def refine_window(O, window):
processed_pdb_file = O.get_processed_pdb_file()
assert (processed_pdb_file is not None)
hierarchy = processed_pdb_file.all_chain_proxies.pdb_hierarchy
pdb_atoms = hierarchy.atoms()
sites_cart = pdb_atoms.extract_xyz()
dxyz = [None] * 3
dxyz[0] = flex.vec3_double(pdb_atoms.size(), (1.0, 0.2, 0.3))
dxyz[1] = flex.vec3_double(pdb_atoms.size(), (-1.0, -0.1, 0.0))
dxyz[2] = dxyz[1]
trials = []
if (window.residue_id_str in [" A PHE 113 ", " A SER 110 "]):
for i in range(3):
trials.append(
alt_confs.trial_result(
sites_cart=(sites_cart + dxyz[i]).select(
window.selection),
min_fofc=4.0,
mean_fofc=4.5,
rmsd=1.2,
max_dev=1.3,
cc=0.99))
elif (window.residue_id_str in [" A ASN 108 "]):
trials.append(
alt_confs.trial_result(sites_cart=(sites_cart +
dxyz[0]).select(
window.selection),
min_fofc=4.0,
mean_fofc=4.5,
rmsd=1.2,
max_dev=1.3,
cc=0.99))
e = sliding_window.ensemble(window=window, sites_trials=trials)
n_keep = e.filter_trials(sites_cart=O.sites_cart,
min_rmsd=O.params.min_rmsd,
min_dev=O.min_required_deviation)
if (n_keep > 0):
return e
return None
def refine_window (O, window) :
processed_pdb_file = O.get_processed_pdb_file()
assert (processed_pdb_file is not None)
hierarchy = processed_pdb_file.all_chain_proxies.pdb_hierarchy
pdb_atoms = hierarchy.atoms()
sites_cart = pdb_atoms.extract_xyz()
dxyz = [ None ] * 3
dxyz[0] = flex.vec3_double(pdb_atoms.size(), (1.0,0.2,0.3))
dxyz[1] = flex.vec3_double(pdb_atoms.size(), (-1.0,-0.1,0.0))
dxyz[2] = dxyz[1]
trials = []
if (window.residue_id_str in [" A PHE 113 ", " A SER 110 "]) :
for i in range(3) :
trials.append(alt_confs.trial_result(
sites_cart=(sites_cart + dxyz[i]).select(window.selection),
min_fofc=4.0,
mean_fofc=4.5,
rmsd=1.2,
max_dev=1.3,
cc=0.99))
elif (window.residue_id_str in [" A ASN 108 "]) :
trials.append(alt_confs.trial_result(
sites_cart=(sites_cart + dxyz[0]).select(window.selection),
min_fofc=4.0,
mean_fofc=4.5,
rmsd=1.2,
max_dev=1.3,
cc=0.99))
e = sliding_window.ensemble(window=window, sites_trials=trials)
n_keep = e.filter_trials(
sites_cart=O.sites_cart,
min_rmsd=O.params.min_rmsd,
min_dev=O.min_required_deviation)
if (n_keep > 0) :
return e
return None
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)
