def macro_cycle(
xray_structure,
target_map,
geometry_restraints,
max_iterations = 50,
expload = False,
n_expload = 1,
log = None):
if(not log):
if(log is None): log = sys.stdout
d_min = maptbx.d_min_from_map(
map_data = target_map,
unit_cell = xray_structure.unit_cell())
all_selection = flex.bool(xray_structure.scatterers().size(),True)
rsr_simple_refiner = individual_sites.simple(
target_map = target_map,
selection = all_selection,
real_space_gradients_delta = d_min/4,
max_iterations = max_iterations,
geometry_restraints_manager = geometry_restraints)
xray_structure = shift_to_center_of_mass(xray_structure=xray_structure,
target_map=target_map)
cm = xray_structure.center_of_mass()
sites_cart = xray_structure.sites_cart()
sc = scorer(
unit_cell = xray_structure.unit_cell(),
sites_frac = xray_structure.sites_frac(),
target_map = target_map,
log = log)
weights = flex.double()
sampling_range = [0, 90, 180, 270]
for the in sampling_range:
for psi in sampling_range:
for phi in sampling_range:
sites_cart_new = apply_rigid_body_shift(
sites_cart=sites_cart,
cm=cm, x=0,y=0,z=0, the=the, psi=psi, phi=phi)
xray_structure = xray_structure.replace_sites_cart(
new_sites=sites_cart_new)
w = run_refine(
rsr_simple_refiner = rsr_simple_refiner,
xray_structure = xray_structure,
scorer = sc,
log = log,
weight = flex.mean_default(weights, 1.0))
weights.append(w)
if(expload):
for i in xrange(n_expload):
xray_structure_ = xray_structure.deep_copy_scatterers()
xray_structure_.shake_sites_in_place(mean_distance=1.0)
run_refine(
rsr_simple_refiner = rsr_simple_refiner,
xray_structure = xray_structure_,
scorer = sc,
log = log)
if(log): print >> log, "Final target:", sc.target
return xray_structure.replace_sites_frac(new_sites=sc.sites_frac_best)
def macro_cycle(xray_structure,
target_map,
geometry_restraints,
max_iterations=50,
expload=False,
n_expload=1,
log=None):
if (not log):
if (log is None): log = sys.stdout
d_min = maptbx.d_min_from_map(map_data=target_map,
unit_cell=xray_structure.unit_cell())
all_selection = flex.bool(xray_structure.scatterers().size(), True)
rsr_simple_refiner = individual_sites.simple(
target_map=target_map,
selection=all_selection,
real_space_gradients_delta=d_min / 4,
max_iterations=max_iterations,
geometry_restraints_manager=geometry_restraints)
xray_structure = shift_to_center_of_mass(xray_structure=xray_structure,
target_map=target_map)
cm = xray_structure.center_of_mass()
sites_cart = xray_structure.sites_cart()
sc = scorer(unit_cell=xray_structure.unit_cell(),
sites_frac=xray_structure.sites_frac(),
target_map=target_map,
log=log)
weights = flex.double()
sampling_range = [0, 90, 180, 270]
for the in sampling_range:
for psi in sampling_range:
for phi in sampling_range:
sites_cart_new = apply_rigid_body_shift(sites_cart=sites_cart,
cm=cm,
x=0,
y=0,
z=0,
the=the,
psi=psi,
phi=phi)
xray_structure = xray_structure.replace_sites_cart(
new_sites=sites_cart_new)
w = run_refine(rsr_simple_refiner=rsr_simple_refiner,
xray_structure=xray_structure,
scorer=sc,
log=log,
weight=flex.mean_default(weights, 1.0))
weights.append(w)
if (expload):
for i in xrange(n_expload):
xray_structure_ = xray_structure.deep_copy_scatterers()
xray_structure_.shake_sites_in_place(mean_distance=1.0)
run_refine(rsr_simple_refiner=rsr_simple_refiner,
xray_structure=xray_structure_,
scorer=sc,
log=log)
if (log): print >> log, "Final target:", sc.target
return xray_structure.replace_sites_frac(new_sites=sc.sites_frac_best)
def exercise():
# Exercise "simple" target
pi = get_pdb_inputs(pdb_str=pdb_str_1)
selection = flex.bool(pi.xrs.scatterers().size(), True)
for d_min in [1, 2, 3]:
print("d_min:", d_min)
f_calc = pi.xrs.structure_factors(d_min=d_min).f_calc()
fft_map = f_calc.fft_map(resolution_factor=0.25)
fft_map.apply_sigma_scaling()
target_map = fft_map.real_map_unpadded()
rsr_simple_refiner = individual_sites.simple(
target_map=target_map,
selection=selection,
real_space_gradients_delta=d_min / 4,
max_iterations=150,
geometry_restraints_manager=pi.grm.geometry)
for shake_size in [
1,
]:
print(" shake_size:", shake_size)
for p in [(0.01, 1.0), (0.03, 3.0), (0.1, 10.0)]:
print(" target:", p)
w_opt = flex.double()
for start_value in [0.001, 0.01, 0.1, 0, 1, 10, 100, 1000]:
xrs_poor = pi.xrs.deep_copy_scatterers()
random.seed(0)
flex.set_random_seed(0)
xrs_poor.shake_sites_in_place(mean_distance=shake_size)
#
refined = individual_sites.refinery(
refiner=rsr_simple_refiner,
xray_structure=xrs_poor,
start_trial_weight_value=start_value,
rms_bonds_limit=p[0],
rms_angles_limit=p[1])
w_opt.append(refined.weight_final)
dist = flex.mean(
flex.sqrt((pi.xrs.sites_cart() -
refined.sites_cart_result).dot()))
print(
" w_start,w_final,b,a,dist: %9.4f %9.4f %6.3f %6.3f %6.3f"
% (start_value, refined.weight_final,
refined.rms_bonds_final, refined.rms_angles_final,
dist))
assert refined.rms_bonds_final <= p[0]
assert refined.rms_angles_final <= p[1]
def exercise():
# Exercise "simple" target
pi = get_pdb_inputs(pdb_str=pdb_str_1)
selection = flex.bool(pi.xrs.scatterers().size(), True)
for d_min in [1, 2, 3]:
print "d_min:", d_min
f_calc = pi.xrs.structure_factors(d_min = d_min).f_calc()
fft_map = f_calc.fft_map(resolution_factor=0.25)
fft_map.apply_sigma_scaling()
target_map = fft_map.real_map_unpadded()
rsr_simple_refiner = individual_sites.simple(
target_map = target_map,
selection = selection,
real_space_gradients_delta = d_min/4,
max_iterations = 150,
geometry_restraints_manager = pi.grm.geometry)
for shake_size in [1,]:
print " shake_size:", shake_size
for p in [(0.01, 1.0), (0.03, 3.0), (0.1, 10.0)]:
print " target:", p
w_opt = flex.double()
for start_value in [0.001, 0.01, 0.1, 0, 1, 10, 100, 1000]:
xrs_poor = pi.xrs.deep_copy_scatterers()
random.seed(0)
flex.set_random_seed(0)
xrs_poor.shake_sites_in_place(mean_distance = shake_size)
#
refined = individual_sites.refinery(
refiner = rsr_simple_refiner,
xray_structure = xrs_poor,
start_trial_weight_value = start_value,
rms_bonds_limit = p[0],
rms_angles_limit = p[1])
w_opt.append(refined.weight_final)
dist = flex.mean(flex.sqrt((pi.xrs.sites_cart() -
refined.sites_cart_result).dot()))
print " w_start,w_final,b,a,dist: %9.4f %9.4f %6.3f %6.3f %6.3f"%(
start_value, refined.weight_final, refined.rms_bonds_final,
refined.rms_angles_final, dist)
assert refined.rms_bonds_final <= p[0]
assert refined.rms_angles_final <= p[1]
def exercise(d_min=5, random_seed=1111111):
inp = get_pdb_inputs(pdb_str=pdb_str)
xrs_good = inp.xrs.deep_copy_scatterers()
target_map = get_tmo(inp=inp, d_min=d_min)
inp.ph.write_pdb_file(file_name="start.pdb")
show(prefix="GOOD",
pdb_hierarchy=inp.ph,
tm=target_map,
xrs=xrs_good,
grm=inp.grm.geometry)
#
sites_cart_reference = []
selections_reference = []
pdb_hierarchy_reference = inp.ph.deep_copy()
pdb_hierarchy_reference.reset_i_seq_if_necessary()
for model in inp.ph.models():
for chain in model.chains():
for residue in chain.residues():
sites_cart_reference.append(residue.atoms().extract_xyz())
selections_reference.append(residue.atoms().extract_i_seq())
#
sites_cart_reference_for_chi_only = []
selections_reference_for_chi_only = []
for model in inp.ph.models():
for chain in model.chains():
for residue in chain.residues():
s1 = flex.vec3_double()
s2 = flex.size_t()
for atom in residue.atoms():
if (not atom.name.strip().upper() in ["O"]):
s1.append(atom.xyz)
s2.append(atom.i_seq)
sites_cart_reference_for_chi_only.append(s1)
selections_reference_for_chi_only.append(s2)
#
xrs_poor = shake_sites(xrs=xrs_good.deep_copy_scatterers(),
random=False,
shift=2.0,
grm=inp.grm)
inp.ph.adopt_xray_structure(xrs_poor)
inp.ph.write_pdb_file(file_name="poor.pdb")
#
for use_reference_torsion in [
"no", "yes_add_once", "yes_add_per_residue", "yes_manual"
]:
es = inp.grm.energies_sites(
sites_cart=xrs_good.sites_cart()) # it's essential to update grm
inp.ph.adopt_xray_structure(xrs_poor)
random.seed(random_seed)
flex.set_random_seed(random_seed)
print "*" * 79
print "use_reference_torsion:", use_reference_torsion
print "*" * 79
show(prefix="START",
pdb_hierarchy=inp.ph,
tm=target_map,
xrs=xrs_poor,
grm=inp.grm.geometry)
#
if (use_reference_torsion == "yes_add_per_residue"):
inp.grm.geometry.remove_chi_torsion_restraints_in_place()
for sites_cart, selection in zip(sites_cart_reference,
selections_reference):
inp.grm.geometry.add_chi_torsion_restraints_in_place(
pdb_hierarchy=pdb_hierarchy_reference,
sites_cart=sites_cart,
selection=selection,
chi_angles_only=True,
sigma=1)
if (use_reference_torsion == "yes_add_once"):
inp.grm.geometry.remove_chi_torsion_restraints_in_place()
inp.grm.geometry.add_chi_torsion_restraints_in_place(
pdb_hierarchy=pdb_hierarchy_reference,
sites_cart=xrs_good.sites_cart(),
chi_angles_only=True,
sigma=1)
if (use_reference_torsion == "yes_manual"):
inp.grm.geometry.remove_chi_torsion_restraints_in_place()
for sites_cart, selection in zip(
sites_cart_reference_for_chi_only,
selections_reference_for_chi_only):
inp.grm.geometry.add_chi_torsion_restraints_in_place(
pdb_hierarchy=pdb_hierarchy_reference,
sites_cart=sites_cart,
selection=selection,
chi_angles_only=True,
sigma=1)
#
tmp = xrs_poor.deep_copy_scatterers()
rsr_simple_refiner = individual_sites.simple(
target_map=target_map.data,
selection=flex.bool(tmp.scatterers().size(), True),
real_space_gradients_delta=d_min / 4,
max_iterations=500,
geometry_restraints_manager=inp.grm.geometry)
refined = individual_sites.refinery(refiner=rsr_simple_refiner,
optimize_weight=True,
xray_structure=tmp,
start_trial_weight_value=50,
rms_bonds_limit=0.02,
rms_angles_limit=2.0)
assert refined.sites_cart_result is not None
tmp = tmp.replace_sites_cart(refined.sites_cart_result)
inp.ph.adopt_xray_structure(tmp)
show(prefix="FINAL",
pdb_hierarchy=inp.ph,
tm=target_map,
xrs=tmp,
grm=inp.grm.geometry)
inp.ph.write_pdb_file(file_name="final_%s.pdb" %
str(use_reference_torsion))
def exercise(d_min=5, random_seed=1111111):
inp = get_pdb_inputs(pdb_str=pdb_str)
xrs_good = inp.xrs.deep_copy_scatterers()
target_map = get_tmo(inp=inp, d_min = d_min)
inp.ph.write_pdb_file(file_name="start.pdb")
show(prefix="GOOD",
pdb_hierarchy = inp.ph,
tm=target_map,
xrs=xrs_good,
grm=inp.grm.geometry)
#
sites_cart_reference = []
selections_reference = []
pdb_hierarchy_reference = inp.ph.deep_copy()
pdb_hierarchy_reference.reset_i_seq_if_necessary()
for model in inp.ph.models():
for chain in model.chains():
for residue in chain.residues():
sites_cart_reference.append(residue.atoms().extract_xyz())
selections_reference.append(residue.atoms().extract_i_seq())
#
sites_cart_reference_for_chi_only = []
selections_reference_for_chi_only = []
for model in inp.ph.models():
for chain in model.chains():
for residue in chain.residues():
s1 = flex.vec3_double()
s2 = flex.size_t()
for atom in residue.atoms():
if(not atom.name.strip().upper() in ["O"]):
s1.append(atom.xyz)
s2.append(atom.i_seq)
sites_cart_reference_for_chi_only.append(s1)
selections_reference_for_chi_only.append(s2)
#
xrs_poor = shake_sites(xrs=xrs_good.deep_copy_scatterers(), random=False,
shift=2.0, grm=inp.grm)
inp.ph.adopt_xray_structure(xrs_poor)
inp.ph.write_pdb_file(file_name="poor.pdb")
#
for use_reference_torsion in ["no", "yes_add_once", "yes_add_per_residue",
"yes_manual"]:
es = inp.grm.energies_sites(sites_cart = xrs_good.sites_cart()) # it's essential to update grm
inp.ph.adopt_xray_structure(xrs_poor)
random.seed(random_seed)
flex.set_random_seed(random_seed)
print "*"*79
print "use_reference_torsion:", use_reference_torsion
print "*"*79
show(prefix="START",pdb_hierarchy = inp.ph, tm=target_map, xrs=xrs_poor, grm=inp.grm.geometry)
#
if(use_reference_torsion == "yes_add_per_residue"):
inp.grm.geometry.remove_chi_torsion_restraints_in_place()
for sites_cart, selection in zip(sites_cart_reference, selections_reference):
inp.grm.geometry.add_chi_torsion_restraints_in_place(
pdb_hierarchy = pdb_hierarchy_reference,
sites_cart = sites_cart,
selection = selection,
chi_angles_only = True,
sigma = 1)
if(use_reference_torsion == "yes_add_once"):
inp.grm.geometry.remove_chi_torsion_restraints_in_place()
inp.grm.geometry.add_chi_torsion_restraints_in_place(
pdb_hierarchy = pdb_hierarchy_reference,
sites_cart = xrs_good.sites_cart(),
chi_angles_only = True,
sigma = 1)
if(use_reference_torsion == "yes_manual"):
inp.grm.geometry.remove_chi_torsion_restraints_in_place()
for sites_cart, selection in zip(sites_cart_reference_for_chi_only,
selections_reference_for_chi_only):
inp.grm.geometry.add_chi_torsion_restraints_in_place(
pdb_hierarchy = pdb_hierarchy_reference,
sites_cart = sites_cart,
selection = selection,
chi_angles_only = True,
sigma = 1)
#
tmp = xrs_poor.deep_copy_scatterers()
rsr_simple_refiner = individual_sites.simple(
target_map = target_map.data,
selection = flex.bool(tmp.scatterers().size(), True),
real_space_gradients_delta = d_min/4,
max_iterations = 500,
geometry_restraints_manager = inp.grm.geometry)
refined = individual_sites.refinery(
refiner = rsr_simple_refiner,
optimize_weight = True,
xray_structure = tmp,
start_trial_weight_value = 50,
rms_bonds_limit = 0.02,
rms_angles_limit = 2.0)
assert refined.sites_cart_result is not None
tmp = tmp.replace_sites_cart(refined.sites_cart_result)
inp.ph.adopt_xray_structure(tmp)
show(prefix="FINAL",pdb_hierarchy = inp.ph, tm=target_map, xrs=tmp, grm=inp.grm.geometry)
inp.ph.write_pdb_file(file_name="final_%s.pdb"%str(use_reference_torsion))
