def exercise () :
from mmtbx.ions import utils as ion_utils
import iotbx.pdb.hierarchy
pdb_in = iotbx.pdb.hierarchy.input(pdb_string="""\
CRYST1 20.000 60.000 50.000 90.00 90.00 90.00 P 1
HETATM 1690 ZN ZN X 1 -14.031 10.147 -2.484 1.00 14.71 ION ZN2+
HETATM 1699 CL CL X 10 -16.305 10.413 -3.294 0.94 15.45 ION CL1-
HETATM 1700 CL CL X 11 -13.440 1.272 -23.660 0.64 21.31 ION CL1-
HETATM 1691 MG MG X 2 -14.099 16.408 -0.840 1.00 14.87 ION MG2+
HETATM 1692 CD CD X 3 -5.302 -1.809 -1.327 1.00 14.78 ION CD2+
HETATM 1693 CD CD X 4 -8.287 -11.927 -43.776 1.00 14.70 ION CD2+
HETATM 1703 CL CL X 14 -2.713 20.673 -12.004 0.79 20.10 ION CL1-
HETATM 1694 NI NI X 5 -5.160 20.798 -11.755 0.93 14.92 ION NI2+
HETATM 1695 CA CA X 6 -7.922 -11.718 -0.402 0.74 16.82 ION CA2+
HETATM 1696 CD CD X 7 -16.886 -19.039 -34.333 0.61 15.22 ION CD2+
HETATM 1701 CL CL X 12 -10.068 -10.650 0.239 0.53 22.83 ION CL1-
""")
xrs = pdb_in.input.xray_structure_simple()
pdb_atoms = pdb_in.hierarchy.atoms()
perm = ion_utils.sort_atoms_permutation(
pdb_atoms=pdb_atoms,
xray_structure=xrs)
pdb_atoms = pdb_atoms.select(perm)
xrs = xrs.select(perm)
hierarchy = iotbx.pdb.hierarchy.root()
model = iotbx.pdb.hierarchy.model()
hierarchy.append_model(model)
chain = iotbx.pdb.hierarchy.chain(id="X")
model.append_chain(chain)
for k, atom in enumerate(pdb_atoms) :
rg = iotbx.pdb.hierarchy.residue_group(resseq="%4d" % (k+1))
chain.append_residue_group(rg)
ag = iotbx.pdb.hierarchy.atom_group(resname=atom.parent().resname)
rg.append_atom_group(ag)
ag.append_atom(atom.detached_copy())
assert (hierarchy.as_pdb_string(crystal_symmetry=xrs) == """\
CRYST1 20.000 60.000 50.000 90.00 90.00 90.00 P 1
SCALE1 0.050000 0.000000 0.000000 0.00000
SCALE2 0.000000 0.016667 0.000000 0.00000
SCALE3 0.000000 0.000000 0.020000 0.00000
HETATM 5 CD CD X 1 -5.302 -1.809 -1.327 1.00 14.78 ION CD2+
HETATM 6 CD CD X 2 -8.287 -11.927 -43.776 1.00 14.70 ION CD2+
HETATM 10 CD CD X 3 -16.886 -19.039 -34.333 0.61 15.22 ION CD2+
HETATM 1 ZN ZN X 4 -14.031 10.147 -2.484 1.00 14.71 ION ZN2+
HETATM 8 NI NI X 5 -5.160 20.798 -11.755 0.93 14.92 ION NI2+
HETATM 9 CA CA X 6 -7.922 -11.718 -0.402 0.74 16.82 ION CA2+
HETATM 2 CL CL X 7 -16.305 10.413 -3.294 0.94 15.45 ION CL1-
HETATM 7 CL CL X 8 -2.713 20.673 -12.004 0.79 20.10 ION CL1-
HETATM 3 CL CL X 9 -13.440 1.272 -23.660 0.64 21.31 ION CL1-
HETATM 11 CL CL X 10 -10.068 -10.650 0.239 0.53 22.83 ION CL1-
HETATM 4 MG MG X 11 -14.099 16.408 -0.840 1.00 14.87 ION MG2+
TER
""")
def exercise():
from mmtbx.ions import utils as ion_utils
import iotbx.pdb.hierarchy
pdb_in = iotbx.pdb.hierarchy.input(pdb_string="""\
CRYST1 20.000 60.000 50.000 90.00 90.00 90.00 P 1
HETATM 1690 ZN ZN X 1 -14.031 10.147 -2.484 1.00 14.71 ION ZN2+
HETATM 1699 CL CL X 10 -16.305 10.413 -3.294 0.94 15.45 ION CL1-
HETATM 1700 CL CL X 11 -13.440 1.272 -23.660 0.64 21.31 ION CL1-
HETATM 1691 MG MG X 2 -14.099 16.408 -0.840 1.00 14.87 ION MG2+
HETATM 1692 CD CD X 3 -5.302 -1.809 -1.327 1.00 14.78 ION CD2+
HETATM 1693 CD CD X 4 -8.287 -11.927 -43.776 1.00 14.70 ION CD2+
HETATM 1703 CL CL X 14 -2.713 20.673 -12.004 0.79 20.10 ION CL1-
HETATM 1694 NI NI X 5 -5.160 20.798 -11.755 0.93 14.92 ION NI2+
HETATM 1695 CA CA X 6 -7.922 -11.718 -0.402 0.74 16.82 ION CA2+
HETATM 1696 CD CD X 7 -16.886 -19.039 -34.333 0.61 15.22 ION CD2+
HETATM 1701 CL CL X 12 -10.068 -10.650 0.239 0.53 22.83 ION CL1-
""")
xrs = pdb_in.input.xray_structure_simple()
pdb_atoms = pdb_in.hierarchy.atoms()
perm = ion_utils.sort_atoms_permutation(pdb_atoms=pdb_atoms,
xray_structure=xrs)
pdb_atoms = pdb_atoms.select(perm)
xrs = xrs.select(perm)
hierarchy = iotbx.pdb.hierarchy.root()
model = iotbx.pdb.hierarchy.model()
hierarchy.append_model(model)
chain = iotbx.pdb.hierarchy.chain(id="X")
model.append_chain(chain)
for k, atom in enumerate(pdb_atoms):
rg = iotbx.pdb.hierarchy.residue_group(resseq="%4d" % (k + 1))
chain.append_residue_group(rg)
ag = iotbx.pdb.hierarchy.atom_group(resname=atom.parent().resname)
rg.append_atom_group(ag)
ag.append_atom(atom.detached_copy())
assert (hierarchy.as_pdb_string(crystal_symmetry=xrs) == """\
CRYST1 20.000 60.000 50.000 90.00 90.00 90.00 P 1
SCALE1 0.050000 0.000000 0.000000 0.00000
SCALE2 0.000000 0.016667 0.000000 0.00000
SCALE3 0.000000 0.000000 0.020000 0.00000
HETATM 5 CD CD X 1 -5.302 -1.809 -1.327 1.00 14.78 ION CD2+
HETATM 6 CD CD X 2 -8.287 -11.927 -43.776 1.00 14.70 ION CD2+
HETATM 10 CD CD X 3 -16.886 -19.039 -34.333 0.61 15.22 ION CD2+
HETATM 1 ZN ZN X 4 -14.031 10.147 -2.484 1.00 14.71 ION ZN2+
HETATM 8 NI NI X 5 -5.160 20.798 -11.755 0.93 14.92 ION NI2+
HETATM 9 CA CA X 6 -7.922 -11.718 -0.402 0.74 16.82 ION CA2+
HETATM 2 CL CL X 7 -16.305 10.413 -3.294 0.94 15.45 ION CL1-
HETATM 7 CL CL X 8 -2.713 20.673 -12.004 0.79 20.10 ION CL1-
HETATM 3 CL CL X 9 -13.440 1.272 -23.660 0.64 21.31 ION CL1-
HETATM 11 CL CL X 10 -10.068 -10.650 0.239 0.53 22.83 ION CL1-
HETATM 4 MG MG X 11 -14.099 16.408 -0.840 1.00 14.87 ION MG2+
TER
""")
def exercise_cmdline():
from mmtbx.command_line import extend_sidechains
from mmtbx.regression import model_1yjp
import iotbx.pdb.hierarchy
pdb_file = "tst_extend_sidechains.pdb"
mtz_file = "tst_extend_sidechains.mtz"
pdb_in = iotbx.pdb.hierarchy.input(pdb_string=model_1yjp)
xrs = pdb_in.input.xray_structure_simple()
f_calc = abs(xrs.structure_factors(d_min=1.5).f_calc())
sel = pdb_in.hierarchy.atom_selection_cache().selection(
"not (resname TYR and not (name c or name o or name n or name oxt or name ca or name cb))"
)
hierarchy = pdb_in.hierarchy.select(sel)
f = open(pdb_file, "w")
f.write(hierarchy.as_pdb_string(crystal_symmetry=xrs))
f.close()
flags = f_calc.generate_r_free_flags(fraction=0.1)
mtz = f_calc.as_mtz_dataset(column_root_label="F")
mtz.add_miller_array(flags, column_root_label="FreeR_flag")
mtz.mtz_object().write(mtz_file)
pdb_out = "tst_extend_sidechains_out.pdb"
if os.path.isfile(pdb_out):
os.remove(pdb_out)
out = StringIO()
extend_sidechains.run(
args=[pdb_file, mtz_file,
"output_model=%s" % pdb_out], out=out)
assert ("1 sidechains extended." in out.getvalue()), out.getvalue()
from mmtbx.validation import rotalyze
pdb_new = iotbx.pdb.hierarchy.input(file_name=pdb_out)
r1 = rotalyze.rotalyze(pdb_hierarchy=pdb_in.hierarchy, outliers_only=False)
r2 = rotalyze.rotalyze(pdb_hierarchy=pdb_new.hierarchy,
outliers_only=False)
for o1, o2 in zip(r1.results, r2.results):
assert o1.rotamer_name == o2.rotamer_name
# Part 2: with sequence corrections
seq_file = "tst_extend_sidechains.fa"
with open(seq_file, "w") as f:
f.write(">1yjp_new\nGNDQQNY")
pdb_out = "tst_extend_sidechains_out2.pdb"
extend_sidechains.run(
args=[pdb_file, mtz_file, seq_file,
"output_model=%s" % pdb_out],
out=out)
assert ("1 sidechains extended." in out.getvalue())
def exercise_cmdline () :
from mmtbx.command_line import extend_sidechains
from mmtbx.regression import model_1yjp
import iotbx.pdb.hierarchy
pdb_file = "tst_extend_sidechains.pdb"
mtz_file = "tst_extend_sidechains.mtz"
pdb_in = iotbx.pdb.hierarchy.input(pdb_string=model_1yjp)
xrs = pdb_in.input.xray_structure_simple()
f_calc = abs(xrs.structure_factors(d_min=1.5).f_calc())
sel = pdb_in.hierarchy.atom_selection_cache().selection(
"not (resname TYR and not (name c or name o or name n or name oxt or name ca or name cb))")
hierarchy = pdb_in.hierarchy.select(sel)
f = open(pdb_file, "w")
f.write(hierarchy.as_pdb_string(crystal_symmetry=xrs))
f.close()
flags = f_calc.generate_r_free_flags(fraction=0.1)
mtz = f_calc.as_mtz_dataset(column_root_label="F")
mtz.add_miller_array(flags, column_root_label="FreeR_flag")
mtz.mtz_object().write(mtz_file)
pdb_out = "tst_extend_sidechains_out.pdb"
if os.path.isfile(pdb_out) :
os.remove(pdb_out)
out = StringIO()
extend_sidechains.run(
args=[pdb_file, mtz_file, "output_model=%s" % pdb_out],
out=out)
assert ("1 sidechains extended." in out.getvalue())
from mmtbx.validation import rotalyze
pdb_new = iotbx.pdb.hierarchy.input(file_name=pdb_out)
r1 = rotalyze.rotalyze(pdb_hierarchy=pdb_in.hierarchy, outliers_only=False)
r2 = rotalyze.rotalyze(pdb_hierarchy=pdb_new.hierarchy, outliers_only=False)
for o1, o2 in zip(r1.results, r2.results) :
assert o1.rotamer_name == o2.rotamer_name
# Part 2: with sequence corrections
seq_file = "tst_extend_sidechains.fa"
open(seq_file, "w").write(">1yjp_new\nGNDQQNY")
pdb_out = "tst_extend_sidechains_out2.pdb"
extend_sidechains.run(
args=[pdb_file, mtz_file, seq_file, "output_model=%s" % pdb_out],
out=out)
assert ("2 sidechains extended." in out.getvalue())
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()
other_sites=sites_cart)
sites_cart_out = sup.other_sites_best_fit()
for site_label,site_cart in zip(atom_names, sites_cart_out):
atom = iotbx.pdb.hierarchy.atom()
atom.name = " %-3s" % site_label
atom.xyz = matrix.col(site_cart) + matrix.col((0,0,i_stack*1.5))
atom.occ = 1
atom.b = 20
atom.element = " " + site_label[0]
atom_group.append_atom(atom)
residue_group = iotbx.pdb.hierarchy.residue_group(
resseq="%4d" % (i_stack+1), icode=" ")
residue_group.append_atom_group(atom_group)
chain.append_residue_group(residue_group)
hierarchy.atoms().reset_serial()
pdb_str = hierarchy.as_pdb_string(append_end=True)
file_name = "puckers.pdb"
print "Writing file:", file_name
open(file_name, "w").write("""\
REMARK random_puckers.py
REMARK 1 = 3'
REMARK 2 = 2'
REMARK 3 = A
REMARK 4 = B
""" + pdb_str)
#
print "OK"
if (__name__ == "__main__"):
run(args=sys.argv[1:])
def run(args):
assert args in [[], ["--verbose"]]
if (len(args) != 0):
cout = sys.stdout
else:
cout = null_out()
edge_list_bonds = [(0, 1), (0, 4), (1, 2), (2, 3), (3, 4)]
bond_list = [(("C1*", "C2*"), 1.529), (("C1*", "O4*"), 1.412),
(("C2*", "C3*"), 1.526), (("C3*", "C4*"), 1.520),
(("C4*", "O4*"), 1.449)]
angle_list = [
(("C1*", "C2*", "C3*"), 101.3), (("C2*", "C3*", "C4*"), 102.3),
(("C3*", "C4*", "O4*"), 104.2), (("C4*", "O4*", "C1*"), 110.0)
]
sites_cart, geo_manager = cctbx.geometry_restraints.manager \
.construct_non_crystallographic_conserving_bonds_and_angles(
sites_cart=sites_cart_3p,
edge_list_bonds=edge_list_bonds,
edge_list_angles=[])
for bond_atom_names, distance_ideal in bond_list:
i, j = [atom_names.index(atom_name) for atom_name in bond_atom_names]
bond_params = geo_manager.bond_params_table[i][j]
assert approx_equal(bond_params.distance_ideal,
distance_ideal,
eps=1.e-2)
bond_params.distance_ideal = distance_ideal
bond_params.weight = 1 / 0.02**2
assert geo_manager.angle_proxies is None
geo_manager.angle_proxies = cctbx.geometry_restraints.shared_angle_proxy()
for angle_atom_names, angle_ideal in angle_list:
i_seqs = [
atom_names.index(atom_name) for atom_name in angle_atom_names
]
geo_manager.angle_proxies.append(
cctbx.geometry_restraints.angle_proxy(i_seqs=i_seqs,
angle_ideal=angle_ideal,
weight=1 / 3**2))
geo_manager.show_sorted(site_labels=atom_names,
sites_cart=sites_cart,
f=cout)
def lbfgs(sites_cart):
for i_lbfgs_restart in range(3):
minimized = cctbx.geometry_restraints.lbfgs.lbfgs(
sites_cart=sites_cart, geometry_restraints_manager=geo_manager)
assert is_below_limit(value=minimized.final_target_value,
limit=1e-10)
return minimized
lbfgs(sites_cart=sites_cart_3p)
lbfgs(sites_cart=sites_cart_2p)
conformer_counts = [0] * 4
sites_cart = sites_cart.deep_copy()
mt = flex.mersenne_twister(seed=0)
for i_trial in range(20):
while True:
for i in range(sites_cart.size()):
sites_cart[i] = mt.random_double_point_on_sphere()
try:
lbfgs(sites_cart=sites_cart)
except RuntimeError as e:
if (not str(e).startswith(
"Bond distance > max_reasonable_bond_distance: ")):
raise
else:
break
rmsd_list = flex.double()
for reference_sites in [
sites_cart_3p, sites_cart_2p, sites_cart_a, sites_cart_b
]:
sup = scitbx.math.superpose.least_squares_fit(
reference_sites=reference_sites, other_sites=sites_cart)
rmsd = reference_sites.rms_difference(sup.other_sites_best_fit())
rmsd_list.append(rmsd)
oline = " ".join(["%.3f" % rmsd for rmsd in rmsd_list])
print(oline, file=cout)
assert is_below_limit(min(rmsd_list), 1e-3)
conformer_counts[flex.min_index(rmsd_list)] += 1
print("conformer_counts:", conformer_counts)
#
if (libtbx.env.has_module("iotbx")):
import iotbx.pdb.hierarchy
hierarchy = iotbx.pdb.hierarchy.root()
model = iotbx.pdb.hierarchy.model(id="")
chain = iotbx.pdb.hierarchy.chain(id="A")
model.append_chain(chain)
hierarchy.append_model(model)
#
sites_cart_pentagon = pentagon_sites_cart()
for i_stack, sites_cart in enumerate(
[sites_cart_3p, sites_cart_2p, sites_cart_a, sites_cart_b]):
atom_group = iotbx.pdb.hierarchy.atom_group(resname=" U",
altloc="")
sup = scitbx.math.superpose.least_squares_fit(
reference_sites=sites_cart_pentagon, other_sites=sites_cart)
sites_cart_out = sup.other_sites_best_fit()
for site_label, site_cart in zip(atom_names, sites_cart_out):
atom = iotbx.pdb.hierarchy.atom()
atom.name = " %-3s" % site_label
atom.xyz = matrix.col(site_cart) + matrix.col(
(0, 0, i_stack * 1.5))
atom.occ = 1
atom.b = 20
atom.element = " " + site_label[0]
atom_group.append_atom(atom)
residue_group = iotbx.pdb.hierarchy.residue_group(resseq="%4d" %
(i_stack + 1),
icode=" ")
residue_group.append_atom_group(atom_group)
chain.append_residue_group(residue_group)
hierarchy.atoms().reset_serial()
pdb_str = hierarchy.as_pdb_string(append_end=True)
file_name = "puckers.pdb"
print("Writing file:", file_name)
open(file_name, "w").write("""\
REMARK random_puckers.py
REMARK 1 = 3'
REMARK 2 = 2'
REMARK 3 = A
REMARK 4 = B
""" + pdb_str)
#
print("OK")
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)
