def exercise_map_utils () :
#
# UNSTABLE
#
hierarchy, fmodel = get_1yjp_pdb_and_fmodel()
sel_cache = hierarchy.atom_selection_cache()
sele = sel_cache.selection("resseq 5 and (name CD or name OE1 or name NE2)")
sele_all = sel_cache.selection("resseq 5")
fmodel.xray_structure.scale_adp(factor=0.5, selection=sele)
fmodel.update_xray_structure(update_f_calc=True)
two_fofc_map, fofc_map = building.get_difference_maps(fmodel)
map_stats = building.local_density_quality(
fofc_map=fofc_map,
two_fofc_map=two_fofc_map,
atom_selection=sele_all,
xray_structure=fmodel.xray_structure)
out = StringIO()
map_stats.show_atoms_outside_density(out=out, two_fofc_cutoff=3.0)
pdb_strs = [ l.split(":")[0] for l in out.getvalue().splitlines() ]
assert len(pdb_strs) > 0
# XXX there seems to be a stochastic effect here
#assert (pdb_strs == ['pdb=" CA GLN A 5 "', 'pdb=" CB GLN A 5 "',
# 'pdb=" CD GLN A 5 "', 'pdb=" NE2 GLN A 5 "']), \
# pdb_strs
fc_map = fmodel.map_coefficients(map_type="Fc").fft_map(
resolution_factor=0.25).apply_sigma_scaling().real_map_unpadded()
assert (map_stats.number_of_atoms_below_fofc_map_level() == 3)
assert (map_stats.fraction_of_nearby_grid_points_above_cutoff() > 0.0)
stats = building.get_model_map_stats(
selection=sele_all,
target_map=two_fofc_map,
model_map=fc_map,
unit_cell=fmodel.xray_structure.unit_cell(),
sites_cart=fmodel.xray_structure.sites_cart(),
pdb_atoms=hierarchy.atoms())
def exercise_map_utils () :
#
# UNSTABLE 2x
#
hierarchy, fmodel = get_1yjp_pdb_and_fmodel()
sel_cache = hierarchy.atom_selection_cache()
sele = sel_cache.selection("resseq 5 and (name CD or name OE1 or name NE2)")
sele_all = sel_cache.selection("resseq 5")
fmodel.xray_structure.scale_adp(factor=0.5, selection=sele)
fmodel.update_xray_structure(update_f_calc=True)
two_fofc_map, fofc_map = building.get_difference_maps(fmodel)
map_stats = building.local_density_quality(
fofc_map=fofc_map,
two_fofc_map=two_fofc_map,
atom_selection=sele_all,
xray_structure=fmodel.xray_structure)
out = StringIO()
map_stats.show_atoms_outside_density(out=out, two_fofc_cutoff=3.0)
pdb_strs = [ l.split(":")[0] for l in out.getvalue().splitlines() ]
assert len(pdb_strs) > 0
# XXX there seems to be a stochastic effect here
#assert (pdb_strs == ['pdb=" CA GLN A 5 "', 'pdb=" CB GLN A 5 "',
# 'pdb=" CD GLN A 5 "', 'pdb=" NE2 GLN A 5 "']), \
# pdb_strs
fc_map = fmodel.map_coefficients(map_type="Fc").fft_map(
resolution_factor=0.25).apply_sigma_scaling().real_map_unpadded()
assert (map_stats.number_of_atoms_below_fofc_map_level() == 3)
assert (map_stats.fraction_of_nearby_grid_points_above_cutoff() > 0.0)
stats = building.get_model_map_stats(
selection=sele_all,
target_map=two_fofc_map,
model_map=fc_map,
unit_cell=fmodel.xray_structure.unit_cell(),
sites_cart=fmodel.xray_structure.sites_cart(),
pdb_atoms=hierarchy.atoms())
def filter_before_build (
pdb_hierarchy,
fmodel,
geometry_restraints_manager,
selection=None,
params=None,
verbose=True,
log=sys.stdout) :
"""
Pick residues suitable for building alternate conformations - by default,
this means no MolProbity/geometry outliers, good fit to map, no missing
atoms, and no pre-existing alternates, but with significant difference
density nearby.
"""
from mmtbx.validation import molprobity
from mmtbx.rotamer import rotamer_eval
import mmtbx.monomer_library.server
from mmtbx import building
from iotbx.pdb import common_residue_names_get_class
from scitbx.array_family import flex
if (selection is None) :
selection = flex.bool(fmodel.xray_structure.scatterers().size(), True)
pdb_atoms = pdb_hierarchy.atoms()
assert (pdb_atoms.size() == fmodel.xray_structure.scatterers().size())
pdb_atoms.reset_i_seq()
full_validation = molprobity.molprobity(
pdb_hierarchy=pdb_hierarchy,
fmodel=fmodel,
geometry_restraints_manager=geometry_restraints_manager,
outliers_only=False,
rotamer_library="8000")
if (verbose) :
full_validation.show(out=log)
multi_criterion = full_validation.as_multi_criterion_view()
if (params is None) :
params = libtbx.phil.parse(filter_params_str).extract()
mon_lib_srv = mmtbx.monomer_library.server.server()
two_fofc_map, fofc_map = building.get_difference_maps(fmodel=fmodel)
residues = []
filters = params.discard_outliers
make_sub_header("Identifying candidates for building", out=log)
# TODO parallelize
for chain in pdb_hierarchy.only_model().chains() :
if (not chain.is_protein()) :
continue
for residue_group in chain.residue_groups() :
atom_groups = residue_group.atom_groups()
id_str = residue_group.id_str()
i_seqs = residue_group.atoms().extract_i_seq()
residue_sel = selection.select(i_seqs)
if (not residue_sel.all_eq(True)) :
continue
if (len(atom_groups) > 1) :
print >> log, " %s is already multi-conformer" % id_str
continue
atom_group = atom_groups[0]
res_class = common_residue_names_get_class(atom_group.resname)
if (res_class != "common_amino_acid") :
print >> log, " %s: non-standard residue" % id_str
continue
missing_atoms = rotamer_eval.eval_residue_completeness(
residue=atom_group,
mon_lib_srv=mon_lib_srv,
ignore_hydrogens=True)
if (len(missing_atoms) > 0) :
# residues modeled as pseudo-ALA are allowed by default; partially
# missing sidechains are more problematic
if ((building.is_stub_residue(atom_group)) and
(not params.ignore_stub_residues)) :
pass
else :
print >> log, " %s: missing or incomplete sidechain" % \
(id_str, len(missing_atoms))
continue
validation = multi_criterion.get_residue_group_data(residue_group)
is_outlier = is_validation_outlier(validation, params)
if (is_outlier) :
print >> log, " %s" % str(validation)
continue
if (params.use_difference_map) :
i_seqs_no_hd = building.get_non_hydrogen_atom_indices(residue_group)
map_stats = building.local_density_quality(
fofc_map=fofc_map,
two_fofc_map=two_fofc_map,
atom_selection=i_seqs_no_hd,
xray_structure=fmodel.xray_structure,
radius=params.sampling_radius)
if ((map_stats.number_of_atoms_below_fofc_map_level() == 0) and
(map_stats.fraction_of_nearby_grid_points_above_cutoff()==0)) :
if (verbose) :
print >> log, " no difference density for %s" % id_str
continue
residues.append(residue_group.only_atom_group())
if (len(residues) == 0) :
raise Sorry("No residues passed the filtering criteria.")
print >> log, ""
print >> log, "Alternate conformations will be tried for %d residue(s):" % \
len(residues)
building.show_chain_resseq_ranges(residues, out=log, prefix=" ")
print >> log, ""
return residues
def __init__(self,
fmodel,
params,
mp_params,
pdb_hierarchy,
processed_pdb_file,
selection=None,
cif_objects=(),
verbose=True,
debug=False,
out=None):
if (out is None) : out = sys.stdout
adopt_init_args(self, locals())
self.asynchronous_output = False
from mmtbx.rotamer import rotamer_eval
from scitbx.array_family import flex
assert (processed_pdb_file is not None) or (len(pdb_file_names) > 0)
assert (0 < self.params.window_radius <= 4)
self.pdb_hierarchy = pdb_hierarchy
self.processed_pdb_file = processed_pdb_file
self.get_processed_pdb_file(log=out)
self.sites_cart = self.pdb_hierarchy.atoms().extract_xyz().deep_copy()
if (self.selection is None):
self.selection = flex.bool(self.sites_cart.size(), True)
self.min_required_deviation = self.params.min_deviation
if (self.min_required_deviation is Auto):
self.min_required_deviation = fmodel.f_obs().d_min() / 2
self._ensembles = []
self.nproc_1 = self.nproc_2 = 1
two_fofc_map, fofc_map = mmtbx.building.get_difference_maps(fmodel=fmodel)
windows = []
r = rotamer_eval.RotamerEval(data_version="8000")
exclude_resnames = []
if (params.exclude_resnames is not None):
exclude_resnames = [ n.upper() for n in params.exclude_resnames ]
for chain in self.pdb_hierarchy.only_model().chains():
if not chain.is_protein():
continue
residues = chain.residue_groups()
fragments = alt_confs.fragment_single_conformer_chain(residues)
for fragment_residues in fragments :
start = params.window_radius
end = - params.window_radius
for i_res, residue in enumerate(fragment_residues[start:end]):
j_res = i_res + start
atom_groups = residue.atom_groups()
main_conf = atom_groups[0]
if (main_conf.resname.upper() in exclude_resnames):
continue
residue_id = main_conf.id_str()
ag_i_seqs = main_conf.atoms().extract_i_seq()
if (not self.selection.select(ag_i_seqs).all_eq(True)):
continue
if (len(atom_groups) != 1):
if (self.verbose):
print(" residue %s already has multiple conformations"%\
residue_id, file=out)
continue
ag_i_seqs_no_hd = flex.size_t()
for atom in main_conf.atoms():
if (atom.element.strip() not in ["H","D"]):
ag_i_seqs_no_hd.append(atom.i_seq)
# XXX this is probably not optimal; what should I do about the
# adjacent residues? it would be good to check Ramachandran plot too
if (self.params.prefilter.rotameric_only):
n_outliers = alt_confs.score_rotamers(hierarchy=hierarchy,
selection=ag_i_seqs)
if (n_outliers > 0):
if (self.verbose):
print(" residue %s is a rotamer outlier" % residue_id, file=out)
continue
if (self.params.prefilter.use_difference_map):
map_stats = building.local_density_quality(
fofc_map=fofc_map,
two_fofc_map=two_fofc_map,
atom_selection=ag_i_seqs_no_hd,
xray_structure=fmodel.xray_structure,
radius=self.params.prefilter.sampling_radius)
if ((map_stats.number_of_atoms_in_difference_holes() == 0) and
(map_stats.fraction_of_nearby_grid_points_above_cutoff()==0)):
if (self.verbose):
print(" no difference density for %s" % residue_id, file=out)
continue
window_selection = flex.size_t()
offset = - self.params.window_radius
while (offset <= self.params.window_radius):
adjacent_group = fragment_residues[j_res+offset].atom_groups()[0]
window_selection.extend(adjacent_group.atoms().extract_i_seq())
offset += 1
windows.append(residue_window(
residue_id_str=residue_id,
selection=window_selection,
residue_selection=ag_i_seqs_no_hd,
sites_reference=self.sites_cart.select(selection),
window_radius=self.params.window_radius))
if (len(windows) == 0):
raise Sorry("No peptide segments meeting the filtering criteria could "+
"be extracted from the selected atoms.")
else :
print("%d fragments will be refined." % len(windows), file=out)
if (self.mp_params.nproc == 1):
pass
elif (self.mp_params.technology == "multiprocessing"):
if (self.params.n_trials == 1) and (len(self.params.partial_occupancy) == 1):
# only one refinement per window, so parallelize residue iteration
self.nproc_1 = self.mp_params.nproc
else :
# multiple refinements per window, so parallelize at that level
# FIXME actually, this needs to be smarter - if the number of
# available processors is greater than the number of refinements per
# window, it will be more efficient to parallelize the window loop
self.nproc_2 = self.mp_params.nproc
else :
# queuing system, so we can only parallelize residue iteration
self.nproc_1 = self.mp_params.nproc
self.out = null_out()
self.processed_pdb_file = None
print("", file=out)
alt_confs.print_trial_header(out)
ensembles = []
if (self.nproc_1 == 1):
self.asynchronous_output = True
for window in windows :
ens = self.refine_window(window)
ensembles.append(ens)
else :
ensembles = easy_mp.parallel_map(
func=self.refine_window,
iterable=windows,
processes=self.nproc_1,
qsub_command=mp_params.qsub_command,
method=mp_params.technology)
self._ensembles = [ e for e in ensembles if (e is not None) ]
# XXX reassert order
print("", file=out)
if (len(self._ensembles) == 0):
print("WARNING: no ensembles passed filtering step", file=out)
print("", file=out)
self._ensembles.sort(lambda a,b: a.selection[0] < b.selection[0])
self.processed_pdb_file = processed_pdb_file
if (debug):
for k, ens in enumerate(filtered):
pdb_out = ens.dump_pdb_file(
pdb_hierarchy=pdb_hierarchy,
crystal_symmetry=fmodel.f_obs())
print("wrote %s" % pdb_out, file=out)
def __init__ (self,
fmodel,
params,
mp_params,
pdb_hierarchy,
processed_pdb_file,
selection=None,
cif_objects=(),
verbose=True,
debug=False,
out=None) :
if (out is None) : out = sys.stdout
adopt_init_args(self, locals())
self.asynchronous_output = False
from mmtbx.rotamer import rotamer_eval
from scitbx.array_family import flex
assert (processed_pdb_file is not None) or (len(pdb_file_names) > 0)
assert (0 < self.params.window_radius <= 4)
self.pdb_hierarchy = pdb_hierarchy
self.processed_pdb_file = processed_pdb_file
self.get_processed_pdb_file(log=out)
self.sites_cart = self.pdb_hierarchy.atoms().extract_xyz().deep_copy()
if (self.selection is None) :
self.selection = flex.bool(self.sites_cart.size(), True)
self.min_required_deviation = self.params.min_deviation
if (self.min_required_deviation is Auto) :
self.min_required_deviation = fmodel.f_obs().d_min() / 2
self._ensembles = []
self.nproc_1 = self.nproc_2 = 1
two_fofc_map, fofc_map = mmtbx.building.get_difference_maps(fmodel=fmodel)
windows = []
r = rotamer_eval.RotamerEval(data_version="8000")
exclude_resnames = []
if (params.exclude_resnames is not None) :
exclude_resnames = [ n.upper() for n in params.exclude_resnames ]
for chain in self.pdb_hierarchy.only_model().chains() :
main_conf = chain.conformers()[0]
if (not main_conf.is_protein()) :
continue
residues = chain.residue_groups()
fragments = alt_confs.fragment_single_conformer_chain(residues)
for fragment_residues in fragments :
start = params.window_radius
end = - params.window_radius
for i_res, residue in enumerate(fragment_residues[start:end]) :
j_res = i_res + start
atom_groups = residue.atom_groups()
main_conf = atom_groups[0]
if (main_conf.resname.upper() in exclude_resnames) :
continue
residue_id = main_conf.id_str()
ag_i_seqs = main_conf.atoms().extract_i_seq()
if (not self.selection.select(ag_i_seqs).all_eq(True)) :
continue
if (len(atom_groups) != 1) :
if (self.verbose) :
print >> out, " residue %s already has multiple conformations"%\
residue_id
continue
ag_i_seqs_no_hd = flex.size_t()
for atom in main_conf.atoms() :
if (atom.element.strip() not in ["H","D"]) :
ag_i_seqs_no_hd.append(atom.i_seq)
# XXX this is probably not optimal; what should I do about the
# adjacent residues? it would be good to check Ramachandran plot too
if (self.params.prefilter.rotameric_only) :
n_outliers = alt_confs.score_rotamers(hierarchy=hierarchy,
selection=ag_i_seqs)
if (n_outliers > 0) :
if (self.verbose) :
print >> out, " residue %s is a rotamer outlier" % residue_id
continue
if (self.params.prefilter.use_difference_map) :
map_stats = building.local_density_quality(
fofc_map=fofc_map,
two_fofc_map=two_fofc_map,
atom_selection=ag_i_seqs_no_hd,
xray_structure=fmodel.xray_structure,
radius=self.params.prefilter.sampling_radius)
if ((map_stats.number_of_atoms_in_difference_holes() == 0) and
(map_stats.fraction_of_nearby_grid_points_above_cutoff()==0)) :
if (self.verbose) :
print >> out, " no difference density for %s" % residue_id
continue
window_selection = flex.size_t()
offset = - self.params.window_radius
while (offset <= self.params.window_radius) :
adjacent_group = fragment_residues[j_res+offset].atom_groups()[0]
window_selection.extend(adjacent_group.atoms().extract_i_seq())
offset += 1
windows.append(residue_window(
residue_id_str=residue_id,
selection=window_selection,
residue_selection=ag_i_seqs_no_hd,
sites_reference=self.sites_cart.select(selection),
window_radius=self.params.window_radius))
if (len(windows) == 0) :
raise Sorry("No peptide segments meeting the filtering criteria could "+
"be extracted from the selected atoms.")
else :
print >> out, "%d fragments will be refined." % len(windows)
if (self.mp_params.nproc == 1) :
pass
elif (self.mp_params.technology == "multiprocessing") :
if (self.params.n_trials == 1) and (len(self.params.partial_occupancy) == 1) :
# only one refinement per window, so parallelize residue iteration
self.nproc_1 = self.mp_params.nproc
else :
# multiple refinements per window, so parallelize at that level
# FIXME actually, this needs to be smarter - if the number of
# available processors is greater than the number of refinements per
# window, it will be more efficient to parallelize the window loop
self.nproc_2 = self.mp_params.nproc
else :
# queuing system, so we can only parallelize residue iteration
self.nproc_1 = self.mp_params.nproc
self.out = null_out()
self.processed_pdb_file = None
print >> out, ""
alt_confs.print_trial_header(out)
ensembles = []
if (self.nproc_1 == 1) :
self.asynchronous_output = True
for window in windows :
ens = self.refine_window(window)
ensembles.append(ens)
else :
ensembles = easy_mp.parallel_map(
func=self.refine_window,
iterable=windows,
processes=self.nproc_1,
qsub_command=mp_params.qsub_command,
method=mp_params.technology)
self._ensembles = [ e for e in ensembles if (e is not None) ]
# XXX reassert order
print >> out, ""
if (len(self._ensembles) == 0) :
print >> out, "WARNING: no ensembles passed filtering step"
print >> out, ""
self._ensembles.sort(lambda a,b: a.selection[0] < b.selection[0])
self.processed_pdb_file = processed_pdb_file
if (debug) :
for k, ens in enumerate(filtered) :
pdb_out = ens.dump_pdb_file(
pdb_hierarchy=pdb_hierarchy,
crystal_symmetry=fmodel.f_obs())
print >> out, "wrote %s" % pdb_out
