def exercise_utils():
#--- coord_stats_with_flips
pdb_1 = iotbx.pdb.hierarchy.input(pdb_string="""\
ATOM 1639 N PHE A 113 18.514 41.994 54.886 1.00 12.36 N
ATOM 1640 CA PHE A 113 19.737 42.742 54.898 1.00 12.97 C
ATOM 1641 C PHE A 113 19.514 44.206 55.111 1.00 11.88 C
ATOM 1642 O PHE A 113 18.442 44.758 54.823 1.00 13.75 O
ATOM 1643 CB PHE A 113 20.587 42.460 53.684 1.00 20.08 C
ATOM 1644 CG PHE A 113 19.893 42.692 52.399 1.00 17.15 C
ATOM 1645 CD1 PHE A 113 19.182 41.689 51.798 1.00 20.34 C
ATOM 1646 CD2 PHE A 113 20.029 43.895 51.745 1.00 21.22 C
ATOM 1647 CE1 PHE A 113 18.577 41.887 50.601 1.00 23.49 C
ATOM 1648 CE2 PHE A 113 19.411 44.105 50.542 1.00 24.87 C
ATOM 1649 CZ PHE A 113 18.675 43.097 49.976 1.00 21.81 C
ATOM 1650 H PHE A 113 17.875 42.342 54.427 1.00 14.83 H
ATOM 1651 HA PHE A 113 20.250 42.435 55.661 1.00 15.57 H
ATOM 1652 HB2 PHE A 113 21.367 43.036 53.708 1.00 24.10 H
ATOM 1653 HB3 PHE A 113 20.865 41.531 53.707 1.00 24.10 H
ATOM 1654 HD1 PHE A 113 19.099 40.866 52.224 1.00 24.40 H
ATOM 1655 HD2 PHE A 113 20.518 44.581 52.138 1.00 25.47 H
ATOM 1656 HE1 PHE A 113 18.077 41.204 50.215 1.00 28.18 H
ATOM 1657 HE2 PHE A 113 19.488 44.927 50.113 1.00 29.84 H
ATOM 1658 HZ PHE A 113 18.261 43.230 49.154 1.00 26.17 H
""")
pdb_2 = iotbx.pdb.hierarchy.input(pdb_string="""\
ATOM 1639 N PHE A 113 18.514 41.994 54.886 1.00 12.36 N
ATOM 1640 CA PHE A 113 19.737 42.742 54.898 1.00 12.97 C
ATOM 1641 C PHE A 113 19.514 44.206 55.111 1.00 11.88 C
ATOM 1642 O PHE A 113 18.442 44.758 54.823 1.00 13.75 O
ATOM 1643 CB PHE A 113 20.587 42.460 53.684 1.00 20.08 C
ATOM 1644 CG PHE A 113 19.893 42.692 52.399 1.00 17.15 C
ATOM 1645 CD1 PHE A 113 20.174 43.797 51.643 1.00 20.34 C
ATOM 1646 CD2 PHE A 113 18.888 41.845 51.991 1.00 21.22 C
ATOM 1647 CE1 PHE A 113 19.512 44.033 50.483 1.00 23.49 C
ATOM 1648 CE2 PHE A 113 18.224 42.071 50.816 1.00 24.87 C
ATOM 1649 CZ PHE A 113 18.548 43.166 50.057 1.00 21.81 C
ATOM 1650 H PHE A 113 17.875 42.342 54.427 1.00 14.83 H
ATOM 1651 HA PHE A 113 20.250 42.435 55.661 1.00 15.57 H
ATOM 1652 HB2 PHE A 113 21.367 43.036 53.708 1.00 24.10 H
ATOM 1653 HB3 PHE A 113 20.865 41.531 53.707 1.00 24.10 H
ATOM 1654 HD1 PHE A 113 20.840 44.386 51.919 1.00 24.40 H
ATOM 1655 HD2 PHE A 113 18.681 41.096 52.501 1.00 25.47 H
ATOM 1656 HE1 PHE A 113 19.730 44.776 49.967 1.00 28.18 H
ATOM 1657 HE2 PHE A 113 17.560 41.484 50.533 1.00 29.84 H
ATOM 1658 HZ PHE A 113 18.093 43.330 49.263 1.00 26.17 H
""")
hierarchy_1 = pdb_1.hierarchy
hierarchy_2 = pdb_2.hierarchy
pdb_atoms_1 = hierarchy_1.atoms()
sites_1 = pdb_atoms_1.extract_xyz()
sites_2 = hierarchy_2.atoms().extract_xyz()
result = alternate_conformations.coord_stats_with_flips(
sites_1, sites_2, pdb_atoms_1)
assert (approx_equal(result.rmsd, 0.2, eps=0.001))
assert (approx_equal(result.max_dev, 0.452427, eps=0.00001))
#--- get_selection_gap
assert (alternate_conformations.get_selection_gap([1, 2, 3, 4, 5],
[6, 7, 8, 9]) == 0)
assert (alternate_conformations.get_selection_gap([6, 7],
[1, 2, 3, 4, 5]) == 0)
assert (alternate_conformations.get_selection_gap([1, 2, 3, 4, 5],
[7, 8, 9]) == 1)
assert (alternate_conformations.get_selection_gap([1, 2, 3, 4, 5],
[4, 5, 7, 8, 9]) == -2)
assert (alternate_conformations.get_selection_gap([4, 5, 7, 8, 9],
[1, 2, 3, 4]) == -1)
#--- score_rotamers
n_outliers = alternate_conformations.score_rotamers(
hierarchy_2, flex.bool(sites_2.size(), True))
assert (n_outliers == 0)
#--- get_partial_omit_map
xrs = pdb_1.input.xray_structure_simple()
f_calc = abs(xrs.structure_factors(d_min=1.5).f_calc())
f_calc.set_observation_type_xray_amplitude()
flags = f_calc.generate_r_free_flags()
fmodel = mmtbx.utils.fmodel_simple(xray_structures=[xrs],
f_obs=f_calc,
r_free_flags=flags,
scattering_table="n_gaussian",
bulk_solvent_and_scaling=False,
skip_twin_detection=True)
sel = pdb_1.hierarchy.atom_selection_cache().selection("name CZ")
assert (sel.count(True) == 1)
two_fofc_map, fofc_map = alternate_conformations.get_partial_omit_map(
fmodel=fmodel, selection=sel)
site = xrs.sites_frac()[sel.iselection()[0]] # CZ coordinate
assert (fofc_map.tricubic_interpolation(site) > 20)
def exercise_utils () :
#--- coord_stats_with_flips
pdb_1 = iotbx.pdb.hierarchy.input(pdb_string="""\
ATOM 1639 N PHE A 113 18.514 41.994 54.886 1.00 12.36 N
ATOM 1640 CA PHE A 113 19.737 42.742 54.898 1.00 12.97 C
ATOM 1641 C PHE A 113 19.514 44.206 55.111 1.00 11.88 C
ATOM 1642 O PHE A 113 18.442 44.758 54.823 1.00 13.75 O
ATOM 1643 CB PHE A 113 20.587 42.460 53.684 1.00 20.08 C
ATOM 1644 CG PHE A 113 19.893 42.692 52.399 1.00 17.15 C
ATOM 1645 CD1 PHE A 113 19.182 41.689 51.798 1.00 20.34 C
ATOM 1646 CD2 PHE A 113 20.029 43.895 51.745 1.00 21.22 C
ATOM 1647 CE1 PHE A 113 18.577 41.887 50.601 1.00 23.49 C
ATOM 1648 CE2 PHE A 113 19.411 44.105 50.542 1.00 24.87 C
ATOM 1649 CZ PHE A 113 18.675 43.097 49.976 1.00 21.81 C
ATOM 1650 H PHE A 113 17.875 42.342 54.427 1.00 14.83 H
ATOM 1651 HA PHE A 113 20.250 42.435 55.661 1.00 15.57 H
ATOM 1652 HB2 PHE A 113 21.367 43.036 53.708 1.00 24.10 H
ATOM 1653 HB3 PHE A 113 20.865 41.531 53.707 1.00 24.10 H
ATOM 1654 HD1 PHE A 113 19.099 40.866 52.224 1.00 24.40 H
ATOM 1655 HD2 PHE A 113 20.518 44.581 52.138 1.00 25.47 H
ATOM 1656 HE1 PHE A 113 18.077 41.204 50.215 1.00 28.18 H
ATOM 1657 HE2 PHE A 113 19.488 44.927 50.113 1.00 29.84 H
ATOM 1658 HZ PHE A 113 18.261 43.230 49.154 1.00 26.17 H
""")
pdb_2 = iotbx.pdb.hierarchy.input(pdb_string="""\
ATOM 1639 N PHE A 113 18.514 41.994 54.886 1.00 12.36 N
ATOM 1640 CA PHE A 113 19.737 42.742 54.898 1.00 12.97 C
ATOM 1641 C PHE A 113 19.514 44.206 55.111 1.00 11.88 C
ATOM 1642 O PHE A 113 18.442 44.758 54.823 1.00 13.75 O
ATOM 1643 CB PHE A 113 20.587 42.460 53.684 1.00 20.08 C
ATOM 1644 CG PHE A 113 19.893 42.692 52.399 1.00 17.15 C
ATOM 1645 CD1 PHE A 113 20.174 43.797 51.643 1.00 20.34 C
ATOM 1646 CD2 PHE A 113 18.888 41.845 51.991 1.00 21.22 C
ATOM 1647 CE1 PHE A 113 19.512 44.033 50.483 1.00 23.49 C
ATOM 1648 CE2 PHE A 113 18.224 42.071 50.816 1.00 24.87 C
ATOM 1649 CZ PHE A 113 18.548 43.166 50.057 1.00 21.81 C
ATOM 1650 H PHE A 113 17.875 42.342 54.427 1.00 14.83 H
ATOM 1651 HA PHE A 113 20.250 42.435 55.661 1.00 15.57 H
ATOM 1652 HB2 PHE A 113 21.367 43.036 53.708 1.00 24.10 H
ATOM 1653 HB3 PHE A 113 20.865 41.531 53.707 1.00 24.10 H
ATOM 1654 HD1 PHE A 113 20.840 44.386 51.919 1.00 24.40 H
ATOM 1655 HD2 PHE A 113 18.681 41.096 52.501 1.00 25.47 H
ATOM 1656 HE1 PHE A 113 19.730 44.776 49.967 1.00 28.18 H
ATOM 1657 HE2 PHE A 113 17.560 41.484 50.533 1.00 29.84 H
ATOM 1658 HZ PHE A 113 18.093 43.330 49.263 1.00 26.17 H
""")
hierarchy_1 = pdb_1.hierarchy
hierarchy_2 = pdb_2.hierarchy
pdb_atoms_1 = hierarchy_1.atoms()
sites_1 = pdb_atoms_1.extract_xyz()
sites_2 = hierarchy_2.atoms().extract_xyz()
result = alternate_conformations.coord_stats_with_flips(sites_1, sites_2, pdb_atoms_1)
assert (approx_equal(result.rmsd, 0.2, eps=0.001))
assert (approx_equal(result.max_dev, 0.452427, eps=0.00001))
#--- get_selection_gap
assert (alternate_conformations.get_selection_gap([1,2,3,4,5],[6,7,8,9]) == 0)
assert (alternate_conformations.get_selection_gap([6,7], [1,2,3,4,5]) == 0)
assert (alternate_conformations.get_selection_gap([1,2,3,4,5],[7,8,9]) == 1)
assert (alternate_conformations.get_selection_gap([1,2,3,4,5],[4,5,7,8,9]) == -2)
assert (alternate_conformations.get_selection_gap([4,5,7,8,9],[1,2,3,4]) == -1)
#--- score_rotamers
n_outliers = alternate_conformations.score_rotamers(hierarchy_2,
flex.bool(sites_2.size(), True))
assert (n_outliers == 0)
#--- get_partial_omit_map
xrs = pdb_1.input.xray_structure_simple()
f_calc = abs(xrs.structure_factors(d_min=1.5).f_calc())
f_calc.set_observation_type_xray_amplitude()
flags = f_calc.generate_r_free_flags()
fmodel = mmtbx.utils.fmodel_simple(
xray_structures=[xrs],
f_obs=f_calc,
r_free_flags=flags,
scattering_table="n_gaussian",
bulk_solvent_and_scaling=False,
skip_twin_detection=True)
sel = pdb_1.hierarchy.atom_selection_cache().selection("name CZ")
assert (sel.count(True) == 1)
two_fofc_map, fofc_map = alternate_conformations.get_partial_omit_map(
fmodel=fmodel,
selection=sel)
site = xrs.sites_frac()[sel.iselection()[0]] # CZ coordinate
assert (fofc_map.tricubic_interpolation(site) > 20)
def run_trial(self, occ):
"""
Actually run the refinement - called in parallel via easy_mp.pool_map
"""
from scitbx.array_family import flex
seed = int(time.time() / os.getpid())
random.seed(seed)
flex.set_random_seed(seed)
fmodel = self.fmodel
selection = self.selection.deep_copy()
iselection = self.iselection.deep_copy()
d_min = fmodel.f_obs().d_min()
xrs = fmodel.xray_structure.deep_copy_scatterers()
hd_sel = xrs.hd_selection()
sites_start = xrs.sites_cart().deep_copy()
assert (len(selection) == len(sites_start))
fmodel.update_xray_structure(xrs, update_f_calc=True)
sites_start_selected = sites_start.select(iselection)
occ_start = xrs.scatterers().extract_occupancies().deep_copy()
u_start = xrs.extract_u_cart_plus_u_iso()
if (self.params.adjust_b_factors_if_poor_density):
fofc_map = fmodel.map_coefficients(
map_type="mFo-DFc",
exclude_free_r_reflections=True).fft_map(
resolution_factor=1/4.).apply_sigma_scaling().real_map_unpadded()
sites_frac = xrs.sites_frac()
b_scale_isel = flex.size_t()
for i_seq in iselection :
map_value = fofc_map.tricubic_interpolation(sites_frac[i_seq])
if (map_value < -2.5):
b_scale_isel.append(i_seq)
if (len(b_scale_isel) > 0):
b_scale_sel = flex.bool(sites_frac.size(), False).set_selected(
b_scale_isel, True)
xrs.scale_adp(factor=0.75, selection=b_scale_sel)
nearby_water_selection = mmtbx.building.get_nearby_water_selection(
pdb_hierarchy=self.pdb_hierarchy,
xray_structure=xrs,
selection=selection)
two_fofc_map, fofc_map = alt_confs.get_partial_omit_map(
fmodel=fmodel,
selection=iselection,
selection_delete=None,#nearby_water_selection,
negate_surrounding=self.params.negate_surrounding,
partial_occupancy=occ)
#xrs.set_occupancies(occ_start)
make_sub_header("Simulated annealing into mFo-DFc map", out=self.out)
if (self.params.shake_sites is not None):
xrs.shake_sites_in_place(self.params.shake_sites, selection=selection)
sites_new = mmtbx.building.run_real_space_annealing(
xray_structure=xrs,
pdb_hierarchy=self.pdb_hierarchy,
processed_pdb_file=self.processed_pdb_file,
selection=selection,
target_map=fofc_map,
d_min=d_min,
params=self.params.simulated_annealing,
#wc=5, # FIXME why does this need to be scaled?
target_map_rsr=two_fofc_map,
rsr_after_anneal=self.params.rsr_after_anneal,
out=self.out,
debug=True)
# now compute CC of refined sites to difference map
fmodel.update_xray_structure(xrs, update_f_calc=True)
fc_coeffs = fmodel.f_model()
fc_fft_map = fc_coeffs.fft_map(resolution_factor=1/4.)
fc_map = fc_fft_map.apply_sigma_scaling().real_map_unpadded()
pdb_atoms = self.pdb_hierarchy.atoms()
# XXX should this only calculate statistics for the central atoms?
map_stats = mmtbx.building.get_model_map_stats(
selection=self.selection_score,
target_map=fofc_map,
model_map=fc_map,
unit_cell=xrs.unit_cell(),
sites_cart=sites_new,
pdb_atoms=pdb_atoms,
local_sampling=False)
# reset xray structure
xrs.set_sites_cart(sites_start)
xrs.set_u_cart(u_start)
fmodel.update_xray_structure(xrs, update_f_calc=True)
# we may only want the rmsd and max. dev. from a subset of sites, e.g.
# the central residue of a sliding window (minus hydrogens)
selection_score = self.selection_score.deep_copy()
if (type(selection_score).__name__ != 'bool'):
selection_score = flex.bool(hd_sel.size(), False).set_selected(
self.selection_score, True)
selection_score &= ~hd_sel
site_stats = alt_confs.coord_stats_with_flips(
sites1=sites_start.select(selection_score),
sites2=sites_new.select(selection_score),
atoms=self.pdb_hierarchy.atoms().select(selection_score))
return alt_confs.trial_result(
sites_cart=sites_new.select(self.iselection),
min_fofc=map_stats.min,
mean_fofc=map_stats.mean,
rmsd=site_stats.rmsd,
max_dev=site_stats.max_dev,
cc=map_stats.cc)
def run_trial (self, occ) :
"""
Actually run the refinement - called in parallel via easy_mp.pool_map
"""
from scitbx.array_family import flex
seed = int(time.time() / os.getpid())
random.seed(seed)
flex.set_random_seed(seed)
fmodel = self.fmodel
selection = self.selection.deep_copy()
iselection = self.iselection.deep_copy()
d_min = fmodel.f_obs().d_min()
xrs = fmodel.xray_structure.deep_copy_scatterers()
hd_sel = xrs.hd_selection()
sites_start = xrs.sites_cart().deep_copy()
assert (len(selection) == len(sites_start))
fmodel.update_xray_structure(xrs, update_f_calc=True)
sites_start_selected = sites_start.select(iselection)
occ_start = xrs.scatterers().extract_occupancies().deep_copy()
u_start = xrs.extract_u_cart_plus_u_iso()
if (self.params.adjust_b_factors_if_poor_density) :
fofc_map = fmodel.map_coefficients(
map_type="mFo-DFc",
exclude_free_r_reflections=True).fft_map(
resolution_factor=1/4.).apply_sigma_scaling().real_map_unpadded()
sites_frac = xrs.sites_frac()
b_scale_isel = flex.size_t()
for i_seq in iselection :
map_value = fofc_map.tricubic_interpolation(sites_frac[i_seq])
if (map_value < -2.5) :
b_scale_isel.append(i_seq)
if (len(b_scale_isel) > 0) :
b_scale_sel = flex.bool(sites_frac.size(), False).set_selected(
b_scale_isel, True)
xrs.scale_adp(factor=0.75, selection=b_scale_sel)
nearby_water_selection = mmtbx.building.get_nearby_water_selection(
pdb_hierarchy=self.pdb_hierarchy,
xray_structure=xrs,
selection=selection)
two_fofc_map, fofc_map = alt_confs.get_partial_omit_map(
fmodel=fmodel,
selection=iselection,
selection_delete=None,#nearby_water_selection,
negate_surrounding=self.params.negate_surrounding,
partial_occupancy=occ)
#xrs.set_occupancies(occ_start)
make_sub_header("Simulated annealing into mFo-DFc map", out=self.out)
if (self.params.shake_sites is not None) :
xrs.shake_sites_in_place(self.params.shake_sites, selection=selection)
sites_new = mmtbx.building.run_real_space_annealing(
xray_structure=xrs,
pdb_hierarchy=self.pdb_hierarchy,
processed_pdb_file=self.processed_pdb_file,
selection=selection,
target_map=fofc_map,
d_min=d_min,
params=self.params.simulated_annealing,
#wc=5, # FIXME why does this need to be scaled?
target_map_rsr=two_fofc_map,
rsr_after_anneal=self.params.rsr_after_anneal,
out=self.out,
debug=True)
# now compute CC of refined sites to difference map
fmodel.update_xray_structure(xrs, update_f_calc=True)
fc_coeffs = fmodel.f_model()
fc_fft_map = fc_coeffs.fft_map(resolution_factor=1/4.)
fc_map = fc_fft_map.apply_sigma_scaling().real_map_unpadded()
pdb_atoms = self.pdb_hierarchy.atoms()
# XXX should this only calculate statistics for the central atoms?
map_stats = mmtbx.building.get_model_map_stats(
selection=self.selection_score,
target_map=fofc_map,
model_map=fc_map,
unit_cell=xrs.unit_cell(),
sites_cart=sites_new,
pdb_atoms=pdb_atoms,
local_sampling=False)
# reset xray structure
xrs.set_sites_cart(sites_start)
xrs.set_u_cart(u_start)
fmodel.update_xray_structure(xrs, update_f_calc=True)
# we may only want the rmsd and max. dev. from a subset of sites, e.g.
# the central residue of a sliding window (minus hydrogens)
selection_score = self.selection_score.deep_copy()
if (type(selection_score).__name__ != 'bool') :
selection_score = flex.bool(hd_sel.size(), False).set_selected(
self.selection_score, True)
selection_score &= ~hd_sel
site_stats = alt_confs.coord_stats_with_flips(
sites1=sites_start.select(selection_score),
sites2=sites_new.select(selection_score),
atoms=self.pdb_hierarchy.atoms().select(selection_score))
return alt_confs.trial_result(
sites_cart=sites_new.select(self.iselection),
min_fofc=map_stats.min,
mean_fofc=map_stats.mean,
rmsd=site_stats.rmsd,
max_dev=site_stats.max_dev,
cc=map_stats.cc)
