def run(args):
assert len(args) == 1
# Read file into pdb_input class
inp = iotbx.pdb.input(file_name=args[0])
# create a model manager
model = mmtbx.model.manager(model_input=inp)
# get number of atoms in the input model
n_atoms = model.get_number_of_atoms()
# extract atom coordinates
old_sites_cart = model.get_sites_cart()
# generate random additions
random_addition = flex.vec3_double(
flex.random_double(size=n_atoms * 3) - 0.5)
# actually add them to old coordinates
new_xyz = old_sites_cart + random_addition
# Update coordinates in model manager
model.set_sites_cart(sites_cart=new_xyz)
# get xray structure
xrs = model.get_xray_structure()
# reset B-factors (min=1, max=20)
# generate array of new B-factors
new_b = flex.random_double(size=n_atoms, factor=19) + 1
# set them in xray structure
xrs.set_b_iso(values=new_b)
# update model manager with this xray structure
model.set_xray_structure(xrs)
# output result in PDB format to the screen
print model.model_as_pdb()
print "END"
def exercise3(pdb_str, type_list_known):
pdb_inp = iotbx.pdb.input(lines=pdb_str.split("\n"), source_info=None)
model = mmtbx.model.manager(
model_input = pdb_inp,
build_grm = True)
pdb_hierarchy = model.get_hierarchy()
sites_cart = model.get_sites_cart()
atoms = pdb_hierarchy.atoms()
model.setup_riding_h_manager()
riding_h_manager = model.get_riding_h_manager()
h_para = riding_h_manager.h_parameterization
diagnostics = riding_h_manager.diagnostics(
sites_cart = sites_cart,
threshold = 0.05)
h_distances = diagnostics.h_distances
type_list = diagnostics.type_list
number_h = model.get_hd_selection().count(True)
number_h_para = len(h_para) - h_para.count(None)
assert (number_h_para == number_h-2), 'Not all H atoms are parameterized'
for ih in h_distances:
labels = atoms[ih].fetch_labels()
assert (h_distances[ih] < 0.2), \
'distance too large: %s atom: %s (%s) residue: %s ' \
% (h_para[ih].htype, atoms[ih].name, ih, labels.resseq.strip())
for type1, type2 in zip(type_list, type_list_known):
assert (type1 == type2)
def run(args):
log = sys.stdout
if (len(args) == 0): args = ["--help"]
command_line = (option_parser(usage="%s [options] pdb_file" %
libtbx.env.dispatcher_name).option(
None,
"--buffer_layer",
action="store",
type="float",
default=5)).process(args=args, nargs=1)
pdb_inp = iotbx.pdb.input(file_name=command_line.args[0])
model = mmtbx.model.manager(model_input=pdb_inp)
box = uctbx.non_crystallographic_unit_cell_with_the_sites_in_its_center(
sites_cart=model.get_sites_cart(),
buffer_layer=command_line.options.buffer_layer)
model.set_sites_cart(box.sites_cart)
# Bad hack, never repeat. In fact, all the boxing functionality should
# go into mmtbx.model.manager
model._crystal_symmetry = box.crystal_symmetry()
print('REMARK %s --buffer-layer=%.6g %s' %
(libtbx.env.dispatcher_name, command_line.options.buffer_layer,
show_string(command_line.args[0])),
file=log)
print('REMARK %s' % date_and_time(), file=log)
print(model.model_as_pdb(), file=log)
def exercise1(pdb_str, cif_str):
model = prepare_inputs(pdb_str, cif_str)
riding_h_manager = model.get_riding_h_manager()
atoms = model.get_hierarchy().atoms()
h_para = riding_h_manager.h_parameterization
diagnostics = riding_h_manager.diagnostics(
sites_cart=model.get_sites_cart(), threshold=0.05)
h_distances = diagnostics.h_distances
type_list = diagnostics.type_list
# number of H atoms
number_h = model.get_hd_selection().count(True)
number_h_para = len(h_para) - h_para.count(None)
assert (number_h_para == number_h), 'Not all H atoms are parameterized'
for ih in h_distances:
# One atom is expected to be moved
if (ih == 16):
continue
labels = atoms[ih].fetch_labels()
assert (h_distances[ih] < 0.1), \
'distance too large: %s atom: %s (%s) residue: %s ' \
% (h_para[ih].htype, atoms[ih].name, ih, labels.resseq.strip())
for type1, type2 in zip(type_list, type_list_known1):
assert (type1 == type2)
def exercise_1():
pdb_inp = iotbx.pdb.input(lines=flex.std_string(pdb_str_1.splitlines()),
source_info=None)
model = mmtbx.model.manager(model_input=pdb_inp)
model.process(make_restraints=True)
grm = model.get_restraints_manager().geometry
pdb_hierarchy = model.get_hierarchy()
sites_cart = model.get_sites_cart()
# c-beta restraints are added by default!!!
assert len(grm.get_c_beta_torsion_proxies()) == 4
#test global selection and removing c-beta restraints
tst_boolsel = pdb_hierarchy.atom_selection_cache().selection("resname TYR")
tst_iselection = tst_boolsel.iselection()
#test global selection
grm2 = grm.select(iselection=tst_iselection)
assert len(grm2.get_c_beta_torsion_proxies()) == 2
grm2 = grm.select(selection=tst_boolsel)
assert len(grm2.get_c_beta_torsion_proxies()) == 2
#remove a selection
grm.remove_c_beta_torsion_restraints_in_place(selection=tst_iselection)
assert len(grm.get_c_beta_torsion_proxies()) == 2
#add a selection
grm.remove_c_beta_torsion_restraints_in_place()
assert len(grm.get_c_beta_torsion_proxies()) == 0
c_beta_torsion_proxies = c_beta.get_c_beta_torsion_proxies(
pdb_hierarchy, selection=tst_iselection, sigma=2.5)
assert len(c_beta_torsion_proxies) == 2
def exercise(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.process(make_restraints=True)
pdb_hierarchy = model.get_hierarchy()
sites_cart = model.get_sites_cart()
atoms = pdb_hierarchy.atoms()
model.setup_riding_h_manager()
riding_h_manager = model.get_riding_h_manager()
h_parameterization = riding_h_manager.h_parameterization
diagnostics = riding_h_manager.diagnostics(sites_cart=sites_cart,
threshold=0.05)
diagnostics = riding_h_manager.diagnostics(sites_cart=sites_cart,
threshold=0.05)
h_distances = diagnostics.h_distances
number_h = model.get_hd_selection().count(True)
number_h_para = len(h_parameterization) - h_parameterization.count(None)
# There are 90 H atoms in pdb_string, check if all of them are recognized
assert (number_h_para == number_h), 'Not all H atoms are parameterized'
for ih in h_distances:
labels = atoms[ih].fetch_labels()
assert (h_distances[ih] < 0.01), \
'distance too large: %s atom: %s (%s) residue: %s ' \
% (h_parameterization[ih].htype, atoms[ih].name, ih, labels.resseq.strip())
def exercise(pdb_str, use_ideal_bonds_angles):
# --------------------------------------------------------------
# code to switch off CDL
# --------------------------------------------------------------
#params_line = grand_master_phil_str
#params = iotbx.phil.parse(
# input_string=params_line, process_includes=True).extract()
#params.pdb_interpretation.restraints_library.cdl=False
# ---------------------------------------------------------------
pdb_inp = iotbx.pdb.input(lines=pdb_str.split("\n"), source_info=None)
model = mmtbx.model.manager(model_input=pdb_inp,
log=null_out(),
build_grm=True)
pdb_hierarchy = model.get_hierarchy()
geometry_restraints = model.get_restraints_manager().geometry
xray_structure = model.get_xray_structure()
sites_cart = model.get_sites_cart()
grf = cctbx.geometry_restraints.flags.flags(default=True)
minimized = mmtbx.refinement.geometry_minimization.lbfgs(
sites_cart=sites_cart,
correct_special_position_tolerance=1.0,
geometry_restraints_manager=geometry_restraints,
geometry_restraints_flags=grf,
lbfgs_termination_params=scitbx.lbfgs.termination_parameters(
max_iterations=500))
xray_structure.set_sites_cart(sites_cart)
pdb_hierarchy.adopt_xray_structure(xray_structure)
atoms = pdb_hierarchy.atoms()
sites_cart = xray_structure.sites_cart()
riding_h_manager = riding.manager(
pdb_hierarchy=pdb_hierarchy,
geometry_restraints=geometry_restraints,
use_ideal_bonds_angles=use_ideal_bonds_angles)
h_parameterization = riding_h_manager.h_parameterization
diagnostics = riding_h_manager.diagnostics(sites_cart=sites_cart,
threshold=0.05)
h_distances = diagnostics.h_distances
number_h = model.get_hd_selection().count(True)
number_h_para = len(h_parameterization) - h_parameterization.count(None)
assert (number_h_para == number_h), 'Not all H atoms are parameterized'
for ih in h_distances:
labels = atoms[ih].fetch_labels()
if use_ideal_bonds_angles:
assert (h_distances[ih] < 0.03), \
'distance too large: %s atom: %s (%s) residue: %s ' \
% (h_parameterization[ih].htype, atoms[ih].name, ih, labels.resseq.strip())
else:
assert (h_distances[ih] < 1e-7), \
'distance too large: %s atom: %s (%s) residue: %s distance %s' \
% (h_parameterization[ih].htype, atoms[ih].name, ih, labels.resseq.strip(), h_distances[ih])
def get_bounds_around_model(
map_manager=None,
model=None,
box_cushion=None,
):
'''
Calculate the lower and upper bounds to box around a model
Allow bounds to go outside the available box (this has to be
dealt with at the boxing stage)
'''
# get items needed to do the shift
cs = map_manager.crystal_symmetry()
uc = cs.unit_cell()
sites_cart = model.get_sites_cart()
sites_frac = uc.fractionalize(sites_cart)
map_data = map_manager.map_data()
# convert box_cushion into fractional vector
cushion_frac = flex.double(uc.fractionalize((box_cushion, ) * 3))
# find fractional corners
frac_min = sites_frac.min()
frac_max = sites_frac.max()
frac_max = list(flex.double(frac_max) + cushion_frac)
frac_min = list(flex.double(frac_min) - cushion_frac)
# find corner grid nodes
all_orig = map_data.all()
lower_bounds = [ifloor(f * n) for f, n in zip(frac_min, all_orig)]
upper_bounds = [iceil(f * n) for f, n in zip(frac_max, all_orig)]
return group_args(
lower_bounds=lower_bounds,
upper_bounds=upper_bounds,
)
def run(pdb_file_name):
pdb_inp = iotbx.pdb.input(file_name=pdb_file_name)
params = mmtbx.model.manager.get_default_pdb_interpretation_params()
model = mmtbx.model.manager(
model_input = pdb_inp,
pdb_interpretation_params = params,
build_grm = True,
stop_for_unknowns = False,
log = null_out())
pdb_hierarchy = model.get_hierarchy()
sites_cart_dc = model.get_sites_cart().deep_copy()
angle = 0.
# points_i_seqs = [483]
# axis = [479,482]
points_i_seqs = [559]
axis = [556,557]
while angle <= 360:
atoms_xyz_tmp = flex.vec3_double(len(points_i_seqs))
atom_xyz_new = rotate_point_around_axis(
axis_point_1 = sites_cart_dc[axis[0]],
axis_point_2 = sites_cart_dc[axis[1]],
point = sites_cart_dc[points_i_seqs[0]],
angle = angle,
deg = True)
print (atom_xyz_new)
atoms_xyz_tmp[0] = atom_xyz_new
sites_cart_dc[points_i_seqs] = atoms_xyz_tmp[0]
model.set_sites_cart(sites_cart = sites_cart_dc)
with open(str(angle)+".pdb", "w") as of:
print (model.model_as_pdb(),file=of)
angle += 30.
def exercise(pdb_str):
params = mmtbx.model.manager.get_default_pdb_interpretation_params()
params.pdb_interpretation.use_neutron_distances = True
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.process(pdb_interpretation_params=params, make_restraints=True)
pdb_hierarchy = model.get_hierarchy()
sites_cart = model.get_sites_cart()
atoms = pdb_hierarchy.atoms()
model.setup_riding_h_manager()
riding_h_manager = model.get_riding_h_manager()
h_parameterization = riding_h_manager.h_parameterization
diagnostics = riding_h_manager.diagnostics(sites_cart=sites_cart,
threshold=0.05)
h_distances = diagnostics.h_distances
number_h = model.get_hd_selection().count(True)
number_h_para = len(h_parameterization) - h_parameterization.count(None)
if (pdb_str != pdb_str_02):
assert (number_h_para == number_h), 'Not all H atoms are parameterized'
# For each H atom, check if distance between computed H and that in input model is
# not too large
for ih in h_distances:
labels = atoms[ih].fetch_labels()
assert (h_distances[ih] < 0.1), \
'distance too large: %s atom: %s (%s) residue: %s ' \
% (h_parameterization[ih].htype, atoms[ih].name, ih, labels.resseq.strip())
def exclude_h_on_SS(model):
rm = model.get_restraints_manager()
bond_proxies_simple, asu = rm.geometry.get_all_bond_proxies(
sites_cart=model.get_sites_cart())
els = model.get_hierarchy().atoms().extract_element()
ss_i_seqs = []
all_proxies = [p for p in bond_proxies_simple]
for proxy in asu:
all_proxies.append(proxy)
for proxy in all_proxies:
if (isinstance(proxy, ext.bond_simple_proxy)): i, j = proxy.i_seqs
elif (isinstance(proxy, ext.bond_asu_proxy)):
i, j = proxy.i_seq, proxy.j_seq
else:
assert 0 # never goes here
if ([els[i], els[j]
].count("S") == 2): # XXX may be coordinated if metal edits used
ss_i_seqs.extend([i, j])
sel_remove = flex.size_t()
for proxy in all_proxies:
if (isinstance(proxy, ext.bond_simple_proxy)): i, j = proxy.i_seqs
elif (isinstance(proxy, ext.bond_asu_proxy)):
i, j = proxy.i_seq, proxy.j_seq
else:
assert 0 # never goes here
if (els[i] in ["H", "D"] and j in ss_i_seqs): sel_remove.append(i)
if (els[j] in ["H", "D"] and i in ss_i_seqs): sel_remove.append(j)
return model.select(~flex.bool(model.size(), sel_remove))
def run(maxnum_residues_in_cluster):
result = []
for clustering in [True, False]:
if 0: print(" clustering", clustering, "-"*30)
model = get_model()
fq = from_cctbx(restraints_manager = model.get_restraints_manager())
if(clustering):
fm = fragments(
working_folder = os.path.split("./ase/tmp_ase.pdb")[0]+ "/",
clustering_method = betweenness_centrality_clustering,
maxnum_residues_in_cluster = maxnum_residues_in_cluster,
altloc_method = "subtract",
charge_embedding = False,
two_buffers = False,
clustering = clustering,
pdb_hierarchy = model.get_hierarchy().deep_copy(),
qm_engine_name = "mopac",
fast_interaction = True,
crystal_symmetry = model.crystal_symmetry())
else:
fc = fq
fc = from_cluster(
restraints_manager = fq,
fragment_manager = fm,
parallel_params = get_master_phil().extract())
energy, gradients = fc.target_and_gradients(sites_cart=model.get_sites_cart())
gradients = gradients.as_double()
result.append(gradients.deep_copy())
diff = flex.abs(result[0] - result[1])
max_diff = flex.max(diff)
#print " max(diff_grad):", max_diff
assert max_diff < 1.e-9
def exercise3(pdb_str, type_list_known):
pdb_inp = iotbx.pdb.input(lines=pdb_str.split("\n"), source_info=None)
params = mmtbx.model.manager.get_default_pdb_interpretation_scope(
).extract()
params.pdb_interpretation.allow_polymer_cross_special_position = True
model = mmtbx.model.manager(model_input=pdb_inp, log=null_out())
model.process(pdb_interpretation_params=params, make_restraints=True)
pdb_hierarchy = model.get_hierarchy()
sites_cart = model.get_sites_cart()
atoms = pdb_hierarchy.atoms()
model.setup_riding_h_manager()
riding_h_manager = model.get_riding_h_manager()
h_para = riding_h_manager.h_parameterization
diagnostics = riding_h_manager.diagnostics(sites_cart=sites_cart,
threshold=0.05)
h_distances = diagnostics.h_distances
type_list = diagnostics.type_list
number_h = model.get_hd_selection().count(True)
number_h_para = len(h_para) - h_para.count(None)
assert (number_h_para == number_h - 2), 'Not all H atoms are parameterized'
for ih in h_distances:
labels = atoms[ih].fetch_labels()
assert (h_distances[ih] < 0.2), \
'distance too large: %s atom: %s (%s) residue: %s ' \
% (h_para[ih].htype, atoms[ih].name, ih, labels.resseq.strip())
for type1, type2 in zip(type_list, type_list_known):
assert (type1 == type2)
def __init__(self,
map_manager,
model,
box_cushion,
wrapping=None,
log=sys.stdout):
self._map_manager = map_manager
self._model = model
self._force_wrapping = wrapping
if wrapping is None:
wrapping = self.map_manager().wrapping()
self.basis_for_boxing_string = 'using_model, wrapping = %s' % (
wrapping)
# safeguards
assert isinstance(map_manager, iotbx.map_manager.map_manager)
assert isinstance(model, mmtbx.model.manager)
assert self._map_manager.map_data().accessor().origin() == (0, 0, 0)
# Make sure working model and map_manager crystal_symmetry match
assert map_manager.is_compatible_model(model)
assert box_cushion >= 0
if self.map_manager().wrapping(): # map must be entire unit cell
assert map_manager.unit_cell_grid == map_manager.map_data().all()
# NOTE: We are going to use crystal_symmetry and sites_frac based on
# the map_manager (the model could still have different crystal_symmetry)
# get items needed to do the shift
cs = map_manager.crystal_symmetry()
uc = cs.unit_cell()
sites_cart = model.get_sites_cart()
sites_frac = uc.fractionalize(sites_cart)
map_data = map_manager.map_data()
# convert box_cushion into fractional vector
cushion_frac = flex.double(uc.fractionalize((box_cushion, ) * 3))
# find fractional corners
frac_min = sites_frac.min()
frac_max = sites_frac.max()
frac_max = list(flex.double(frac_max) + cushion_frac)
frac_min = list(flex.double(frac_min) - cushion_frac)
# find corner grid nodes
all_orig = map_data.all()
self.gridding_first = [
ifloor(f * n) for f, n in zip(frac_min, all_orig)
]
self.gridding_last = [iceil(f * n) for f, n in zip(frac_max, all_orig)]
# 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()
def run(model,
max_cutoff=4.0,
min_cutoff=1.5,
hd=["H", "D"],
acceptors=["O", "N", "S", "F", "CL"],
protein_only=True):
atoms = list(model.get_hierarchy().atoms())
sites_cart = model.get_sites_cart()
crystal_symmetry = model.crystal_symmetry()
fm = crystal_symmetry.unit_cell().fractionalization_matrix()
om = crystal_symmetry.unit_cell().orthogonalization_matrix()
pg = get_pair_generator(crystal_symmetry=crystal_symmetry,
buffer_thickness=max_cutoff,
sites_cart=sites_cart)
get_class = iotbx.pdb.common_residue_names_get_class
for p in pg.pair_generator:
i, j = p.i_seq, p.j_seq
ei, ej = atoms[i].element, atoms[j].element
altloc_i = atoms[i].parent().altloc
altloc_j = atoms[j].parent().altloc
resseq_i = atoms[i].parent().parent().resseq
resseq_j = atoms[j].parent().parent().resseq
# pre-screen candidates begin
one_is_hd = ei in hd or ej in hd
other_is_acceptor = ei in acceptors or ej in acceptors
dist = math.sqrt(p.dist_sq)
assert dist <= max_cutoff
is_candidate = one_is_hd and other_is_acceptor and dist >= min_cutoff and \
altloc_i == altloc_j and resseq_i != resseq_j
if (protein_only):
for it in [i, j]:
resname = atoms[it].parent().resname
is_candidate &= get_class(name=resname) == "common_amino_acid"
if (not is_candidate): continue
# pre-screen candidates end
rt_mx_i = pg.conn_asu_mappings.get_rt_mx_i(p)
rt_mx_j = pg.conn_asu_mappings.get_rt_mx_j(p)
rt_mx_ji = rt_mx_i.inverse().multiply(rt_mx_j)
print "%5.3f"%math.sqrt(p.dist_sq), \
"<", atoms[i].parent().resname, resseq_i, ei, atoms[i].name, ">", \
"<", atoms[j].parent().resname, resseq_j, ej, atoms[j].name, ">", \
rt_mx_ji, i, j,
### re-confirm distance between pairs
ai = atoms[i]
aj = atoms[j]
if (str(rt_mx_ji) == "x,y,z"):
d = math.sqrt((ai.xyz[0] - aj.xyz[0])**2 +
(ai.xyz[1] - aj.xyz[1])**2 +
(ai.xyz[2] - aj.xyz[2])**2)
else:
t1 = fm * flex.vec3_double([aj.xyz])
t2 = rt_mx_ji * t1[0]
t3 = om * flex.vec3_double([t2])
d = math.sqrt((ai.xyz[0] - t3[0][0])**2 +
(ai.xyz[1] - t3[0][1])**2 +
(ai.xyz[2] - t3[0][2])**2)
print "dist=%5.3f" % d
def run():
rmsd_dirs = ["0.3/", "0.6/", "0.9/", "1.2/", "1.5/"]
base_names = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
ref = get_reference()
tmp_cntr1 = 0
print "Hbond analysis"
print "model min max mean recovered bond_rmsd rama_favored clashscore"
for sub_root in [
"./perturbed/", "./cctbx_opt/", "./xtb_opt_stpmax_0.3/",
"./xtb_opt_stpmax_0.4/", "./xtb_opt_stpmax_0.5/",
"./terachem_opt_final/"
]:
print sub_root
for rmsd_dir in rmsd_dirs:
h_bonds = flex.double()
rmsd_bonds = flex.double()
rama_fav = flex.double()
clashscore = flex.double()
cntr = 0
for fn in base_names:
file_name = sub_root + rmsd_dir + fn + ".pdb"
if (not os.path.exists(file_name)): assert 0, file_name
model = get_model(file_name)
assert ref.h.is_similar_hierarchy(model.get_hierarchy())
g = model.geometry_statistics(use_hydrogens=False).result()
rmsd_bonds.append(g.bond.mean)
rama_fav.append(g.ramachandran.favored)
clashscore.append(g.clash.score)
#print g.bond.mean, g.clash.score, g.rotamer.outliers, g.c_beta.outliers, \
# g.ramachandran.outliers, g.ramachandran.allowed, g.ramachandran.favored
sites_cart = model.get_sites_cart()
if (sub_root.count("perturbed") > 0):
tmp_cntr1 += 1
assert approx_equal(
float(rmsd_dir.replace("/", "")),
flex.mean(
flex.sqrt((ref.sites_cart - sites_cart).dot())),
0.01)
for pair in ref.h_bonds_i_seqs:
d = dist(sites_cart[pair[0]], sites_cart[pair[1]])
h_bonds.append(d)
cntr += 1
assert cntr == 10, cntr
sel = h_bonds < 2.3
sel &= h_bonds > 1.6
if (h_bonds.size() > 0):
print rmsd_dir, "%8.3f %8.3f %8.3f"%h_bonds.min_max_mean().as_tuple(), \
" %7.2f"%(sel.count(True)*100./(len(ref.h_bonds_i_seqs)*cntr)),\
" %6.4f %7.2f %4.2f"%(flex.mean(rmsd_bonds), flex.mean(rama_fav), flex.mean(clashscore))
else:
print rmsd_dir, "N/A"
#
assert tmp_cntr1 == 50, tmp_cntr1
def compare_XH_bond_length_to_ideal(model):
geometry = model.get_restraints_manager().geometry
atoms = model.get_hierarchy().atoms()
sites_cart = model.get_sites_cart()
bond_proxies_simple, asu = \
geometry.get_all_bond_proxies(sites_cart = sites_cart)
hd_selection = model.get_hd_selection()
for bp in bond_proxies_simple:
i, j = bp.i_seqs
if hd_selection[i] or hd_selection[j]:
assert approx_equal(atoms[i].distance(atoms[j]),
bp.distance_ideal,
eps=0.001)
def exercise1():
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.process(make_restraints=True)
pdb_hierarchy = model.get_hierarchy()
sites_cart = model.get_sites_cart()
atoms = pdb_hierarchy.atoms()
model.setup_riding_h_manager()
riding_h_manager = model.get_riding_h_manager()
h_parameterization = riding_h_manager.h_parameterization
number_h = model.get_hd_selection().count(True)
number_h_para = len(h_parameterization) - h_parameterization.count(None)
assert (number_h_para == number_h-1), 'Not all H atoms are parameterized'
rc = h_parameterization[20]
new_manager = riding_h_manager.deep_copy()
new_h_parameterization = new_manager.h_parameterization
rc_new = new_h_parameterization[20]
assert (rc_new.htype == rc.htype)
assert (rc_new.ih == rc.ih)
assert (rc_new.a0 == rc.a0)
assert (rc_new.a1 == rc.a1)
assert (rc_new.a2 == rc.a2)
assert (rc_new.a3 == rc.a3)
assert (rc_new.a == rc.a)
assert (rc_new.b == rc.b)
assert (rc_new.h == rc.h)
assert (rc_new.n == rc.n)
assert (rc_new.disth == rc.disth)
rc_new.htype = 'unk'
rc_new.ih = 0
rc_new.a0 = 1
rc_new.a1 = 2
rc_new.a2 = 3
rc_new.a3 = 4
assert (rc_new.htype != rc.htype)
assert (rc_new.ih != rc.ih)
assert (rc_new.a0 != rc.a0)
assert (rc_new.a1 != rc.a1)
assert (rc_new.a2 != rc.a2)
assert (rc_new.a3 != rc.a3)
def get_CNCO_bond_angle(model):
geometry = model.get_restraints_manager()
bond_proxies_simple, asu = geometry.geometry.get_all_bond_proxies(
sites_cart=model.get_sites_cart())
bps_dict = {}
[bps_dict.setdefault(p.i_seqs, True) for p in bond_proxies_simple]
hierarchy = model.get_hierarchy()
atom1s = []
atom2s = []
atom3s = []
results = []
for a in hierarchy.atoms():
if (not a.parent().resname == "MTN"): continue
if a.element.strip().upper() == "C":
atom1s.append(a)
if a.element.strip().upper() == "N":
atom2s.append(a)
if a.element.strip().upper() == "O":
atom3s.append(a)
for i, a1 in enumerate(atom1s):
for a2 in atom2s:
if (not a1.is_in_same_conformer_as(a2)): continue
if (not is_bonded(a1, a2, bps_dict)): continue
for j, a4 in enumerate(atom1s):
if (j <= i): continue
if (not a4.is_in_same_conformer_as(a2)): continue
if (not is_bonded(a4, a2, bps_dict)): continue
if (not a1.is_in_same_conformer_as(a4)): continue
if (is_bonded(a1, a4, bps_dict)): continue
for a3 in atom3s:
if (not a2.is_in_same_conformer_as(a3)): continue
if (not is_bonded(a3, a2, bps_dict)): continue
d12 = a1.distance(a2)
d23 = a2.distance(a3)
d24 = a2.distance(a4)
angle123 = a2.angle(a1, a3, deg=True)
angle124 = a2.angle(a1, a4, deg=True)
angle324 = a2.angle(a3, a4, deg=True)
result = group_args(atom_1=a1,
atom_2=a2,
atom_3=a3,
atom_4=a4,
d_C_L_N=d12,
d_O_N=d23,
d_C_R_N=d24,
a_C_L_N_O=angle123,
a_C_N_C=angle124,
a_C_R_N_O=angle324)
if (result is not None): results.append(result)
return results
def run(prefix):
"""
Exercise combined energy and gradients from cluster qm.
"""
for restraints in ["cctbx", "qm"]:
if 0:
print("Using restraints:", restraints)
result = []
for clustering in [True, False]:
if 0:
print(" clustering", clustering, "-" * 30)
model = get_model()
if (restraints == "qm"):
fq = from_qm(pdb_hierarchy=model.get_hierarchy(),
qm_engine_name="mopac",
method="PM3",
crystal_symmetry=model.crystal_symmetry(),
clustering=clustering)
else:
fq = from_cctbx(
restraints_manager=model.get_restraints_manager())
if (clustering):
fm = fragments(
working_folder=os.path.split("./ase/tmp_ase.pdb")[0] + "/",
clustering_method=betweenness_centrality_clustering,
maxnum_residues_in_cluster=8,
charge_embedding=False,
two_buffers=False,
fast_interaction=True,
pdb_hierarchy=model.get_hierarchy().deep_copy(
), # deep copy just in case
qm_engine_name="mopac",
crystal_symmetry=model.crystal_symmetry())
fc = from_cluster(restraints_manager=fq,
fragment_manager=fm,
parallel_params=get_master_phil().extract())
else:
fc = fq
energy, gradients = fc.target_and_gradients(
sites_cart=model.get_sites_cart())
if (restraints == "qm"):
energy = energy * (kcal / mol) * (kcal / mol) / Hartree
gradients = gradients * (kcal / mol) * (kcal / mol) * (Bohr /
Hartree)
gradients = gradients.as_double()
result.append(gradients.deep_copy())
#
diff = flex.abs(result[0] - result[1])
max_diff = flex.max(diff)
def exercise2(pdb_str, cif_str):
model = prepare_inputs(pdb_str, cif_str)
riding_h_manager = model.get_riding_h_manager()
atoms = model.get_hierarchy().atoms()
h_para = riding_h_manager.h_parameterization
diagnostics = riding_h_manager.diagnostics(
sites_cart=model.get_sites_cart(), threshold=0.05)
h_distances = diagnostics.h_distances
type_list = diagnostics.type_list
# number of H atoms
number_h = model.get_hd_selection().count(True)
number_h_para = len(h_para) - h_para.count(None)
assert (number_h_para == 0), 'Not all H atoms are parameterized'
def exercise():
pdb_inp = iotbx.pdb.input(lines=pdb_str.split("\n"), source_info=None)
model = mmtbx.model.manager(
model_input = pdb_inp,
build_grm = True)
pdb_hierarchy = model.get_hierarchy()
sites_cart = model.get_sites_cart()
atoms = pdb_hierarchy.atoms()
model.setup_riding_h_manager()
riding_h_manager = model.get_riding_h_manager()
h_parameterization = riding_h_manager.h_parameterization
diagnostics = riding_h_manager.diagnostics(
sites_cart = sites_cart,
threshold = 0.05)
h_distances = diagnostics.h_distances
type_list = diagnostics.type_list
# number of H atoms in model
number_h = model.get_hd_selection().count(True)
# number of H in parameterization
number_h_para = len(h_parameterization) - h_parameterization.count(None)
# There are 152 H atoms in pdb_string, check if all of them are parameterized
assert (number_h_para == number_h), 'Not all H atoms are parameterized'
# For each H atom, check if distance compared to input model is not > 0.05
# 0.0014 = uncertainty for distance if uncertainty of coordinate = 0.001
for ih in h_distances:
labels = atoms[ih].fetch_labels()
assert (h_distances[ih] < 0.05), \
'distance too large: %s atom: %s (%s) residue: %s ' \
% (h_parameterization[ih].htype, atoms[ih].name, ih, labels.resseq.strip())
# KEEP: useful for debugging
#for ih in h_parameterization.keys():
# hp = h_parameterization[ih]
# print "'"+hp.htype+"'"+',',
for type1, type2 in zip(type_list, type_list_known):
assert (type1 == type2)
def exclude_h_on_coordinated_S(model): # XXX if edits used it should be like in exclude_h_on_SS
rm = model.get_restraints_manager().geometry
els = model.get_hierarchy().atoms().extract_element()
# Find possibly coordinated S
exclusion_list = ["H","D","T","S","O","P","N","C","SE"]
sel_s = []
for proxy in rm.pair_proxies().nonbonded_proxies.simple:
i,j = proxy.i_seqs
if(els[i] == "S" and not els[j] in exclusion_list): sel_s.append(i)
if(els[j] == "S" and not els[i] in exclusion_list): sel_s.append(j)
# Find H attached to possibly coordinated S
bond_proxies_simple, asu = rm.get_all_bond_proxies(
sites_cart = model.get_sites_cart())
sel_remove = flex.size_t()
for proxy in bond_proxies_simple:
i,j = proxy.i_seqs
if(els[i] in ["H","D"] and j in sel_s): sel_remove.append(i)
if(els[j] in ["H","D"] and i in sel_s): sel_remove.append(j)
return model.select(~flex.bool(model.size(), sel_remove))
def exercise():
pdb_inp = iotbx.pdb.input(lines=pdb_str.split("\n"), source_info=None)
cif_object = iotbx.cif.reader(input_string=cif_str).model()
# bla.cif does not exist, but cif_objects needs a filename in first position
# of the tuple
cif_objects = [('bla.cif', cif_object)]
model = mmtbx.model.manager(model_input=pdb_inp,
build_grm=True,
restraint_objects=cif_objects)
pdb_hierarchy = model.get_hierarchy()
sites_cart = model.get_sites_cart()
atoms = pdb_hierarchy.atoms()
model.setup_riding_h_manager()
riding_h_manager = model.get_riding_h_manager()
h_para = riding_h_manager.h_parameterization
diagnostics = riding_h_manager.diagnostics(sites_cart=sites_cart,
threshold=0.05)
h_distances = diagnostics.h_distances
type_list = diagnostics.type_list
# number of H atoms
number_h = model.get_hd_selection().count(True)
number_h_para = len(h_para) - h_para.count(None)
assert (number_h_para == number_h), 'Not all H atoms are parameterized'
for ih in h_distances:
# One atom is expected to be moved
if (ih == 16):
continue
labels = atoms[ih].fetch_labels()
assert (h_distances[ih] < 0.1), \
'distance too large: %s atom: %s (%s) residue: %s ' \
% (h_para[ih].htype, atoms[ih].name, ih, labels.resseq.strip())
for type1, type2 in zip(type_list, type_list_known):
assert (type1 == type2)
def validate(pdb_str, threshold_bonds=0.02 * 4, threshold_angles=2.5 * 4):
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
params = mmtbx.model.manager.get_default_pdb_interpretation_params()
params.pdb_interpretation.use_neutron_distances = True
params.pdb_interpretation.restraints_library.cdl = False
model = mmtbx.model.manager(
model_input=pdb_inp,
build_grm=True,
stop_for_unknowns=True, #False,
pdb_interpretation_params=params,
log=null_out())
grm = model.get_restraints_manager().geometry
sites_cart = model.get_sites_cart()
b_deltas = flex.abs(
grm.get_all_bond_proxies()[0].deltas(sites_cart=sites_cart))
b_outl = b_deltas.select(b_deltas > threshold_bonds)
if (b_outl.size() > 0): return None
a_deltas = flex.abs(
grm.get_all_angle_proxies().deltas(sites_cart=sites_cart))
a_outl = a_deltas.select(a_deltas > threshold_angles)
if (a_outl.size() > 0): return None
return pdb_str
def exercise():
pdb_inp = iotbx.pdb.input(lines=pdb_str.split("\n"), source_info=None)
model = mmtbx.model.manager(
model_input = pdb_inp,
build_grm = True,
log = null_out())
pdb_hierarchy = model.get_hierarchy()
sites_cart = model.get_sites_cart()
atoms = pdb_hierarchy.atoms()
model.setup_riding_h_manager()
riding_h_manager = model.get_riding_h_manager()
h_parameterization = riding_h_manager.h_parameterization
diagnostics = riding_h_manager.diagnostics(
sites_cart = sites_cart,
threshold = 0.05)
h_distances = diagnostics.h_distances
type_list = diagnostics.type_list
number_h = model.get_hd_selection().count(True)
number_h_para = len(h_parameterization) - h_parameterization.count(None)
# There are 90 H atoms in pdb_string, check if all of them are recognized
assert (number_h_para == number_h), 'Not all H atoms are parameterized'
# For every H , check if distance between computed H and H in input model is
# < 0.03 A
for ih in h_distances:
labels = atoms[ih].fetch_labels()
assert (h_distances[ih] < 0.03), 'distance too large: %s atom: %s (%s) residue: %s ' \
% (h_parameterization[ih].htype, atoms[ih].name, ih, labels.resseq.strip())
# Check if parameterization types are correct
for type1, type2 in zip(type_list, type_list_known):
assert (type1 == type2)
def exercise1():
pdb_str = """
CRYST1 21.850 24.325 24.603 90.00 90.00 90.00 P 1
SCALE1 0.045767 0.000000 0.000000 0.00000
SCALE2 0.000000 0.041110 0.000000 0.00000
SCALE3 0.000000 0.000000 0.040645 0.00000
ATOM 1 N CYS D 37 8.132 18.794 15.343 1.00143.27 D N
ANISOU 1 N CYS D 37 26762 17628 10046 -3652 3033 538 D N
ATOM 2 CA CYS D 37 9.229 18.179 14.592 1.00143.16 D C
ANISOU 2 CA CYS D 37 26739 17512 10144 -3661 2916 574 D C
ATOM 3 C CYS D 37 10.551 18.924 14.744 1.00143.18 D C
ANISOU 3 C CYS D 37 26760 17474 10166 -3627 2815 455 D C
ATOM 4 O CYS D 37 11.615 18.295 14.696 1.00141.47 D O
ANISOU 4 O CYS D 37 26571 17211 9972 -3636 2700 489 D O
ATOM 5 CB CYS D 37 8.857 18.048 13.112 1.00144.61 D C
ANISOU 5 CB CYS D 37 26841 17595 10509 -3672 2948 608 D C
ATOM 6 SG CYS D 37 7.159 17.517 12.843 1.00142.64 D S
ANISOU 6 SG CYS D 37 26553 17390 10253 -3704 3086 716 D S
ATOM 7 H CYS D 37 7.635 19.325 14.885 1.00172.08 D H
ATOM 8 HA CYS D 37 9.365 17.282 14.934 1.00171.95 D H
ATOM 9 HB2 CYS D 37 8.970 18.910 12.683 1.00173.69 D H
ATOM 10 HB3 CYS D 37 9.442 17.394 12.699 1.00173.69 D H
ATOM 11 N CYS D 40 13.775 17.698 16.644 1.00154.41 D N
ANISOU 11 N CYS D 40 28359 18912 11399 -3629 2475 474 D N
ATOM 12 CA CYS D 40 14.636 16.639 16.128 1.00149.28 D C
ANISOU 12 CA CYS D 40 27712 18183 10825 -3653 2369 564 D C
ATOM 13 C CYS D 40 15.084 16.830 14.685 1.00148.00 D C
ANISOU 13 C CYS D 40 27476 17888 10868 -3647 2342 540 D C
ATOM 14 O CYS D 40 16.158 16.339 14.335 1.00149.38 D O
ANISOU 14 O CYS D 40 27657 17989 11113 -3652 2233 563 D O
ATOM 15 CB CYS D 40 13.921 15.279 16.208 1.00140.80 D C
ANISOU 15 CB CYS D 40 26653 17143 9703 -3698 2398 729 D C
ATOM 16 SG CYS D 40 13.262 14.779 17.830 1.00136.64 D S
ANISOU 16 SG CYS D 40 26211 16771 8936 -3714 2439 794 D S
ATOM 17 H CYS D 40 13.032 17.777 16.218 1.00185.45 D H
ATOM 18 HA CYS D 40 15.432 16.588 16.680 1.00179.29 D H
ATOM 19 HB2 CYS D 40 13.174 15.295 15.590 1.00169.12 D H
ATOM 20 HB3 CYS D 40 14.548 14.592 15.932 1.00169.12 D H
HETATM 21 NB HEC D1001 9.421 13.220 11.858 1.00140.59 N
ANISOU 21 NB HEC D1001 26354 16953 10112 -3810 2765 1093 N
HETATM 22 ND HEC D1001 12.886 11.034 12.753 1.00144.24 N
ANISOU 22 ND HEC D1001 26954 17334 10517 -3839 2396 1214 N
HETATM 23 C1A HEC D1001 12.227 10.592 9.833 1.00139.00 C
ANISOU 23 C1A HEC D1001 26141 16470 10203 -3864 2452 1283 C
HETATM 24 C1B HEC D1001 8.860 13.248 10.595 1.00143.63 C
ANISOU 24 C1B HEC D1001 26666 17264 10645 -3817 2816 1110 C
HETATM 25 C1C HEC D1001 10.125 13.804 14.697 1.00139.27 C
ANISOU 25 C1C HEC D1001 26337 16981 9599 -3780 2714 1003 C
HETATM 26 C1D HEC D1001 13.435 10.960 14.028 1.00134.69 C
ANISOU 26 C1D HEC D1001 25819 16201 9156 -3833 2339 1204 C
HETATM 27 C2A HEC D1001 11.928 10.386 8.429 1.00142.28 C
ANISOU 27 C2A HEC D1001 26487 16789 10784 -3875 2476 1314 C
HETATM 28 C2B HEC D1001 7.789 14.214 10.617 1.00141.94 C
ANISOU 28 C2B HEC D1001 26417 17097 10418 -3798 2937 1044 C
HETATM 29 C2C HEC D1001 10.451 14.023 16.100 1.00140.94 C
ANISOU 29 C2C HEC D1001 26625 17289 9635 -3766 2687 965 C
HETATM 30 C2D HEC D1001 14.546 10.022 13.991 1.00142.48 C
ANISOU 30 C2D HEC D1001 26834 17133 10168 -3850 2214 1270 C
HETATM 31 C3A HEC D1001 10.804 11.059 8.149 1.00136.41 C
ANISOU 31 C3A HEC D1001 25701 16075 10056 -3866 2591 1279 C
HETATM 32 C3B HEC D1001 7.744 14.781 11.826 1.00143.82 C
ANISOU 32 C3B HEC D1001 26706 17431 10509 -3780 2957 983 C
HETATM 33 C3C HEC D1001 11.528 13.271 16.400 1.00137.87 C
ANISOU 33 C3C HEC D1001 26282 16879 9224 -3777 2567 1011 C
HETATM 34 C3D HEC D1001 14.632 9.534 12.538 1.00144.35 C
ANISOU 34 C3D HEC D1001 27005 17250 10590 -3867 2201 1319 C
HETATM 35 C4A HEC D1001 10.358 11.714 9.366 1.00144.69 C
ANISOU 35 C4A HEC D1001 26789 17236 10949 -3849 2645 1224 C
HETATM 36 C4B HEC D1001 8.768 14.145 12.649 1.00137.78 C
ANISOU 36 C4B HEC D1001 26012 16687 9651 -3787 2848 1015 C
HETATM 37 C4C HEC D1001 11.920 12.537 15.214 1.00134.63 C
ANISOU 37 C4C HEC D1001 25826 16356 8971 -3798 2515 1081 C
HETATM 38 C4D HEC D1001 13.570 10.218 11.849 1.00138.72 C
ANISOU 38 C4D HEC D1001 26232 16533 9944 -3858 2317 1280 C
HETATM 39 CAA HEC D1001 12.769 9.532 7.451 1.00132.04 C
ANISOU 39 CAA HEC D1001 25171 15378 9621 -3893 2385 1376 C
HETATM 40 CAB HEC D1001 6.719 15.866 12.240 1.00138.71 C
ANISOU 40 CAB HEC D1001 26042 16858 9803 -3757 3079 901 C
HETATM 41 CAC HEC D1001 12.297 13.190 17.738 1.00135.76 C
ANISOU 41 CAC HEC D1001 26101 16686 8794 -3768 2488 993 C
HETATM 42 CAD HEC D1001 15.637 8.535 11.925 1.00138.75 C
ANISOU 42 CAD HEC D1001 26295 16445 9980 -3888 2089 1394 C
HETATM 43 CBA HEC D1001 11.998 8.251 7.139 1.00132.66 C
ANISOU 43 CBA HEC D1001 25237 15456 9711 -3938 2416 1524 C
HETATM 44 CBB HEC D1001 5.650 15.212 13.142 1.00135.28 C
ANISOU 44 CBB HEC D1001 25642 16531 9227 -3784 3158 997 C
HETATM 45 CBC HEC D1001 11.427 12.956 18.987 1.00146.99 C
ANISOU 45 CBC HEC D1001 27580 18242 10026 -3778 2561 1040 C
HETATM 46 CBD HEC D1001 14.919 7.222 11.642 1.00139.54 C
ANISOU 46 CBD HEC D1001 26386 16545 10089 -3933 2120 1549 C
HETATM 47 CGA HEC D1001 12.527 7.624 5.875 1.00128.84 C
ANISOU 47 CGA HEC D1001 24714 14851 9389 -3953 2359 1570 C
HETATM 48 CGD HEC D1001 15.891 6.207 11.098 1.00141.03 C
ANISOU 48 CGD HEC D1001 26575 16642 10368 -3955 2011 1625 C
HETATM 49 CHA HEC D1001 13.299 10.063 10.513 1.00146.02 C
ANISOU 49 CHA HEC D1001 27088 17363 11029 -3868 2342 1305 C
HETATM 50 CHB HEC D1001 9.227 12.490 9.500 1.00144.37 C
ANISOU 50 CHB HEC D1001 26723 17255 10876 -3837 2764 1179 C
HETATM 51 CHC HEC D1001 9.083 14.384 13.975 1.00144.74 C
ANISOU 51 CHC HEC D1001 26964 17660 10370 -3774 2826 976 C
HETATM 52 CHD HEC D1001 12.978 11.644 15.133 1.00137.25 C
ANISOU 52 CHD HEC D1001 26183 16634 9330 -3815 2394 1143 C
HETATM 53 CMA HEC D1001 10.090 11.136 6.782 1.00134.85 C
ANISOU 53 CMA HEC D1001 25424 15802 10009 -3872 2656 1294 C
HETATM 54 CMB HEC D1001 6.909 14.580 9.402 1.00138.00 C
ANISOU 54 CMB HEC D1001 25835 16541 10059 -3798 3022 1038 C
HETATM 55 CMC HEC D1001 9.714 15.017 17.030 1.00140.17 C
ANISOU 55 CMC HEC D1001 26549 17298 9410 -3741 2781 878 C
HETATM 56 CMD HEC D1001 15.472 9.587 15.143 1.00143.64 C
ANISOU 56 CMD HEC D1001 27061 17330 10186 -3851 2112 1287 C
HETATM 57 NA HEC D1001 11.246 11.403 10.381 1.00142.02 N
ANISOU 57 NA HEC D1001 26521 16938 10501 -3848 2558 1228 N
HETATM 58 NC HEC D1001 11.031 12.874 14.214 1.00135.40 N
ANISOU 58 NC HEC D1001 25852 16413 9183 -3799 2607 1074 N
HETATM 59 O1A HEC D1001 13.768 7.499 5.723 1.00127.91 O
ANISOU 59 O1A HEC D1001 24612 14669 9319 -3944 2254 1548 O
HETATM 60 O1D HEC D1001 15.705 5.000 11.405 1.00143.73 O
ANISOU 60 O1D HEC D1001 26943 17007 10662 -3991 1995 1754 O
HETATM 61 O2A HEC D1001 11.708 7.243 5.000 1.00126.20 O
ANISOU 61 O2A HEC D1001 24330 14482 9138 -3973 2418 1629 O
HETATM 62 O2D HEC D1001 16.850 6.595 10.375 1.00138.38 O
ANISOU 62 O2D HEC D1001 26214 16212 10153 -3936 1943 1558 O
HETATM 63 FE HEC D1001 11.115 12.157 12.415 1.00158.39 Fe
ANISOU 63 FE HEC D1001 28683 19178 12321 -3824 2585 1151 Fe
HETATM 64 HAB HEC D1001 6.266 16.109 11.430 1.00166.61 H
HETATM 65 HAC HEC D1001 13.016 12.556 17.688 1.00163.06 H
HETATM 66 HHA HEC D1001 13.931 9.523 9.993 1.00175.38 H
HETATM 67 HHB HEC D1001 8.617 12.507 8.733 1.00173.40 H
HETATM 68 HHC HEC D1001 8.518 15.025 14.454 1.00173.84 H
HETATM 69 HHD HEC D1001 13.462 11.479 15.969 1.00164.85 H
HETATM 70 HAA1 HEC D1001 13.619 9.309 7.860 1.00158.61 H
HETATM 71 HAA2 HEC D1001 12.922 10.028 6.632 1.00158.61 H
HETATM 72 HAD1 HEC D1001 16.364 8.380 12.549 1.00166.66 H
HETATM 73 HAD2 HEC D1001 15.991 8.897 11.097 1.00166.66 H
HETATM 74 HBA1 HEC D1001 11.058 8.462 7.024 1.00159.35 H
HETATM 75 HBA2 HEC D1001 12.100 7.627 7.874 1.00159.35 H
HETATM 76 HBB1 HEC D1001 5.203 14.504 12.651 1.00162.49 H
HETATM 77 HBB2 HEC D1001 5.000 15.880 13.409 1.00162.49 H
HETATM 78 HBB3 HEC D1001 6.076 14.841 13.930 1.00162.49 H
HETATM 79 HBC1 HEC D1001 11.697 12.130 19.419 1.00176.54 H
HETATM 80 HBC2 HEC D1001 10.495 12.894 18.725 1.00176.54 H
HETATM 81 HBC3 HEC D1001 11.541 13.696 19.603 1.00176.54 H
HETATM 82 HBD1 HEC D1001 14.216 7.374 10.991 1.00167.61 H
HETATM 83 HBD2 HEC D1001 14.529 6.885 12.463 1.00167.61 H
HETATM 84 HMA1 HEC D1001 9.181 10.779 6.867 1.00161.97 H
HETATM 85 HMA2 HEC D1001 10.588 10.607 6.123 1.00161.97 H
HETATM 86 HMA3 HEC D1001 10.048 12.069 6.487 1.00161.97 H
HETATM 87 HMB1 HEC D1001 7.003 15.525 9.208 1.00165.76 H
HETATM 88 HMB2 HEC D1001 5.981 14.382 9.604 1.00165.76 H
HETATM 89 HMB3 HEC D1001 7.190 14.061 8.632 1.00165.76 H
HETATM 90 HMC1 HEC D1001 9.327 14.535 17.777 1.00168.36 H
HETATM 91 HMC2 HEC D1001 9.010 15.463 16.534 1.00168.36 H
HETATM 92 HMC3 HEC D1001 10.343 15.677 17.362 1.00168.36 H
HETATM 93 HMD1 HEC D1001 16.399 9.817 14.922 1.00172.53 H
HETATM 94 HMD2 HEC D1001 15.399 8.618 15.274 1.00172.53 H
HETATM 95 HMD3 HEC D1001 15.209 10.048 15.966 1.00172.53 H
TER
END
"""
edits = """
geometry_restraints.edits {
bond {
atom_selection_1 = chain D and resseq 40 and name SG
atom_selection_2 = chain D and resseq 1001 and name CAC
distance_ideal = 1.81
sigma = 0.05
}
bond {
atom_selection_1 = chain D and resseq 37 and name SG
atom_selection_2 = chain D and resseq 1001 and name CAB
distance_ideal = 1.81
sigma = 0.05
}
angle {
atom_selection_1 = chain D and resseq 40 and name SG
atom_selection_2 = chain D and resseq 1001 and name CAC
atom_selection_3 = chain D and resseq 1001 and name CBC
angle_ideal = 109
sigma = 2
}
angle {
atom_selection_1 = chain D and resseq 40 and name SG
atom_selection_2 = chain D and resseq 1001 and name CAC
atom_selection_3 = chain D and resseq 1001 and name C3C
angle_ideal = 109
sigma = 2
}
angle {
atom_selection_1 = chain D and resseq 37 and name SG
atom_selection_2 = chain D and resseq 1001 and name CAB
atom_selection_3 = chain D and resseq 1001 and name CBB
angle_ideal = 109
sigma = 2
}
angle {
atom_selection_1 = chain D and resseq 1001 and name HAB
atom_selection_2 = chain D and resseq 1001 and name CAB
atom_selection_3 = chain D and resseq 37 and name SG
angle_ideal = 109
sigma = 1.1
}
angle {
atom_selection_1 = chain D and resseq 1001 and name HAC
atom_selection_2 = chain D and resseq 1001 and name CAC
atom_selection_3 = chain D and resseq 40 and name SG
angle_ideal = 109
sigma = 1.1
}
}
"""
gm_phil = iotbx.phil.parse(input_string=grand_master_phil_str,
process_includes=True)
edits_phil = iotbx.phil.parse(edits)
working_phil = gm_phil.fetch(edits_phil)
params = working_phil.extract()
# Make sure the angle edit is present
assert (params.geometry_restraints.edits.angle[3].atom_selection_1 == \
"chain D and resseq 1001 and name HAB")
pdb_inp = iotbx.pdb.input(lines=pdb_str.split("\n"), source_info=None)
model = mmtbx.model.manager(model_input=pdb_inp,
build_grm=True,
pdb_interpretation_params=params)
pdb_hierarchy = model.get_hierarchy()
sites_cart = model.get_sites_cart()
atoms = pdb_hierarchy.atoms()
model.setup_riding_h_manager()
riding_h_manager = model.get_riding_h_manager()
h_para = riding_h_manager.h_parameterization
diagnostics = riding_h_manager.diagnostics(sites_cart=sites_cart,
threshold=0.05)
h_distances = diagnostics.h_distances
type_list = diagnostics.type_list
number_h = model.get_hd_selection().count(True)
number_h_para = len(h_para) - h_para.count(None)
for rc in h_para:
if rc:
assert (rc.ih != 63), 'Wrong atom not recognized.'
# Test if number of paramterized H atoms is correct
assert (number_h == number_h_para + 1), 'Not all H atoms are parameterized'
type_list_known = [
'alg1b', '3neigbs', '2tetra', '2tetra', 'alg1b', '3neigbs', '2tetra',
'2tetra', '3neigbs', 'flat_2neigbs', 'flat_2neigbs', 'flat_2neigbs',
'flat_2neigbs', '2tetra', '2tetra', '2tetra', '2tetra', '2tetra',
'2tetra', 'prop', 'prop', 'prop', 'prop', 'prop', 'prop', '2tetra',
'2tetra', 'prop', 'prop', 'prop', 'prop', 'prop', 'prop', 'prop',
'prop', 'prop', 'prop', 'prop', 'prop'
]
for ih in h_distances:
# One H atom is expected to be far (HAC)
if (ih == 64):
continue
labels = atoms[ih].fetch_labels()
if (h_distances[ih] > 0.1):
assert (h_distances[ih] < 0.1), \
'distance too large: %s atom: %s (%s) residue: %s ' \
% (h_para[ih].htype, atoms[ih].name, ih, labels.resseq.strip())
#
for type1, type2 in zip(type_list, type_list_known):
assert (type1 == type2)
def exercise2():
pdb_str = """
CRYST1 16.660 12.742 18.240 90.00 90.00 90.00 P 1
SCALE1 0.060024 0.000000 0.000000 0.00000
SCALE2 0.000000 0.078481 0.000000 0.00000
SCALE3 0.000000 0.000000 0.054825 0.00000
ATOM 1 N TYR A 7 9.837 5.000 6.625 1.00 15.00 N
ATOM 2 CA TYR A 7 10.084 6.426 6.798 1.00 15.00 C
ATOM 3 C TYR A 7 11.431 6.813 6.197 1.00 15.00 C
ATOM 4 O TYR A 7 11.660 6.642 5.000 1.00 15.00 O
ATOM 5 CB TYR A 7 10.042 6.803 8.281 1.00 15.00 C
ATOM 6 CG TYR A 7 8.697 6.593 8.948 1.00 15.00 C
ATOM 7 CD1 TYR A 7 7.540 6.413 8.198 1.00 15.00 C
ATOM 8 CD2 TYR A 7 8.586 6.575 10.332 1.00 15.00 C
ATOM 9 CE1 TYR A 7 6.315 6.222 8.807 1.00 15.00 C
ATOM 10 CE2 TYR A 7 7.364 6.384 10.950 1.00 15.00 C
ATOM 11 CZ TYR A 7 6.233 6.208 10.183 1.00 15.00 C
ATOM 12 OH TYR A 7 5.015 6.018 10.794 1.00 15.00 O
ATOM 13 HA TYR A 7 9.398 6.930 6.331 1.00 15.00 H
ATOM 14 HB2 TYR A 7 10.693 6.264 8.757 1.00 15.00 H
ATOM 15 HB3 TYR A 7 10.270 7.742 8.369 1.00 15.00 H
ATOM 16 HD1 TYR A 7 7.589 6.422 7.269 1.00 15.00 H
ATOM 17 HD2 TYR A 7 9.347 6.694 10.853 1.00 15.00 H
ATOM 18 HE1 TYR A 7 5.550 6.103 8.292 1.00 15.00 H
ATOM 19 HE2 TYR A 7 7.306 6.375 11.878 1.00 15.00 H
ATOM 20 HH TYR A 7 5.000 6.415 11.534 1.00 15.00 H
TER
HETATM 21 O HOH B 1 5.307 7.545 13.240 1.00 30.00 O
TER
END
"""
edits = """
geometry_restraints.edits {
bond {
atom_selection_1 = chain A and resseq 7 and name HH
atom_selection_2 = chain B and resseq 1 and name O
distance_ideal = 1.81
sigma = 0.05
}
}
"""
type_list_known = [
'3neigbs', '2tetra', '2tetra', 'flat_2neigbs', 'flat_2neigbs',
'flat_2neigbs', 'flat_2neigbs', 'alg1b'
]
gm_phil = iotbx.phil.parse(input_string=grand_master_phil_str,
process_includes=True)
edits_phil = iotbx.phil.parse(edits)
working_phil = gm_phil.fetch(edits_phil)
params = working_phil.extract()
# Make sure the angle edit is present
assert (params.geometry_restraints.edits.bond[0].atom_selection_1 == \
"chain A and resseq 7 and name HH")
pdb_inp = iotbx.pdb.input(lines=pdb_str.split("\n"), source_info=None)
model = mmtbx.model.manager(model_input=pdb_inp,
build_grm=True,
pdb_interpretation_params=params)
pdb_hierarchy = model.get_hierarchy()
sites_cart = model.get_sites_cart()
atoms = pdb_hierarchy.atoms()
model.setup_riding_h_manager()
riding_h_manager = model.get_riding_h_manager()
h_para = riding_h_manager.h_parameterization
diagnostics = riding_h_manager.diagnostics(sites_cart=sites_cart,
threshold=0.05)
h_distances = diagnostics.h_distances
type_list = diagnostics.type_list
number_h = model.get_hd_selection().count(True)
number_h_para = len(h_para) - h_para.count(None)
connectivity_manager = connectivity.determine_connectivity(
pdb_hierarchy=pdb_hierarchy,
geometry_restraints=model.get_restraints_manager().geometry)
double_H = connectivity_manager.double_H
# Test if number of paramterized H atoms is correct
assert (number_h == number_h_para), 'Not all H atoms are parameterized'
assert (double_H[19] == [11,
20]), 'H bound to two atoms wrongly recognized'
assert (number_h_para == 8), 'Not all H atoms are parameterized'
for ih in h_distances:
labels = atoms[ih].fetch_labels()
if (h_distances[ih] > 0.1):
assert (h_distances[ih] < 0.1), \
'distance too large: %s atom: %s (%s) residue: %s ' \
% (h_para[ih].htype, atoms[ih].name, ih, labels.resseq.strip())
for type1, type2 in zip(type_list, type_list_known):
assert (type1 == type2)
def __init__(
self,
model,
Hs=["H", "D"],
As=["O", "N", "S", "F", "CL"],
Ds=["O", "N", "S"],
d_HA_cutoff=[1.4, 3.0], # original: [1.4, 2.4],
d_DA_cutoff=[2.5, 3.5], # not used
a_DHA_cutoff=120, # should be greater than this
a_YAH_cutoff=[90, 180], # should be within this interval, not used
protein_only=False):
self.result = []
self.atoms = model.get_hierarchy().atoms()
geometry = model.get_restraints_manager()
bond_proxies_simple, asu = geometry.geometry.get_all_bond_proxies(
sites_cart=model.get_sites_cart())
h_bonded_to = {}
for p in bond_proxies_simple:
i, j = p.i_seqs
ei, ej = self.atoms[p.i_seqs[0]].element, self.atoms[
p.i_seqs[1]].element
if (ei in Hs): h_bonded_to[i] = self.atoms[j]
if (ej in Hs): h_bonded_to[j] = self.atoms[i]
#
sites_cart = model.get_sites_cart()
crystal_symmetry = model.crystal_symmetry()
fm = crystal_symmetry.unit_cell().fractionalization_matrix()
om = crystal_symmetry.unit_cell().orthogonalization_matrix()
pg = get_pair_generator(crystal_symmetry=crystal_symmetry,
buffer_thickness=d_HA_cutoff[1],
sites_cart=sites_cart)
get_class = iotbx.pdb.common_residue_names_get_class
for p in pg.pair_generator:
i, j = p.i_seq, p.j_seq
ei, ej = self.atoms[i].element, self.atoms[j].element
altloc_i = self.atoms[i].parent().altloc
altloc_j = self.atoms[j].parent().altloc
resseq_i = self.atoms[i].parent().parent().resseq
resseq_j = self.atoms[j].parent().parent().resseq
# pre-screen candidates begin
one_is_Hs = ei in Hs or ej in Hs
other_is_acceptor = ei in As or ej in As
d_HA = math.sqrt(p.dist_sq)
assert d_HA <= d_HA_cutoff[1]
is_candidate = one_is_Hs and other_is_acceptor and \
d_HA >= d_HA_cutoff[0] and \
altloc_i == altloc_j and resseq_i != resseq_j
if (protein_only):
for it in [i, j]:
resname = self.atoms[it].parent().resname
is_candidate &= get_class(
name=resname) == "common_amino_acid"
if (not is_candidate): continue
if (ei in Hs and not h_bonded_to[i].element in As): continue
if (ej in Hs and not h_bonded_to[j].element in As): continue
# pre-screen candidates end
rt_mx_i = pg.conn_asu_mappings.get_rt_mx_i(p)
rt_mx_j = pg.conn_asu_mappings.get_rt_mx_j(p)
rt_mx_ji = rt_mx_i.inverse().multiply(rt_mx_j)
#
if (ei in Hs):
H = self.atoms[i]
D = self.atoms[h_bonded_to[H.i_seq].i_seq]
A = self.atoms[j]
if (str(rt_mx_ji) != "x,y,z"):
A = apply_symop_to_copy(A, rt_mx_ji, fm, om)
if (ej in Hs):
H = self.atoms[j]
D = self.atoms[h_bonded_to[H.i_seq].i_seq]
A = self.atoms[i]
if (str(rt_mx_ji) != "x,y,z"):
H = apply_symop_to_copy(H, rt_mx_ji, fm, om)
D = apply_symop_to_copy(D, rt_mx_ji, fm, om)
assert H.distance(D) < 1.15
# filter by a_DHA
a_DHA = H.angle(A, D, deg=True)
if (a_DHA < a_DHA_cutoff): continue
#
assert approx_equal(d_HA, H.distance(A), 1.e-3)
self.result.append(
group_args(i=i,
j=j,
symop=rt_mx_ji,
d_HA=d_HA,
a_DHA=a_DHA,
d_AD=A.distance(D)))
def run(args, command_name="phenix.tls"):
if (len(args) == 0): args = ["--help"]
usage_fmt = "%s pdb_file [parameters: file or command line string]"
des_fmt = "Example: %s model.pdb fit_tls_to.selection='%s' fit_tls_to.selection='%s'"
command_line = (iotbx_option_parser(
usage=usage_fmt % command_name, description=banner).option(
"--show_defaults",
action="store_true",
help="Do not output to the screen (except errors).").option(
"--silent",
action="store_true",
help="Suppress output to the screen.")).process(args=args)
#
log = sys.stdout
if (not command_line.options.silent):
utils.print_header("TLS tools", out=log)
if (command_line.options.show_defaults):
master_params.show(out=log)
print(file=log)
return
if (not command_line.options.silent):
print(banner, file=log)
#
processed_args = utils.process_command_line_args(
args=command_line.args, master_params=master_params, log=log)
reflection_files = processed_args.reflection_files
if (processed_args.crystal_symmetry is None):
raise Sorry("No crystal symmetry found.")
if (len(processed_args.pdb_file_names) == 0):
raise Sorry("No PDB file found.")
params = processed_args.params
if (not command_line.options.silent):
utils.print_header("Input parameters", out=log)
params.show(out=log)
params = params.extract()
#
if (processed_args.crystal_symmetry.unit_cell() is None
or processed_args.crystal_symmetry.space_group() is None):
raise Sorry("No CRYST1 record found.")
pdb_combined = iotbx.pdb.combine_unique_pdb_files(
file_names=processed_args.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_records = pdb_combined.raw_records
try:
pdb_inp = iotbx.pdb.input(source_info=None,
lines=flex.std_string(raw_records))
except ValueError as e:
raise Sorry("Model format (PDB or mmCIF) error:\n%s" % str(e))
model = mmtbx.model.manager(
model_input=pdb_inp,
restraint_objects=processed_args.cif_objects,
crystal_symmetry=processed_args.crystal_symmetry,
log=log)
if (not command_line.options.silent):
utils.print_header("TLS groups from PDB file header", out=log)
pdb_inp_tls = mmtbx.tls.tools.tls_from_pdb_inp(
remark_3_records=model._model_input.extract_remark_iii_records(3),
pdb_hierarchy=model.get_hierarchy())
#
tls_groups = []
if (pdb_inp_tls.tls_present):
if (pdb_inp_tls.error_string is not None):
raise Sorry(pdb_inp_tls.error_string)
pdb_tls = mmtbx.tls.tools.extract_tls_from_pdb(pdb_inp_tls=pdb_inp_tls,
model=model)
tls_groups = pdb_tls.pdb_inp_tls.tls_params
#
tls_selections_strings = []
#
if (len(tls_groups) == 0 and not command_line.options.silent):
print("No TLS groups found in PDB file header.", file=log)
else:
for i_seq, tls_group in enumerate(tls_groups):
tls_selections_strings.append(tls_group.selection_string)
if (not command_line.options.silent):
print("TLS group %d: %s" %
(i_seq + 1, tls_group.selection_string),
file=log)
mmtbx.tls.tools.show_tls_one_group(tlso=tls_group, out=log)
print(file=log)
#
if (len(tls_selections_strings) > 0 and len(params.selection) > 0):
raise Sorry(
"Two TLS selection sources found: PDB file header and parameters.")
if (len(params.selection) > 0):
tls_selections_strings = params.selection
if ([params.combine_tls, params.extract_tls].count(True) > 1):
raise Sorry(
"Cannot simultaneously pereform: combine_tls and extract_tls")
if ([params.combine_tls, params.extract_tls].count(True) > 0):
if (len(tls_selections_strings) == 0):
raise Sorry("No TLS selections found.")
#
if (len(tls_selections_strings)):
if (not command_line.options.silent):
utils.print_header("TLS groups selections", out=log)
selections = utils.get_atom_selections(
model=model, selection_strings=tls_selections_strings)
if (not command_line.options.silent):
print("Number of TLS groups: ", len(selections), file=log)
print("Number of atoms: %d" % model.get_number_of_atoms(),
file=log)
n_atoms_in_tls = 0
for sel_a in selections:
n_atoms_in_tls += sel_a.size()
if (not command_line.options.silent):
print("Number of atoms in TLS groups: %d" % n_atoms_in_tls,
file=log)
print(file=log)
assert len(tls_selections_strings) == len(selections)
if (not command_line.options.silent):
for sel_a, sel_s in zip(selections, tls_selections_strings):
print("Selection string:\n%s" % sel_s, file=log)
print("selects %d atoms." % sel_a.size(), file=log)
print(file=log)
print("Ready-to-use in phenix.refine:\n", file=log)
for sel_a, sel_s in zip(selections, tls_selections_strings):
print(sel_s, file=log)
#
ofn = params.output_file_name
if (ofn is None):
ofn = os.path.splitext(
os.path.basename(processed_args.pdb_file_names[0]))[0]
if (len(processed_args.pdb_file_names) > 1):
ofn = ofn + "_el_al"
if (params.combine_tls):
ofn = ofn + "_combine_tls.pdb"
elif (params.extract_tls):
ofn = ofn + "_extract_tls.pdb"
else:
ofn = None
if (ofn is not None):
ofo = open(ofn, "w")
#
if (params.extract_tls):
utils.print_header(
"Fit TLS matrices to B-factors of selected sets of atoms", out=log)
tlsos = mmtbx.tls.tools.generate_tlsos(
selections=selections,
xray_structure=model.get_xray_structure(),
value=0.0)
for rt, rl, rs in [[1, 0, 1], [1, 1, 1], [0, 1, 1], [1, 0, 0],
[0, 1, 0], [0, 0, 1], [1, 1, 1], [0, 0, 1]] * 10:
tlsos = mmtbx.tls.tools.tls_from_uanisos(
xray_structure=model.get_xray_structure(),
selections=selections,
tlsos_initial=tlsos,
number_of_macro_cycles=10,
max_iterations=100,
refine_T=rt,
refine_L=rl,
refine_S=rs,
enforce_positive_definite_TL=params.
enforce_positive_definite_TL,
verbose=-1,
out=log)
mmtbx.tls.tools.show_tls(tlsos=tlsos, out=log)
u_cart_from_tls = mmtbx.tls.tools.u_cart_from_tls(
sites_cart=model.get_sites_cart(),
selections=selections,
tlsos=tlsos)
unit_cell = model.get_xray_structure().unit_cell()
for i_seq, sc in enumerate(model.get_xray_structure().scatterers()):
if (u_cart_from_tls[i_seq] != (0, 0, 0, 0, 0, 0)):
u_star_tls = adptbx.u_cart_as_u_star(
unit_cell, tuple(u_cart_from_tls[i_seq]))
sc.u_star = tuple(
flex.double(sc.u_star) - flex.double(u_star_tls))
for sel in selections:
model.get_xray_structure().convert_to_isotropic(selection=sel)
mmtbx.tls.tools.remark_3_tls(tlsos=tlsos,
selection_strings=tls_selections_strings,
out=ofo)
#
if (params.combine_tls):
utils.print_header("Combine B_tls with B_residual", out=log)
mmtbx.tls.tools.combine_tls_and_u_local(
xray_structure=model.get_xray_structure(),
tls_selections=selections,
tls_groups=tls_groups)
print("All done.", file=log)
#
if (ofn is not None):
utils.print_header("Write output PDB file %s" % ofn, out=log)
model.set_sites_cart_from_xrs()
pdb_str = model.model_as_pdb()
ofo.write(pdb_str)
ofo.close()
print("All done.", file=log)