx.regression import tst_build_alt_confs
pdb_in = iotbx.pdb.hierarchy.input(pdb_string=tst_build_alt_confs.pdb_raw)
xrs = pdb_in.input.xray_structure_simple()
fc = abs(xrs.structure_factors(d_min=1.5).f_calc())
flags = fc.resolution_filter(d_min=1.6).generate_r_free_flags()
ls = fc.lone_set(other=flags)
# case 1: no work set in high-res shell
flags2 = ls.array(data=flex.bool(ls.size(), True))
flags_all = flags.concatenate(other=flags2)
mtz_out = fc.as_mtz_dataset(column_root_label="F")
mtz_out.add_miller_array(flags_all, column_root_label="FreeR_flag")
mtz_out.mtz_object().write("tst_molprobity_1.mtz")
open("tst_molprobity_1.pdb", "w").write(tst_build_alt_confs.pdb_raw)
args = [
"tst_molprobity_1.pdb",
"tst_molprobity_1.mtz",
"--kinemage",
"--maps",
"flags.clashscore=False",
"flags.xtriage=True",
]
result = molprobity.run(args=args,
ignore_missing_modules=True,
out=null_out()).validation
out = StringIO()
result.show(out=out)
# case 2: no test set in high-res shell
flags2 = ls.array(data=flex.bool(ls.size(), False))
flags_all = flags.concatenate(other=flags2)
mtz_out = fc.as_mtz_dataset(column_root_label="F")
mtz_out.add_miller_array(flags_all, column_root_label="FreeR_flag")
result = molprobity.run(args=args,
ignore_missing_modules=True,
out=null_out()).validation
out = StringIO()
result.show(out=out)
# case 3: multi-MODEL structure
# XXX This is not a very sophisticated test - it only ensures that the
# program does not crash. We need a test for expected output...
hierarchy = pdb_in.hierarchy
model2 = hierarchy.only_model().detached_copy()
hierarchy.append_model(model2)
hierarchy.models()[0].id = "1"
hierarchy.models()[1].id = "2"
open("tst_molprobity_multi_model.pdb", "w").write(hierarchy.as_pdb_string())
args = [
"tst_molprobity_multi_model.pdb",
"tst_molprobity_1.mtz",
"--kinemage",
"--maps",
]
result = molprobity.run(args=args,
ignore_missing_modules=True,
out=null_out()).validation
out = StringIO()
result.show(out=out)
# test rotamer distributions
open("tst_molprobity_misc1.pdb", "w").write(tst_build_alt_confs.pdb_raw)
args = [
"tst_molprobity_1.pdb",
"rotamer_library=8000",
]
out = StringIO()
result = molprobity.run(args=args,
ignore_missing_modules=True,
out=null_out()).validation
result.show(outliers_only=False, out=out)
def exercise () :
pdb_str = """\
MODEL 1
ATOM 1 CA ALA A 73 40.400 8.490 10.792 0.01 13.69 C
ATOM 2 CA ARG A 74 36.672 7.864 11.359 0.01 13.55 C
ATOM 3 CA LYS A 75 37.406 4.181 10.841 0.01 13.28 C
ENDMDL
MODEL 2
ATOM 1 CA ALA A 73 40.320 8.758 11.103 0.01 13.69 C
ATOM 2 CA ARG A 74 36.630 7.923 11.568 0.01 13.55 C
ATOM 3 CA LYS A 75 36.856 4.141 11.040 0.01 13.28 C
ENDMDL
MODEL 3
ATOM 1 CA ALA A 73 40.192 9.182 11.213 0.01 13.69 C
ATOM 2 CA ARG A 74 36.644 7.871 11.649 0.01 13.55 C
ATOM 3 CA LYS A 75 37.564 4.235 11.057 0.01 13.28 C
ENDMDL
MODEL 4
ATOM 1 CA ALA A 73 39.924 9.073 10.840 0.01 13.69 C
ATOM 2 CA ARG A 74 36.434 7.623 11.282 0.01 13.55 C
ATOM 3 CA LYS A 75 38.120 4.233 11.095 0.01 13.28 C
ENDMDL
MODEL 5
ATOM 1 CA ALA A 73 39.870 8.700 11.195 0.01 13.69 C
ATOM 2 CA ARG A 74 36.328 7.379 11.083 0.01 13.55 C
ATOM 3 CA LYS A 75 37.543 3.760 10.681 0.01 13.28 C
ENDMDL
MODEL 6
ATOM 1 CA ALA A 73 40.236 9.111 11.122 0.01 13.69 C
ATOM 2 CA ARG A 74 36.653 7.850 11.378 0.01 13.55 C
ATOM 3 CA LYS A 75 37.252 4.117 10.669 0.01 13.28 C
ENDMDL
MODEL 7
ATOM 1 CA ALA A 73 40.532 8.599 10.939 0.01 13.69 C
ATOM 2 CA ARG A 74 36.830 7.868 11.208 0.01 13.55 C
ATOM 3 CA LYS A 75 37.397 4.094 11.107 0.01 13.28 C
ENDMDL
MODEL 8
ATOM 1 CA ALA A 73 40.340 9.520 11.303 0.01 13.69 C
ATOM 2 CA ARG A 74 36.701 8.474 11.515 0.01 13.55 C
ATOM 3 CA LYS A 75 37.522 4.781 11.633 0.01 13.28 C
ENDMDL
MODEL 9
ATOM 1 CA ALA A 73 40.296 8.642 10.668 0.01 13.69 C
ATOM 2 CA ARG A 74 36.832 7.269 11.324 0.01 13.55 C
ATOM 3 CA LYS A 75 38.541 3.900 11.400 0.01 13.28 C
ENDMDL
MODEL 10
ATOM 1 CA ALA A 73 39.396 8.850 11.064 0.01 13.69 C
ATOM 2 CA ARG A 74 35.915 7.291 10.970 0.01 13.55 C
ATOM 3 CA LYS A 75 37.173 3.719 10.662 0.01 13.28 C
ENDMDL
"""
pdb_in = iotbx.pdb.hierarchy.input(pdb_string=pdb_str)
hierarchy = pdb_in.hierarchy
disorder.set_ensemble_b_factors_to_xyz_displacement(
pdb_hierarchy=hierarchy,
method="rmsf",
log=null_out())
model = hierarchy.models()[0]
assert approx_equal(list(model.atoms().extract_b()),
[0.480574, 0.478505, 0.616683])
disorder.set_ensemble_b_factors_to_xyz_displacement(
pdb_hierarchy=hierarchy,
method="mcs",
log=null_out())
model = hierarchy.models()[0]
assert approx_equal(list(model.atoms().extract_b()),
[0.684434, 0.779022, 0.881886])
