def exercise():
pdb_file_names = raw_input(
"pdb file name here,space interval,such as : 2ona.pdb 2whb.pdb")
p_f = pdb_file_names.split(' ')
for pdb_file_name in p_f:
easy_run.call("phenix.fetch_pdb {0}".format(pdb_file_name[0:4]))
easy_run.call("phenix.ready_set {0}".format(pdb_file_name))
print(pdb_file_name, "-" * 50)
cif_file_name = pdb_file_name[0:4] + ".ligands.cif"
if os.path.exists(cif_file_name):
cif_file_names = cif_file_name
else:
cif_file_names = None
model = get_model(pdb_file_name=pdb_file_name,
cif_file_name=cif_file_names)
resnames = raw_input(
'list of resnames ,comma interval,such as: resname PHE,resname HIS'
)
resn = resnames.split(',')
ss = " or ".join(resn)
m_sel = model.selection(ss)
new_model = model.select(m_sel)
hierarchy = new_model.get_hierarchy()
crystal_symmetry = new_model.crystal_symmetry()
hierarchy.write_pdb_file(file_name=pdb_file_name[0:4] + "new.pdb",
crystal_symmetry=crystal_symmetry)
def test_3():
""" test for command-line tool iotbx.unique_with_biomt"""
inp = iotbx.pdb.input(source_info=None, lines=pdb_str_0)
model = mmtbx.model.manager(model_input=inp)
model.expand_with_BIOMT_records()
model = shift_and_box_model(model)
sel = model.selection("chain '0' or chain 'C' or chain 'F' or chain 'I' or chain 'L' or chain 'O' or chain 'R' or chain 'U' or chain 'X'")
model = model.select(sel)
pdb_str = model.model_as_pdb()
fname = 'tst_reduce_model_with_biomt_test3.pdb'
with open(fname, 'w') as f:
f.write(pdb_str)
assert os.path.isfile(fname)
cmd = "iotbx.unique_with_biomt %s" % fname
print(cmd)
easy_run.call(cmd)
res_fname = 'tst_reduce_model_with_biomt_test3_unique_biomt_000.cif'
assert_lines_in_file(res_fname, """
ATOM 1 N . LYS 0 151 ? 72.74200 43.65400 193.22800 14.010 14.01000 N ? A ? 1 1""")
assert_lines_in_file(res_fname, """
_pdbx_struct_assembly_gen.asym_id_list
1 (1-9) A""")
cmd = "iotbx.unique_with_biomt %s chain_id_to_leave='C' output.serial=1" % fname
print(cmd)
easy_run.call(cmd)
res_fname = 'tst_reduce_model_with_biomt_test3_unique_biomt_001.cif'
assert_lines_in_file(res_fname, """
ATOM 1 N . LYS C 151 ? 186.74500 185.38200 236.77300 14.010 14.01000 N ? A ? 1 1""")
assert_lines_in_file(res_fname, """
_pdbx_struct_assembly_gen.asym_id_list
1 (1-9) A""")
def exercise_no_sidechains(prefix="tst_one_resid_rotation_no_sidechains"):
pdb_inp = iotbx.pdb.input(lines=pdb_str.split('\n'), source_info=None)
model = mmtbx.model.manager(
model_input = pdb_inp)
with open("%s_start.pdb" % prefix, 'w') as f:
f.write(model.model_as_pdb())
s = model.selection("name N or name CA or name C or name O")
model = model.select(s)
ci = cablam_idealization(model = model, params=master_phil.extract().cablam_idealization, log=sys.stdout)
pdb_txt = model.model_as_pdb()
def main(filename):
t0 = time.time()
pdb_inp = iotbx.pdb.input(file_name=filename)
model = mmtbx.model.manager(model_input=pdb_inp, log=null_out())
sel = model.selection(string="protein")
model = model.select(selection=sel)
model.crystal_symmetry()
a = aev.AEV(model=model)
CC_value = aev.compare(a)
print(CC_value)
recs = aev.format_HELIX_records_from_AEV(CC_value)
print("\n".join(recs))
print('time', time.time() - t0)
def exercise_05(verbose):
if (verbose): log = sys.stdout
else: log = StringIO()
model = get_model(file_name=None, log=log, pdb_str=pdb_str_2)
#model.get_hierarchy().write_pdb_file(file_name="haha.pdb")
model.de_deuterate()
assert (len(model.hd_group_selections()) == 0)
ph = model.get_hierarchy()
atoms = ph.atoms()
assert (atoms.extract_element().count('D') == 0)
#print(model.get_hd_selection().count(True))
assert (model.get_hd_selection().count(True) == 35)
assert (model.selection('resname DOD').count(True) == 0)
def test_1():
inp = iotbx.pdb.input(source_info=None, lines=pdb_str_0)
model = mmtbx.model.manager(model_input=inp)
model.expand_with_BIOMT_records()
model = shift_and_box_model(model)
sel = model.selection("chain '0' or chain 'C' or chain 'F' or chain 'I' or chain 'L' or chain 'O' or chain 'R' or chain 'U' or chain 'X'")
model = model.select(sel)
model.search_for_ncs()
model.setup_ncs_constraints_groups(filter_groups=True)
n1 = model.get_number_of_atoms()
assert n1 == 648, n1
assert model.ncs_constraints_present()
nrgl = model.get_ncs_groups()
assert len(nrgl[0].master_iselection) == 72
assert len(nrgl[0].copies) == 8
# nrgl._show()
# print (model.can_be_unique_with_biomt())
cif_txt = model.model_as_mmcif(try_unique_with_biomt=True)
# print (cif_txt)
assert_lines_in_text(cif_txt, """
loop_
_pdbx_struct_assembly_gen.assembly_id
_pdbx_struct_assembly_gen.oper_expression
_pdbx_struct_assembly_gen.asym_id_list
1 (1-9) A""")
assert_lines_in_text(cif_txt, """
loop_
_pdbx_struct_assembly.id
_pdbx_struct_assembly.details
_pdbx_struct_assembly.method_details
_pdbx_struct_assembly.oligomeric_details
_pdbx_struct_assembly.oligomeric_count
1 'Symmetry assembly' ? ? ? """)
assert_lines_in_text(cif_txt, """
_pdbx_struct_oper_list.vector[1]
_pdbx_struct_oper_list.vector[2]
_pdbx_struct_oper_list.vector[3]
1 'point symmetry operation' ? ? 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0""")
inp = iotbx.pdb.input(source_info=None, lines=cif_txt)
m2 = mmtbx.model.manager(model_input=inp)
n2_1 = m2.get_number_of_atoms()
assert n2_1 == 72
m2.expand_with_BIOMT_records()
n2_2 = m2.get_number_of_atoms()
# print (n1, n2)
assert n1 == n2_2, "%d, %d" % (n1, n2)
def run(args=(), params=None, out=None):
if (out is None):
out = sys.stdout
if (params is None):
import iotbx.phil
cmdline = iotbx.phil.process_command_line_with_files(
args=args,
master_phil_string=master_phil,
pdb_file_def="adp_statistics.pdb_file",
cif_file_def="adp_statistics.cif_file",
usage_string="""\
phenix.b_factor_statistics model.pdb [restraints.cif] [selection=...]
Show statistics for atomic displacement parameters (ADPs) or B-factors,
including TLS contribution if present.""")
params = cmdline.work.extract()
validate_params(params)
import mmtbx.model
m_input = iotbx.pdb.input(file_name=params.adp_statistics.pdb_file)
restraint_objects = []
for fn in params.adp_statistics.cif_file:
cif_object = iotbx.cif.reader(file_path=fn).model()
restraint_objects.append((fn, cif_object))
model = mmtbx.model.manager(model_input=m_input,
restraint_objects=restraint_objects,
build_grm=True,
log=out)
make_sub_header("Analyzing model B-factors", out=out)
if (params.adp_statistics.selection is not None):
selection = model.selection(params.adp_statistics.selection)
n_sel = selection.count(True)
if (n_sel == 0):
raise Sorry("No atoms in selection!")
else:
model = model.select(selection)
print >> out, "Extracted %d atoms in selection:" % n_sel
print >> out, " %s" % params.adp_statistics.selection
print >> out, ""
stats = model.adp_statistics()
stats.file_name = params.adp_statistics.pdb_file
stats.selection = params.adp_statistics.selection
stats.show_1(out=out)
return stats
def exercise_2(eps = 1.e-6):
###> Get started from PDB
mon_lib_srv = monomer_library.server.server()
ener_lib = monomer_library.server.ener_lib()
pdb_file = libtbx.env.find_in_repositories(
relative_path="phenix_regression/pdb/phe_abc_tlsanl_out_geometry_minimized.pdb",
test=os.path.isfile)
model = mmtbx.model.manager(
model_input=iotbx.pdb.input(file_name=pdb_file),
build_grm=True)
model.setup_scattering_dictionaries(scattering_table="wk1995")
model.get_xray_structure().convert_to_isotropic()
u_iso_start = model.get_xray_structure().extract_u_iso_or_u_equiv()
model.get_xray_structure().convert_to_anisotropic()
selections = []
selection_strings = ["chain A", "chain B", "chain C"]
for string in selection_strings:
selections.append(model.selection(string = string))
################
selection = flex.bool(model.get_number_of_atoms(), True)
class refinement_flags: pass
refinement_flags.adp_tls = selections
model.set_refinement_flags(refinement_flags)
model.determine_tls_groups(selection_strings=selections, generate_tlsos=selections)
model.set_refinement_flags(refinement_flags)
xray_structure = model.get_xray_structure()
################
###> Get TLS <-> Ucart
T_initial = []
L_initial = []
S_initial = []
T_initial.append([0.11,0.22,0.33,0.12,0.13,0.23])
L_initial.append([1.11,1.22,1.33,1.12,1.13,1.23])
S_initial.append([0.11,0.12,0.13,0.21,0.22,0.23,0.31,0.32,-0.33])
T_initial.append([0.22,0.44,0.66,0.24,0.26,0.46])
L_initial.append([2.22,2.44,2.66,2.24,2.26,2.46])
S_initial.append([0.22,0.24,0.26,0.42,0.44,0.46,0.62,0.64,-0.66])
T_initial.append([0.33,0.66,0.99,0.36,0.39,0.69])
L_initial.append([2.33,2.66,2.99,2.36,2.39,2.69])
S_initial.append([0.22,0.24,0.26,0.42,0.44,0.46,0.62,0.64,-0.66])
tlsosA = tools.generate_tlsos(selections = selections,
xray_structure = xray_structure,
T = T_initial,
L = L_initial,
S = S_initial)
tlsos = tools.generate_tlsos(selections = selections,
xray_structure = xray_structure,
T = T_initial,
L = L_initial,
S = S_initial)
tlsos = tools.make_tlso_compatible_with_u_positive_definite(
tlsos = tlsos,
xray_structure = xray_structure.deep_copy_scatterers(),
selections = selections,
max_iterations = 50,
number_of_u_nonpositive_definite = 0,
eps = eps,
number_of_macro_cycles_for_tls_from_uanisos = 30)
u_cart_answer = tools.u_cart_from_tls(sites_cart = xray_structure.sites_cart(),
selections = selections,
tlsos = tlsos)
xray_structure.scatterers().set_u_cart(xray_structure.unit_cell(),
u_cart_answer)
assert approx_equal(u_cart_answer,
xray_structure.scatterers().extract_u_cart(xray_structure.unit_cell()))
tools.show_tls(tlsos = tlsos, text = "ANSWER")
###> Set up fmodel
sfg_params = mmtbx.f_model.sf_and_grads_accuracy_master_params.extract()
sfg_params.algorithm = "direct"
sfg_params.cos_sin_table = False
dummy = xray_structure.structure_factors(algorithm = sfg_params.algorithm,
d_min = 2.0).f_calc()
f_obs = abs(dummy.structure_factors_from_scatterers(
xray_structure = xray_structure,
algorithm = sfg_params.algorithm,
cos_sin_table = sfg_params.cos_sin_table).f_calc())
flags = f_obs.generate_r_free_flags(fraction=0.01, max_free=2000)
fmodel = mmtbx.f_model.manager(xray_structure = xray_structure,
f_obs = f_obs,
r_free_flags = flags,
target_name = "ls_wunit_k1",
sf_and_grads_accuracy_params = sfg_params)
fmodel.info(free_reflections_per_bin=250, max_number_of_bins=30).show_all()
xray_structure.convert_to_isotropic()
xray_structure.set_b_iso(value = 25.0)
fmodel.update_xray_structure(xray_structure = xray_structure,
update_f_calc = True)
fmodel.info(free_reflections_per_bin=250, max_number_of_bins=30).show_all()
print("*"*80)
###> TLS refinement against xray data
if (not "--comprehensive" in sys.argv[1:]):
number_of_macro_cycles = 1
max_number_of_iterations = 3
else:
number_of_macro_cycles = 100
max_number_of_iterations = 50
for start_tls_value in [None]:#[0.0, tlsosA, None]:
#for start_tls_value in [None]:
print(" \n "+str(start_tls_value) + " \n ")
fmodel_cp = fmodel.deep_copy()
#for sc in fmodel_cp.xray_structure.scatterers():
# sc.flags.set_use_u_aniso(True)
fmodel_cp.xray_structure.convert_to_anisotropic()
if(start_tls_value is None):
run_finite_differences_test = True
else: run_finite_differences_test = False
model.set_xray_structure(fmodel_cp.xray_structure)
tls_refinement_manager = tools.tls_refinement(
fmodel = fmodel_cp,
model = model,
selections = selections,
selections_1d = None,
refine_T = 1,
refine_L = 1,
refine_S = 1,
number_of_macro_cycles = number_of_macro_cycles,
max_number_of_iterations = max_number_of_iterations,
start_tls_value = start_tls_value,
run_finite_differences_test = run_finite_differences_test,
eps = eps)
u_cart = tls_refinement_manager.fmodel.xray_structure.scatterers().extract_u_cart(
xray_structure.unit_cell())
if("--comprehensive" in sys.argv[1:]):
format = "%10.6f %10.6f %10.6f %10.6f %10.6f %10.6f"
counter = 0
if(start_tls_value == tlsosA): tolerance = 1.e-6
else: tolerance = 0.02
for m1,m2 in zip(u_cart_answer, u_cart):
counter += 1
if(counter < 10):
print("1=" + format % (m1[0],m1[1],m1[2],m1[3],m1[4],m1[5]))
print("2=" + format % (m2[0],m2[1],m2[2],m2[3],m2[4],m2[5]))
assert approx_equal(m1,m2, tolerance)
def stats(model, prefix, no_ticks=True):
# Get rid of H, multi-model, no-protein and single-atom residue models
if (model.percent_of_single_atom_residues() > 20):
return None
sel = model.selection(string="protein")
if (sel.count(True) == 0):
return None
ssr = "protein and not (element H or element D or resname UNX or resname UNK or resname UNL)"
sel = model.selection(string=ssr)
model = model.select(sel)
if (len(model.get_hierarchy().models()) > 1):
return None
# Add H; this looses CRYST1 !
rr = run_reduce_with_timeout(
stdin_lines=model.get_hierarchy().as_pdb_string().splitlines(),
file_name=None,
parameters="-oh -his -flip -keep -allalt -pen9999 -",
override_auto_timeout_with=None)
# Create model; this is a single-model pure protein with new H added
pdb_inp = iotbx.pdb.input(source_info=None, lines=rr.stdout_lines)
model = mmtbx.model.manager(model_input=None,
build_grm=True,
pdb_hierarchy=pdb_inp.construct_hierarchy(),
process_input=True,
log=null_out())
box = uctbx.non_crystallographic_unit_cell_with_the_sites_in_its_center(
sites_cart=model.get_sites_cart(), buffer_layer=5)
model.set_sites_cart(box.sites_cart)
model._crystal_symmetry = box.crystal_symmetry()
#
N = 10
SS = get_ss_selections(hierarchy=model.get_hierarchy())
HB_all = find(
model=model.select(flex.bool(model.size(), True)),
a_DHA_cutoff=90).get_params_as_arrays(replace_with_empty_threshold=N)
HB_alpha = find(
model=model.select(SS.both.h_sel),
a_DHA_cutoff=90).get_params_as_arrays(replace_with_empty_threshold=N)
HB_beta = find(
model=model.select(SS.both.s_sel),
a_DHA_cutoff=90).get_params_as_arrays(replace_with_empty_threshold=N)
print(HB_all.d_HA.size())
result_dict = {}
result_dict["all"] = HB_all
result_dict["alpha"] = HB_alpha
result_dict["beta"] = HB_beta
# result_dict["loop"] = get_selected(sel=loop_sel)
# Load histograms for reference high-resolution d_HA and a_DHA
pkl_fn = libtbx.env.find_in_repositories(
relative_path="mmtbx") + "/nci/d_HA_and_a_DHA_high_res.pkl"
assert os.path.isfile(pkl_fn)
ref = easy_pickle.load(pkl_fn)
#
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10, 10))
kwargs = dict(histtype='bar', bins=20, range=[1.6, 3.0], alpha=.8)
for j, it in enumerate([["alpha", 1], ["beta", 3], ["all", 5]]):
key, i = it
ax = plt.subplot(int("32%d" % i))
if (no_ticks):
#ax.set_xticks([])
ax.set_yticks([])
if (j in [0, 1]):
ax.tick_params(bottom=False)
ax.set_xticklabels([])
ax.tick_params(axis="x", labelsize=12)
ax.tick_params(axis="y", labelsize=12, left=False, pad=-2)
ax.text(0.98,
0.92,
key,
size=12,
horizontalalignment='right',
transform=ax.transAxes)
HB = result_dict[key]
if HB is None: continue
w1 = np.ones_like(HB.d_HA) / HB.d_HA.size()
ax.hist(HB.d_HA, color="orangered", weights=w1, rwidth=0.3, **kwargs)
#
start, end1, end2 = 0, max(ref.distances[key].vals), \
round(max(ref.distances[key].vals),2)
if (not no_ticks):
plt.yticks([0.01, end1], ["0", end2],
visible=True,
rotation="horizontal")
if (key == "alpha"): plt.ylim(0, end2 + 0.02)
elif (key == "beta"): plt.ylim(0, end2 + 0.02)
elif (key == "all"): plt.ylim(0, end2 + 0.02)
else: assert 0
#
if (j == 0): ax.set_title("Distance", size=15)
bins = list(flex.double(ref.distances[key].bins))
ax.bar(bins, ref.distances[key].vals, alpha=.3, width=0.07)
#
kwargs = dict(histtype='bar', bins=20, range=[90, 180], alpha=.8)
for j, it in enumerate([["alpha", 2], ["beta", 4], ["all", 6]]):
key, i = it
ax = plt.subplot(int("32%d" % i))
if (j in [0, 1]):
ax.tick_params(bottom=False)
ax.set_xticklabels([])
if (no_ticks):
#ax.set_xticks([])
ax.set_yticks([])
ax.tick_params(axis="x", labelsize=12)
ax.tick_params(axis="y", labelsize=12, left=False, pad=-2)
ax.text(0.98,
0.92,
key,
size=12,
horizontalalignment='right',
transform=ax.transAxes)
ax.text(0.98,
0.92,
key,
size=12,
horizontalalignment='right',
transform=ax.transAxes)
#if(j in [0,1]): ax.plot_params(bottom=False)
HB = result_dict[key]
if HB is None: continue
w1 = np.ones_like(HB.a_DHA) / HB.a_DHA.size()
ax.hist(HB.a_DHA, color="orangered", weights=w1, rwidth=0.3, **kwargs)
#
start, end1, end2 = 0, max(ref.angles[key].vals), \
round(max(ref.angles[key].vals),2)
if (not no_ticks):
plt.yticks([0.01, end1], ["0", end2],
visible=True,
rotation="horizontal")
if (key == "alpha"): plt.ylim(0, end2 + 0.02)
elif (key == "beta"): plt.ylim(0, end2 + 0.02)
elif (key == "all"): plt.ylim(0, end2 + 0.02)
else: assert 0
#
if (j == 0): ax.set_title("Angle", size=15)
ax.bar(ref.angles[key].bins, ref.angles[key].vals, width=4.5, alpha=.3)
plt.subplots_adjust(wspace=0.12, hspace=0.025)
if (no_ticks):
plt.subplots_adjust(wspace=0.025, hspace=0.025)
#fig.savefig("%s.png"%prefix, dpi=1000)
fig.savefig("%s.pdf" % prefix)
def exercise_nonstd_residue():
""" When loop closure need to put back side chain for non-standard residue,
here is TPO
"""
tst_pdb_2 = """\
CRYST1 114.270 114.270 170.840 90.00 90.00 120.00 P 32 2 1 6
ATOM 2808 N GLY A 495 -21.779 41.479 -17.193 1.00 99.35 N
ATOM 2809 CA GLY A 495 -21.593 42.735 -17.896 1.00 99.35 C
ATOM 2810 C GLY A 495 -22.691 43.004 -18.907 1.00 99.35 C
ATOM 2811 O GLY A 495 -22.977 44.156 -19.232 1.00 99.35 O
ATOM 2812 N VAL A 496 -23.308 41.937 -19.403 1.00 93.50 N
ATOM 2813 CA VAL A 496 -24.379 42.055 -20.385 1.00 93.50 C
ATOM 2814 C VAL A 496 -25.614 42.689 -19.751 1.00 93.50 C
ATOM 2815 O VAL A 496 -25.859 42.530 -18.555 1.00 93.50 O
ATOM 2816 CB VAL A 496 -24.766 40.672 -20.951 1.00109.49 C
ATOM 2817 CG1 VAL A 496 -25.690 40.837 -22.146 1.00109.49 C
ATOM 2818 CG2 VAL A 496 -23.515 39.902 -21.340 1.00109.49 C
ATOM 2819 N THR A 497 -26.391 43.407 -20.558 1.00 89.25 N
ATOM 2820 CA THR A 497 -27.597 44.065 -20.068 1.00 89.25 C
ATOM 2821 C THR A 497 -28.779 43.853 -21.009 1.00 89.25 C
ATOM 2822 O THR A 497 -28.783 42.929 -21.822 1.00 89.25 O
ATOM 2823 CB THR A 497 -27.264 45.433 -19.439 1.00109.67 C
ATOM 2824 OG1 THR A 497 -28.479 46.116 -19.107 1.00109.67 O
ATOM 2825 CG2 THR A 497 -26.458 46.284 -20.409 1.00109.67 C
ATOM 2826 N THR A 498 -29.782 44.718 -20.887 1.00 88.67 N
ATOM 2827 CA THR A 498 -30.974 44.647 -21.724 1.00 88.67 C
ATOM 2828 C THR A 498 -32.417 45.138 -21.787 1.00 88.67 C
ATOM 2829 O THR A 498 -33.305 44.408 -22.229 1.00 88.67 O
ATOM 2830 CB THR A 498 -31.649 43.319 -22.121 1.00 90.48 C
ATOM 2831 OG1 THR A 498 -32.602 43.559 -23.164 1.00 90.48 O
ATOM 2832 CG2 THR A 498 -32.357 42.701 -20.925 1.00 90.48 C
ATOM 2833 N LYS A 499 -32.654 46.372 -21.352 1.00 81.24 N
ATOM 2834 CA LYS A 499 -34.004 46.920 -21.362 1.00 81.24 C
ATOM 2835 C LYS A 499 -35.350 46.867 -22.083 1.00 81.24 C
ATOM 2836 O LYS A 499 -35.726 47.804 -22.788 1.00 81.24 O
ATOM 2837 CB LYS A 499 -33.470 48.359 -21.348 1.00138.53 C
ATOM 2838 CG LYS A 499 -32.387 48.644 -22.386 1.00138.53 C
ATOM 2839 CD LYS A 499 -32.946 49.210 -23.682 1.00138.53 C
ATOM 2840 CE LYS A 499 -33.295 50.684 -23.534 1.00138.53 C
ATOM 2841 NZ LYS A 499 -33.775 51.276 -24.813 1.00138.53 N
HETATM 2842 N TPO A 500 -36.075 45.767 -21.897 1.00 93.19 N
HETATM 2843 CA TPO A 500 -37.391 45.606 -22.507 1.00 93.19 C
HETATM 2844 CB TPO A 500 -37.442 44.363 -23.396 1.00112.60 C
HETATM 2845 CG2 TPO A 500 -38.746 44.383 -24.204 1.00112.60 C
HETATM 2846 OG1 TPO A 500 -36.327 44.384 -24.298 1.00112.60 O
HETATM 2847 P TPO A 500 -35.745 42.879 -24.411 1.00112.60 P
HETATM 2848 O1P TPO A 500 -36.794 41.971 -24.933 1.00112.60 O
HETATM 2849 O2P TPO A 500 -34.491 42.875 -25.421 1.00112.60 O
HETATM 2850 O3P TPO A 500 -35.274 42.380 -22.956 1.00112.60 O
HETATM 2851 C TPO A 500 -38.390 46.538 -21.826 1.00 93.19 C
HETATM 2852 O TPO A 500 -38.487 47.718 -22.163 1.00 93.19 O
ATOM 2853 N PHE A 501 -39.132 45.991 -20.868 1.00 94.45 N
ATOM 2854 CA PHE A 501 -40.138 46.751 -20.134 1.00 94.45 C
ATOM 2855 C PHE A 501 -41.172 45.642 -20.302 1.00 94.45 C
ATOM 2856 O PHE A 501 -42.021 45.697 -21.192 1.00 94.45 O
ATOM 2857 CB PHE A 501 -40.812 48.126 -20.206 1.00 71.72 C
ATOM 2858 CG PHE A 501 -41.372 48.607 -18.894 1.00 71.72 C
ATOM 2859 CD1 PHE A 501 -41.285 49.951 -18.547 1.00 71.72 C
ATOM 2860 CD2 PHE A 501 -41.992 47.727 -18.010 1.00 71.72 C
ATOM 2861 CE1 PHE A 501 -41.804 50.415 -17.341 1.00 71.72 C
ATOM 2862 CE2 PHE A 501 -42.516 48.181 -16.801 1.00 71.72 C
ATOM 2863 CZ PHE A 501 -42.421 49.529 -16.467 1.00 71.72 C
ATOM 2864 N CYS A 502 -41.089 44.634 -19.439 1.00 84.28 N
ATOM 2865 CA CYS A 502 -42.009 43.503 -19.480 1.00 84.28 C
ATOM 2866 C CYS A 502 -42.191 42.910 -18.087 1.00 84.28 C
ATOM 2867 O CYS A 502 -41.624 43.404 -17.113 1.00 84.28 O
ATOM 2868 CB CYS A 502 -41.477 42.426 -20.428 1.00117.39 C
ATOM 2869 SG CYS A 502 -39.879 41.732 -19.943 1.00117.39 S
ATOM 2870 N GLY A 503 -42.984 41.847 -17.999 1.00109.92 N
ATOM 2871 CA GLY A 503 -43.219 41.207 -16.718 1.00109.92 C
ATOM 2872 C GLY A 503 -44.627 41.425 -16.198 1.00109.92 C
ATOM 2873 O GLY A 503 -45.559 41.639 -16.974 1.00109.92 O
ATOM 2874 N THR A 504 -44.779 41.375 -14.879 1.00102.36 N
ATOM 2875 CA THR A 504 -46.079 41.561 -14.246 1.00102.36 C
ATOM 2876 C THR A 504 -46.092 42.865 -13.449 1.00102.36 C
ATOM 2877 O THR A 504 -45.101 43.222 -12.812 1.00102.36 O
ATOM 2878 CB THR A 504 -46.396 40.395 -13.292 1.00 94.96 C
ATOM 2879 OG1 THR A 504 -45.797 39.194 -13.793 1.00 94.96 O
ATOM 2880 CG2 THR A 504 -47.899 40.185 -13.187 1.00 94.96 C
END
"""
pdb_inp = iotbx.pdb.input(source_info=None, lines=tst_pdb_2)
model = mmtbx.model.manager(model_input=pdb_inp)
model.process(make_restraints=True)
model.get_hierarchy().write_pdb_file("tst_loop_closure_2_start.pdb")
assert model.get_hierarchy().atoms_size() == 73
loop_ideal_params = loop_idealization.master_phil.extract()
loop_ideal_params.loop_idealization.enabled = True
loop_ideal_params.loop_idealization.variant_search_level = 1
loop_ideal_params.loop_idealization.variant_number_cutoff = 10
loop_ideal_params.loop_idealization.number_of_ccd_trials = 1
loop_ideal_params.loop_idealization.minimize_whole = False
loop_ideal = loop_idealization.loop_idealization(model=model,
params=loop_ideal_params,
verbose=False)
model.get_hierarchy().write_pdb_file("tst_loop_closure_2_result.pdb")
assert model.get_hierarchy().atoms_size() == 73
sel = model.selection("resname TPO")
assert model.get_hierarchy().select(sel).atoms_size() == 11
