def exercise_01 () :
"""
Sanity check - don't crash when mean intensity for a bin is zero.
"""
xrs = random_structure.xray_structure(
unit_cell=(50,50,50,90,90,90),
space_group_symbol="P1",
n_scatterers=1200,
elements="random")
fc = abs(xrs.structure_factors(d_min=1.5).f_calc())
fc = fc.set_observation_type_xray_amplitude()
cs = fc.complete_set(d_min=1.4)
ls = cs.lone_set(other=fc)
f_zero = ls.array(data=flex.double(ls.size(), 0))
f_zero.set_observation_type_xray_amplitude()
fc = fc.concatenate(other=f_zero)
sigf = flex.double(fc.size(), 0.1) + (fc.data() * 0.03)
fc = fc.customized_copy(sigmas=sigf)
try :
fc_fc = french_wilson.french_wilson_scale(miller_array=fc, log=null_out())
except Sorry :
pass
else :
raise Exception_expected
ic = fc.f_as_f_sq().set_observation_type_xray_intensity()
fc_fc = french_wilson.french_wilson_scale(miller_array=ic, log=null_out())
def exercise_intensity_output () :
if (os.path.isfile("tst_fmodel_anomalous.mtz")) :
os.remove("tst_fmodel_anomalous.mtz")
pdb_file = make_fake_anomalous_data.write_pdb_input_cd_cl(
file_base="tst_fmodel_anomalous")
# phenix.fmodel (with wavelength)
args = [
pdb_file,
"high_resolution=1.0",
"wavelength=1.116",
"obs_type=intensities",
"type=real",
"output.file_name=tst_fmodel_intensity.mtz",
"r_free_flags_fraction=0.1",
]
args2 = args + ["label=Imodel"]
fmodel.run(args=args2, log=null_out())
assert os.path.isfile("tst_fmodel_intensity.mtz")
mtz_in = file_reader.any_file("tst_fmodel_intensity.mtz")
assert mtz_in.file_server.miller_arrays[0].is_xray_intensity_array()
try :
fmodel.run(args=args, log=null_out())
except Sorry :
pass
else :
raise Exception_expected
try :
fmodel.run(args=args+["format=cns"], log=null_out())
except Sorry :
pass
else :
raise Exception_expected
def exercise () :
from mmtbx.regression.make_fake_anomalous_data import generate_calcium_inputs
from mmtbx.command_line import find_peaks_holes
mtz_file, pdb_file = generate_calcium_inputs(
file_base = "tst_find_peaks_holes", anonymize = True)
out = StringIO()
peaks_holes = find_peaks_holes.run(
args=[pdb_file, mtz_file],
out=out)
peaks_holes.save_pdb_file(file_name="tst_fph_peaks.pdb", log=null_out())
p = easy_pickle.dumps(peaks_holes)
s = peaks_holes.get_summary()
sp = easy_pickle.dumps(s)
out2 = StringIO()
s.show(out=out2)
lines = out2.getvalue().splitlines()
assert (""" anomalous H2O (anomalous > 3): 1""" in lines)
assert (""" anomalous non-water atoms: 0""" in lines)
assert (""" mFo-DFc > 9: 0""" in lines)
peaks_holes = find_peaks_holes.run(
args=[pdb_file, mtz_file, "filter_peaks_by_2fofc=1.0"],
out=null_out())
out3 = StringIO()
peaks_holes.get_summary().show(out=out3)
lines = out3.getvalue().splitlines()
assert (""" anomalous > 3: 0""" in lines)
out3 = StringIO()
peaks_holes = find_peaks_holes.run(
args=[pdb_file, mtz_file, "include_peaks_near_model=True",],
out=out3)
lines = out3.getvalue().splitlines()
assert (""" mFo-DFc > 9: 1""" in lines)
os.remove(mtz_file)
os.remove(pdb_file)
def exercise_heavy () :
from mmtbx.regression import make_fake_anomalous_data
from mmtbx.command_line import validate_waters
import mmtbx.ions.utils
from iotbx.file_reader import any_file
file_base = "tst_validate_waters_1"
pdb_file = make_fake_anomalous_data.write_pdb_input_cd_cl(file_base=file_base)
mtz_file = make_fake_anomalous_data.generate_mtz_file(
file_base="tst_validate_waters_1",
d_min=1.5,
anomalous_scatterers=[
group_args(selection="element CD", fp=-0.29, fdp=2.676),
group_args(selection="element CL", fp=0.256, fdp=0.5),
])
pdb_in = any_file(pdb_file)
hierarchy = pdb_in.file_object.hierarchy
hierarchy, n = mmtbx.ions.utils.anonymize_ions(hierarchy, log=null_out())
hierarchy.write_pdb_file("%s_start.pdb" % file_base,
crystal_symmetry=pdb_in.file_object.crystal_symmetry())
args = ["tst_validate_waters_1_start.pdb", "tst_validate_waters_1.mtz",
"skip_twin_detection=True"]
results = validate_waters.run(args=args, out=null_out())
out = StringIO()
results.show(out=out)
s = easy_pickle.dumps(results)
r2 = easy_pickle.loads(s)
out2 = StringIO()
r2.show(out=out2)
assert not show_diff(out.getvalue(), out2.getvalue())
assert (results.n_bad >= 1) and (results.n_heavy == 2)
def find_sec_str(self, pdb_hierarchy):
if self.params.secondary_structure.protein.search_method == "ksdssp":
pdb_str = pdb_hierarchy.as_pdb_string()
print >> self.log, " running ksdssp..."
(records, stderr) = run_ksdssp_direct(pdb_str)
return iotbx.pdb.secondary_structure.annotation.from_records(
records=records,
log=self.log)
elif self.params.secondary_structure.protein.search_method == "mmtbx_dssp":
from mmtbx.secondary_structure import dssp
print >> self.log, " running mmtbx.dssp..."
return dssp.dssp(
pdb_hierarchy=pdb_hierarchy,
pdb_atoms=self.pdb_atoms,
out=null_out()).get_annotation()
elif self.params.secondary_structure.protein.search_method == "from_ca":
from mmtbx.secondary_structure import find_ss_from_ca
print >> self.log, " running find_ss_from_ca..."
fss = find_ss_from_ca.find_secondary_structure(
hierarchy=pdb_hierarchy,
out=null_out())
return fss.get_annotation()
elif self.params.secondary_structure.protein.search_method == "cablam":
from mmtbx.validation import cablam
print >> self.log, " running cablam..."
cablam_results = cablam.cablamalyze(
pdb_hierarchy = pdb_hierarchy,
outliers_only=False,
out=null_out(),
quiet=False)
return cablam_results.as_secondary_structure()
else:
print >> self.log, " WARNING: Unknown search method for SS. No SS found."
return iotbx.pdb.secondary_structure.annotation.from_records()
def get_phaser_sad_llg_map_coefficients (
fmodel,
pdb_hierarchy,
log=None,
verbose=False) :
"""
Calculates an anomalous log-likelihood gradient (LLG) map using the SAD
target in Phaser. This is essentially similar to an anomalous difference-
difference map, but more sensitive.
"""
if (not libtbx.env.has_module("phaser")) :
raise Sorry("Phaser not available - required for SAD LLG maps.")
from phaser.phenix_adaptors import sad_target
assert (fmodel.f_model().anomalous_flag())
f_obs = fmodel.f_obs().select(fmodel.f_obs().data()>0)
r_free_flags = fmodel.r_free_flags().common_set(other=f_obs)
data = sad_target.data_adaptor(
f_obs=f_obs,
r_free_flags=r_free_flags,
verbose=True)
if (verbose) and (log is not None) :
data.output.setPackagePhenix(log)
else :
data.output.setPackagePhenix(null_out())
t = data.target(
xray_structure=fmodel.xray_structure,
pdb_hierarchy=pdb_hierarchy,
log=null_out())
t.set_f_calc(fmodel.f_model())
map_coeffs = t.llg_map_coeffs()
return map_coeffs
def refine_window (self, window) :
from mmtbx.building.alternate_conformations import real_space_annealing
processed_pdb_file = self.get_processed_pdb_file()
assert (processed_pdb_file is not None)
hierarchy = processed_pdb_file.all_chain_proxies.pdb_hierarchy
#print window.residue_id_str
log = null_out()
if (self.debug) :
log = sys.stdout
refinements = real_space_annealing.refine_into_difference_density(
fmodel=self.fmodel,
pdb_hierarchy=hierarchy.deep_copy(),
processed_pdb_file=processed_pdb_file,
selection=window.selection,
selection_score=window.residue_selection,
params=self.params,
nproc=self.nproc_2,
out=null_out()).get_filtered_trials(log=log)
result = ensemble(
window=window,
sites_trials=refinements)
n_keep = result.filter_trials(
sites_cart=self.sites_cart,
min_rmsd=self.params.min_rmsd,
min_dev=self.min_required_deviation)
if (n_keep > 0) :
if (self.asynchronous_output) :
print >> self.out, result
return result
return None
def format_all_for_phenix_refine(self,quiet=False,out=None,
prefix="refinement.pdb_interpretation.ncs_group"):
'''
This function is an older version of creating phil for phenix refine,
it is modified to replicate a new phil parameters that can handle
selection to the level of atoms, "format_phil_for_phenix_refine".
When it will still can be used in the older form, which allows only
residue level selection.
'''
if hasattr(self._ncs_obj,'show'):
if prefix == 'refinement.pdb_interpretation.ncs_group':
prefix="pdb_interpretation"
if quiet:
out = null_out()
elif out is None:
out=sys.stdout
all_text = self._ncs_obj.show(format='phil',log=null_out(),header=False)
all_text = convert_phil_format(all_text,to_type=prefix)
if all_text:
if not quiet:
print >> out, all_text + '\n'
return all_text
else:
# this is only being used when only a spec file is provided
if out == None:
out=sys.stdout
all_text=""
for ncs_group in self._ncs_groups:
text= ncs_group.format_for_phenix_refine(prefix=prefix)
if text:
if not quiet: out.write(text+'\n')
all_text+="\n"+text
return all_text
def exercise_twin_detwin () :
random.seed(12345)
flex.set_random_seed(12345)
xrs = random_structure.xray_structure(
unit_cell=(12,5,12,90,90,90),
space_group_symbol="P1",
n_scatterers=12,
elements="random")
fc = abs(xrs.structure_factors(d_min=1.5).f_calc())
fc = fc.set_observation_type_xray_amplitude()
mtz_file = "tmp_massage_in.mtz"
fc.as_mtz_dataset(column_root_label="F").mtz_object().write(mtz_file)
massage_data.run(
args=[
mtz_file,
"aniso.action=None",
"outlier.action=None",
"symmetry.action=twin",
"twin_law='l,-k,h'",
"fraction=0.3",
"hklout=tmp_massage_twinned.mtz",
],
out=null_out())
assert op.isfile("tmp_massage_twinned.mtz")
mtz_in = file_reader.any_file("tmp_massage_twinned.mtz")
fc_twin = mtz_in.file_server.miller_arrays[0].f_sq_as_f()
fc_twin, fc_tmp = fc_twin.common_sets(other=fc)
for hkl, f1, f2 in zip(fc_tmp.indices(), fc_tmp.data(), fc_twin.data()) :
if (abs(hkl[0]) != abs(hkl[2])) :
assert not approx_equal(f1, f2, eps=0.01, out=null_out()), (hkl, f1, f2)
massage_data.run(
args=[
mtz_file,
"aniso.action=None",
"outlier.action=None",
"symmetry.action=twin",
"twin_law='l,-k,h'",
"fraction=0.3",
"hklout=tmp_massage_twinned.sca",
],
out=null_out())
assert op.isfile("tmp_massage_twinned.sca")
massage_data.run(
args=[
"tmp_massage_twinned.mtz",
"aniso.action=None",
"outlier.action=None",
"symmetry.action=detwin",
"twin_law='l,-k,h'",
"fraction=0.3",
"hklout=tmp_massage_detwinned.mtz",
],
out=null_out())
mtz_in = file_reader.any_file("tmp_massage_detwinned.mtz")
fc_detwin = mtz_in.file_server.miller_arrays[0].f_sq_as_f()
fc_detwin, fc_tmp = fc_detwin.common_sets(other=fc)
# XXX we appear to lose some accuracy here, possibly due to the use of
# MTZ format
for hkl, f1, f2 in zip(fc_tmp.indices(), fc_tmp.data(), fc_detwin.data()) :
assert approx_equal(f1, f2, eps=0.01), hkl
def exercise_full_validation () :
from phenix.validation import analyze_all
import iotbx.phil
open("tmp_validation_neutron.pdb", "w").write(pdb_str_1)
test_phil = iotbx.phil.parse("""
model_vs_data {
pdb_file = tmp_validation_neutron.pdb
scattering_table = *neutron
}
""")
working_phil = analyze_all.model_vs_data_params.fetch(source=test_phil)
params = working_phil.extract()
validation = analyze_all.validation_result(
params=params,
tmp_dir=os.getcwd(),
out=null_out(),
quiet=True)
c_score = validation.molprobity_result.get_clashscore()
assert approx_equal(c_score, 58.82, eps=0.01)
params.model_vs_data.scattering_table = "n_gaussian"
validation = analyze_all.validation_result(
params=params,
tmp_dir=os.getcwd(),
out=null_out(),
quiet=True)
c_score = validation.molprobity_result.get_clashscore()
assert approx_equal(c_score, 35.29, eps=0.01)
def exercise () :
#
# Test command-line program
#
pdb_in, mtz_in = make_inputs()
pdb_file = file_reader.any_file(pdb_in, force_type="pdb")
hierarchy = pdb_file.file_object.hierarchy
old_ligand = None
for chain in hierarchy.only_model().chains() :
if (chain.id != "B") : continue
for residue_group in chain.residue_groups() :
atom_group = residue_group.only_atom_group()
if (atom_group.resname == "ACT") :
old_ligand = atom_group.detached_copy()
residue_group.remove_atom_group(atom_group)
break
assert old_ligand is not None
open("tst_ligand_ncs_start.pdb", "w").write(hierarchy.as_pdb_string(
crystal_symmetry=pdb_file.file_object.crystal_symmetry()))
args = [
"tst_ligand_ncs_start.pdb",
mtz_in,
"ligand_code=ACT",
]
from mmtbx.command_line import apply_ncs_to_ligand
if op.isfile("ncs_ligands.pdb") :
os.remove("ncs_ligands.pdb")
result = apply_ncs_to_ligand.run(args=args, out=null_out())
assert result.n_ligands_new == 1
assert op.isfile("ncs_ligands.pdb")
pdb_out = file_reader.any_file("ncs_ligands.pdb", force_type="pdb")
hierarchy_new = pdb_out.file_object.hierarchy
new_ligand = None
for chain in hierarchy_new.only_model().chains() :
if (chain.id != "B") : continue
for residue_group in chain.residue_groups() :
atom_group = residue_group.only_atom_group()
if (atom_group.resname == "ACT") :
new_ligand = atom_group.detached_copy()
assert new_ligand is not None
rmsd = old_ligand.atoms().extract_xyz().rms_difference(
new_ligand.atoms().extract_xyz())
assert (rmsd < 0.5)
#
# Unit tests
#
import mmtbx.ncs.ligands
operators = mmtbx.ncs.ligands.find_ncs_operators(hierarchy,
log=null_out())
assert len(operators) == 1
group_ops = operators[0]
assert len(group_ops) == 2
assert (len(group_ops[0].selection) == 7)
for g_op in group_ops:
out = StringIO()
g_op.show_summary(out=out, prefix=" ")
assert out.getvalue().count("Rotation:") == 1
def __init__ (self,
xray_structure,
pdb_hierarchy,
f_obs,
r_free_flags,
rigid_body_refine=False,
optimize_b_factors=False,
skip_twin_detection=False,
scattering_table="n_gaussian") :
self.r_work = None
self.r_free = None
self.xray_structure = None
from mmtbx.utils import fmodel_simple
from cctbx import crystal
combined_symmetry = crystal.symmetry(
unit_cell=f_obs.unit_cell(),
space_group=xray_structure.space_group())
xray_structure = xray_structure.customized_copy(
crystal_symmetry=combined_symmetry)
f_obs = f_obs.customized_copy(
crystal_symmetry=combined_symmetry).eliminate_sys_absent()
r_free_flags = r_free_flags.customized_copy(
crystal_symmetry=combined_symmetry).eliminate_sys_absent()
fmodel = fmodel_simple(
f_obs=f_obs,
r_free_flags=r_free_flags,
xray_structures=[xray_structure],
skip_twin_detection=skip_twin_detection,
scattering_table=scattering_table)
self.r_work_start = fmodel.r_work()
self.r_free_start = fmodel.r_free()
if (not rigid_body_refine) :
self.r_work = self.r_work_start
self.r_free = self.r_free_start
self.xray_structure = xray_structure
else :
from mmtbx.refinement import rigid_body
selection_strings = rigid_body.rigid_groups_from_pdb_chains(
pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
group_all_by_chain=True,
check_for_atoms_on_special_positions=True,
log=null_out())
selections = []
for sele_str in selection_strings :
sele = pdb_hierarchy.atom_selection_cache().selection(sele_str)
selections.append(sele.iselection())
refined = rigid_body.manager(
fmodel=fmodel,
selections=selections,
params=rigid_body.master_params.extract(),
log=null_out())
self.xray_structure = refined.fmodel.xray_structure
self.r_work = refined.fmodel.r_work()
self.r_free = refined.fmodel.r_free()
def exercise_combine_symmetry () :
"""
Test the extraction of symmetry from both a PDB file and an MTZ file.
"""
from mmtbx.regression import model_1yjp
import mmtbx.command_line
import iotbx.pdb.hierarchy
from cctbx import sgtbx
from cctbx import uctbx
# 1yjp, as usual
pdb_in = iotbx.pdb.hierarchy.input(pdb_string=model_1yjp)
xrs = pdb_in.input.xray_structure_simple()
f = open("tst_combine_symmetry.pdb", "w")
f.write(pdb_in.hierarchy.as_pdb_string(crystal_symmetry=xrs))
f.close()
f_calc = abs(xrs.structure_factors(d_min=1.5).f_calc())
# Make up slightly more exact unit cell, but set SG to P2
f_calc = f_calc.customized_copy(
crystal_symmetry=f_calc.crystal_symmetry().customized_copy(
space_group_info=sgtbx.space_group_info("P2"),
unit_cell=uctbx.unit_cell((21.9371, 4.8659, 23.4774, 90.0, 107.0832,
90.00))))
flags = f_calc.generate_r_free_flags()
mtz = f_calc.as_mtz_dataset(column_root_label="F")
mtz.add_miller_array(flags, column_root_label="FreeR_flag")
mtz.mtz_object().write("tst_combine_symmetry.mtz")
cmdline = mmtbx.command_line.load_model_and_data(
args=["tst_combine_symmetry.pdb", "tst_combine_symmetry.mtz"],
master_phil=mmtbx.command_line.generate_master_phil_with_inputs(""),
process_pdb_file=False,
create_fmodel=True,
out=null_out())
symm = cmdline.xray_structure.crystal_symmetry()
assert (approx_equal(symm.unit_cell().parameters(),
(21.9371, 4.8659, 23.4774, 90.0, 107.0832, 90.0)))
assert (str(symm.space_group_info()) == "P 1 21 1")
# Part 2: incompatible space groups
f_calc_2 = f_calc.customized_copy(
crystal_symmetry=f_calc.crystal_symmetry().customized_copy(
space_group_info=sgtbx.space_group_info("P1")))
flags_2 = f_calc_2.generate_r_free_flags()
mtz = f_calc_2.as_mtz_dataset(column_root_label="F")
mtz.add_miller_array(flags_2, column_root_label="FreeR_flag")
mtz.mtz_object().write("tst_combine_symmetry_2.mtz")
try :
cmdline = mmtbx.command_line.load_model_and_data(
args=["tst_combine_symmetry.pdb", "tst_combine_symmetry_2.mtz"],
master_phil=mmtbx.command_line.generic_simple_input_phil(),
process_pdb_file=False,
create_fmodel=True,
out=null_out())
except Sorry, s :
assert ("Incompatible space groups" in str(s))
def exercise () :
from mmtbx.wwpdb import utils as wwpdb_utils
for fn in ["3qgg.pdb", "3gqq.mtz", "3gqq-sf.cif"] :
if (os.path.isfile(fn)) : os.remove(fn)
pdb_id = "3gqq"
wwpdb_utils.fetch_pdb_data("3gqq")
program, program_full = wwpdb_utils.get_program("3gqq.pdb")
assert (program == "PHENIX.REFINE")
data = wwpdb_utils.find_data_arrays("3gqq.mtz", log=null_out())
filter = wwpdb_utils.filter_pdb_file("3gqq.pdb", log=null_out())
assert (filter.n_semet == 24) and (filter.n_unknown == 30)
print "OK"
def exercise_space_group_handling () :
flex.set_random_seed(123456)
random.seed(123456)
base = "tst_cc_star_space_group"
pdb_in = iotbx.pdb.hierarchy.input(pdb_string=model_1yjp)
xrs = pdb_in.xray_structure_simple()
xrs.set_inelastic_form_factors(
photon=1.54,
table="sasaki")
fc = abs(xrs.structure_factors(d_min=1.5).f_calc()).average_bijvoet_mates()
fc.set_observation_type_xray_amplitude()
flags = fc.generate_r_free_flags()
mtz = fc.as_mtz_dataset(column_root_label="F")
mtz.add_miller_array(flags, column_root_label="FreeR_flag")
mtz.mtz_object().write(base + ".mtz")
xrs_p1 = xrs.expand_to_p1()
xrs_p1.shake_sites_in_place(rms_difference=0.1)
fc_p1 = xrs_p1.structure_factors(d_min=1.4).f_calc()
fc_p1_extra = fc_p1.randomize_amplitude_and_phase(amplitude_error=1.0,
phase_error_deg=0,
random_seed=123456)
fc_p1 = abs(fc_p1.concatenate(other=fc_p1_extra)).sort(
by_value="packed_indices")
fc_p1.set_observation_type_xray_amplitude()
sg_p2 = sgtbx.space_group_info("P2")
ic = fc_p1.f_as_f_sq().customized_copy(
space_group_info=sg_p2,
sigmas=flex.double(fc_p1.size(), 10.0))
ic.export_as_scalepack_unmerged(file_name=base + ".sca")
open(base + ".pdb", "w").write(model_1yjp)
args = [
base + ".mtz",
base + ".pdb",
"unmerged_data=%s.sca" % base,
]
cc_star.run(args=args, out=null_out())
# now with .sca in P1 (raises Sorry)
ic2 = fc_p1.f_as_f_sq().customized_copy(
sigmas=flex.double(fc_p1.size(), 10.0))
ic2.export_as_scalepack_unmerged(file_name=base + "_p1.sca")
args = [
base + ".mtz",
base + ".pdb",
"unmerged_data=%s_p1.sca" % base,
]
try :
cc_star.run(args=args, out=null_out())
except Sorry, s :
assert (str(s) == "Incompatible space groups in merged and unmerged data:P 1 21 1 versus P 1"), s
def run(fn):
''' '''
pdb_hierarchy = pdb.hierarchy.input(file_name=fn).hierarchy
ss = 'pepnames and (name ca or name n or name c) and altloc " "'
select_str = 'chain L and resseq 104:end and %s' % ss
select_str = 'chain H and resseq 114:end and pepnames and (name ca or name n or name c) and altloc " "'
pdb_selection_bool = pdb_hierarchy.atom_selection_cache().selection(select_str)
const_L = pdb_hierarchy.select(pdb_selection_bool)
ss = 'pepnames and (name ca or name n or name c) and altloc " "'
select_str = 'chain H and resseq 114:end and %s' % ss
pdb_selection_bool = pdb_hierarchy.atom_selection_cache().selection(select_str)
const_H = pdb_hierarchy.select(pdb_selection_bool)
params = superpose_pdbs.master_params.extract()
x = superpose_pdbs.manager(
params,
log=null_out(),
write_output=False,
save_lsq_fit_obj=True,
pdb_hierarchy_fixed=const_H,
pdb_hierarchy_moving=const_L)
print 'ok'
def __init__ (self, fmodel, pdb_hierarchy, cc_min=0.8,
molprobity_map_params=None) :
from iotbx.pdb.amino_acid_codes import one_letter_given_three_letter
from mmtbx import real_space_correlation
validation.__init__(self)
# arrays for different components
self.everything = list()
self.protein = list()
self.other = list()
self.water = list()
aa_codes = one_letter_given_three_letter.keys()
# redo real_space_corelation.simple to use map objects instead of filenames
try :
rsc_params = real_space_correlation.master_params().extract()
rsc_params.detail="residue"
rsc_params.map_1.fill_missing_reflections = False
rsc_params.map_2.fill_missing_reflections = False
if (molprobity_map_params is not None):
rsc_params.map_file_name = molprobity_map_params.map_file_name
rsc_params.map_coefficients_file_name = \
molprobity_map_params.map_coefficients_file_name
rsc_params.map_coefficients_label = \
molprobity_map_params.map_coefficients_label
rsc = real_space_correlation.simple(
fmodel=fmodel,
pdb_hierarchy=pdb_hierarchy,
params=rsc_params,
log=null_out())
except Exception, e :
raise e
def exercise () :
import mmtbx.validation.ligands
import iotbx.pdb.hierarchy
pdb_1 = iotbx.pdb.hierarchy.input(pdb_string="""\
HETATM 1 C ACT X 1 0.496 0.209 0.702 1.00 20.00 A C
HETATM 2 O ACT X 1 1.691 0.458 0.395 1.00 20.00 A O
HETATM 3 OXT ACT X 1 0.100 0.388 1.883 1.00 20.00 A O-1
HETATM 4 CH3 ACT X 1 -0.463 -0.305 -0.349 1.00 20.00 A C
HETATM 5 H1 ACT X 1 -0.904 0.449 -0.784 1.00 20.00 A H
HETATM 6 H2 ACT X 1 -1.135 -0.874 0.074 1.00 20.00 A H
HETATM 7 H3 ACT X 1 0.029 -0.823 -1.014 1.00 20.00 A H
END""")
pdb_2 = iotbx.pdb.hierarchy.input(pdb_string="""\
HETATM 1 C ACT X 1 0.412 -0.000 0.714 1.00 20.00 A C
HETATM 2 O ACT X 1 1.671 -0.000 0.714 1.00 20.00 A O
HETATM 3 OXT ACT X 1 -0.217 -0.000 1.804 1.00 20.00 A O-1
HETATM 4 CH3 ACT X 1 -0.344 -0.000 -0.597 1.00 20.00 A C
HETATM 5 H1 ACT X 1 -0.507 0.920 -0.878 1.00 20.00 A H
HETATM 6 H2 ACT X 1 -1.197 -0.460 -0.480 1.00 20.00 A H
HETATM 7 H3 ACT X 1 0.183 -0.460 -1.277 1.00 20.00 A H
""")
rmsds = mmtbx.validation.ligands.compare_ligands(
ligand_code="ACT",
hierarchy_1=pdb_1.hierarchy,
hierarchy_2=pdb_2.hierarchy,
out=null_out())
assert (len(rmsds) == 1)
assert approx_equal(rmsds[0][0], 0.444, eps=0.0001)
print "OK"
def clean_up_ions (fmodel, model, params, log=None, verbose=True) :
"""
Parameters
----------
fmodel : mmtbx.f_model.manager
model : mmtbx.model.manager
params : libtbx.phil.scope_extract
log : file, optional
verbose : bool, optional
Returns
-------
mmtbx.model.manager
An updated model with ions corrected.
"""
if (log is None) :
log = null_out()
import mmtbx.ions.utils
ion_selection = model.pdb_hierarchy().atom_selection_cache().selection(
"segid ION")
ion_iselection = ion_selection.iselection()
if (len(ion_iselection) == 0) :
print >> log, " No ions (segid=ION) found."
return model
n_sites_start = model.xray_structure.scatterers().size()
new_model = model.select(~ion_selection)
ion_model = model.select(ion_selection)
ion_pdb_hierarchy = ion_model.pdb_hierarchy(sync_with_xray_structure=True)
ion_atoms = ion_pdb_hierarchy.atoms()
ion_xrs = ion_model.xray_structure
perm = mmtbx.ions.utils.sort_atoms_permutation(
pdb_atoms=ion_pdb_hierarchy.atoms(),
xray_structure=ion_model.xray_structure)
nonbonded_types = ion_model.restraints_manager.geometry.nonbonded_types
nonbonded_charges = ion_model.restraints_manager.geometry.nonbonded_charges
ion_atoms = ion_atoms.select(perm)
new_model.append_single_atoms(
new_xray_structure=ion_xrs.select(perm),
atom_names=[ atom.name for atom in ion_atoms ],
residue_names=[ atom.fetch_labels().resname for atom in ion_atoms ],
nonbonded_types=nonbonded_types.select(perm),
nonbonded_charges=nonbonded_charges.select(perm),
chain_id=params.ion_chain_id,
segids=[ "ION" for atom in ion_atoms ],
refine_occupancies=params.refine_ion_occupancies,
refine_adp="isotropic",
reset_labels=True)
n_sites_end = new_model.xray_structure.scatterers().size()
new_hierarchy = new_model.pdb_hierarchy()
n_sites_pdb = new_hierarchy.atoms().size()
assert (n_sites_start == n_sites_end == n_sites_pdb)
new_selection = new_hierarchy.atom_selection_cache().selection("segid ION")
ion_atoms = new_hierarchy.atoms().select(new_selection)
if (verbose) :
print >> log, " Final list of ions:"
for atom in ion_atoms :
print >> log, " %s" % atom.id_str()
print >> log, ""
fmodel.update_xray_structure(new_model.xray_structure)
return new_model
def write_pdb_files (self, output_base, serial, serial_format="%04d",
pause=0, pause_at_end=False, log=None) :
if (log is None) : log = null_out()
file_format = "%s_%s.pdb" % (output_base, serial_format)
k = serial
if (pause != 0) :
for j in range(pause) :
self.pdb_hierarchy.atoms().set_xyz(self._frames[0])
file_name = file_format % k
self._write_pdb(file_name)
print >> log, " wrote %s" % os.path.basename(file_name)
k += 1
for sites in self._frames :
self.pdb_hierarchy.atoms().set_xyz(sites)
file_name = file_format % k
self._write_pdb(file_name)
print >> log, " wrote %s" % os.path.basename(file_name)
k += 1
if (pause_at_end) and (pause != 0) :
for j in range(pause) :
self.pdb_hierarchy.atoms().set_xyz(self._frames[-1])
file_name = file_format % k
self._write_pdb(file_name)
print >> log, " wrote %s" % os.path.basename(file_name)
k += 1
return k
def exercise_get_atom_selections (verbose=False) :
pdb_in = """\
CRYST1 15.000 15.000 15.000 90.00 90.00 90.00 P 212121
HETATM 115 O HOH A 18 3.000 5.000 5.000 1.00 10.00 O
HETATM 115 O HOH A 19 5.000 5.000 8.000 1.00 10.00 O
HETATM 115 O HOH A 20 5.000 5.000 8.000 1.00 10.00 O
END"""
log = null_out()
if (verbose) :
log = sys.stdout
processed_pdb_files_srv = utils.process_pdb_file_srv(log=log)
processed_pdb_file, pdb_inp = processed_pdb_files_srv.process_pdb_files(
raw_records=pdb_in.splitlines())
selections1 = utils.get_atom_selections(
all_chain_proxies=processed_pdb_file.all_chain_proxies,
xray_structure=processed_pdb_file.xray_structure(),
selection_strings=["resseq 18", "resseq 19", "resseq 20"],
parameter_name="refine.occupancy")
try :
selections2 = utils.get_atom_selections(
all_chain_proxies=processed_pdb_file.all_chain_proxies,
xray_structure=processed_pdb_file.xray_structure(),
selection_strings=["resseq 18:19", "resseq 19:20"],
parameter_name="refine.occupancy")
except Sorry, s :
assert (str(s) == """\
One or more overlapping selections for refine.occupancy:
resseq 18:19
resseq 19:20""")
def prepare_inputs (prefix="tst_build_alt_confs") :
pdb_in = "%s_in.pdb" % prefix
open(pdb_in, "w").write(pdb_raw)
args = [
pdb_in,
"high_resolution=1.2",
"type=real",
"label=F",
"add_sigmas=True",
"r_free_flags_fraction=0.1",
"random_seed=12345",
"output.file_name=%s.mtz" % prefix,
]
fmodel.run(args=args, log=null_out())
pdb_file = file_reader.any_file(pdb_in)
hierarchy = pdb_file.file_object.hierarchy
xrs = pdb_file.file_object.xray_structure_simple()
for chain in hierarchy.only_model().chains() :
for residue_group in chain.residue_groups() :
atom_groups = residue_group.atom_groups()
if (len(atom_groups) > 1) :
while (len(atom_groups) > 1) :
residue_group.remove_atom_group(atom_groups[-1])
del atom_groups[-1]
for atom in residue_group.atoms() :
atom.occ = 1.0
atom_groups[0].altloc = ''
assert hierarchy.atoms().extract_occ().all_eq(1.0)
open("%s_start.pdb" % prefix, "w").write(
hierarchy.as_pdb_string(crystal_symmetry=xrs))
def exercise_2():
import iotbx.pdb
import mmtbx.maps.utils
pdb_str1="""
CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P 1
HETATM 1 C C 1 4.271 0.000 0.000 1.00 5.00 C
HETATM 2 ? ? 2 5.729 0.000 0.000 1.00 5.00 ?
HETATM 1 X D 1 4.271 0.000 0.000 1.00 5.00 X
HETATM 1 Z E 1 4.271 0.000 0.000 1.00 5.00 Z
END
"""
pdb_str2="""
CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P 1
HETATM 1 C C 1 4.271 0.000 0.000 1.00 5.00 C
END
"""
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str1)
pdb_inp.write_pdb_file(file_name = "tst_exercise_2_map_utils.pdb")
fc = iotbx.pdb.input(source_info=None,
lines=pdb_str2).xray_structure_simple().structure_factors(d_min=2).f_calc()
class dummy:
def amplitudes(self): return "2FOFCWT"
def phases(self,root_label=None): return "PH2FOFCWT"
mtz_dataset = fc.as_mtz_dataset(column_root_label=dummy().amplitudes(),
label_decorator=dummy())
mtz_dataset.add_miller_array(miller_array=abs(fc), column_root_label="FOBS_X")
mtz_object = mtz_dataset.mtz_object()
mtz_object.write(file_name = "tst_exercise_2_map_utils.mtz")
mfn1 = "tst_exercise_2_map_utils_output.mtz"
mmtbx.maps.utils.create_map_from_pdb_and_mtz(
pdb_file="tst_exercise_2_map_utils.pdb",
mtz_file="tst_exercise_2_map_utils.mtz",
output_file=mfn1,
out=null_out())
def get_validated_residues_in_selection (self,
selection,
require_n_residues=None,
log=None) :
if (log is None) : log = null_out()
results = []
i_model = 0
while (i_model < len(self.pdb_hierarchies)) :
hierarchy = self.pdb_hierarchies[i_model]
sel_cache = self.selection_caches[i_model]
rama, rota = self.validations[i_model]
i_model += 1
isel = sel_cache.selection(selection).iselection()
hierarchy_sel = hierarchy.select(isel)
residue_groups = hierarchy_sel.only_model().only_chain().residue_groups()
if (require_n_residues is not None) :
if (len(residue_groups) != require_n_residues) :
results.append(None)
continue
reject = False
for residue_group in residue_groups :
atom_group = residue_group.only_atom_group()
rama_result = rama.find_atom_group(other=atom_group)
rota_result = rota.find_atom_group(other=atom_group)
assert (not None in [rama_result, rota_result]), atom_group.id_str()
if (rama_result.is_outlier()) or (rota_result.is_outlier()) :
reject = True
break
if (reject) :
results.append(None)
else :
results.append(hierarchy_sel)
return results
def exercise () :
if (os.path.isfile("tst_fmodel_anomalous.mtz")) :
os.remove("tst_fmodel_anomalous.mtz")
pdb_file = make_fake_anomalous_data.write_pdb_input_cd_cl(
file_base="tst_fmodel_anomalous")
# phenix.fmodel (with wavelength)
args = [
pdb_file,
"high_resolution=1.0",
"wavelength=1.116",
"label=F",
"type=real",
"output.file_name=tst_fmodel_anomalous.mtz",
"r_free_flags_fraction=0.1",
]
fmodel.run(args=args, log=null_out())
assert os.path.isfile("tst_fmodel_anomalous.mtz")
mtz_in = file_reader.any_file("tst_fmodel_anomalous.mtz")
array = mtz_in.file_server.miller_arrays[0]
assert (array.anomalous_flag())
anom_diffs = array.anomalous_differences()
assert approx_equal(flex.max(anom_diffs.data()), 5.72, eps=0.01)
# mmtbx.fmodel_simple
result = easy_run.call(
"mmtbx.fmodel_simple \"%s\" tst_fmodel_anomalous.mtz high_resolution=2.0"
% pdb_file)
print "OK"
def exercise_emringer_residue_scan():
pdb_file = libtbx.env.find_in_repositories(
relative_path="phenix_regression/mmtbx/em_ringer/tst_emringer_model.pdb",
test=os.path.isfile)
map_file = libtbx.env.find_in_repositories(
relative_path="phenix_regression/mmtbx/em_ringer/tst_emringer_map.ccp4",
test=os.path.isfile)
assert (not None in [pdb_file, map_file])
results, scoring, rolling = emringer.run([pdb_file, map_file], out=null_out())
# Make sure the right number of residues (22 out of 28) get scanned
assert len(results)==22
modelled_list = [290.742121792,192.844056257,45.4781110306,294.247825632,303.618891108,58.7694040824,331.70068496,46.7136045049,290.167261226,304.261231829,282.651244586,268.729721112,195.972333785,305.321933311,314.81066224,286.028424514,311.180807466,313.004918133,296.67781565,296.949191638,169.644245088,192.496265164]
peak_list = [270,180,260,75,305,30,310,90,265,270,270,240,280,260,310,285,295,100,260,165,155,200]
peak_rhos = [0.175600306502,0.351591946536,0.206238983746,0.3269057296,0.68375562882,0.251143527693,0.29106077218,0.199922124642,0.298461589197,0.563313760047,0.412696803251,0.511080434089,0.310001828446,0.228239176285,0.563148497472,0.490755919184,0.200978032127,0.274929619102,0.299229846335,0.179215798655,0.150783734124,0.210869945593]
for i in range(22):
# Make sure the modelled angle is correctly read
assert approx_equal(results[i]._angles[1].angle_current, modelled_list[i])
# Make sure the peak is chosen correctly
assert approx_equal(results[i]._angles[1].peak_chi, peak_list[i])
# Make sure the peak rhos are correct
assert approx_equal(results[i]._angles[1].peak_rho, peak_rhos[i])
results, scoring2, rolling2 = emringer.run([pdb_file, map_file, "rolling_window_threshold=0.5"], out=null_out())
assert rolling.threshold == 0
assert rolling2.threshold == 0.5
#print rolling.results_a[0]
#print rolling2.results_a[0]
# just making sure this doesn't break!
results, scoring2, rolling = emringer.run([pdb_file, map_file, "sampling_angle=2"], out=null_out())
def run(args):
assert len(args) in [0,2]
if (len(args) == 0):
n_trials = 3
n_dynamics_steps = 30
out = null_out()
else:
n_trials = max(1, int(args[0]))
n_dynamics_steps = max(1, int(args[1]))
out = sys.stdout
show_times_at_exit()
if (1):
exercise_six_dof(
out=out, n_trials=n_trials, n_dynamics_steps=n_dynamics_steps)
if (1):
exercise_six_dof2(
out=out, n_trials=n_trials, n_dynamics_steps=n_dynamics_steps)
if (1):
exercise_spherical(
out=out, n_trials=n_trials, n_dynamics_steps=n_dynamics_steps)
if (1):
exercise_revolute(
out=out, n_trials=n_trials, n_dynamics_steps=n_dynamics_steps)
if (1):
exercise_revolute2(
out=out, n_trials=n_trials, n_dynamics_steps=n_dynamics_steps)
print "OK"
def get_structure_factors():
"""
Get f_obs and r_free_flags
From cif file if available
"""
f_obs = None
full_path_cif = '2qvz-sf.cif'
iotbx.cif.reader()
miller_arrays = iotbx.cif.reader(
file_path=full_path_cif).\
as_miller_arrays(force_symmetry=True)
# print miller_arrays[0].completeness()
for ma in miller_arrays:
if ma.is_xray_amplitude_array():
# Consider using Bijvoet mates
ma = ma.average_bijvoet_mates()
f_obs = abs(ma)
break
elif not f_obs and ma.is_xray_intensity_array():
# Consider using Bijvoet mates
ma = ma.average_bijvoet_mates()
# convert i_obs to f_obs
f_obs = abs(ma.french_wilson(log=null_out()))
if f_obs:
r_free_flags = f_obs.generate_r_free_flags()
# f_obs.show_summary()
else:
raise RuntimeError("Missing amplitude array.")
return f_obs,r_free_flags
def enable_multiprocessing_if_possible (nproc=Auto, log=None) :
"""
Switch for using multiple CPUs with the pool_map function, usually called at
the beginning of an app. If nproc is Auto or None and we are running
Windows, it will be reset to 1.
:param nproc: default number of processors to use
:returns: number of processors to use (None or Auto means automatic)
"""
if (nproc == 1) or (nproc == 0) :
return 1
if (log is None) :
from libtbx.utils import null_out
log = null_out()
problems = detect_problem()
if (problems is not None) and (problems is not Auto) :
if (nproc is Auto) or (nproc is None) :
return 1
else :
from libtbx.utils import Sorry
raise Sorry("%s. Please use nproc=1 or nproc=Auto." % str(problems))
else :
print >> log, """
******************************************************************
INFO: Some parts of this job will make use of multiple processors:
******************************************************************
nproc = %s
Please ask your system administrator for advice about this, in particular if
you run this job through a queuing system.
""" % str(nproc)
return nproc
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 exercise_1():
pdb_raw = """\
CRYST1 23.000 6.666 25.000 90.00 107.08 90.00 P 1 21 1 2
ATOM 1 N GLY A 1 -9.009 4.612 6.102 1.00 16.77 N
ATOM 2 CA GLY A 1 -9.052 4.207 4.651 1.00 16.57 C
ATOM 3 C GLY A 1 -8.015 3.140 4.419 1.00 16.16 C
ATOM 4 O GLY A 1 -7.523 2.521 5.381 1.00 16.78 O
ATOM 5 N ASN A 2 -7.656 2.923 3.155 1.00 15.02 N
ATOM 6 CA ASN A 2 -6.522 2.038 2.831 1.00 14.10 C
ATOM 7 C ASN A 2 -5.241 2.537 3.427 1.00 13.13 C
ATOM 8 O ASN A 2 -4.978 3.742 3.426 1.00 11.91 O
ATOM 9 CB ASN A 2 -6.346 1.881 1.341 1.00 15.38 C
ATOM 10 CG ASN A 2 -7.584 1.342 0.692 1.00 14.08 C
ATOM 11 OD1 ASN A 2 -8.025 0.227 1.016 1.00 17.46 O
ATOM 12 ND2 ASN A 2 -8.204 2.155 -0.169 1.00 11.72 N
ATOM 13 N ASN A 3 -4.438 1.590 3.905 1.00 12.26 N
ATOM 14 CA ASN A 3 -3.193 1.904 4.589 1.00 11.74 C
ATOM 15 C ASN A 3 -1.955 1.332 3.895 1.00 11.10 C
ATOM 16 O ASN A 3 -1.872 0.119 3.648 1.00 10.42 O
ATOM 17 CB ASN A 3 -3.259 1.378 6.042 1.00 12.15 C
ATOM 18 CG ASN A 3 -2.006 1.739 6.861 1.00 12.82 C
ATOM 19 OD1 ASN A 3 -1.702 2.925 7.072 1.00 15.05 O
ATOM 20 ND2 ASN A 3 -1.271 0.715 7.306 1.00 13.48 N
ATOM 21 N MET A 4 -1.005 2.228 3.598 1.00 10.29 N
ATOM 22 CA MET A 4 0.384 1.888 3.199 1.00 10.53 C
ATOM 23 C MET A 4 1.435 2.606 4.088 1.00 10.24 C
ATOM 24 O MET A 4 1.547 3.843 4.115 1.00 8.86 O
ATOM 25 CB MET A 4 0.616 2.241 1.729 1.00 20.00 C
ATOM 26 CG MET A 4 -0.207 1.416 0.754 1.00 20.00 C
ATOM 27 SD MET A 4 0.132 -0.349 0.876 1.00 20.00 S
ATOM 28 CE MET A 4 1.822 -0.411 0.285 1.00 20.00 C
ATOM 29 N GLN A 5 2.154 1.821 4.871 1.00 10.38 N
ATOM 30 CA GLN A 5 3.270 2.361 5.640 1.00 11.39 C
ATOM 31 C GLN A 5 4.594 1.768 5.172 1.00 11.52 C
ATOM 32 O GLN A 5 4.768 0.546 5.054 1.00 12.05 O
ATOM 33 CB GLN A 5 3.056 2.183 7.147 1.00 11.96 C
ATOM 34 CG GLN A 5 1.829 2.950 7.647 1.00 10.81 C
ATOM 35 CD GLN A 5 1.344 2.414 8.954 1.00 13.10 C
ATOM 36 OE1 GLN A 5 0.774 1.325 9.002 1.00 10.65 O
ATOM 37 NE2 GLN A 5 1.549 3.187 10.039 1.00 12.30 N
ATOM 38 N ASN A 6 5.514 2.664 4.856 1.00 11.99 N
ATOM 39 CA ASN A 6 6.831 2.310 4.318 1.00 12.30 C
ATOM 40 C ASN A 6 7.854 2.761 5.324 1.00 13.40 C
ATOM 41 O ASN A 6 8.219 3.943 5.374 1.00 13.92 O
ATOM 42 CB ASN A 6 7.065 3.016 2.993 1.00 12.13 C
ATOM 43 CG ASN A 6 5.961 2.735 2.003 1.00 12.77 C
ATOM 44 OD1 ASN A 6 5.798 1.604 1.551 1.00 14.27 O
ATOM 45 ND2 ASN A 6 5.195 3.747 1.679 1.00 10.07 N
ATOM 46 N TYR A 7 8.292 1.817 6.147 1.00 14.70 N
ATOM 47 CA TYR A 7 9.159 2.144 7.299 1.00 15.18 C
ATOM 48 C TYR A 7 10.603 2.331 6.885 1.00 15.91 C
ATOM 49 O TYR A 7 11.041 1.811 5.855 1.00 15.76 O
ATOM 50 CB TYR A 7 9.061 1.065 8.369 1.00 15.35 C
ATOM 51 CG TYR A 7 7.665 0.929 8.902 1.00 14.45 C
ATOM 52 CD1 TYR A 7 6.771 0.021 8.327 1.00 15.68 C
ATOM 53 CD2 TYR A 7 7.210 1.756 9.920 1.00 14.80 C
ATOM 54 CE1 TYR A 7 5.480 -0.094 8.796 1.00 13.46 C
ATOM 55 CE2 TYR A 7 5.904 1.649 10.416 1.00 14.33 C
ATOM 56 CZ TYR A 7 5.047 0.729 9.831 1.00 15.09 C
ATOM 57 OH TYR A 7 3.766 0.589 10.291 1.00 14.39 O
ATOM 58 OXT TYR A 7 11.358 2.999 7.612 1.00 17.49 O
TER 59 TYR A 7
HETATM 1 CA CA A 8 10.431 1.858 3.216 1.00 30.00 CA
HETATM 60 O HOH A 9 -6.471 5.227 7.124 1.00 22.62 O
HETATM 62 O HOH A 10 -11.286 1.756 -1.468 1.00 17.08 O
HETATM 63 O HOH A 11 11.808 4.179 9.970 1.00 23.99 O
HETATM 64 O HOH A 12 13.605 1.327 9.198 1.00 26.17 O
HETATM 65 O HOH A 13 -2.749 3.429 10.024 1.00 39.15 O
HETATM 66 O HOH A 14 -1.500 0.682 10.967 1.00 43.49 O
END
"""
pdb_file = "tst_xtriage_in.pdb"
open(pdb_file, "w").write(pdb_raw)
fmodel_args = [
pdb_file,
"high_resolution=1.5",
"k_sol=0.35",
"b_sol=20",
"wavelength=1.54",
"add_random_error_to_amplitudes_percent=3",
"random_seed=12345",
"output.type=real",
"output.label=F",
"output.file_name=tst_xtriage_fmodel.mtz",
]
# read it instead so python3 will be the same
# fmodel.run(args=fmodel_args, log=null_out())
hkl_file = libtbx.env.find_in_repositories(
relative_path="mmtbx/regression/mtz/tst_xtriage_fmodel.mtz",
test=os.path.isfile)
mtz_in = file_reader.any_file(hkl_file).assert_file_type("hkl")
f_obs = mtz_in.file_server.miller_arrays[0].remove_cone(0.1)
data = f_obs.data()
# add some outliers
#data[17] = 20
#data[334] = 26
#data[1908] = 13
# and sigmas
sigf = flex.double(f_obs.size(), 0.1) + (f_obs.data() * 0.03)
f_obs = f_obs.customized_copy(sigmas=sigf)
mtz_file = "tst_xtriage_in.mtz"
f_obs.as_mtz_dataset(column_root_label="F").mtz_object().write(mtz_file)
seq_file = "tst_xtriage_in.fa"
open(seq_file, "w").write("> tst_xtriage\nGNNMQNY")
# check with completeness_as_non_anomalous=True
xtriage_args = [
mtz_file,
pdb_file,
seq_file,
"log=tst_xtriage_1.log",
"l_test_dhkl=2,2,2",
"completeness_as_non_anomalous=True",
]
result = xtriage.run(args=xtriage_args, out=null_out())
test_pickle_consistency_and_size(result)
assert (result.matthews.n_copies == 1)
assert (str(result.matthews.table) == """\
Solvent content analysis
Copies Solvent content Matthews coeff. P(solvent content)
1 0.472 2.33 1.000
""")
data_strength = result.data_strength_and_completeness
assert approx_equal(data_strength.data_strength.resolution_cut,
1.5351,
eps=0.001)
out1 = data_strength.low_resolution_completeness.format()
assert (out1 == """\
---------------------------------------------------------
| Resolution range | N(obs)/N(possible) | Completeness |
---------------------------------------------------------
| 21.9858 - 10.4368 | [6/7] | 0.857 |
| 10.4368 - 8.4369 | [3/3] | 1.000 |
| 8.4369 - 7.4172 | [3/4] | 0.750 |
| 7.4172 - 6.7606 | [4/4] | 1.000 |
| 6.7606 - 6.2882 | [5/5] | 1.000 |
| 6.2882 - 5.9252 | [3/4] | 0.750 |
| 5.9252 - 5.6337 | [7/7] | 1.000 |
| 5.6337 - 5.3922 | [5/5] | 1.000 |
| 5.3922 - 5.1874 | [4/4] | 1.000 |
| 5.1874 - 5.0106 | [4/4] | 1.000 |
---------------------------------------------------------"""), out1
# ANOMALOUS SIGNAL
a_meas = result.anomalous_info.measurability
#assert approx_equal(a_meas.high_d_cut, 4.7636, eps=0.0001) # Why it's None?
assert approx_equal(a_meas.low_d_cut, 2.3566, eps=0.0001)
# ABSOLUTE SCALING
ws = result.wilson_scaling
assert ("%.2f" % ws.iso_p_scale) == "0.65", ws.iso_p_scale
assert ("%.2f" % ws.iso_b_wilson) == "14.42", ws.iso_b_wilson
# FIXME these may need to be adjusted for different hardware/OS
assert approx_equal(ws.aniso_p_scale, 0.64723, eps=0.001)
assert approx_equal(
ws.aniso_u_star,
[0.00034229, 0.00475982, 0.000285989, -0.0, 8.95386085999e-05, 0.0])
assert approx_equal(ws.aniso_b_cart,
(13.218423, 16.840142, 12.948426, 1.0354e-15,
-0.0685311, -7.92862e-16), 0.3)
# convenience methods for GUI
assert approx_equal(result.aniso_b_min, 12.895580)
assert approx_equal(result.aniso_range_of_b, 3.804215)
#
assert approx_equal(
ws.outlier_shell_table.data[0], # d_spacing
[9.865131, 8.369653, 4.648634])
assert approx_equal(
ws.outlier_shell_table.data[1], # z_score
[5.306713, 18.068284, 5.319230])
assert (len(ws.outliers.acentric_outliers_table.data[0]) == 2)
assert (ws.outliers.acentric_outliers_table.data[1] == [(0, -1, -1),
(0, 1, 1)])
assert approx_equal(ws.outliers.acentric_outliers_table.data[2],
[3.507247, 3.315550])
assert (ws.outliers.centric_outliers_table.data is None)
assert (len(ws.ice_rings.table._rows) == 10)
assert (ws.ice_rings.table._rows[0] ==
[' 3.897', ' 1.000', ' 0.76', ' 1.00']), \
ws.ice_rings.table._rows[0]
tw = result.twin_results
wm = tw.wilson_moments
out = StringIO()
wm.show(out)
assert not show_diff(
out.getvalue(), """
----------Wilson ratio and moments----------
Acentric reflections:
<I^2>/<I>^2 :2.063 (untwinned: 2.000; perfect twin 1.500)
<F>^2/<F^2> :0.778 (untwinned: 0.785; perfect twin 0.885)
<|E^2 - 1|> :0.745 (untwinned: 0.736; perfect twin 0.541)
Centric reflections:
<I^2>/<I>^2 :3.076 (untwinned: 3.000; perfect twin 2.000)
<F>^2/<F^2> :0.628 (untwinned: 0.637; perfect twin 0.785)
<|E^2 - 1|> :0.999 (untwinned: 0.968; perfect twin 0.736)
""")
# XXX PDB validation server
assert approx_equal(result.iso_b_wilson, 14.51, eps=0.1)
assert approx_equal(result.aniso_b_ratio, 0.271, eps=0.1)
assert (result.number_of_wilson_outliers == 2)
assert approx_equal(result.l_test_mean_l, 0.481, eps=0.1)
assert approx_equal(result.l_test_mean_l_squared, 0.322, eps=0.1)
assert approx_equal(result.i_over_sigma_outer_shell, 10.71, eps=0.01)
assert ("indicating pseudo-translationa" in result.patterson_verdict)
# check relative Wilson
# FIXME
#result.relative_wilson.show()
#assert (result.relative_wilson.n_outliers() == 0)
#show_pickled_object_sizes(result)
#
# check with completeness_as_non_anomalous=False
xtriage_args = [
mtz_file,
pdb_file,
seq_file,
"log=tst_xtriage_1.log",
"l_test_dhkl=2,2,2",
"completeness_as_non_anomalous=False",
]
result = xtriage.run(args=xtriage_args, out=null_out())
test_pickle_consistency_and_size(result)
assert (result.matthews.n_copies == 1)
assert (str(result.matthews.table) == """\
Solvent content analysis
Copies Solvent content Matthews coeff. P(solvent content)
1 0.472 2.33 1.000
""")
data_strength = result.data_strength_and_completeness
assert approx_equal(data_strength.data_strength.resolution_cut,
1.5351,
eps=0.001)
out1 = data_strength.low_resolution_completeness.format()
assert (out1 == """\
---------------------------------------------------------
| Resolution range | N(obs)/N(possible) | Completeness |
---------------------------------------------------------
| 21.9858 - 10.4368 | [ 6/7 ] | 0.857 |
| 10.4368 - 8.4369 | [ 3/3 ] | 1.000 |
| 8.4369 - 7.4172 | [ 3/4 ] | 0.750 |
| 7.4172 - 6.7606 | [ 4/4 ] | 1.000 |
| 6.7606 - 6.2882 | [ 8/8 ] | 1.000 |
| 6.2882 - 5.9252 | [ 4/5 ] | 0.800 |
| 5.9252 - 5.6337 | [11/11] | 1.000 |
| 5.6337 - 5.3922 | [ 7/7 ] | 1.000 |
| 5.3922 - 5.1874 | [ 6/6 ] | 1.000 |
| 5.1874 - 5.0106 | [ 7/7 ] | 1.000 |
---------------------------------------------------------"""), out1
# ANOMALOUS SIGNAL
a_meas = result.anomalous_info.measurability
#assert approx_equal(a_meas.high_d_cut, 4.7636, eps=0.0001) # Why?
assert approx_equal(a_meas.low_d_cut, 2.3565, eps=0.0001)
# ABSOLUTE SCALING
ws = result.wilson_scaling
assert ("%.2f" % ws.iso_p_scale) == "0.65", ws.iso_p_scale
assert ("%.2f" % ws.iso_b_wilson) == "14.42", ws.iso_b_wilson
# FIXME these may need to be adjusted for different hardware/OS
assert approx_equal(ws.aniso_p_scale, 0.64723, eps=0.001)
assert approx_equal(
ws.aniso_u_star,
[0.00034473, 0.00479983, 0.000287162, -0.0, 9.00962e-05, 0.0], 6.e-5)
assert approx_equal(ws.aniso_b_cart, [13.12, 16.69, 12.89, 0, -0.08, 0],
0.01)
# convenience methods for GUI
assert approx_equal(result.aniso_b_min, 12.9, 0.1)
assert approx_equal(result.aniso_range_of_b, 3.8, 0.1)
#
assert approx_equal(
ws.outlier_shell_table.data[0], # d_spacing
[9.86, 8.36, 4.64],
0.02)
assert approx_equal(
ws.outlier_shell_table.data[1], # z_score
[5.30, 18.06, 5.31],
0.01)
assert (len(ws.outliers.acentric_outliers_table.data[0]) == 2)
assert (ws.outliers.acentric_outliers_table.data[1] == [(0, -1, -1),
(0, 1, 1)])
assert approx_equal(ws.outliers.acentric_outliers_table.data[2],
[3.5, 3.3], 0.1)
assert (ws.outliers.centric_outliers_table.data is None)
assert (len(ws.ice_rings.table._rows) == 10)
assert (ws.ice_rings.table._rows[0] ==
[' 3.897', ' 1.000', ' 0.76', ' 1.00']), \
ws.ice_rings.table._rows[0]
tw = result.twin_results
wm = tw.wilson_moments
out = StringIO()
wm.show(out)
assert not show_diff(
out.getvalue(), """
----------Wilson ratio and moments----------
Acentric reflections:
<I^2>/<I>^2 :2.063 (untwinned: 2.000; perfect twin 1.500)
<F>^2/<F^2> :0.778 (untwinned: 0.785; perfect twin 0.885)
<|E^2 - 1|> :0.745 (untwinned: 0.736; perfect twin 0.541)
Centric reflections:
<I^2>/<I>^2 :3.076 (untwinned: 3.000; perfect twin 2.000)
<F>^2/<F^2> :0.628 (untwinned: 0.637; perfect twin 0.785)
<|E^2 - 1|> :0.999 (untwinned: 0.968; perfect twin 0.736)
""")
# XXX PDB validation server
assert approx_equal(result.iso_b_wilson, 14.51, eps=0.1)
assert approx_equal(result.aniso_b_ratio, 0.271, eps=0.1)
assert (result.number_of_wilson_outliers == 2)
assert approx_equal(result.l_test_mean_l, 0.481, eps=0.1)
assert approx_equal(result.l_test_mean_l_squared, 0.322, eps=0.1)
assert approx_equal(result.i_over_sigma_outer_shell, 10.71, eps=0.01)
assert ("indicating pseudo-translationa" in result.patterson_verdict)
# check relative Wilson
# FIXME
#result.relative_wilson.show()
#assert (result.relative_wilson.n_outliers() == 0)
#show_pickled_object_sizes(result)
#
# test without sigmas
f_obs_2 = f_obs.customized_copy(sigmas=None)
mtz_file = "tst_xtriage_in_2.mtz"
f_obs_2.as_mtz_dataset(column_root_label="F").mtz_object().write(mtz_file)
xtriage_args = [
mtz_file,
pdb_file,
seq_file,
"log=tst_xtriage_1.log",
]
result = xtriage.run(args=xtriage_args, out=null_out())
result.summarize_issues()
# test in lower symmetry
f_obs_3 = f_obs.expand_to_p1()
mtz_file = "tst_xtriage_in_3.mtz"
f_obs_3.as_mtz_dataset(column_root_label="F").mtz_object().write(mtz_file)
xtriage_args = [
mtz_file,
seq_file,
"log=tst_xtriage_2.log",
]
result = xtriage.run(args=xtriage_args, out=null_out())
assert ((
1,
'One or more symmetry operators suggest that the data has a higher crystallographic symmetry (P 2 1 1).',
'Point group and R-factor analysis')
in result.summarize_issues()._issues)
# test with elliptical truncation
f_obs_3 = f_obs.customized_copy(
crystal_symmetry=crystal.symmetry((23, 5, 20, 90, 107.8, 90), "P 21"))
f_obs_3 = f_obs_3.resolution_filter(d_min=1.5)
f_obs_3 = f_obs_3.customized_copy(
crystal_symmetry=f_obs.crystal_symmetry())
reso = ds.analyze_resolution_limits(f_obs_3)
out = StringIO()
reso.show(out=out)
assert ("max. difference between axes = 0.652" in out.getvalue()), \
out.getvalue()
assert ("elliptically truncated" in out.getvalue())
# make sure the elliptical truncation detection still works in higher space
# groups - we only need a miller.set for this
miller_set = miller.build_set(crystal_symmetry=crystal.symmetry(
(20, 20, 20, 90, 90, 90), "P422"),
d_min=1.5,
anomalous_flag=False)
reso = ds.analyze_resolution_limits(miller_set)
out = StringIO()
reso.show(out=out)
assert ("Resolution limits are within expected tolerances"
in out.getvalue())
# log binning
out = StringIO()
log_binned = ds.log_binned_completeness(f_obs_3)
log_binned.show(out=out)
assert ("""| 1.9724 - 1.5094 | 368/1230 | 29.9% |"""
in out.getvalue()), out.getvalue()
# test with no acentrics
cf = f_obs.centric_flags().data()
centrics = f_obs.select(cf)
acentrics = f_obs.select(~cf)
mtz_file = "tst_xtriage_in_3.mtz"
centrics.as_mtz_dataset(column_root_label="F").mtz_object().write(mtz_file)
args = [
mtz_file,
pdb_file,
seq_file,
"log=tst_xtriage_3.log",
]
try:
xtriage.run(args=args, out=null_out())
except Sorry:
pass
else:
raise Exception_expected
# with only a handful of acentrics
sel = flex.bool(acentrics.size(), False)
for i in range(10):
sel[i] = True
f_obs_4 = centrics.concatenate(acentrics.select(sel))
f_obs_4.as_mtz_dataset(column_root_label="F").mtz_object().write(mtz_file)
try:
xtriage.run(args=args, out=null_out())
except Sorry:
pass
else:
raise Exception_expected
def rama_z_score(self): return rama_z.rama_z(models = [self.model], log = null_out()).get_result()
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_inp = iotbx.pdb.input(lines=pdb_str.split("\n"), source_info=None)
model = mmtbx.model.manager(model_input=pdb_inp, log=null_out())
restraints_manager = model.get_restraints_manager()
angle_proxies = restraints_manager.geometry.get_all_angle_proxies()
connectivity_manager = connectivity.determine_connectivity(
pdb_hierarchy=model.get_hierarchy(),
geometry_restraints=restraints_manager.geometry)
h_connectivity = connectivity_manager.h_connectivity
# get bonds stored in connectivity
bond_list = {}
angle_list = {}
for neighbors in h_connectivity:
if (neighbors is None): continue
ih = neighbors.ih
a0 = neighbors.a0
i_a0 = a0['iseq']
a1 = neighbors.a1
i_a1 = a1['iseq']
bond_list[ih] = [i_a0, a0['dist_ideal']]
selected_atoms = tuple(sorted([ih, i_a0, i_a1]))
angle_list[selected_atoms] = a1['angle_ideal']
if neighbors.a2:
a2 = neighbors.a2
selected_atoms2 = tuple(sorted([ih, i_a0, a2['iseq']]))
angle_list[selected_atoms2] = a2['angle_ideal']
if neighbors.a3:
a3 = neighbors.a3
selected_atoms3 = tuple(sorted([ih, i_a0, a3['iseq']]))
angle_list[selected_atoms3] = a3['angle_ideal']
if neighbors.h1:
h1 = neighbors.h1
selected_atoms4 = tuple(sorted([ih, i_a0, h1['iseq']]))
angle_list[selected_atoms4] = h1['angle_ideal']
if neighbors.b1:
i_b1 = neighbors.b1['iseq']
third_nb_dict = {ih: i_b1}
bond_ctrl = {}
for i in model.xh_connectivity_table():
bond_ctrl[i[1]] = [i[0], i[3]]
# List of angle restraints
angles = [(4, 1, 12), (0, 1, 12), (2, 1, 12), (13, 4, 14), (5, 4, 14),
(5, 4, 13), (1, 4, 13), (1, 4, 14), (8, 6, 15), (5, 6, 15),
(9, 7, 16), (5, 7, 16), (10, 8, 17), (6, 8, 17), (10, 11, 19),
(7, 9, 18), (10, 9, 18)]
angle_ctrl = {}
for ap in angle_proxies:
if (ap.i_seqs in angles):
angle_ctrl[tuple(sorted(list(ap.i_seqs)))] = ap.angle_ideal
# HH needs also third neighbors:
third_nb_ctrl = {19: 8}
assert (
bond_list == bond_ctrl), '1-2 neighbors and distance_ideal are wrong'
assert (
angle_list == angle_ctrl), '1-3 neighbors and angle_ideal are wrong'
assert (third_nb_dict == third_nb_ctrl), '1-4 neighbors are wrong'
def real_space_refine(
pdb_hierarchy,
fmodel,
cif_objects,
params,
out,
nproc=None,
max_cycles=100, # arbitrarily large
remediate=False):
from scitbx.array_family import flex
i_cycle = 0
while (i_cycle < max_cycles):
print(" Cycle %d:" % (i_cycle + 1), file=out)
# this keeps track of which residues were split in the previous cycle -
# we only refine segments that have had residues added
rebuilt_flags = pdb_hierarchy.atoms().extract_tmp_as_size_t()
processed_pdb_file = building.reprocess_pdb(
pdb_hierarchy=pdb_hierarchy,
cif_objects=cif_objects,
crystal_symmetry=fmodel.xray_structure,
out=null_out())
# get the 2mFo-DFc map without new alternates!
#two_fofc_map = fmodel.two_fofc_map(exclude_free_r_reflections=True)
pdb_hierarchy = processed_pdb_file.all_chain_proxies.pdb_hierarchy
pdb_atoms = pdb_hierarchy.atoms()
xray_structure = processed_pdb_file.xray_structure()
geometry_restraints_manager = \
processed_pdb_file.geometry_restraints_manager(show_energies=False)
fmodel.update_xray_structure(xray_structure)
sele_cache = pdb_hierarchy.atom_selection_cache()
# FIXME very inefficient when looping!
# this will include both the newly built residues and the original atoms,
# including residues split to allow for backbone flexibility.
sele_split = sele_cache.selection(alt_confs.SELECTION_MODIFIED)
sele_main_conf = sele_cache.selection(alt_confs.SELECTION_OLD)
assert (len(sele_split) > 0)
k = 0
fragments = []
while (k < len(sele_split)):
if (sele_split[k]):
current_fragment = flex.size_t()
while (sele_split[k]):
current_fragment.append(k)
k += 1
atom_start = pdb_atoms[current_fragment[0]].fetch_labels()
atom_end = pdb_atoms[current_fragment[-1]].fetch_labels()
frag_selection = flex.bool(sele_split.size(), current_fragment)
if (i_cycle > 0):
flags = rebuilt_flags.select(frag_selection)
if flags.all_eq(0):
continue
fragments.append(current_fragment)
else:
k += 1
if (len(fragments) == 0):
pass
refine_fragments = rsr_fragments_parallel(
pdb_hierarchy=pdb_hierarchy,
fmodel=fmodel,
processed_pdb_file=processed_pdb_file,
sele_main_conf=sele_main_conf,
rsr_fofc_map_target=(i_cycle == 0
and params.cleanup.rsr_fofc_map_target))
refined = easy_mp.pool_map(fixed_func=refine_fragments,
iterable=fragments,
processes=nproc)
sites_refined = pdb_atoms.extract_xyz()
for result in refined:
assert (result is not None)
result.show(out=out, prefix=" ")
sites_refined.set_selected(result.selection, result.sites_cart)
pdb_atoms.set_xyz(sites_refined)
xray_structure.set_sites_cart(sites_refined)
fmodel.update_xray_structure(xray_structure)
if (not remediate) or (max_cycles == 1):
break
else:
for atom in pdb_hierarchy.atoms():
if (atom.segid == alt_confs.SEGID_NEW_SPLIT):
atom.segid = alt_confs.SEGID_NEW_REBUILT
print(" checking for conformational strain...", file=out)
n_split = alt_confs.spread_alternates(
pdb_hierarchy=pdb_hierarchy,
new_occupancy=params.residue_fitting.expected_occupancy,
split_all_adjacent=False,
selection=alt_confs.SELECTION_NEW_REBUILT)
if (n_split > 0):
print(" split another %d residue(s) - will re-run RSR" % \
n_split, file=out)
else:
break
i_cycle += 1
xray_structure = pdb_hierarchy.extract_xray_structure(
crystal_symmetry=fmodel.xray_structure)
fmodel.update_xray_structure(xray_structure,
update_f_mask=True,
update_f_calc=True)
#fmodel.info().show_targets(out=out, text="After real-space refinement")
t2 = time.time()
return pdb_hierarchy
def write_map_file(self):
# output MTZ file with map coefficients
class map_coeffs_mtz_label_manager:
def __init__(self, amplitudes, phases):
self._amplitudes = amplitudes
self._phases = phases
def amplitudes(self):
return self._amplitudes
def phases(self, root_label, anomalous_sign=None):
assert anomalous_sign is None or not anomalous_sign
return self._phases
mtz_history_buffer = flex.std_string()
lbl_mgr = map_coeffs_mtz_label_manager(amplitudes = "FoFo", phases = "PHFc")
mtz_dataset = self.map_coeff.as_mtz_dataset(
column_root_label=lbl_mgr.amplitudes(),
label_decorator=lbl_mgr)
mtz_history_buffer.append("> column label %s = phenix %s" % (
lbl_mgr.amplitudes(), "FoFoPHFc"))
if self.output_file is not None :
file_name = self.output_file
else :
file_name = "FoFoPHFc.mtz"
mtz_history_buffer.append("file name %s"%file_name)
mtz_object = mtz_dataset.mtz_object()
mtz_object.add_history(mtz_history_buffer)
mtz_object.write(file_name=file_name)
self.file_names = [ file_name ]
if (self.peak_search):
from mmtbx.command_line import find_peaks_holes
from mmtbx import find_peaks
peak_search_log = self.log
if (self.silent) : peak_search_log = null_out()
fmodel = self.fmodel
peaks = find_peaks.manager(
fmodel=fmodel,
map_type=None,
map_coeffs=self.map_coeff,
map_cutoff=self.map_cutoff,
params=self.peak_search_params,
log=peak_search_log).peaks_mapped()
peaks.sites = fmodel.xray_structure.unit_cell().orthogonalize(peaks.sites)
holes = find_peaks.manager(
fmodel=fmodel,
map_type=None,
map_coeffs=self.map_coeff,
map_cutoff=-self.map_cutoff,
params=self.peak_search_params,
log=peak_search_log).peaks_mapped()
holes.sites = fmodel.xray_structure.unit_cell().orthogonalize(holes.sites)
result = find_peaks_holes.peaks_holes_container(
peaks=peaks,
holes=holes,
map_cutoff=self.map_cutoff)
pdb_out = os.path.splitext(file_name)[0] + "_peaks.pdb"
result.save_pdb_file(
file_name=pdb_out,
include_anom=False,
include_water=False,
log=peak_search_log)
self.file_names.append(pdb_out)
return self.file_names
def _try_as_ccp4_map(self): from iotbx.map_manager import map_manager from libtbx.utils import null_out map_object=map_manager(file_name=str(self.file_name),log=null_out()) self._file_type = "ccp4_map" self._file_object = map_object
def exercise_space_group_handling():
flex.set_random_seed(123456)
random.seed(123456)
base = "tst_cc_star_space_group"
pdb_in = iotbx.pdb.hierarchy.input(pdb_string=model_1yjp)
xrs = pdb_in.xray_structure_simple()
xrs.set_inelastic_form_factors(photon=1.54, table="sasaki")
fc = abs(xrs.structure_factors(d_min=1.5).f_calc()).average_bijvoet_mates()
fc.set_observation_type_xray_amplitude()
flags = fc.generate_r_free_flags()
mtz = fc.as_mtz_dataset(column_root_label="F")
mtz.add_miller_array(flags, column_root_label="FreeR_flag")
mtz.mtz_object().write(base + ".mtz")
xrs_p1 = xrs.expand_to_p1()
xrs_p1.shake_sites_in_place(rms_difference=0.1)
fc_p1 = xrs_p1.structure_factors(d_min=1.4).f_calc()
fc_p1_extra = fc_p1.randomize_amplitude_and_phase(amplitude_error=1.0,
phase_error_deg=0,
random_seed=123456)
fc_p1 = abs(
fc_p1.concatenate(other=fc_p1_extra)).sort(by_value="packed_indices")
fc_p1.set_observation_type_xray_amplitude()
sg_p2 = sgtbx.space_group_info("P2")
ic = fc_p1.f_as_f_sq().customized_copy(space_group_info=sg_p2,
sigmas=flex.double(
fc_p1.size(), 10.0))
ic.export_as_scalepack_unmerged(file_name=base + ".sca")
open(base + ".pdb", "w").write(model_1yjp)
args = [
base + ".mtz",
base + ".pdb",
"unmerged_data=%s.sca" % base,
]
cc_star.run(args=args, out=null_out())
# now with .sca in P1 (raises Sorry)
ic2 = fc_p1.f_as_f_sq().customized_copy(
sigmas=flex.double(fc_p1.size(), 10.0))
ic2.export_as_scalepack_unmerged(file_name=base + "_p1.sca")
args = [
base + ".mtz",
base + ".pdb",
"unmerged_data=%s_p1.sca" % base,
]
try:
cc_star.run(args=args, out=null_out())
except Sorry as s:
assert (
str(s) ==
"Incompatible space groups in merged and unmerged data:P 1 21 1 versus P 1"
), s
else:
raise Exception_expected
# now with CIF (complete symmetry)
f = open(base + ".cif", "w")
ic.as_cif_simple(array_type="meas", out=f)
f.close()
args = [
base + ".mtz",
base + ".pdb",
"unmerged_data=%s.cif" % base,
]
cc_star.run(args=args, out=null_out())
# bad unit cell
uc2 = uctbx.unit_cell((23, 6.5, 23.5, 90, 108, 90))
ic3 = ic.customized_copy(unit_cell=uc2)
f = open(base + "_new_uc.cif", "w")
ic3.as_cif_simple(array_type="meas", out=f)
f.close()
args = [
base + ".mtz",
base + ".pdb",
"unmerged_data=%s_new_uc.cif" % base,
]
try:
cc_star.run(args=args, out=null_out())
except Sorry as s:
assert ("Incompatible symmetry definitions:" in str(s)), s
else:
raise Exception_expected
def __init__(self,
map_manager = None,
resolution = None,
molecular_mass = None,
sequence = None,
solvent_content = None):
'''
Create a mask (map object) with values of 1 near molecule
Parameters are:
map_manager: source of information about density
resolution : optional resolution of map
molecular_mass: optional mass (Da) of object in density
sequence: optional sequence of object in density
solvent_content : optional solvent_content of map
'''
assert (map_manager is not None)
if not resolution:
from cctbx.maptbx import d_min_from_map
resolution = d_min_from_map(
map_data=map_manager.map_data(),
unit_cell=map_manager.crystal_symmetry().unit_cell())
self._crystal_symmetry = map_manager.crystal_symmetry()
if (molecular_mass or sequence ) and (
not solvent_content):
# Try to get a good starting value of solvent_content
from cctbx.maptbx.segment_and_split_map import get_solvent_fraction
solvent_content = get_solvent_fraction(
params = None,
molecular_mass = molecular_mass,
sequence = sequence,
do_not_adjust_dalton_scale = True,
crystal_symmetry = self._crystal_symmetry,
out = null_out())
# Now use automatic procedure to get a mask
from cctbx.maptbx.segment_and_split_map import \
get_iterated_solvent_fraction
self._mask, self._solvent_content = get_iterated_solvent_fraction(
crystal_symmetry = self._crystal_symmetry,
fraction_of_max_mask_threshold = 0.05, #
solvent_content = solvent_content,
cell_cutoff_for_solvent_from_mask = 1, # Use low-res method always
use_solvent_content_for_threshold = True,
mask_resolution = resolution,
return_mask_and_solvent_fraction = True,
map = map_manager.map_data(),
verbose = False,
out = null_out())
if self._solvent_content is None:
raise Sorry("Unable to get solvent content in auto-masking")
# Set up map_manager with this mask
self._map_manager = map_manager.customized_copy(map_data = self._mask)
self._map_manager.set_is_mask(True)
# Initialize soft mask
self._is_soft_mask = False
self._is_soft_mask_around_edges = False
def exercise_2():
hkl_file = libtbx.env.find_in_repositories(
relative_path="phenix_regression/wizards/data/p9_se_w2.sca",
test=os.path.isfile)
if (hkl_file is None):
warnings.warn("phenix_regression not available, skipping test")
return
hkl_in = file_reader.any_file(hkl_file).assert_file_type("hkl")
i_obs_raw = hkl_in.file_object.as_miller_arrays(
merge_equivalents=False,
crystal_symmetry=crystal.symmetry(space_group_symbol="I4",
unit_cell=(113.949, 113.949, 32.474,
90, 90, 90)))[0]
i_obs = i_obs_raw.merge_equivalents().array()
# completeness and data strength
cstats = ds.i_sigi_completeness_stats(i_obs)
d_min_cut = cstats.resolution_cut
assert approx_equal(d_min_cut, 2.150815)
ws = ds.wilson_scaling(miller_array=i_obs, n_residues=120)
# outliers - this shouldn't actually work, since it requires additional
# processing steps on the input data
try:
outliers = ds.possible_outliers(i_obs)
except AssertionError:
pass
else:
raise Exception_expected
######################################################################
# OVERALL ANALYSIS
pdb_file = libtbx.env.find_in_repositories(
relative_path="phenix_examples/p9-build/p9.pdb", test=os.path.isfile)
f_calc = None
if (pdb_file is not None):
pdb_in = file_reader.any_file(pdb_file).assert_file_type("pdb")
hierarchy = pdb_in.file_object.hierarchy
xrs = pdb_in.file_object.xray_structure_simple(crystal_symmetry=i_obs)
f_calc = xrs.structure_factors(d_min=i_obs.d_min()).f_calc()
f_calc = abs(f_calc).generate_bijvoet_mates()
f_calc = f_calc.set_observation_type_xray_amplitude()
i_obs, f_calc = i_obs.common_sets(other=f_calc)
open("tmp_xtriage.pdb",
"w").write(hierarchy.as_pdb_string(crystal_symmetry=i_obs))
pdb_file = "tmp_xtriage.pdb"
params = xtriage.master_params.extract()
params.scaling.input.asu_contents.n_residues = 141
result = xtriage.xtriage_analyses(miller_obs=i_obs,
miller_calc=f_calc,
params=params,
unmerged_obs=i_obs_raw,
text_out=open("logfile3.log",
"w")) #sys.stdout)
# XXX there appears to be some system-dependence here, hence sloppy limits
assert (15.5 < result.aniso_b_min < 15.9)
assert (10 < result.aniso_range_of_b < 11)
# check relative Wilson
if (pdb_file is not None):
assert (result.relative_wilson is not None)
# FIXME
#assert (result.relative_wilson.n_outliers() == 34)
#show_pickled_object_sizes(result)
test_pickle_consistency_and_size(result)
# XXX PDB validation server
assert approx_equal(result.iso_b_wilson, 18.33, eps=0.1)
assert approx_equal(result.aniso_b_ratio, 0.546, eps=0.1)
assert (result.number_of_wilson_outliers == 0)
assert approx_equal(result.l_test_mean_l, 0.493, eps=0.1)
assert approx_equal(result.l_test_mean_l_squared, 0.326, eps=0.1)
assert approx_equal(result.i_over_sigma_outer_shell, 3.25, eps=0.1)
assert approx_equal(result.overall_i_sig_i, 10.34, eps=0.1)
assert approx_equal(
result.anomalous_info.plan_sad_experiment_stats.get_overall(
item="i_over_sigma_dict"),
10.61,
eps=0.1)
assert approx_equal(
result.anomalous_info.plan_sad_experiment_stats.get_overall(
item="anom_signal_dict"),
15.35,
eps=0.1)
assert ("No significant pseudotranslation is detected"
in result.patterson_verdict)
# test consistency of output after pickling and unpickling
try:
from phenix_dev.phenix_cloud import xtriage_json
except ImportError:
pass
else:
json_out = xtriage_json.json_output("p9.sca")
result.show(out=json_out)
open("xtriage.json", "w").write(json_out.export())
# unmerged data
assert result.merging_stats is not None
out = StringIO()
result.merging_stats.show(out=out)
assert ("R-merge: 0.073" in out.getvalue())
assert approx_equal(result.estimate_d_min(min_i_over_sigma=10),
1.9645,
eps=0.001)
# FIXME PDB doesn't actually have unit cell!
# test detection of symmetry in reference file
if (pdb_file is not None):
args = [hkl_file, pdb_file]
result = xtriage.run(args=args, out=null_out())
def find_alternate_residue(residue,
pdb_hierarchy,
fmodel,
restraints_manager,
params,
verbose=False,
debug=None,
log=None):
if (log is None):
log = null_out()
t1 = time.time()
from scitbx.array_family import flex
selection = flex.size_t()
window = building.get_window_around_residue(residue,
window_size=params.window_size)
for pdb_object in window:
selection.extend(pdb_object.atoms().extract_i_seq())
assert (len(selection) > 0) and (not selection.all_eq(0))
occupancies = []
if (params.expected_occupancy is not None):
assert (0.0 <= params.expected_occupancy <= 1.0)
occupancies = [params.expected_occupancy]
else:
occupancies = [0.2, 0.3, 0.4, 0.5]
trials = []
sites_start_1d = pdb_hierarchy.atoms().extract_xyz().as_double()
from mmtbx.rotamer import rotamer_eval
rotamer_manager = rotamer_eval.RotamerEval(data_version="8000")
id_str = residue.id_str()
delete_selection = None
if (params.omit_waters):
delete_selection = building.get_nearby_water_selection(
pdb_hierarchy=pdb_hierarchy,
xray_structure=fmodel.xray_structure,
selection=selection)
for occupancy in occupancies:
prefix = "%s_%.2f" % (id_str.replace(" ", "_"), occupancy)
map_file_name = None
if (debug > 1):
map_file_name = prefix + ".mtz"
two_fofc_map, fofc_map = alt_confs.get_partial_omit_map(
fmodel=fmodel.deep_copy(),
selection=selection,
selection_delete=delete_selection,
negate_surrounding=True,
map_file_name=map_file_name,
partial_occupancy=1.0 - occupancy)
rebuild = rebuild_residue(
target_map=fofc_map,
pdb_hierarchy=pdb_hierarchy,
xray_structure=fmodel.xray_structure,
geometry_restraints_manager=restraints_manager,
rotamer_eval=rotamer_manager,
d_min=fmodel.f_obs().d_min())
new_hierarchy = rebuild(
atom_group=residue,
window_size=params.window_size,
backbone_sample_angle=params.backbone_sample_angle,
anneal=params.anneal,
annealing_temperature=params.annealing_temperature,
use_chi1_sampling=params.simple_chi1_sampling,
log=log)
trial = residue_trial(residue=residue,
new_hierarchy=new_hierarchy,
occupancy=occupancy,
rotamer_eval=rotamer_manager,
fmodel=fmodel,
two_fofc_map=two_fofc_map,
fofc_map=fofc_map)
trials.append(trial)
if (debug > 1):
open("%s.pdb" % prefix,
"w").write(trial.new_hierarchy.as_pdb_string())
sites_end_1d = pdb_hierarchy.atoms().extract_xyz().as_double()
assert sites_start_1d.all_eq(sites_end_1d)
t2 = time.time()
if (debug > 1):
print(" %d build trials (%s): %.3fs" %
(len(occupancies), residue.id_str(), t2 - t1),
file=log)
return trials
def run(args, command_name = "phenix.fobs_minus_fobs_map", log=None):
if(len(args) == 0): args = ["--help"]
examples = """Examples:
phenix.fobs_minus_fobs_map f_obs_1_file=data1.mtz f_obs_2_file=data2.sca \
f_obs_1_label=FOBS1 f_obs_2_label=FOBS2 model.pdb
phenix.fobs_minus_fobs_map f_obs_1_file=data.mtz f_obs_2_file=data.mtz \
f_obs_1_label=FOBS1 f_obs_2_label=FOBS2 phase_source=model.pdb \
high_res=2.0 sigma_cutoff=2 scattering_table=neutron"""
command_line = (iotbx_option_parser(
usage="%s [options]" % command_name,
description=examples)
.option("--silent",
action="store_true",
help="Suppress output to the screen.")
.enable_symmetry_comprehensive()
).process(args=args)
#
if (log is None):
log = sys.stdout
if(not command_line.options.silent):
utils.print_header("phenix.fobs_minus_fobs_map", out = log)
print("Command line arguments: ", file=log)
print(args, file=log)
print(file=log)
#
processed_args = utils.process_command_line_args(
args=command_line.args,
cmd_cs=command_line.symmetry,
master_params=fo_minus_fo_master_params(),
absolute_angle_tolerance=5,
absolute_length_tolerance=1,
log=log,
suppress_symmetry_related_errors=True)
working_phil = processed_args.params
if(not command_line.options.silent):
print("*** Parameters:", file=log)
working_phil.show(out = log)
print(file=log)
params = working_phil.extract()
consensus_symmetry = None
if (params.ignore_non_isomorphous_unit_cells):
if (None in [params.f_obs_1_file_name, params.f_obs_2_file_name,
params.phase_source]):
raise Sorry("The file parameters (f_obs_1_file_name, f_obs_2_file_name, "+
"phase_source) must be specified explicitly when "+
"ignore_non_isomorphous_unit_cells=True.")
symm_manager = iotbx.symmetry.manager()
pdb_in = iotbx.file_reader.any_file(params.phase_source, force_type="pdb")
symm_manager.process_pdb_file(pdb_in)
hkl_in_1 = iotbx.file_reader.any_file(params.f_obs_1_file_name,
force_type="hkl")
sg_err_1, uc_err_1 = symm_manager.process_reflections_file(hkl_in_1)
hkl_in_2 = iotbx.file_reader.any_file(params.f_obs_2_file_name,
force_type="hkl")
sg_err_2, uc_err_2 = symm_manager.process_reflections_file(hkl_in_2)
out = StringIO()
symm_manager.show(out=out)
if (sg_err_1) or (sg_err_2):
raise Sorry(("Incompatible space groups in input files:\n%s\nAll files "+
"must have the same point group (and ideally the same space group). "+
"Please note that any symmetry information in the PDB file will be "+
"used first.") % out.getvalue())
elif (uc_err_1) or (uc_err_2):
libtbx.call_back(message="warn",
data=("Crystal symmetry mismatch:\n%s\nCalculations will continue "+
"using the symmetry in the PDB file (or if not available, the "+
"first reflection file), but the maps should be treated with "+
"extreme suspicion.") % out.getvalue())
crystal_symmetry = symm_manager.as_symmetry_object()
else :
processed_args = utils.process_command_line_args(
args=command_line.args,
cmd_cs=command_line.symmetry,
master_params=fo_minus_fo_master_params(),
suppress_symmetry_related_errors = False,
absolute_angle_tolerance=5,
absolute_length_tolerance=1,
log=StringIO())
crystal_symmetry = processed_args.crystal_symmetry
#
pdb_file_names = processed_args.pdb_file_names
if(len(processed_args.pdb_file_names) == 0):
if(params.phase_source is not None):
pdb_file_names = [params.phase_source]
else:
raise Sorry("No PDB file found.")
# Extaract Fobs1, Fobs2
f_obss = []
if(len(processed_args.reflection_files)==2):
for reflection_file in processed_args.reflection_files:
reflection_file_server = reflection_file_utils.reflection_file_server(
crystal_symmetry = crystal_symmetry,
force_symmetry = True,
reflection_files = [reflection_file],
err = null_out())
# XXX UGLY !!!
try:
parameters = extract_xtal_data.data_and_flags_master_params().extract()
if(params.f_obs_1_label is not None):
parameters.labels = [params.f_obs_1_label]
determine_data_and_flags_result = extract_xtal_data.run(
reflection_file_server = reflection_file_server,
keep_going = True,
parameters = parameters)
except: # intentional
parameters = extract_xtal_data.data_and_flags_master_params().extract()
if(params.f_obs_2_label is not None):
parameters.labels = [params.f_obs_2_label]
determine_data_and_flags_result = extract_xtal_data.run(
reflection_file_server = reflection_file_server,
keep_going = True,
parameters = parameters)
f_obss.append(determine_data_and_flags_result.f_obs)
else:
if([params.f_obs_1_file_name,params.f_obs_2_file_name].count(None)==2):
raise Sorry("No reflection data file found.")
for file_name, label in zip([params.f_obs_1_file_name,params.f_obs_2_file_name],
[params.f_obs_1_label,params.f_obs_2_label]):
reflection_file = reflection_file_reader.any_reflection_file(
file_name = file_name, ensure_read_access = False)
reflection_file_server = reflection_file_utils.reflection_file_server(
crystal_symmetry = crystal_symmetry,
force_symmetry = True,
reflection_files = [reflection_file],
err = null_out())
parameters = extract_xtal_data.data_and_flags_master_params().extract()
if(label is not None):
parameters.labels = [label]
determine_data_and_flags_result = extract_xtal_data.run(
reflection_file_server = reflection_file_server,
parameters = parameters,
keep_going = True)
f_obss.append(determine_data_and_flags_result.f_obs)
if(len(f_obss)!=2):
raise Sorry(" ".join(errors))
if(not command_line.options.silent):
for ifobs, fobs in enumerate(f_obss):
print("*** Summary for data set %d:"%ifobs, file=log)
fobs.show_comprehensive_summary(f = log)
print(file=log)
pdb_combined = combine_unique_pdb_files(file_names = pdb_file_names)
pdb_combined.report_non_unique(out = log)
if(len(pdb_combined.unique_file_names) == 0):
raise Sorry("No coordinate file given.")
#
raw_recs = flex.std_string()
for rec in pdb_combined.raw_records:
if(rec.upper().count("CRYST1")==0):
raw_recs.append(rec)
raw_recs.append(iotbx.pdb.format_cryst1_record(
crystal_symmetry = crystal_symmetry))
#
pdb_in = iotbx.pdb.input(source_info = None, lines = raw_recs)
model = mmtbx.model.manager(model_input = pdb_in)
d_min = min(f_obss[0].d_min(), f_obss[1].d_min())
model.setup_scattering_dictionaries(
scattering_table = params.scattering_table,
d_min = d_min)
xray_structure = model.get_xray_structure()
hierarchy = model.get_hierarchy()
#
omit_sel = flex.bool(hierarchy.atoms_size(), False)
if (params.advanced.omit_selection is not None):
print("Will omit selection from phasing model:", file=log)
print(" " + params.advanced.omit_selection, file=log)
omit_sel = hierarchy.atom_selection_cache().selection(
params.advanced.omit_selection)
print("%d atoms selected for removal" % omit_sel.count(True), file=log)
del hierarchy
xray_structure = xray_structure.select(~omit_sel)
if(not command_line.options.silent):
print("*** Model summary:", file=log)
xray_structure.show_summary(f = log)
print(file=log)
info0 = f_obss[0].info()
info1 = f_obss[1].info()
f_obss[0] = f_obss[0].resolution_filter(d_min = params.high_resolution,
d_max = params.low_resolution).set_info(info0)
f_obss[1] = f_obss[1].resolution_filter(d_min = params.high_resolution,
d_max = params.low_resolution).set_info(info1)
if(params.sigma_cutoff is not None):
for i in [0,1]:
if(f_obss[i].sigmas() is not None):
sel = f_obss[i].data() > f_obss[i].sigmas()*params.sigma_cutoff
f_obss[i] = f_obss[i].select(sel).set_info(info0)
for k, f_obs in enumerate(f_obss):
if (f_obs.indices().size() == 0):
raise Sorry("No data left in array %d (labels=%s) after filtering!" % (k+1,
f_obs.info().label_string()))
output_file_name = params.output_file
if (output_file_name is None) and (params.file_name_prefix is not None):
output_file_name = "%s_%s.mtz" % (params.file_name_prefix, params.job_id)
output_files = compute_fo_minus_fo_map(
data_arrays = f_obss,
xray_structure = xray_structure,
log = log,
silent = command_line.options.silent,
output_file = output_file_name,
peak_search=params.find_peaks_holes,
map_cutoff=params.map_cutoff,
peak_search_params=params.peak_search,
multiscale=params.advanced.multiscale,
anomalous=params.advanced.anomalous).file_names
return output_files
def __call__(self,
atom_group,
log,
window_size=2,
backbone_sample_angle=10,
anneal=False,
annealing_temperature=1000,
use_chi1_sampling=False):
import iotbx.pdb.hierarchy
from scitbx.array_family import flex
assert (atom_group is not None)
pdb_hierarchy = self.pdb_hierarchy.deep_copy()
xray_structure = self.xray_structure.deep_copy_scatterers()
geometry_restraints_manager = self.geometry_restraints_manager
# FIXME this doesn't work - can't recover the atom_group afterwards!
#hd_sel = xray_structure.hd_selection()
#n_hydrogen = hd_sel.count(True)
#if (n_hydrogen > 0):
# non_hd_sel = ~hd_sel
# pdb_hierarchy = pdb_hierarchy.select(non_hd_sel)
# xray_structure = xray_structure.select(non_hd_sel)
# geometry_restraints_manager = geometry_restraints_manager.select(
# non_hd_sel)
pdb_atoms = pdb_hierarchy.atoms()
pdb_atoms.reset_i_seq()
isel = building.extract_iselection([atom_group])
atom_group = pdb_atoms[isel[0]].parent()
atom_group_start = atom_group.detached_copy()
needs_rebuild = not building.is_stub_residue(atom_group)
residue_group = atom_group.parent()
assert (len(residue_group.atom_groups()) == 1)
sel_residues = building.get_window_around_residue(
residue=atom_group, window_size=window_size)
# get rid of sidechains for surrounding residues only
adjacent_residues = []
for other_rg in sel_residues:
if (other_rg != residue_group):
adjacent_residues.append(other_rg)
building.remove_sidechain_atoms(adjacent_residues)
pdb_atoms = pdb_hierarchy.atoms()
adjacent_trimmed_atom_names = pdb_atoms.extract_name()
adjacent_trimmed_sel = pdb_atoms.extract_i_seq()
xrs_adjacent_trimmed = xray_structure.select(adjacent_trimmed_sel)
grm_adjacent_trimmed = geometry_restraints_manager.select(
adjacent_trimmed_sel)
pdb_atoms.reset_i_seq()
# get rid of central sidechain and refine mainchain for entire window
truncate = (not atom_group.resname in ["GLY", "ALA"]) # XXX PRO?
if (truncate):
building.remove_sidechain_atoms([atom_group])
pdb_atoms = pdb_hierarchy.atoms()
all_mc_sel = pdb_atoms.extract_i_seq()
xrs_mc = xrs_adjacent_trimmed.select(all_mc_sel)
pdb_atoms.reset_i_seq()
window_mc_sel = building.extract_iselection(sel_residues)
selection = flex.bool(pdb_atoms.size(),
False).set_selected(window_mc_sel, True)
restraints_manager = grm_adjacent_trimmed.select(all_mc_sel)
box = building.box_build_refine_base(
xray_structure=xrs_mc,
pdb_hierarchy=pdb_hierarchy,
selection=selection,
processed_pdb_file=None,
target_map=self.target_map,
geometry_restraints_manager=restraints_manager.geometry,
d_min=self.d_min,
out=null_out(),
debug=True)
box.restrain_atoms(selection=box.others_in_box, reference_sigma=0.1)
box.real_space_refine(selection=box.selection_in_box)
sites_new = box.update_original_coordinates()
pdb_atoms.set_xyz(sites_new)
# extend and replace existing residue. this is done in such a way that
# the original atom ordering for the central residue is preserved, which
# allows us to use the pre-existing geometry restraints instead of
# re-calculating them every time this function is called.
target_atom_group = self.ideal_dict[atom_group.resname.lower()].\
only_model().only_chain().only_residue_group().only_atom_group()
new_atom_group_base = extend_sidechains.extend_residue(
residue=atom_group,
target_atom_group=target_atom_group,
mon_lib_srv=self.mon_lib_srv)
new_atom_group = iotbx.pdb.hierarchy.atom_group(
resname=atom_group.resname)
for atom in atom_group_start.atoms():
for new_atom in new_atom_group_base.atoms():
if (new_atom.name == atom.name):
new_atom_group.append_atom(new_atom.detached_copy())
n_atoms_new = len(new_atom_group.atoms())
n_atoms_start = len(atom_group_start.atoms())
if (n_atoms_new != n_atoms_start):
raise RuntimeError(
("Inconsistent atom counts for residue %s after " +
"building (%d versus %d).") %
(atom_group.id_str(), n_atoms_start, n_atoms_new))
rg = atom_group.parent()
rg.remove_atom_group(atom_group)
rg.append_atom_group(new_atom_group)
pdb_atoms = pdb_hierarchy.atoms()
pdb_atoms.reset_i_seq()
new_names = pdb_atoms.extract_name()
assert new_names.all_eq(adjacent_trimmed_atom_names)
# get new box around this residue
residue_sel = building.extract_iselection([new_atom_group])
selection = flex.bool(pdb_atoms.size(),
False).set_selected(residue_sel, True)
xrs_adjacent_trimmed.set_sites_cart(pdb_atoms.extract_xyz())
box = building.box_build_refine_base(
xray_structure=xrs_adjacent_trimmed,
pdb_hierarchy=pdb_hierarchy,
selection=selection,
processed_pdb_file=None,
target_map=self.target_map,
geometry_restraints_manager=grm_adjacent_trimmed.geometry,
d_min=self.d_min,
out=null_out(),
debug=True)
# place sidechain using mmtbx.refinement.real_space.fit_residue
if ((atom_group.resname in rotatable_sidechain_atoms)
and (use_chi1_sampling)):
fit_chi1_simple(residue=box.only_residue(),
unit_cell=box.unit_cell_box,
target_map=box.target_map_box,
rotamer_eval=self.rotamer_eval)
box.update_sites_from_pdb_atoms()
else:
box.fit_residue_in_box(backbone_sample_angle=backbone_sample_angle)
if (anneal):
box.anneal(start_temperature=annealing_temperature)
#box.real_space_refine()
sites_new = box.update_original_coordinates()
pdb_hierarchy.atoms().set_xyz(sites_new)
return building.atom_group_as_hierarchy(new_atom_group)
def __init__(self,
pdb_hierarchy,
xray_structure=None,
pdb_atoms=None,
params=None,
out=None,
log=None):
t0 = time.time()
if (out is None): out = sys.stdout
if (log is None): log = null_out()
if (params is None): params = master_phil.extract()
if (pdb_atoms is None): pdb_atoms = pdb_hierarchy.atoms()
if (xray_structure is None):
xray_structure = pdb_hierarchy.extract_xray_structure()
self.hbonds = []
self.pdb_hierarchy = pdb_hierarchy
self.pdb_atoms = pdb_atoms
self.params = params
self.log = log
t1 = time.time()
assert (not pdb_atoms.extract_i_seq().all_eq(0))
unit_cell = xray_structure.unit_cell()
pair_asu_table = xray_structure.pair_asu_table(
distance_cutoff=params.distance_cutoff)
asu_mappings = pair_asu_table.asu_mappings()
self.asu_table = pair_asu_table.table()
self.pdb_labels = []
# first mark atoms in each residue with position in sequence
k = 0
for chain in pdb_hierarchy.only_model().chains():
last_resseq = None
for residue_group in chain.residue_groups():
resseq = residue_group.resseq_as_int()
if (last_resseq is not None) and ((resseq - last_resseq) > 1):
self.pdb_labels.append(None)
k += 1 # extra increment to handle probable chain breaks
last_resseq = resseq
atom_group = residue_group.atom_groups()[0]
self.pdb_labels.append(get_pdb_fields(atom_group))
for atom in atom_group.atoms():
#if (atom.name.strip() in ["N","C","O"]):
atom.tmp = k
k += 1
# now iterate over backbone O atoms and look for H-bonds
# XXX this loop takes up most of the runtime
t1 = time.time()
if (self.params.verbosity >= 1):
print("Time to initialize: %.3fs" % (t1 - t0), file=log)
t_process = 0
t_find = 0
for chain in pdb_hierarchy.only_model().chains():
if (not chain.is_protein()):
continue
for residue_group in chain.residue_groups():
atom_group = residue_group.atom_groups()[0]
ag_atoms = atom_group.atoms()
for atom in ag_atoms:
if (atom.name == " O "):
tf0 = time.time()
n_atoms = self.find_nearby_backbone_n(atom)
t_find += time.time() - tf0
tp0 = time.time()
for n_atom in n_atoms:
hbond = self.process_o_n_interaction(atom, n_atom)
if (hbond is not None):
self.hbonds.append(hbond)
t_process += time.time() - tp0
break
t2 = time.time()
if (self.params.verbosity >= 1):
print("Time to find H-bonds: %.3f" % (t2 - t1), file=log)
print(" local atom detection: %.3f" % t_find, file=log)
print(" analysis: %.3f" % t_process, file=log)
if (self.params.verbosity >= 2):
print("All hydrogen bonds:", file=log)
for hbond in self.hbonds:
hbond.show(log, prefix=" ")
if (self.params.pymol_script is not None):
self._pml = open(self.params.pymol_script, "w")
else:
self._pml = null_out()
t1 = time.time()
self.helices = self.find_helices()
t2 = time.time()
if (self.params.verbosity >= 1):
print("Time to find helices: %.3f" % (t2 - t1), file=log)
self.sheets = self.find_sheets()
t3 = time.time()
if (self.params.verbosity >= 1):
print("Time to find sheets: %.3f" % (t3 - t2), file=log)
self.show(out=out)
self._pml.close()
self.log = None
def __init__(self,
obs,
r_free_flags,
test_flag_value,
phases=None,
d_min=None,
d_max=None,
r_free_flags_params=None,
merge_anomalous=False,
log=sys.stdout,
verbose=True):
assert (log is not None) and (obs is not None)
if (r_free_flags_params is None):
from cctbx.r_free_utils import generate_r_free_params_str
r_free_flags_params = libtbx.phil.parse(
generate_r_free_params_str).extract()
obs_info = obs.info()
r_free_flags_info = phases_info = None
sg = obs.space_group_info()
obs = obs.map_to_asu().merge_equivalents().array()
obs = obs.eliminate_sys_absent(log=log)
obs = obs.resolution_filter(d_min=d_min, d_max=d_max)
if (obs.is_xray_intensity_array()):
from cctbx import french_wilson
if (verbose):
fw_out = log
else :
fw_out = null_out()
obs = french_wilson.french_wilson_scale(
miller_array=obs,
params=None,
log=fw_out)
assert (obs is not None)
merged_obs = obs.average_bijvoet_mates()
if (merged_obs.completeness() < 0.9):
print >> log, """
WARNING: data are incomplete (%.1f%% of possible reflections measured to
%.2fA). This may cause problems if you plan to use the maps for building
and/or ligand fitting!
""" % (100*merged_obs.completeness(), merged_obs.d_min())
# XXX this is kind of a hack (the reconstructed arrays break some of my
# assumptions about labels)
if (merge_anomalous):
obs = obs.average_bijvoet_mates()
if (r_free_flags is not None):
r_free_flags_info = r_free_flags.info()
format = "cns"
if (test_flag_value == 0):
format = "ccp4"
elif (test_flag_value == -1):
format = "shelx"
if (r_free_flags.anomalous_flag()):
r_free_flags = r_free_flags.average_bijvoet_mates()
is_compatible_symmetry = False
obs_pg = obs.space_group().build_derived_point_group()
flags_pg = r_free_flags.space_group().build_derived_point_group()
if (obs_pg.type().number() == flags_pg.type().number()):
is_compatible_symmetry = True
else :
pass # TODO unit cell comparison?
if (is_compatible_symmetry):
r_free_flags = r_free_flags.map_to_asu().merge_equivalents().array()
r_free_flags = r_free_flags.eliminate_sys_absent(log=log)
if (format == "cns"):
r_free_flags = r_free_flags.customized_copy(
crystal_symmetry=obs.crystal_symmetry(),
data=(r_free_flags.data() == test_flag_value))
test_flag_value = True
obs_tmp = obs.deep_copy()
if (obs.anomalous_flag()):
obs_tmp = obs.average_bijvoet_mates()
r_free_flags = r_free_flags.common_set(other=obs_tmp)
n_r_free = r_free_flags.indices().size()
n_obs = obs_tmp.indices().size()
if ((test_flag_value is None) or
(r_free_flags.data().all_eq(r_free_flags.data()[0]))):
print >> log, """
WARNING: uniform R-free flags detected; a new test set will be generated,
but this will bias the refinement statistics.
"""
r_free_flags = None
elif (n_r_free != n_obs):
missing_set = obs_tmp.lone_set(other=r_free_flags)
n_missing = missing_set.indices().size()
if (n_missing > 0):
print >> log, """
WARNING: R-free flags are incomplete relative to experimental
data (%d vs. %d reflections). The flags will be extended to
complete the set, but we recommend supplying flags that are already
generated to the maximum expected resolution.
""" % (n_r_free, n_obs)
if (n_missing < 20) : # FIXME
if (format == "cns"):
missing_flags = missing_set.array(data=flex.bool(n_missing,
False))
else :
missing_flags = missing_set.array(data=flex.int(n_missing, 1))
else :
missing_flags = missing_set.generate_r_free_flags(
fraction=(r_free_flags.data().count(test_flag_value)/n_r_free),
max_free=None,
use_lattice_symmetry=True,
format=format)
r_free_flags = r_free_flags.concatenate(other=missing_flags)
if (r_free_flags is not None):
assert (r_free_flags.indices().size() == obs_tmp.indices().size())
else :
print >> log, """
NOTE: incompatible symmetry between the data and the R-free flags:
Data : %s %s
Flags : %s %s
A new test set will be generated.
""" % (str(obs.space_group_info()),
" ".join([ "%g" % x for x in obs.unit_cell().parameters() ]),
str(r_free_flags.space_group_info()),
" ".join(["%g" % x for x in r_free_flags.unit_cell().parameters()]))
else :
print >> log, """
WARNING: R-free flags not supplied. This may bias the refinement if the
structures are very nearly isomorphous!
"""
self._generate_new = False
if (r_free_flags is None):
r_free_flags = obs.generate_r_free_flags(
fraction=r_free_flags_params.fraction,
max_free=r_free_flags_params.max_free,
use_lattice_symmetry=r_free_flags_params.use_lattice_symmetry,
use_dataman_shells=r_free_flags_params.use_dataman_shells,
n_shells=r_free_flags_params.n_shells,
format="ccp4")
test_flag_value = 0
self._generate_new = True
if (r_free_flags.anomalous_flag()):
r_free_flags = r_free_flags.average_bijvoet_mates()
if (phases is not None):
phases_info = phases.info()
phases = phases.map_to_asu().resolution_filter(d_min=d_min, d_max=d_max)
assert (obs.is_xray_amplitude_array())
self.f_obs = obs.set_info(obs_info)
self.r_free_flags = r_free_flags.set_info(r_free_flags_info)
self.test_flag_value = test_flag_value
self.phases = None
if (phases is not None):
self.phases = phases.set_info(phases_info)
def run(args):
assert len(args) in [0,2], "n_sites, n_trials"
if (len(args) == 0):
n_sites, n_trials = 3, 2
out = null_out()
else:
n_sites, n_trials = [int(arg) for arg in args]
out = sys.stdout
#
show_times_at_exit()
class type_info(object):
def __init__(O, type, use_analytical_gradients):
O.type = type
O.use_analytical_gradients = use_analytical_gradients
def __str__(O):
return "%s(use_analytical_gradients=%s)" % (
O.type.__name__, str(O.use_analytical_gradients))
spherical_types = [
type_info(euler_params, False),
type_info(euler_params, True),
type_info(euler_angles_xyz, False),
type_info(euler_angles_xyz, True),
type_info(euler_angles_zxz, False),
type_info(euler_angles_zxz, True),
type_info(euler_angles_yxyz, False),
type_info(euler_angles_xyzy, False),
type_info(inf_euler_params, False),
type_info(inf_axis_angle, False)]
nfun_accu = {}
n_failed = {}
for ti in spherical_types:
nfun_accu[str(ti)] = flex.size_t()
n_failed[str(ti)] = 0
mersenne_twister = flex.mersenne_twister(seed=0)
for i_trial in range(n_trials):
sites = [matrix.col(s) for s in flex.vec3_double(
mersenne_twister.random_double(size=n_sites*3)*2-1)]
c = center_of_mass_from_sites(sites)
r = matrix.sqr(mersenne_twister.random_double_r3_rotation_matrix())
wells = [r*(s-c)+c for s in sites]
for ti in spherical_types:
r = refinery(spherical_type_info=ti, sites=sites, wells=wells, out=out)
nfun_accu[str(ti)].append(r.nfun)
if (r.failed):
n_failed[str(ti)] += 1
nfun_sums = []
annotations = []
for ti in spherical_types:
print(ti, file=out)
nfuns = nfun_accu[str(ti)]
stats = nfuns.as_double().min_max_mean()
stats.show(out=out, prefix=" ")
nfun_sums.append((str(ti), flex.sum(nfuns)))
if (n_failed[str(ti)] == 0):
annotations.append(None)
else:
annotations.append("failed: %d" % n_failed[str(ti)])
print(file=out)
show_sorted_by_counts(
label_count_pairs=nfun_sums,
reverse=False,
out=out,
annotations=annotations)
print(file=out)
print("OK")
def __init__(self,
pdb_file,
output_file=None,
log=None,
quiet=False,
set_se_occ=True,
remove_atoms_with_zero_occupancy=False):
from iotbx.file_reader import any_file
import iotbx.pdb
if (log is None):
log = null_out()
pdb_in = any_file(pdb_file, force_type="pdb")
pdb_in.assert_file_type("pdb")
hierarchy = pdb_in.file_object.hierarchy
if (len(hierarchy.models()) > 1):
raise Sorry("Multi-MODEL PDB files are not supported.")
n_unknown = 0
all_atoms = hierarchy.atoms()
cache = hierarchy.atom_selection_cache()
# resname UNK is now okay (with some restrictions)
known_sel = cache.selection("not (element X or resname UNX or resname UNL)")
semet_sel = cache.selection("element SE and resname MSE")
zero_occ_sel = all_atoms.extract_occ() == 0
self.n_unknown = known_sel.count(False)
self.n_semet = semet_sel.count(True)
self.n_zero_occ = zero_occ_sel.count(True)
keep_sel = known_sel
modified = False
if ((self.n_unknown > 0) or
((self.n_semet > 0) and (set_se_occ)) or
(self.n_zero_occ > 0) and (remove_atoms_with_zero_occupancy)):
modified = True
if (output_file is None):
output_file = pdb_file
if (self.n_unknown > 0) and (not quiet):
print >> log, "Warning: %d unknown atoms or ligands removed:" % \
self.n_unknown
for i_seq in (~known_sel).iselection():
print >> log, " %s" % all_atoms[i_seq].id_str()
if (self.n_zero_occ > 0):
msg = "Warning: %d atoms with zero occupancy present in structure:"
if (remove_atoms_with_zero_occupancy):
msg = "Warning: %d atoms with zero occupancy removed:"
keep_sel &= ~zero_occ_sel
if (not quiet):
print >> log, msg % self.n_zero_occ
for i_seq in zero_occ_sel.iselection():
print >> log, " %s" % all_atoms[i_seq].id_str()
hierarchy_filtered = hierarchy.select(keep_sel)
if (self.n_semet > 0) and (set_se_occ):
for atom in hierarchy_filtered.atoms():
if (atom.element == "SE") and (atom.fetch_labels().resname == "MSE"):
if (atom.occ == 1.0):
if (not quiet):
print >> log, "Set occupancy of %s to 0.99" % atom.id_str()
atom.occ = 0.99 # just enough to trigger occupancy refinement
if (modified):
f = open(output_file, "w")
# if the input file is actually from the PDB, we need to preserve the
# header information for downstream code.
print >> f, "\n".join(pdb_in.file_object.input.title_section())
print >> f, "\n".join(pdb_in.file_object.input.remark_section())
print >> f, iotbx.pdb.format_cryst1_record(
crystal_symmetry=pdb_in.file_object.crystal_symmetry())
print >> f, hierarchy_filtered.as_pdb_string()
f.close()
def get_model(pdb_str):
pdb_inp = iotbx.pdb.input(lines=pdb_str.split("\n"), source_info=None)
model = mmtbx.model.manager(model_input=pdb_inp, log=null_out())
model_with_h = mmtbx.hydrogens.add(model=model)
return model_with_h
def strip_model(pdb_hierarchy=None,
xray_structure=None,
file_name=None,
params=None,
remove_waters=True,
remove_hydrogens=True,
remove_alt_confs=True,
convert_semet_to_met=True,
convert_to_isotropic=True,
reset_occupancies=True,
remove_ligands=False,
reset_hetatm_flag=False,
preserve_remarks=False,
preserve_symmetry=True,
add_remarks=None,
output_file=None,
log=None):
"""
Utility for removing extraneous records from a model intended for use in
molecular replacement, etc., including waters, alternate conformations,
and other features specific to a particular dataset.
"""
if (params is not None):
remove_waters = params.remove_waters
remove_hydrogens = params.remove_hydrogens
remove_alt_confs = params.remove_alt_confs
convert_semet_to_met = params.convert_semet_to_met
convert_to_isotropic = params.convert_to_isotropic
reset_occupancies = params.reset_occupancies
remove_ligands = params.remove_ligands
reset_hetatm_flag = params.reset_hetatm_flag
if (log is None):
log = null_out()
make_sub_header("Processing input model", out=log)
remarks = None
if (file_name is not None):
print >> log, "Reading model from %s" % file_name
assert ([pdb_hierarchy, xray_structure] == [None, None])
from iotbx import file_reader
pdb_in = file_reader.any_file(file_name,
force_type="pdb",
raise_sorry_if_errors=True)
pdb_in.check_file_type("pdb")
remarks = pdb_in.file_object.input.remark_section()
pdb_hierarchy = pdb_in.file_object.hierarchy
xray_structure = pdb_in.file_object.xray_structure_simple()
else:
# XXX work with copies, not the original structure
pdb_hierarchy = pdb_hierarchy.deep_copy()
xray_structure = xray_structure.deep_copy_scatterers()
pdb_hierarchy.atoms().reset_i_seq()
if (len(pdb_hierarchy.models()) > 1):
raise Sorry("Multiple models not supported.")
if (remove_hydrogens):
sele = ~(xray_structure.hd_selection())
n_hd = sele.count(False)
if (n_hd > 0):
pdb_hierarchy = pdb_hierarchy.select(sele)
xray_structure = xray_structure.select(sele)
print >> log, " removed %d hydrogens" % n_hd
pdb_hierarchy.atoms().reset_i_seq()
if (remove_waters):
sele = pdb_hierarchy.atom_selection_cache().selection(
"not (resname HOH)")
n_wat = sele.count(False)
if (n_wat > 0):
pdb_hierarchy = pdb_hierarchy.select(sele)
xray_structure = xray_structure.select(sele)
print >> log, " removed %d waters" % n_wat
pdb_hierarchy.atoms().reset_i_seq()
assert_identical_id_str = True
if (remove_alt_confs):
n_atoms_start = xray_structure.scatterers().size()
pdb_hierarchy.remove_alt_confs(always_keep_one_conformer=False)
i_seqs = pdb_hierarchy.atoms().extract_i_seq()
n_atoms_end = i_seqs.size()
if (n_atoms_end != n_atoms_start):
print >> log, " removed %d atoms in alternate conformations" % \
(n_atoms_end - n_atoms_start)
assert_identical_id_str = False
xray_structure = xray_structure.select(i_seqs)
pdb_hierarchy.atoms().reset_i_seq()
if (convert_semet_to_met):
# XXX need to start from a copy here because the atom-parent relationship
# seems to be messed up otherwise. this is probably a bug.
pdb_hierarchy = pdb_hierarchy.deep_copy()
pdb_hierarchy.convert_semet_to_met()
if (convert_to_isotropic):
xray_structure.convert_to_isotropic()
pdb_hierarchy.adopt_xray_structure(
xray_structure, assert_identical_id_str=assert_identical_id_str)
print >> log, " converted all atoms to isotropic B-factors"
if (reset_occupancies):
assert (remove_alt_confs)
xray_structure.adjust_occupancy(occ_max=1.0, occ_min=1.0)
pdb_hierarchy.adopt_xray_structure(
xray_structure, assert_identical_id_str=assert_identical_id_str)
print >> log, " reset occupancy to 1.0 for all atoms"
if (reset_hetatm_flag):
for atom in pdb_hierarchy.atoms():
atom.hetero = False
if (remove_ligands):
pdb_hierarchy.atoms().reset_i_seq()
model = pdb_hierarchy.only_model()
for chain in model.chains():
if (not chain.is_protein()) and (not chain.is_na()):
print >> log, " removing %d ligand atoms in chain '%s'" % \
(len(chain.atoms()), chain.id)
model.remove_chain(chain)
i_seqs = pdb_hierarchy.atoms().extract_i_seq()
xray_structure = xray_structure.select(i_seqs)
pdb_hierarchy.atoms().reset_i_seq()
assert xray_structure.scatterers().size() == pdb_hierarchy.atoms_size()
if (output_file is not None):
f = open(output_file, "w")
if (add_remarks is not None):
f.write("\n".join(add_remarks))
f.write("\n")
if (preserve_remarks) and (remarks is not None):
f.write("\n".join(remarks))
f.write("\n")
symm = None
if (preserve_symmetry):
symm = xray_structure
f.write(pdb_hierarchy.as_pdb_string(crystal_symmetry=symm))
f.close()
print >> log, " wrote model to %s" % output_file
return pdb_hierarchy, xray_structure
def run(self):
'''
Function that places H atoms
'''
model_has_bogus_cs = False
# TODO temporary fix until the code is moved to model class
# check if box cussion of 5 A is enough to prevent symm contacts
cs = self.model.crystal_symmetry()
if (cs is None) or (cs.unit_cell() is None):
self.model = shift_and_box_model(model = self.model)
model_has_bogus_cs = True
# Remove existing H if requested
self.n_H_initial = self.model.get_hd_selection().count(True)
if not self.keep_existing_H:
self.model = self.model.select(~self.model.get_hd_selection())
# Add H atoms and place them at center of coordinates
pdb_hierarchy = self.add_missing_H_atoms_at_bogus_position()
pdb_hierarchy.sort_atoms_in_place()
pdb_hierarchy.atoms().reset_serial()
#pdb_hierarchy.sort_atoms_in_place()
p = mmtbx.model.manager.get_default_pdb_interpretation_params()
p.pdb_interpretation.clash_guard.nonbonded_distance_threshold=None
p.pdb_interpretation.use_neutron_distances = self.use_neutron_distances
p.pdb_interpretation.proceed_with_excessive_length_bonds=True
#p.pdb_interpretation.automatic_linking.link_metals = True
#p.pdb_interpretation.restraints_library.cdl=False # XXX this triggers a bug !=360
ro = self.model.get_restraint_objects()
self.model = mmtbx.model.manager(
model_input = None,
pdb_hierarchy = pdb_hierarchy,
build_grm = True,
stop_for_unknowns = self.stop_for_unknowns,
crystal_symmetry = self.model.crystal_symmetry(),
restraint_objects = ro,
pdb_interpretation_params = p,
log = null_out())
#f = open("intermediate1.pdb","w")
#f.write(self.model.model_as_pdb())
# Only keep H that have been parameterized in riding H procedure
sel_h = self.model.get_hd_selection()
if sel_h.count(True) == 0:
return
# get rid of isolated H atoms.
#For example when heavy atom is missing, H needs not to be placed
sel_isolated = self.model.isolated_atoms_selection()
self.sel_lone_H = sel_h & sel_isolated
self.model = self.model.select(~self.sel_lone_H)
# get riding H manager --> parameterize all H atoms
sel_h = self.model.get_hd_selection()
self.model.setup_riding_h_manager(use_ideal_dihedral = True)
sel_h_in_para = flex.bool(
[bool(x) for x in self.model.riding_h_manager.h_parameterization])
sel_h_not_in_para = sel_h_in_para.exclusive_or(sel_h)
self.site_labels_no_para = [atom.id_str().replace('pdb=','').replace('"','')
for atom in self.model.get_hierarchy().atoms().select(sel_h_not_in_para)]
#
self.model = self.model.select(~sel_h_not_in_para)
self.exclude_H_on_disulfides()
#self.exclude_h_on_coordinated_S()
# f = open("intermediate2.pdb","w")
# f.write(model.model_as_pdb())
# Reset occupancies, ADPs and idealize H atom positions
self.model.reset_adp_for_hydrogens(scale = self.adp_scale)
self.model.reset_occupancy_for_hydrogens_simple()
self.model.idealize_h_riding()
self.exclude_h_on_coordinated_S()
#
self.n_H_final = self.model.get_hd_selection().count(True)
def __init__ (self,
model,
pdb_hierarchy=None, # keep for mmtbx.validation_summary (multiple models)
fmodel=None,
fmodel_neutron=None,
sequences=None,
flags=None,
header_info=None,
raw_data=None,
unmerged_data=None,
keep_hydrogens=True,
nuclear=False,
save_probe_unformatted_file=None,
show_hydrogen_outliers=False,
min_cc_two_fofc=0.8,
n_bins_data=10,
count_anomalous_pairs_separately=False,
use_internal_variance=True,
outliers_only=True,
use_pdb_header_resolution_cutoffs=False,
file_name=None,
ligand_selection=None,
rotamer_library="8000",
map_params=None) :
assert rotamer_library == "8000", "data_version given to RotamerEval not recognized."
for name in self.__slots__ :
setattr(self, name, None)
# use objects from model
self.model = model
if (self.model is not None):
pdb_hierarchy = self.model.get_hierarchy()
xray_structure = self.model.get_xray_structure()
geometry_restraints_manager = self.model.get_restraints_manager().geometry
crystal_symmetry = self.model.crystal_symmetry()
all_chain_proxies = self.model.all_chain_proxies
else:
assert (pdb_hierarchy is not None)
xray_structure = None
geometry_restraints_manager = None
crystal_symmetry = None
all_chain_proxies = None
# very important - the i_seq attributes may be extracted later
pdb_hierarchy.atoms().reset_i_seq()
self.pdb_hierarchy = pdb_hierarchy
if (xray_structure is None) :
if (fmodel is not None) :
xray_structure = fmodel.xray_structure
elif (crystal_symmetry is not None) :
xray_structure = pdb_hierarchy.extract_xray_structure(
crystal_symmetry=crystal_symmetry)
self.crystal_symmetry = crystal_symmetry
if (crystal_symmetry is None) and (fmodel is not None) :
self.crystal_symmetry = fmodel.f_obs().crystal_symmetry()
# use maps (fmodel is not used)
# run earlier since pdb_hierarchy gets modified
use_maps = False
if (map_params is not None):
use_maps = ( (map_params.input.maps.map_file_name) or
( (map_params.input.maps.map_coefficients_file_name) and
(map_params.input.maps.map_coefficients_label) ) )
if (use_maps):
if (flags.real_space):
self.real_space = experimental.real_space(
fmodel=None,
model=self.model,
cc_min=min_cc_two_fofc,
molprobity_map_params=map_params.input.maps)
if (flags.waters):
self.waters = waters.waters(
pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
fmodel=None,
collect_all=True,
molprobity_map_params=map_params.input.maps)
self.header_info = header_info
if (flags is None) :
flags = molprobity_flags()
import mmtbx.model.statistics
self.model_statistics_geometry = mmtbx.model.statistics.geometry(
pdb_hierarchy = pdb_hierarchy,
geometry_restraints_manager = geometry_restraints_manager,
use_hydrogens = keep_hydrogens,
use_nuclear = nuclear)
self.model_statistics_geometry_result = \
self.model_statistics_geometry.result()
self.ramalyze = self.model_statistics_geometry_result.ramachandran.ramalyze
self.omegalyze = self.model_statistics_geometry_result.omega.omegalyze
self.rotalyze = self.model_statistics_geometry_result.rotamer.rotalyze
self.cbetadev = self.model_statistics_geometry_result.c_beta.cbetadev
self.clashes = self.model_statistics_geometry_result.clash.clashes
if pdb_hierarchy.contains_protein() :
self.find_missing_atoms(out=null_out())
if (flags.nqh) :
self.nqh_flips = clashscore.nqh_flips(
pdb_hierarchy=pdb_hierarchy)
if (pdb_hierarchy.contains_rna() and flags.rna and
libtbx.env.has_module(name="suitename")) :
if (geometry_restraints_manager is not None) :
self.rna = rna_validate.rna_validation(
pdb_hierarchy=pdb_hierarchy,
geometry_restraints_manager=geometry_restraints_manager,
outliers_only=outliers_only,
params=None)
if (flags.model_stats) and (xray_structure is not None) :
self.model_stats = model_properties.model_statistics(
pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
all_chain_proxies=all_chain_proxies,
ignore_hd=(not nuclear),
ligand_selection=ligand_selection)
if (geometry_restraints_manager is not None) and (flags.restraints) :
assert (xray_structure is not None)
self.restraints = restraints.combined(
pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
geometry_restraints_manager=geometry_restraints_manager,
ignore_hd=(not nuclear),
cdl=getattr(all_chain_proxies, "use_cdl", None))
if (sequences is not None) and (flags.seq) :
self.sequence = sequence.validation(
pdb_hierarchy=pdb_hierarchy,
sequences=sequences,
log=null_out(),
include_secondary_structure=True,
extract_coordinates=True)
if (fmodel is not None) :
if (use_pdb_header_resolution_cutoffs) and (header_info is not None) :
fmodel = fmodel.resolution_filter(
d_min=header_info.d_min,
d_max=header_info.d_max)
if (flags.rfactors) :
self.data_stats = experimental.data_statistics(fmodel,
raw_data=raw_data,
n_bins=n_bins_data,
count_anomalous_pairs_separately=count_anomalous_pairs_separately)
if (not use_maps): # if maps are used, keep previous results
if (flags.real_space):
self.real_space = experimental.real_space(
model=model,
fmodel=fmodel,
cc_min=min_cc_two_fofc)
if (flags.waters) :
self.waters = waters.waters(
pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
fmodel=fmodel,
collect_all=True)
if (unmerged_data is not None) :
self.merging = experimental.merging_and_model_statistics(
f_obs=fmodel.f_obs(),
f_model=fmodel.f_model(),
r_free_flags=fmodel.r_free_flags(),
unmerged_i_obs=unmerged_data,
anomalous=count_anomalous_pairs_separately,
use_internal_variance=use_internal_variance,
n_bins=n_bins_data)
if (flags.xtriage) :
import mmtbx.scaling.xtriage
f_model = abs(fmodel.f_model()).set_observation_type_xray_amplitude()
if (raw_data is not None) :
f_model, obs = f_model.common_sets(other=raw_data)
else :
obs = fmodel.f_obs()
self.xtriage = mmtbx.scaling.xtriage.xtriage_analyses(
miller_obs=obs,
miller_calc=f_model,
unmerged_obs=unmerged_data, # XXX some redundancy here...
text_out=null_out())
if (fmodel_neutron is not None) and (flags.rfactors) :
self.neutron_stats = experimental.data_statistics(fmodel_neutron,
n_bins=n_bins_data,
count_anomalous_pairs_separately=False)
if (pdb_hierarchy.models_size() == 1) :
self._multi_criterion = multi_criterion_view(pdb_hierarchy)
# wilson B
self.wilson_b = None
if (fmodel is not None):
self.wilson_b = fmodel.wilson_b()
elif (fmodel_neutron is not None):
self.wilson_b = fmodel_neutron.wilson_b()
# validate hydrogens
self.hydrogens = None
if self.model is not None and self.model.has_hd():
# import here to avoid circular import issues
from mmtbx.hydrogens.validate_H import validate_H, validate_H_results
hydrogens = validate_H(model, nuclear)
hydrogens.validate_inputs()
hydrogens.run()
self.hydrogens = validate_H_results(hydrogens.get_results())
# write probe file if needed (CLI and GUI)
if (save_probe_unformatted_file is not None):
pcm = self.clashes.probe_clashscore_manager
try:
with open(save_probe_unformatted_file, 'w') as f:
f.write(pcm.probe_unformatted)
self.clashes.probe_file = save_probe_unformatted_file
except IOError as err:
raise Sorry('%s could not be written correctly.\n%s' %
(save_probe_unformatted_file, err))
def whole_minimization(self):
t3 = time()
# pre_result_h = edited_h
# pre_result_h.reset_i_seq_if_necessary()
bsel = flex.bool(self.model.get_number_of_atoms(), False)
helix_selection = flex.bool(self.model.get_number_of_atoms(), False)
sheet_selection = flex.bool(self.model.get_number_of_atoms(), False)
other_selection = flex.bool(self.model.get_number_of_atoms(), False)
ss_for_tors_selection = flex.bool(self.model.get_number_of_atoms(), False)
nonss_for_tors_selection = flex.bool(self.model.get_number_of_atoms(), False)
# set all CA atoms to True for other_selection
#isel = self.model.get_atom_selection_cache().iselection("name ca")
isel = self.model.get_atom_selection_cache().iselection("name ca or name n or name o or name c")
other_selection.set_selected(isel, True)
n_main_chain_atoms = other_selection.count(True)
isel = self.model.get_atom_selection_cache().iselection("name ca or name n or name o or name c")
nonss_for_tors_selection.set_selected(isel, True)
main_chain_selection_prefix = "(name ca or name n or name o or name c) %s"
t4 = time()
print("Preparing selections...", file=self.log)
self.log.flush()
# Here we are just preparing selections
for h in self.ss_annotation.helices:
ss_sels = h.as_atom_selections()[0]
selstring = main_chain_selection_prefix % ss_sels
isel = self.model.get_atom_selection_cache().iselection(selstring)
helix_selection.set_selected(isel, True)
other_selection.set_selected(isel, False)
isel = self.model.get_atom_selection_cache().iselection(selstring)
ss_for_tors_selection.set_selected(isel, True)
nonss_for_tors_selection.set_selected(isel, False)
for sheet in self.ss_annotation.sheets:
for ss_sels in sheet.as_atom_selections():
selstring = main_chain_selection_prefix % ss_sels
isel = self.model.get_atom_selection_cache().iselection(selstring)
sheet_selection.set_selected(isel, True)
other_selection.set_selected(isel, False)
isel = self.model.get_atom_selection_cache().iselection(selstring)
ss_for_tors_selection.set_selected(isel, True)
nonss_for_tors_selection.set_selected(isel, False)
t5 = time()
# print("N idealized elements: %d" % n_idealized_elements, file=self.log)
# print("Initial checking, init : %.4f" % (t1-t0), file=self.log)
# print("Checking SS : %.4f" % (t2-t1), file=self.log)
# print("Changing SS : %.4f" % (t3-t2), file=self.log)
# print("Initializing selections : %.4f" % (t4-t3), file=self.log)
# print("Looping for selections : %.4f" % (t5-t4), file=self.log)
# with open('idealized.pdb', 'w') as f:
# f.write(self.model.model_as_pdb())
# return
isel = self.model.get_atom_selection_cache().iselection(
"not name ca and not name n and not name o and not name c")
other_selection.set_selected(isel, False)
helix_sheet_intersection = helix_selection & sheet_selection
if helix_sheet_intersection.count(True) > 0:
sheet_selection = sheet_selection & ~helix_sheet_intersection
assert ((helix_selection | sheet_selection) & other_selection).count(True)==0
from mmtbx.monomer_library.pdb_interpretation import grand_master_phil_str
params_line = grand_master_phil_str
params_line += "secondary_structure {%s}" % secondary_structure.sec_str_master_phil_str
# print "params_line"
# print params_line
params = iotbx.phil.parse(input_string=params_line, process_includes=True)#.extract()
# This does not work the same way for a strange reason. Need to investigate.
# The number of resulting hbonds is different later.
# w_params = params.extract()
# w_params.pdb_interpretation.secondary_structure.protein.remove_outliers = False
# w_params.pdb_interpretation.peptide_link.ramachandran_restraints = True
# w_params.pdb_interpretation.c_beta_restraints = True
# w_params.pdb_interpretation.secondary_structure.enabled = True
# params.format(python_object=w_params)
# params.show()
# print "="*80
# print "="*80
# print "="*80
grm = self.model.get_restraints_manager()
ssm_log = null_out()
if self.processed_params.verbose:
ssm_log = self.log
ss_params = secondary_structure.sec_str_master_phil.fetch().extract()
ss_params.secondary_structure.protein.remove_outliers=False
ss_manager = secondary_structure.manager(
pdb_hierarchy=self.model.get_hierarchy(),
geometry_restraints_manager=grm.geometry,
sec_str_from_pdb_file=self.ss_annotation,
params=ss_params.secondary_structure,
mon_lib_srv=None,
verbose=-1,
log=ssm_log)
grm.geometry.set_secondary_structure_restraints(
ss_manager=ss_manager,
hierarchy=self.model.get_hierarchy(),
log=ssm_log)
self.model.get_hierarchy().reset_i_seq_if_necessary()
from mmtbx.geometry_restraints import reference
if self.reference_map is None:
if self.processed_params.verbose:
print("Adding reference coordinate restraints...", file=self.log)
grm.geometry.append_reference_coordinate_restraints_in_place(
reference.add_coordinate_restraints(
sites_cart = self.model.get_sites_cart().select(helix_selection),
selection = helix_selection,
sigma = self.processed_params.sigma_on_reference_helix))
grm.geometry.append_reference_coordinate_restraints_in_place(
reference.add_coordinate_restraints(
sites_cart = self.model.get_sites_cart().select(sheet_selection),
selection = sheet_selection,
sigma = self.processed_params.sigma_on_reference_sheet))
grm.geometry.append_reference_coordinate_restraints_in_place(
reference.add_coordinate_restraints(
sites_cart = self.model.get_sites_cart().select(other_selection),
selection = other_selection,
sigma = self.processed_params.sigma_on_reference_non_ss))
# XXX Somewhere here we actually should check placed side-chains for
# clashes because we used ones that were in original model and just moved
# them to nearest allowed rotamer. The idealization may affect a lot
# the orientation of side chain thus justifying changing rotamer on it
# to avoid clashes.
if self.processed_params.fix_rotamer_outliers:
print("Fixing/checking rotamers...", file=self.log)
# pre_result_h.write_pdb_file(file_name="before_rotamers.pdb")
br_txt = self.model.model_as_pdb()
with open("before_rotamers.pdb", 'w') as f:
f.write(br_txt)
if(self.reference_map is None):
backbone_sample=False
else:
backbone_sample=True
result = mmtbx.refinement.real_space.fit_residues.run(
pdb_hierarchy = self.model.get_hierarchy(),
crystal_symmetry = self.model.crystal_symmetry(),
map_data = self.reference_map,
rotamer_manager = mmtbx.idealized_aa_residues.rotamer_manager.load(
rotamers="favored"),
sin_cos_table = scitbx.math.sin_cos_table(n=10000),
backbone_sample = backbone_sample,
mon_lib_srv = self.model.get_mon_lib_srv(),
log = self.log)
self.model.set_sites_cart(
sites_cart = result.pdb_hierarchy.atoms().extract_xyz())
if self.processed_params.verbose:
print("Adding chi torsion restraints...", file=self.log)
# only backbone
grm.geometry.add_chi_torsion_restraints_in_place(
pdb_hierarchy = self.model.get_hierarchy(),
sites_cart = self.model.get_sites_cart().\
select(ss_for_tors_selection),
selection = ss_for_tors_selection,
chi_angles_only = False,
sigma = self.processed_params.sigma_on_torsion_ss)
grm.geometry.add_chi_torsion_restraints_in_place(
pdb_hierarchy = self.model.get_hierarchy(),
sites_cart = self.model.get_sites_cart().\
select(nonss_for_tors_selection),
selection = nonss_for_tors_selection,
chi_angles_only = False,
sigma = self.processed_params.sigma_on_torsion_nonss)
# real_h.atoms().set_xyz(pre_result_h.atoms().extract_xyz())
#
# Check and correct for special positions
#
real_h = self.model.get_hierarchy() # just a shortcut here...
special_position_settings = crystal.special_position_settings(
crystal_symmetry = self.model.crystal_symmetry())
site_symmetry_table = \
special_position_settings.site_symmetry_table(
sites_cart = self.model.get_sites_cart(),
unconditional_general_position_flags=(
self.model.get_atoms().extract_occ() != 1))
spi = site_symmetry_table.special_position_indices()
if spi.size() > 0:
print("Moving atoms from special positions:", file=self.log)
for spi_i in spi:
if spi_i not in self.original_spi:
new_coords = (
real_h.atoms()[spi_i].xyz[0]+0.2,
real_h.atoms()[spi_i].xyz[1]+0.2,
real_h.atoms()[spi_i].xyz[2]+0.2)
print(" ", real_h.atoms()[spi_i].id_str(), end=' ', file=self.log)
print(tuple(real_h.atoms()[spi_i].xyz), "-->", new_coords, file=self.log)
real_h.atoms()[spi_i].set_xyz(new_coords)
self.model.set_sites_cart_from_hierarchy()
self.model_before_regularization = self.model.deep_copy()
t9 = time()
if self.processed_params.file_name_before_regularization is not None:
grm.geometry.pair_proxies(sites_cart=self.model.get_sites_cart())
grm.geometry.update_ramachandran_restraints_phi_psi_targets(
hierarchy=self.model.get_hierarchy())
print("Outputting model before regularization %s" % self.processed_params.file_name_before_regularization, file=self.log)
m_txt = self.model.model_as_pdb()
g_txt = self.model.restraints_as_geo()
with open(self.processed_params.file_name_before_regularization, 'w') as f:
f.write(m_txt)
geo_fname = self.processed_params.file_name_before_regularization[:-4]+'.geo'
print("Outputting geo file for regularization %s" % geo_fname, file=self.log)
with open(geo_fname, 'w') as f:
f.write(g_txt)
#testing number of restraints
assert grm.geometry.get_n_den_proxies() == 0
if self.reference_map is None:
assert grm.geometry.get_n_reference_coordinate_proxies() == n_main_chain_atoms, "" +\
"%d %d" % (grm.geometry.get_n_reference_coordinate_proxies(), n_main_chain_atoms)
refinement_log = null_out()
self.log.write(
"Refining geometry of substituted secondary structure elements\n")
self.log.write(
" for %s macro_cycle(s).\n" % self.processed_params.n_macro)
self.log.flush()
if self.processed_params.verbose:
refinement_log = self.log
t10 = time()
if self.reference_map is None:
n_cycles = self.processed_params.n_macro
if self.processed_params.n_macro == Auto:
n_cycles=5
minimize_wrapper_for_ramachandran(
model = self.model,
original_pdb_h = None,
excl_string_selection = "",
log = refinement_log,
number_of_cycles = n_cycles)
else:
ref_xrs = self.model.crystal_symmetry()
minimize_wrapper_with_map(
model = self.model,
target_map=self.reference_map,
refine_ncs_operators=False,
number_of_cycles=self.processed_params.n_macro,
min_mode='simple_cycles',
log=self.log)
self.model.set_sites_cart_from_hierarchy()
self.log.write(" Done\n")
self.log.flush()
t11 = time()
# print("Initial checking, init : %.4f" % (t1-t0), file=self.log)
# print("Checking SS : %.4f" % (t2-t1), file=self.log)
# print("Initializing selections : %.4f" % (t4-t3), file=self.log)
# print("Looping for selections : %.4f" % (t5-t4), file=self.log)
# print("Finalizing selections : %.4f" % (t6-t5), file=self.log)
# print("PDB interpretation : %.4f" % (t7-t6), file=self.log)
# print("Get GRM : %.4f" % (t8-t7), file=self.log)
# print("Adding restraints to GRM : %.4f" % (t9-t8), file=self.log)
# print("Running GM : %.4f" % (t11-t10), file=self.log)
# print_hbond_proxies(grm.geometry,real_h)
grm.geometry.remove_reference_coordinate_restraints_in_place()
grm.geometry.remove_chi_torsion_restraints_in_place(nonss_for_tors_selection)
return grm.geometry.get_chi_torsion_proxies()
def exercise():
from mmtbx.regression.make_fake_anomalous_data import generate_cd_cl_inputs
from mmtbx.command_line import mtz2map
import mmtbx.utils
from iotbx import file_reader
from scitbx.array_family import flex
mtz_file, pdb_file = generate_cd_cl_inputs(file_base = "tst_mmtbx_mtz2map")
pdb_in = file_reader.any_file(pdb_file)
hierarchy = pdb_in.file_object.hierarchy
xrs = pdb_in.file_object.xray_structure_simple()
mtz_in = file_reader.any_file(mtz_file)
f_obs = mtz_in.file_server.miller_arrays[0]
f_obs_mean = f_obs.average_bijvoet_mates()
flags = mtz_in.file_server.miller_arrays[1]
flags = flags.customized_copy(data=flags.data()==1)
fmodel = mmtbx.utils.fmodel_simple(
f_obs=f_obs,
r_free_flags=flags,
xray_structures=[xrs],
scattering_table="n_gaussian",
skip_twin_detection=True)
assert f_obs.anomalous_flag()
mtz_data = f_obs.as_mtz_dataset(column_root_label="F")
#mtz_data.add_miller_array(
# miller_array=f_obs.average_bijvoet_mates(),
# column_root_label="F_mean")
mtz_data.add_miller_array(
miller_array=fmodel.f_model(),
column_root_label="FMODEL")
mtz_data.add_miller_array(
miller_array=fmodel.f_model().average_bijvoet_mates().phases(deg=True),
column_root_label="PHI",
column_types="P")
mtz_data.add_miller_array(
miller_array=f_obs_mean.customized_copy(
data=flex.double(f_obs_mean.data().size(), 0.95),
sigmas=None).set_observation_type(None),
column_root_label="FOM",
column_types="W")
two_fofc_map = fmodel.map_coefficients(map_type="2mFo-DFc")
fofc_map = fmodel.map_coefficients(map_type="mFo-Dfc")
anom_map = fmodel.map_coefficients(map_type="anom")
mtz_data.add_miller_array(
miller_array=two_fofc_map.average_bijvoet_mates(),
column_root_label="2FOFCWT")
mtz_data.add_miller_array(
miller_array=fmodel.map_coefficients(map_type="mFo-DFc"),
column_root_label="FOFCWT")
mtz_data.add_miller_array(
miller_array=fmodel.map_coefficients(map_type="anom"),
column_root_label="ANOM")
mtz_data.add_miller_array(flags,
column_root_label="FreeR_flag")
map_file = "tst_mmtbx_mtz2map_map_coeffs.mtz"
mtz_data.mtz_object().write(map_file)
# exercise defaults with PDB file
file_info = mtz2map.run([pdb_file, map_file], log=null_out())
file_info = [ (os.path.basename(fn), desc) for fn, desc in file_info ]
assert (file_info == [
('tst_mmtbx_mtz2map_map_coeffs_2mFo-DFc.ccp4', 'CCP4 map'),
('tst_mmtbx_mtz2map_map_coeffs_mFo-DFc.ccp4', 'CCP4 map'),
('tst_mmtbx_mtz2map_map_coeffs_anom.ccp4', 'CCP4 map'),
('tst_mmtbx_mtz2map_map_coeffs_4.ccp4', 'CCP4 map')
])
# without PDB file
file_info_2 = mtz2map.run([map_file], log=null_out())
file_info_2 = [ (os.path.basename(fn), desc) for fn, desc in file_info_2 ]
assert file_info_2 == file_info, file_info_2
# with FMODEL
file_info_3 = mtz2map.run([pdb_file, map_file, "include_fmodel=True"],
log=null_out())
file_info_3 = [ (os.path.basename(fn), desc) for fn, desc in file_info_3 ]
assert (file_info_3 == [
('tst_mmtbx_mtz2map_map_coeffs_fmodel.ccp4', 'CCP4 map'),
('tst_mmtbx_mtz2map_map_coeffs_2mFo-DFc.ccp4', 'CCP4 map'),
('tst_mmtbx_mtz2map_map_coeffs_mFo-DFc.ccp4', 'CCP4 map'),
('tst_mmtbx_mtz2map_map_coeffs_anom.ccp4', 'CCP4 map'),
('tst_mmtbx_mtz2map_map_coeffs_5.ccp4', 'CCP4 map')
])
# exercise bad parameter
try :
file_info = mtz2map.run([pdb_file, "1yjp_mtz2map_map_coeffs.mtz",
"output.directory=1yjp_mtz2map_map_coeffs.mtz"],
log=null_out())
except Sorry :
pass
else :
raise Exception_expected
# bad atom selection
try :
file_info = mtz2map.run([pdb_file, map_file, "selection=\"resname ZN\""],
log=null_out())
except Sorry, s :
assert (str(s) == "No atoms found matching the specified selection.")
def ramalyze_parallel(hierarchy):
return ramalyze(hierarchy, out=null_out())
def fetch(id,
data_type="pdb",
format="pdb",
mirror="rcsb",
log=None,
force_download=False,
local_cache=None):
"""
Locate and open a data file for the specified PDB ID and format, either in a
local mirror or online.
:param id: 4-character PDB ID (e.g. '1hbb')
:param data_type: type of content to download: pdb, xray, or fasta
:param format: format of data: cif, pdb, or xml
:param mirror: remote site to use, either rcsb, pdbe, pdbj or pdb-redo
:returns: a filehandle-like object (with read() method)
"""
assert data_type in ["pdb", "xray", "fasta", "seq"]
assert format in ["cif", "pdb", "xml"]
assert mirror in ["rcsb", "pdbe", "pdbj", "pdb-redo"]
validate_pdb_id(id)
if (log is None): log = null_out()
id = id.lower()
if (not force_download):
if (local_cache is not None) and (data_type == "pdb"):
from iotbx.file_reader import guess_file_type
if (local_cache is Auto):
local_cache = os.getcwd()
cache_files = os.listdir(local_cache)
for file_name in cache_files:
if (len(file_name) > 4):
file_id = re.sub("^pdb", "", file_name)[0:4]
if (file_id.lower() == id):
if (guess_file_type(file_name) == "pdb"):
file_name = os.path.join(local_cache, file_name)
print("Reading from cache directory:", file=log)
print(" " + file_name, file=log)
f = smart_open.for_reading(file_name)
return f
# try local mirror for PDB and X-ray data files first, if it exists
if (data_type == "pdb") and (format == "pdb") and \
("PDB_MIRROR_PDB" in os.environ):
subdir = os.path.join(os.environ["PDB_MIRROR_PDB"], id[1:3])
if (os.path.isdir(subdir)):
file_name = os.path.join(subdir, "pdb%s.ent.gz" % id)
if (os.path.isfile(file_name)):
print("Reading from local mirror:", file=log)
print(" " + file_name, file=log)
f = smart_open.for_reading(file_name)
return f
if (data_type == "pdb") and (format == "cif") and \
("PDB_MIRROR_MMCIF" in os.environ):
subdir = os.path.join(os.environ["PDB_MIRROR_MMCIF"], id[1:3])
if (os.path.isdir(subdir)):
file_name = os.path.join(subdir, "%s.cif.gz" % id)
if (os.path.isfile(file_name)):
print("Reading from local mirror:", file=log)
print(" " + file_name, file=log)
f = smart_open.for_reading(file_name)
return f
if ((data_type == "xray")
and ("PDB_MIRROR_STRUCTURE_FACTORS" in os.environ)):
sf_dir = os.environ["PDB_MIRROR_STRUCTURE_FACTORS"]
subdir = os.path.join(sf_dir, id[1:3])
if (os.path.isdir(subdir)):
file_name = os.path.join(subdir, "r%ssf.ent.gz" % id)
if (os.path.isfile(file_name)):
print("Reading from local mirror:", file=log)
print(" " + file_name, file=log)
f = smart_open.for_reading(file_name)
return f
# No mirror found (or out of date), default to HTTP download
url = None
compressed = False
if (mirror == "rcsb"):
url_base = 'https://files.rcsb.org/download/'
pdb_ext = ".pdb"
sf_prefix = ""
sf_ext = "-sf.cif"
elif (mirror == "pdbe"):
url_base = "https://www.ebi.ac.uk/pdbe-srv/view/files/"
pdb_ext = ".ent"
sf_prefix = "r"
sf_ext = "sf.ent"
elif (mirror == "pdbj"):
url_base = "ftp://ftp.pdbj.org/pub/pdb/data/structures/divided/"
if (data_type == "pdb"):
compressed = True
if (format == "pdb"):
url = url_base + "pdb/%s/pdb%s.ent.gz" % (id[1:3], id)
elif (format == "cif"):
url = url_base + "mmCIF/%s/%s.cif.gz" % (id[1:3], id)
elif (data_type == "xray"):
compressed = True
url = url_base + "structure_factors/%s/r%ssf.ent.gz" % (id[1:3],
id)
elif (data_type in ["fasta", "seq"]):
url = "https://pdbj.org/rest/downloadPDBfile?format=fasta&id=%s" % id
if (url is None) and (data_type != "fasta"):
raise Sorry(
"Can't determine PDBj download URL for this data/format " +
"combination.")
elif mirror == "pdb-redo":
url_base = "https://pdb-redo.eu/db/"
pdb_ext = "_final.pdb"
cif_ext = "_final.cif"
sf_prefix = ""
sf_ext = "_final.mtz"
if (data_type == 'pdb'):
if (format == 'pdb'):
url = url_base + "{id}/{id}{format}".format(id=id,
format=pdb_ext)
elif (format == 'cif'):
url = url_base + "{id}/{id}{format}".format(id=id,
format=cif_ext)
elif (data_type == 'xray'):
url = url_base + "{id}/{id}{format}".format(id=id, format=sf_ext)
if (data_type in ["fasta", "seq"]):
if (url is None): # TODO PDBe equivalent doesn't exist?
# Seems that this url should be working:
url = "https://www.rcsb.org/fasta/entry/%s" % id
try:
data = libtbx.utils.urlopen(url)
except HTTPError as e:
if e.getcode() == 404:
raise RuntimeError("Couldn't download sequence for %s." % id)
else:
raise
elif data_type == "xray":
if (url is None):
url = url_base + sf_prefix + id + sf_ext
try:
data = libtbx.utils.urlopen(url)
except HTTPError as e:
if e.getcode() == 404:
raise RuntimeError(
"Couldn't download structure factors for %s." % id)
else:
raise
else:
if (url is None):
if format == "pdb":
url = url_base + id + pdb_ext
else:
url = url_base + id + "." + format
try:
data = libtbx.utils.urlopen(url)
except HTTPError as e:
if e.getcode() == 404:
raise RuntimeError("Couldn't download model for %s." % id)
else:
raise
if (compressed):
try:
import gzip
except ImportError:
raise Sorry(
"gzip module not available - please use an uncompressed " +
"source of PDB data.")
else:
# XXX due to a bug in urllib2, we can't pass the supposedly file-like
# object directly, so we read the data into a StringIO object instead
return gzip.GzipFile(fileobj=StringIO(data.read()))
return data
def exercise_protein():
pdb_file = libtbx.env.find_in_repositories(
relative_path="phenix_regression/pdb/3ifk.pdb", test=op.isfile)
hkl_file = libtbx.env.find_in_repositories(
relative_path="phenix_regression/reflection_files/3ifk.mtz",
test=op.isfile)
if (pdb_file is None):
print("phenix_regression not available, skipping.")
return
args1 = [
pdb_file,
"outliers_only=True",
"output.prefix=tst_molprobity",
"--pickle",
"flags.xtriage=True",
]
result = molprobity.run(args=args1, out=null_out()).validation
out1 = StringIO()
result.show(out=out1)
result = loads(dumps(result))
out2 = StringIO()
result.show(out=out2)
assert (result.nqh_flips.n_outliers == 6)
assert (not "RNA validation" in out2.getvalue())
assert (out2.getvalue() == out1.getvalue())
dump("tst_molprobity.pkl", result)
mc = result.as_multi_criterion_view()
assert (result.neutron_stats is None)
mpscore = result.molprobity_score()
# percentiles
out4 = StringIO()
result.show_summary(out=out4, show_percentiles=True)
assert (""" Clashscore = 49.59 (percentile: 0.2)"""
in out4.getvalue())
# misc
assert approx_equal(result.r_work(), 0.237) # from PDB header
assert approx_equal(result.r_free(), 0.293) # from PDB header
assert approx_equal(result.d_min(), 2.03) # from PDB header
assert (result.d_max_min() is None)
assert approx_equal(result.rms_bonds(), 0.02586, 1e-5)
assert approx_equal(result.rms_angles(), 2.35285, 1e-5)
assert approx_equal(result.rama_favored(), 96.47059)
assert (result.cbeta_outliers() == 10)
assert approx_equal(result.molprobity_score(), 3.39, eps=0.01)
summary = result.summarize()
gui_fields = list(summary.iter_molprobity_gui_fields())
assert (len(gui_fields) == 6)
#result.show()
assert (str(mc.data()[2]) == ' A 5 THR rota,cb,clash')
import mmtbx.validation.molprobity
from iotbx import file_reader
pdb_in = file_reader.any_file(pdb_file)
model = mmtbx.model.manager(pdb_in.file_object.input)
result = mmtbx.validation.molprobity.molprobity(model)
out3 = StringIO()
result.show_summary(out=out3)
assert """\
Ramachandran outliers = 1.76 %
favored = 96.47 %
Rotamer outliers = 20.00 %
""" in out3.getvalue()
# now with data
args2 = args1 + [hkl_file, "--maps"]
result, cmdline = molprobity.run(args=args2,
out=null_out(),
return_input_objects=True)
out = StringIO()
result.show(out=out)
stats = result.get_statistics_for_phenix_gui()
#print stats
stats = result.get_polygon_statistics([
"r_work", "r_free", "adp_mean_all", "angle_rmsd", "bond_rmsd",
"clashscore"
])
#print stats
assert approx_equal(result.r_work(), 0.2291, eps=0.001)
assert approx_equal(result.r_free(), 0.2804, eps=0.001)
assert approx_equal(result.d_min(), 2.0302, eps=0.0001)
assert approx_equal(result.d_max_min(), [34.546125, 2.0302], eps=0.0001)
assert approx_equal(result.rms_bonds(), 0.02586, 1e-5)
assert approx_equal(result.rms_angles(), 2.35285, 1e-5)
assert approx_equal(result.rama_favored(), 96.47059)
assert (result.cbeta_outliers() == 10)
assert approx_equal(result.unit_cell().parameters(),
(55.285, 58.851, 67.115, 90, 90, 90))
assert (str(result.space_group_info()) == "P 21 21 21")
bins = result.fmodel_statistics_by_resolution()
assert (len(bins) == 10)
assert approx_equal(result.atoms_to_observations_ratio(),
0.09755,
eps=0.0001)
assert approx_equal(result.b_iso_mean(), 31.11739)
assert op.isfile("tst_molprobity_maps.mtz")
bins = result.fmodel_statistics_by_resolution()
#bins.show()
bin_plot = result.fmodel_statistics_graph_data()
lg = bin_plot.format_loggraph()
# fake fmodel_neutron
fmodel_neutron = cmdline.fmodel.deep_copy()
result2 = mmtbx.validation.molprobity.molprobity(
cmdline.model,
fmodel=cmdline.fmodel,
fmodel_neutron=fmodel_neutron,
nuclear=True,
keep_hydrogens=True)
stats = result2.get_statistics_for_phenix_gui()
assert ('R-work (neutron)' in [label for (label, stat) in stats])
def run(args=None,
target_hierarchy=None,
chain_hierarchy=None,
target_file=None, # model
chain_file=None, # query
crystal_symmetry=None,
max_dist=None,
quiet=None,
verbose=None,
use_crystal_symmetry=None,
chain_type=None,
params=None,
target_length_from_matching_chains=None,
distance_per_site=None,
out=sys.stdout):
if not args: args=[]
if not params:
params=get_params(args,out=out)
if params.input_files.pdb_in:
print >>out,"Using %s as target" %(params.input_files.pdb_in[0])
elif chain_file or chain_hierarchy:
pass # it is fine
else:
raise Sorry("Need target model (pdb_in)")
if params.input_files.unique_target_pdb_in and params.input_files.unique_only:
print >>out,"Using %s as target for unique chains" %(
params.input_files.unique_target_pdb_in)
if params.input_files.query_dir and \
os.path.isdir(params.input_files.query_dir):
print >>out,"\nUsing all files in %s as queries\n" %(
params.input_files.query_dir)
return run_all(params=params,out=out)
if verbose is None:
verbose=params.control.verbose
if quiet is None:
quiet=params.control.quiet
if chain_type is None:
chain_type=params.crystal_info.chain_type
if use_crystal_symmetry is None:
use_crystal_symmetry=params.crystal_info.use_crystal_symmetry
params.crystal_info.use_crystal_symmetry=use_crystal_symmetry
if max_dist is None:
max_dist=params.comparison.max_dist
if distance_per_site is None:
distance_per_site=params.comparison.distance_per_site
if target_length_from_matching_chains is None:
target_length_from_matching_chains=\
params.comparison.target_length_from_matching_chains
if verbose:
local_out=out
else:
local_out=null_out()
if not target_file and len(params.input_files.pdb_in)>0:
target_file=params.input_files.pdb_in[0] # model
if not chain_file and len(params.input_files.pdb_in)>1:
chain_file=params.input_files.pdb_in[1] # query
# get the hierarchies
if not chain_hierarchy or not target_hierarchy:
assert chain_file and target_file
pdb_inp=get_pdb_inp(file_name=chain_file )
if params.input_files.unique_target_pdb_in:
target_unique_hierarchy=get_pdb_inp(
file_name=params.input_files.unique_target_pdb_in).construct_hierarchy()
else:
target_unique_hierarchy=None
if not crystal_symmetry:
crystal_symmetry=pdb_inp.crystal_symmetry_from_cryst1()
chain_hierarchy=pdb_inp.construct_hierarchy()
if params.input_files.unique_only:
print >>out,"\nUsing only unique part of query\n"
chain_hierarchy=extract_unique_part_of_hierarchy(
chain_hierarchy,target_ph=target_unique_hierarchy,out=local_out)
target_pdb_inp=get_pdb_inp(file_name=target_file)
if not crystal_symmetry or not crystal_symmetry.unit_cell():
crystal_symmetry=target_pdb_inp.crystal_symmetry_from_cryst1()
target_hierarchy=target_pdb_inp.construct_hierarchy()
if params.crystal_info.use_crystal_symmetry is None: # set default
if crystal_symmetry and crystal_symmetry.space_group() and \
(not crystal_symmetry.space_group().type().number() in [0,1]):
params.crystal_info.use_crystal_symmetry=True
else:
params.crystal_info.use_crystal_symmetry=False
crystal_symmetry=None
elif params.crystal_info.use_crystal_symmetry==False:
crystal_symmetry=None
if not crystal_symmetry or not crystal_symmetry.unit_cell():
crystal_symmetry=get_pdb_inp(
text="CRYST1 1000.000 1000.000 1000.000 90.00 90.00 90.00 P 1"
).crystal_symmetry_from_cryst1()
print >>out,"\nCrystal symmetry will not be used in comparison.\n"
if use_crystal_symmetry:
raise Sorry("Please set use_crystal_symmetry"+
"=False (no crystal symmetry supplied)")
else:
print >>out,"\nCrystal symmetry will be used in comparison.\n"
print>>out, "Space group: %s" %(crystal_symmetry.space_group().info()), \
"Unit cell: %7.2f %7.2f %7.2f %7.2f %7.2f %7.2f \n" %(
crystal_symmetry.unit_cell().parameters())
use_crystal_symmetry=True
if not quiet:
print >>out,"Looking for chain similarity for "+\
"%s (%d residues) in the model %s (%d residues)" %(
chain_file,chain_hierarchy.overall_counts().n_residues,
target_file,target_hierarchy.overall_counts().n_residues)
if verbose:
print >>out,"Chain type is: %s" %(chain_type)
if crystal_symmetry is None or crystal_symmetry.unit_cell() is None:
raise Sorry("Need crystal symmetry in at least one input file")
# get the CA residues
if chain_type in ["RNA","DNA"]:
atom_selection="name P"
if not distance_per_site:
distance_per_site=8.
else:
atom_selection="name ca and (not element Ca)"
if not distance_per_site:
distance_per_site=3.8
chain_ca=apply_atom_selection(atom_selection,chain_hierarchy)
chain_ca_lines=select_atom_lines(chain_ca)
target_ca=apply_atom_selection(atom_selection,target_hierarchy)
target_xyz_lines=select_atom_lines(target_ca)
chain_xyz_cart=chain_ca.atoms().extract_xyz()
target_xyz_cart=target_ca.atoms().extract_xyz()
# for each xyz in chain, figure out closest atom in target and dist
best_i=None
best_i_dd=None
best_pair=None
pair_list=[]
from scitbx.array_family import flex
chain_xyz_fract=crystal_symmetry.unit_cell().fractionalize(chain_xyz_cart)
target_xyz_fract=crystal_symmetry.unit_cell().fractionalize(target_xyz_cart)
far_away_match_list=[]
far_away_match_rmsd_list=flex.double()
if use_crystal_symmetry:
working_crystal_symmetry=crystal_symmetry
else:
working_crystal_symmetry=None
for i in xrange(chain_xyz_fract.size()):
best_j=None
best_dd=None
distance=None
if working_crystal_symmetry:
info=get_best_match(
flex.vec3_double([chain_xyz_fract[i]]),target_xyz_fract,
crystal_symmetry=working_crystal_symmetry,
distance_per_site=distance_per_site)
if info:
distance=info.dist()
else:
info=get_best_match(
flex.vec3_double([chain_xyz_cart[i]]),target_xyz_cart)
distance=info.distance
if info and (best_dd is None or distance<best_dd):
best_dd=distance
best_j=info.j
if best_dd > max_dist:
far_away_match_list.append(i)
far_away_match_rmsd_list.append(best_dd**2)
if (not quiet) and verbose:
print >>out,"%s" %(chain_ca_lines[i])
continue
if best_i is None or best_dd<best_i_dd:
best_i=i
best_i_dd=best_dd
best_pair=[i,best_j]
pair_list.append([i,best_j,best_dd])
n_forward=0
n_reverse=0
forward_match_list=[]
reverse_match_list=[]
forward_match_rmsd_list=flex.double()
reverse_match_rmsd_list=flex.double()
unaligned_match_list=[]
unaligned_match_rmsd_list=flex.double()
close_match_rmsd_list=flex.double()
close_match_list=[]
last_i=None
last_j=None
for [i,j,dd],[next_i,next_j,next_dd] in zip(
pair_list,pair_list[1:]+[[None,None,None]]):
if i is None or j is None: continue
found=False
if last_i is None: # first time
if next_i==i+1: # starting a segment
if next_j==j+1:
n_forward+=1
forward_match_list.append([i,j])
close_match_list.append([i,j])
forward_match_rmsd_list.append(dd**2)
close_match_rmsd_list.append(dd**2)
found=True
elif next_j==j-1:
n_reverse+=1
reverse_match_list.append([i,j])
close_match_list.append([i,j])
reverse_match_rmsd_list.append(dd**2)
close_match_rmsd_list.append(dd**2)
found=True
else: # not the first time
if i==last_i+1: # continuing a segment
if j==last_j+1:
n_forward+=1
forward_match_list.append([i,j])
close_match_list.append([i,j])
forward_match_rmsd_list.append(dd**2)
close_match_rmsd_list.append(dd**2)
found=True
elif j==last_j-1:
n_reverse+=1
reverse_match_list.append([i,j])
close_match_list.append([i,j])
reverse_match_rmsd_list.append(dd**2)
close_match_rmsd_list.append(dd**2)
found=True
if not found:
last_i=None
last_j=None
unaligned_match_list.append([i,j])
close_match_list.append([i,j])
unaligned_match_rmsd_list.append(dd**2)
close_match_rmsd_list.append(dd**2)
else:
last_i=i
last_j=j
if n_forward==n_reverse==0:
direction='none'
elif n_forward>= n_reverse:
direction='forward'
else:
direction='reverse'
if (not quiet) and verbose:
print >>out,"%s %d %d N: %d" %(
direction,n_forward,n_reverse,chain_xyz_fract.size())
rv=rmsd_values()
if forward_match_rmsd_list.size():
id='forward'
rmsd=forward_match_rmsd_list.min_max_mean().mean**0.5
n=forward_match_rmsd_list.size()
rv.add_rmsd(id=id,rmsd=rmsd,n=n)
if reverse_match_rmsd_list.size():
id='reverse'
rmsd=reverse_match_rmsd_list.min_max_mean().mean**0.5
n=reverse_match_rmsd_list.size()
rv.add_rmsd(id=id,rmsd=rmsd,n=n)
if unaligned_match_rmsd_list.size():
id='unaligned'
rmsd=unaligned_match_rmsd_list.min_max_mean().mean**0.5
n=unaligned_match_rmsd_list.size()
rv.add_rmsd(id=id,rmsd=rmsd,n=n)
if close_match_rmsd_list.size():
id='close'
rmsd=close_match_rmsd_list.min_max_mean().mean**0.5
n=close_match_rmsd_list.size()
rv.add_rmsd(id=id,rmsd=rmsd,n=n)
if far_away_match_rmsd_list.size():
id='far_away'
rmsd=far_away_match_rmsd_list.min_max_mean().mean**0.5
n=far_away_match_rmsd_list.size()
rv.add_rmsd(id=id,rmsd=rmsd,n=n)
if not quiet:
if verbose:
print >>out,"Total CA: %d Too far to match: %d " %(
chain_xyz_fract.size(),len(far_away_match_list))
rmsd,n=rv.get_values(id='forward')
if n:
print >>out,\
"\nResidues matching in forward direction: %4d RMSD: %6.2f" %(
n,rmsd)
if verbose:
for i,j in forward_match_list:
print >>out,"ID:%d:%d RESIDUES: \n%s\n%s" %( i,j, chain_ca_lines[i],
target_xyz_lines[j])
rmsd,n=rv.get_values(id='reverse')
if n:
print >>out,\
"Residues matching in reverse direction: %4d RMSD: %6.2f" %(
n,rmsd)
if verbose:
for i,j in reverse_match_list:
print >>out,"ID:%d:%d RESIDUES: \n%s\n%s" %(
i,j, chain_ca_lines[i],
target_xyz_lines[j])
rmsd,n=rv.get_values(id='unaligned')
if n:
print >>out,\
"Residues near but not matching one-to-one:%4d RMSD: %6.2f" %(
n,rmsd)
if verbose:
for i,j in unaligned_match_list:
print >>out,"ID:%d:%d RESIDUES: \n%s\n%s" %(i,j, chain_ca_lines[i],
target_xyz_lines[j])
rmsd,n=rv.get_values(id='close')
if n:
lines_chain_ca=[]
lines_target_xyz=[]
for i,j in close_match_list:
lines_chain_ca.append(chain_ca_lines[i])
lines_target_xyz.append(target_xyz_lines[j])
seq_chain_ca=get_seq_from_lines(lines_chain_ca)
seq_target_xyz=get_seq_from_lines(lines_target_xyz)
target_chain_ids=get_chains_from_lines(lines_target_xyz)
target_length=get_target_length(target_chain_ids=target_chain_ids,
hierarchy=target_ca)
rv.add_target_length(id='close',target_length=target_length)
if verbose:
print "SEQ1:",seq_chain_ca,len(lines_chain_ca)
print "SEQ2:",seq_target_xyz,len(lines_target_xyz)
match_n,match_percent=get_match_percent(seq_chain_ca,seq_target_xyz)
rv.add_match_percent(id='close',match_percent=match_percent)
percent_close=rv.get_close_to_target_percent('close')
print >>out,\
"\nAll residues near target: "+\
"%4d RMSD: %6.2f Seq match (%%):%5.1f %% Found: %5.1f" %(
n,rmsd,match_percent,percent_close)
if verbose:
for i,j in close_match_list:
print >>out,"ID:%d:%d RESIDUES: \n%s\n%s" %(i,j, chain_ca_lines[i],
target_xyz_lines[j])
rmsd,n=rv.get_values(id='far_away')
if n:
print >>out,\
"Residues far from target: %4d RMSD: %6.2f" %(
n,rmsd)
if verbose:
for i in far_away_match_list:
print >>out,"ID:%d RESIDUES: \n%s" %(i,chain_ca_lines[i])
rv.n_forward=n_forward
rv.n_reverse=n_reverse
rv.n=len(pair_list)
return rv
def format_miller_arrays(self, iparams):
'''
Read in mtz file and format to miller_arrays_out object with
index[0] --> FP, SIGFP
index[1] --> PHIB
index[2] --> FOM
index[3] --> HLA, HLB, HLC, HLD
index[4] --> optional PHIC
'''
#readin reflection file
reflection_file = reflection_file_reader.any_reflection_file(
iparams.data)
file_content = reflection_file.file_content()
column_labels = file_content.column_labels()
col_name = iparams.column_names.split(',')
miller_arrays = reflection_file.as_miller_arrays()
flex_centric_flags = miller_arrays[0].centric_flags().data()
crystal_symmetry = crystal.symmetry(
unit_cell=miller_arrays[0].unit_cell(),
space_group=miller_arrays[0].space_group())
#grab all required columns
flag_fp_found = 0
flag_phib_found = 0
flag_fom_found = 0
flag_hl_found = 0
ind_miller_array_fp = 0
ind_miller_array_phib = 0
ind_miller_array_fom = 0
ind_miller_array_hl = 0
for i in range(len(miller_arrays)):
label_string = miller_arrays[i].info().label_string()
labels = label_string.split(',')
#only look at first index string
if labels[0] == col_name[0]:
#grab FP, SIGFP
flex_fp_all = miller_arrays[i].data()
flex_sigmas_all = miller_arrays[i].sigmas()
flag_fp_found = 1
ind_miller_array_fp = i
elif labels[0] == col_name[2]:
#grab PHIB
flex_phib_all = miller_arrays[i].data()
flag_phib_found = 1
ind_miller_array_phib = i
elif labels[0] == col_name[3]:
#grab FOM
flex_fom_all = miller_arrays[i].data()
flag_fom_found = 1
ind_miller_array_fom = i
elif labels[0] == col_name[4]:
#grab HLA,HLB,HLC,HLD
flex_hl_all = miller_arrays[i].data()
flag_hl_found = 1
ind_miller_array_hl = i
if flag_hl_found == 1 and flag_phib_found == 0:
#calculate PHIB and FOM from HL
miller_array_phi_fom = miller_arrays[
ind_miller_array_hl].phase_integrals()
flex_phib_all = miller_array_phi_fom.phases(deg=True).data()
flex_fom_all = miller_array_phi_fom.amplitudes().data()
flag_phib_found = 1
flag_fom_found = 1
if flag_fp_found == 0 or flag_phib_found == 0 or flag_fom_found == 0 or flag_hl_found == 0:
print "couldn't find all required columns"
sys.exit()
miller_indices_sel = miller_arrays[ind_miller_array_fp].indices()
print 'No. reflections for read-in miller arrays - indices:%6.0f fp:%6.0f phib:%6.0f fom:%6.0f HL:%6.0f)'%( \
len(miller_indices_sel), len(flex_fp_all), len(flex_phib_all), len(flex_fom_all), len(flex_hl_all))
miller_indices = flex.miller_index()
flex_fp = flex.double()
flex_sigmas = flex.double()
flex_phib = flex.double()
flex_fom = flex.double()
flex_hl = flex.hendrickson_lattman()
#format all miller arrays to the same length
for miller_index in miller_indices_sel:
fp_cn, phib_cn, fom_cn, hl_cn = (0, 0, 0, 0)
matches = miller.match_multi_indices(
miller_indices_unique=flex.miller_index([miller_index]),
miller_indices=miller_arrays[ind_miller_array_fp].indices())
if len(matches.pairs()) > 0:
fp_cn = 1
fp = flex_fp_all[matches.pairs()[0][1]]
sigmas = flex_sigmas_all[matches.pairs()[0][1]]
matches = miller.match_multi_indices(
miller_indices_unique=flex.miller_index([miller_index]),
miller_indices=miller_arrays[ind_miller_array_phib].indices())
if len(matches.pairs()) > 0:
phib_cn = 1
phib = flex_phib_all[matches.pairs()[0][1]]
matches = miller.match_multi_indices(
miller_indices_unique=flex.miller_index([miller_index]),
miller_indices=miller_arrays[ind_miller_array_fom].indices())
if len(matches.pairs()) > 0:
fom_cn = 1
fom = flex_fom_all[matches.pairs()[0][1]]
matches = miller.match_multi_indices(
miller_indices_unique=flex.miller_index([miller_index]),
miller_indices=miller_arrays[ind_miller_array_hl].indices())
if len(matches.pairs()) > 0:
hl_cn = 1
hl = flex_hl_all[matches.pairs()[0][1]]
if (fp_cn + phib_cn + fom_cn + hl_cn) == 4:
miller_indices.append(miller_index)
flex_fp.append(fp)
flex_sigmas.append(sigmas)
flex_phib.append(phib)
flex_fom.append(fom)
flex_hl.append(hl)
print 'No. reflections after format - indices:%6.0f fp:%6.0f phib:%6.0f fom:%6.0f HL:%6.0f)'%( \
len(miller_indices), len(flex_fp), len(flex_phib), len(flex_fom), len(flex_hl))
flex_hla = flex.double()
flex_hlb = flex.double()
flex_hlc = flex.double()
flex_hld = flex.double()
for i in range(len(flex_hl)):
data_hl_row = flex_hl[i]
flex_hla.append(data_hl_row[0])
flex_hlb.append(data_hl_row[1])
flex_hlc.append(data_hl_row[2])
flex_hld.append(data_hl_row[3])
'''
Read benchmark MTZ (PHICalc) for MPE calculation
'''
flex_phic = flex.double([0] * len(flex_fp))
if iparams.hklrefin is not None:
reflection_file = reflection_file_reader.any_reflection_file(
iparams.hklrefin)
miller_arrays_bench = reflection_file.as_miller_arrays()
flex_phic_raw = None
for i in range(len(miller_arrays_bench)):
label_string = miller_arrays_bench[i].info().label_string()
labels = label_string.split(',')
#only look at first index string
if labels[0] == iparams.column_phic:
#grab PHIC
if miller_arrays_bench[i].is_complex_array():
flex_phic_raw = miller_arrays_bench[i].phases(
deg=True).data()
else:
flex_phic_raw = miller_arrays_bench[i].data()
miller_indices_phic = miller_arrays_bench[i].indices()
if flex_phic is not None:
matches = miller.match_multi_indices(
miller_indices_unique=miller_indices,
miller_indices=miller_indices_phic)
flex_phic = flex.double(
[flex_phic_raw[pair[1]] for pair in matches.pairs()])
#format miller_arrays_out
miller_set = miller.set(crystal_symmetry=crystal_symmetry,
indices=miller_indices,
anomalous_flag=False)
miller_array_out = miller_set.array(
data=flex_fp,
sigmas=flex_sigmas).set_observation_type_xray_amplitude()
#check if Wilson B-factor is applied
flex_fp_for_sort = flex_fp[:]
if iparams.flag_apply_b_factor:
try:
#get wilson_plot
from mmtbx.scaling import xtriage
from libtbx.utils import null_out
xtriage_args = [iparams.data, "", "", "log=tst_xtriage_1.log"]
result = xtriage.run(args=xtriage_args, out=null_out())
ws = result.wilson_scaling
print 'Wilson K=%6.2f B=%6.2f' % (ws.iso_p_scale,
ws.iso_b_wilson)
sin_theta_over_lambda_sq = miller_array_out.two_theta(wavelength=iparams.wavelength) \
.sin_theta_over_lambda_sq().data()
wilson_expect = flex.exp(-2 * ws.iso_b_wilson *
sin_theta_over_lambda_sq)
flex_fp_for_sort = wilson_expect * flex_fp
except Exception:
print 'Error calculating Wilson scale factors. Continue without applying B-factor.'
flex_d_spacings = miller_array_out.d_spacings().data()
mtz_dataset = miller_array_out.as_mtz_dataset(column_root_label="FP")
for data, lbl, typ in [(flex_phib, "PHIB", "P"),
(flex_fom, "FOMB", "W"), (flex_hla, "HLA", "A"),
(flex_hlb, "HLB", "A"), (flex_hlc, "HLC", "A"),
(flex_hld, "HLD", "A"),
(flex_phic, "PHIC", "P")]:
mtz_dataset.add_miller_array(miller_array_out.array(data=data),
column_root_label=lbl,
column_types=typ)
miller_arrays_out = mtz_dataset.mtz_object().as_miller_arrays()
'''
getting sorted indices for the selected reflections in input mtz file
list_fp_sort_index: stores indices of sorted FP in descending order
'''
import operator
fp_sort_index = [
i for (i, j) in sorted(enumerate(flex_fp_for_sort),
key=operator.itemgetter(1))
]
fp_sort_index.reverse()
"""
for i in range(100):
print miller_indices[fp_sort_index[i]], flex_d_spacings[fp_sort_index[i]], flex_fp[fp_sort_index[i]], flex_sigmas[fp_sort_index[i]], wilson_expect[fp_sort_index[i]]
exit()
"""
#calculate sum of fp^2 from percent_f_squared
flex_fp_squared = flex_fp**2
f_squared_per_stack = (iparams.percent_f_squared *
np.sum(flex_fp_squared)) / 100
fp_sort_index_stacks = []
sum_fp_now, i_start = (0, 0)
for i in range(len(fp_sort_index)):
i_sel = fp_sort_index[i_start:i + 1]
sum_fp_now = np.sum([flex_fp_squared[ii_sel] for ii_sel in i_sel])
if sum_fp_now >= f_squared_per_stack:
fp_sort_index_stacks.append(fp_sort_index[i_start:i + 1])
i_start = i + 1
if len(fp_sort_index_stacks) == iparams.n_stacks:
break
txt_out = 'stack_no sum(f_squared) %total n_refl\n'
for i in range(len(fp_sort_index_stacks)):
sum_fp = np.sum([
flex_fp_squared[ii_sel] for ii_sel in fp_sort_index_stacks[i]
])
txt_out += '%6.0f %14.2f %8.2f %6.0f\n'%(i+1, sum_fp, \
(sum_fp/np.sum(flex_fp_squared))*100, len(fp_sort_index_stacks[i]))
return miller_arrays_out, fp_sort_index_stacks, txt_out
def exercise_bond_over_symmetry_2(mon_lib_srv, ener_lib):
""" This test is to illustrate that bond over symmetry actually
adds 2 proxies.
"""
from cctbx.geometry_restraints.linking_class import linking_class
origin_ids = linking_class()
pdb_inp = iotbx.pdb.input(source_info=None, lines=raw_records10)
params = mmtbx.model.manager.get_default_pdb_interpretation_params()
params.pdb_interpretation.restraints_library.mcl = False
model = mmtbx.model.manager(model_input=pdb_inp, log=null_out())
model.process(pdb_interpretation_params=params, make_restraints=True)
grm = model.get_restraints_manager().geometry
simple, asu = grm.get_all_bond_proxies()
assert (simple.size(), asu.size()) == (0, 0)
h = model.get_hierarchy()
sites_cart = h.atoms().extract_xyz()
site_labels = model.get_xray_structure().scatterers().extract_labels()
pair_proxies = grm.pair_proxies(flags=None, sites_cart=sites_cart)
out = StringIO()
pair_proxies.bond_proxies.show_sorted(by_value="residual",
sites_cart=sites_cart,
site_labels=site_labels,
f=out,
prefix="")
outtxt = out.getvalue()
# print(outtxt)
proxy = geometry_restraints.bond_simple_proxy(
i_seqs=(0, 1),
distance_ideal=2.9,
weight=400,
origin_id=origin_ids.get_origin_id('hydrogen bonds'))
grm.add_new_bond_restraints_in_place(proxies=[proxy],
sites_cart=h.atoms().extract_xyz())
simple, asu = grm.get_all_bond_proxies()
# print(simple.size(), asu.size())
assert (simple.size(), asu.size()) == (0, 2)
sites_cart = h.atoms().extract_xyz()
site_labels = model.get_xray_structure().scatterers().extract_labels()
pair_proxies = grm.pair_proxies(flags=None, sites_cart=sites_cart)
out = StringIO()
pair_proxies.bond_proxies.show_sorted(by_value="residual",
sites_cart=sites_cart,
site_labels=site_labels,
f=out,
prefix="")
outtxt = out.getvalue()
# print(outtxt)
assert_lines_in_text(
outtxt, """\
bond pdb=" CA HIS A 2 "
pdb=" N MET A 1 "
ideal model delta sigma weight residual sym.op.
2.900 1.998 0.902 5.00e-02 4.00e+02 3.25e+02 x,y+1,z
bond pdb=" N MET A 1 "
pdb=" CA HIS A 2 "
ideal model delta sigma weight residual sym.op.
2.900 1.998 0.902 5.00e-02 4.00e+02 3.25e+02 x,y-1,z
""")
es = grm.energies_sites(sites_cart=sites_cart, compute_gradients=True)
out = StringIO()
es.show(f=out)
outtxt = out.getvalue()
# print(outtxt)
# do for x coordinate
# ATOM 1 N MET A 1 9.821 1.568 5.000 1.00 66.07 N
# ATOM 2 CA HIS A 2 9.946 12.171 5.357 1.00 66.55 C
# calculation is from geometry_restraints/bond.h: gradient_0()
# weight * 2 * delta_slack * d_distance_d_site_0(epsilon);
# print("X gradient:", 400*2*0.902*(9.946-9.821)) # 90
# Note that n=2 but residual sum is 325.349. 349 is chopped off in rounding in
# cctbx/geometry_restraints/__init__py, def _bond_show_sorted_impl(...)
# where %6.2e is used. in cctbx/geometry_restraints/energies.py: def show()
# %.6g is used which is showing more numbers.
assert_lines_in_text(outtxt, """\
bond_residual_sum (n=2): 325.349""")
# print("Gradients:", list(es.gradients))
# Seems that gradients were splitted in half (note the X gradient is 90 8 lines above)
assert approx_equal(
list(es.gradients),
[(45.135801792665134, -708.451544937652, 128.90784991984805),
(-45.13580179266516, 708.4515449376522, -128.90784991984813)])
def __init__(
self,
map_manager,
model=None,
target_ncs_au_model=None,
regions_to_keep=None,
solvent_content=None,
resolution=None,
sequence=None,
molecular_mass=None,
symmetry=None,
chain_type='PROTEIN',
keep_low_density=True, # default from map_box
box_cushion=5,
soft_mask=True,
mask_expand_ratio=1,
wrapping=None,
log=None):
self.model_can_be_outside_bounds = None # not used but required to be set
self._map_manager = map_manager
self._model = model
self._mask_data = None
self._force_wrapping = wrapping
if wrapping is None:
wrapping = self.map_manager().wrapping()
self.basis_for_boxing_string = 'around_unique, wrapping = %s' % (
wrapping)
if log is None:
log = null_out() # Print only if a log is supplied
assert isinstance(map_manager, iotbx.map_manager.map_manager)
assert self._map_manager.map_data().accessor().origin() == (0, 0, 0)
assert resolution is not None
if model is not None:
assert isinstance(model, mmtbx.model.manager)
assert map_manager.is_compatible_model(model)
if self.map_manager().wrapping(): # map must be entire unit cell
assert map_manager.unit_cell_grid == map_manager.map_data().all()
# Get crystal_symmetry
self.crystal_symmetry = map_manager.crystal_symmetry()
# Convert to map_data
from cctbx.maptbx.segment_and_split_map import run as segment_and_split_map
assert self._map_manager.map_data().origin() == (0, 0, 0)
args = []
ncs_group_obj, remainder_ncs_group_obj, tracking_data = \
segment_and_split_map(args,
map_data = self._map_manager.map_data(),
crystal_symmetry = self.crystal_symmetry,
ncs_obj = self._map_manager.ncs_object(),
target_model = target_ncs_au_model,
write_files = False,
auto_sharpen = False,
add_neighbors = False,
density_select = False,
save_box_map_ncs_au = True,
resolution = resolution,
solvent_content = solvent_content,
chain_type = chain_type,
sequence = sequence,
molecular_mass = molecular_mass,
symmetry = symmetry,
keep_low_density = keep_low_density,
regions_to_keep = regions_to_keep,
box_buffer = box_cushion,
soft_mask_extract_unique = soft_mask,
mask_expand_ratio = mask_expand_ratio,
out = log)
from scitbx.matrix import col
if not hasattr(tracking_data, 'box_mask_ncs_au_map_data'):
raise Sorry(" Extraction of unique part of map failed...")
ncs_au_mask_data = tracking_data.box_mask_ncs_au_map_data
lower_bounds = ncs_au_mask_data.origin()
upper_bounds = tuple(col(ncs_au_mask_data.focus()) - col((1, 1, 1)))
print("\nBounds for unique part of map: %s to %s " %
(str(lower_bounds), str(upper_bounds)),
file=log)
# shift the map so it is in the same position as the box map will be in
ncs_au_mask_data.reshape(flex.grid(ncs_au_mask_data.all()))
assert col(ncs_au_mask_data.all()) == \
col(upper_bounds)-col(lower_bounds)+col((1, 1, 1))
self.gridding_first = lower_bounds
self.gridding_last = upper_bounds
# Ready with gridding...set up shifts and box crystal_symmetry
self.set_shifts_and_crystal_symmetry()
# Apply boxing to model, ncs, and map (if available)
self.apply_to_model_ncs_and_map()
# Note that at this point, self._map_manager has been boxed
assert ncs_au_mask_data.all() == self._map_manager.map_data().all()
self._mask_data = ncs_au_mask_data
# Now separately apply the mask to the boxed map
self.apply_around_unique_mask(self._map_manager,
resolution=resolution,
soft_mask=soft_mask)