def c_beta(self):
result = cbetadev(pdb_hierarchy = self.pdb_hierarchy,
outliers_only = True, out = null_out()) # XXX Why it is different from others?
return group_args(
outliers = result.get_outlier_percent(),
cbetadev = result #XXX Bulky object -- REMOVE!
)
def run(self):
hierarchy = self.data_manager.get_model().get_hierarchy()
hierarchy.atoms().reset_i_seq()
result = cbetadev(
pdb_hierarchy=hierarchy,
outliers_only=self.params.outliers_only,
out=self.logger,
quiet=False)
if self.params.cbetadev.output == "kin":
self.logger.write(result.as_kinemage())
elif self.params.verbose:
#pdb_file_str = os.path.basename(self.params.model)[:-4]
#get input file name from data manager, strip file extension
pdb_file_str = os.path.basename(self.data_manager.get_model_names()[0])[:-4]
result.show_old_output(out=self.logger, prefix=pdb_file_str, verbose=True)
def __init__(
self,
pdb_hierarchy,
molprobity_scores=False,
):
self.pdb_hierarchy = pdb_hierarchy
self.clashscore = None
self.ramachandran_outliers = None
self.ramachandran_allowed = None
self.ramachandran_favored = None
self.rotamer_outliers = None
self.c_beta_dev = None
self.mpscore = None
self.omglz = None
self.n_cis_proline = None
self.n_cis_general = None
self.n_twisted_proline = None
self.n_twisted_general = None
if(molprobity_scores):
self.ramalyze_obj = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
self.ramachandran_outliers = self.ramalyze_obj.percent_outliers
self.ramachandran_allowed = self.ramalyze_obj.percent_allowed
self.ramachandran_favored = self.ramalyze_obj.percent_favored
self.rotalyze_obj = rotalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
self.rotamer_outliers = self.rotalyze_obj.percent_outliers
self.cbetadev_obj = cbetadev(
pdb_hierarchy = pdb_hierarchy,
outliers_only = True,
out = null_out())
self.c_beta_dev = self.cbetadev_obj.get_outlier_count()
self.clashscore = clashscore(pdb_hierarchy=pdb_hierarchy).get_clashscore()
self.mpscore = molprobity_score(
clashscore = self.clashscore,
rota_out = self.rotamer_outliers,
rama_fav = self.ramachandran_favored)
self.omglz = omegalyze.omegalyze(
pdb_hierarchy=self.pdb_hierarchy, quiet=True)
self.n_cis_proline = self.omglz.n_cis_proline()
self.n_cis_general = self.omglz.n_cis_general()
self.n_twisted_proline = self.omglz.n_twisted_proline()
self.n_twisted_general = self.omglz.n_twisted_general()
def __init__(
self,
pdb_hierarchy,
molprobity_scores=False,
):
self.pdb_hierarchy = pdb_hierarchy
self.clashscore = None
self.ramachandran_outliers = None
self.ramachandran_allowed = None
self.ramachandran_favored = None
self.rotamer_outliers = None
self.c_beta_dev = None
self.mpscore = None
self.omglz = None
self.n_cis_proline = None
self.n_cis_general = None
self.n_twisted_proline = None
self.n_twisted_general = None
if(molprobity_scores):
self.ramalyze_obj = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
self.ramachandran_outliers = self.ramalyze_obj.percent_outliers
self.ramachandran_allowed = self.ramalyze_obj.percent_allowed
self.ramachandran_favored = self.ramalyze_obj.percent_favored
self.rotalyze_obj = rotalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
self.rotamer_outliers = self.rotalyze_obj.percent_outliers
self.cbetadev_obj = cbetadev(
pdb_hierarchy = pdb_hierarchy,
outliers_only = True,
out = null_out())
self.c_beta_dev = self.cbetadev_obj.get_outlier_count()
self.clashscore = clashscore(pdb_hierarchy=pdb_hierarchy).get_clashscore()
self.mpscore = molprobity_score(
clashscore = self.clashscore,
rota_out = self.rotamer_outliers,
rama_fav = self.ramachandran_favored)
self.omglz = omegalyze.omegalyze(
pdb_hierarchy=self.pdb_hierarchy, quiet=True)
self.n_cis_proline = self.omglz.n_cis_proline()
self.n_cis_general = self.omglz.n_cis_general()
self.n_twisted_proline = self.omglz.n_twisted_proline()
self.n_twisted_general = self.omglz.n_twisted_general()
def get_scores(self, model):
rama_fav = ramalyze(pdb_hierarchy=model.get_hierarchy(),
outliers_only=False).percent_favored
cbeta = cbetadev(pdb_hierarchy=model.get_hierarchy(),
outliers_only=True,
out=null_out()).get_outlier_percent()
rota = rotalyze(pdb_hierarchy=model.get_hierarchy(),
outliers_only=False).percent_outliers
b_rmsd = get_bonds_rmsd(
restraints_manager=self.geometry_rmsd_manager.geometry,
xrs=model.get_xray_structure())
clash = clashscore(pdb_hierarchy=model.get_hierarchy(),
keep_hydrogens=False,
fast=True,
condensed_probe=True).get_clashscore()
print "DEV: b_rmsd= %7.4f clash= %6.4f rota= %6.4f rama_fav= %5.4f cbeta= %6.4f" % (
b_rmsd, clash, rota, rama_fav, cbeta)
return group_args(rama_fav=rama_fav,
cbeta=cbeta,
rota=rota,
b_rmsd=b_rmsd,
clash=clash)
def __init__(self,
pdb_hierarchy,
xray_structure=None,
fmodel=None,
fmodel_neutron=None,
geometry_restraints_manager=None,
crystal_symmetry=None,
sequences=None,
flags=None,
header_info=None,
raw_data=None,
unmerged_data=None,
all_chain_proxies=None,
keep_hydrogens=True,
nuclear=False,
save_probe_unformatted_file=None,
show_hydrogen_outliers=False,
min_cc_two_fofc=0.8,
n_bins_data=10,
count_anomalous_pairs_separately=False,
use_internal_variance=True,
outliers_only=True,
use_pdb_header_resolution_cutoffs=False,
file_name=None,
ligand_selection=None,
rotamer_library="8000",
map_params=None):
assert rotamer_library == "8000", "data_version given to RotamerEval not recognized."
for name in self.__slots__:
setattr(self, name, None)
# very important - the i_seq attributes may be extracted later
pdb_hierarchy.atoms().reset_i_seq()
self.pdb_hierarchy = pdb_hierarchy
if (xray_structure is None):
if (fmodel is not None):
xray_structure = fmodel.xray_structure
elif (crystal_symmetry is not None):
xray_structure = pdb_hierarchy.extract_xray_structure(
crystal_symmetry=crystal_symmetry)
self.crystal_symmetry = crystal_symmetry
if (crystal_symmetry is None) and (fmodel is not None):
self.crystal_symmetry = fmodel.f_obs().crystal_symmetry()
self.header_info = header_info
if (flags is None):
flags = molprobity_flags()
if pdb_hierarchy.contains_protein():
self.find_missing_atoms(out=null_out())
if (flags.ramalyze):
self.ramalyze = ramalyze.ramalyze(pdb_hierarchy=pdb_hierarchy,
outliers_only=outliers_only,
out=null_out(),
quiet=True)
##### omegalyze ################################################################
if (flags.omegalyze):
self.omegalyze = omegalyze.omegalyze(
pdb_hierarchy=pdb_hierarchy,
nontrans_only=outliers_only,
out=null_out(),
quiet=True)
##### omegalyze ################################################################
if (flags.rotalyze):
self.rotalyze = rotalyze.rotalyze(pdb_hierarchy=pdb_hierarchy,
data_version=rotamer_library,
outliers_only=outliers_only,
out=null_out(),
quiet=True)
if (flags.cbetadev):
self.cbetadev = cbetadev.cbetadev(pdb_hierarchy=pdb_hierarchy,
outliers_only=outliers_only,
out=null_out(),
quiet=True)
if (flags.nqh):
self.nqh_flips = clashscore.nqh_flips(
pdb_hierarchy=pdb_hierarchy)
if (pdb_hierarchy.contains_rna() and flags.rna
and libtbx.env.has_module(name="suitename")):
if (geometry_restraints_manager is not None):
self.rna = rna_validate.rna_validation(
pdb_hierarchy=pdb_hierarchy,
geometry_restraints_manager=geometry_restraints_manager,
outliers_only=outliers_only,
params=None)
if (flags.clashscore):
self.clashes = clashscore.clashscore(
pdb_hierarchy=pdb_hierarchy,
save_probe_unformatted_file=save_probe_unformatted_file,
nuclear=nuclear,
keep_hydrogens=keep_hydrogens,
out=null_out(),
verbose=False)
if (flags.model_stats) and (xray_structure is not None):
self.model_stats = model_properties.model_statistics(
pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
all_chain_proxies=all_chain_proxies,
ignore_hd=(not nuclear),
ligand_selection=ligand_selection)
if (geometry_restraints_manager is not None) and (flags.restraints):
assert (xray_structure is not None)
self.restraints = restraints.combined(
pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
geometry_restraints_manager=geometry_restraints_manager,
ignore_hd=(not nuclear),
cdl=getattr(all_chain_proxies, "use_cdl", None))
if (sequences is not None) and (flags.seq):
self.sequence = sequence.validation(
pdb_hierarchy=pdb_hierarchy,
sequences=sequences,
log=null_out(),
include_secondary_structure=True,
extract_coordinates=True)
# use maps (fmodel is not used)
use_maps = False
if (map_params is not None):
use_maps = ((map_params.input.maps.map_file_name) or
((map_params.input.maps.map_coefficients_file_name) and
(map_params.input.maps.map_coefficients_label)))
if (use_maps):
if (flags.real_space):
self.real_space = experimental.real_space(
fmodel=None,
pdb_hierarchy=pdb_hierarchy,
cc_min=min_cc_two_fofc,
molprobity_map_params=map_params.input.maps)
if (flags.waters):
self.waters = waters.waters(
pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
fmodel=None,
collect_all=True,
molprobity_map_params=map_params.input.maps)
if (fmodel is not None):
if (use_pdb_header_resolution_cutoffs) and (header_info
is not None):
fmodel = fmodel.resolution_filter(d_min=header_info.d_min,
d_max=header_info.d_max)
if (flags.rfactors):
self.data_stats = experimental.data_statistics(
fmodel,
raw_data=raw_data,
n_bins=n_bins_data,
count_anomalous_pairs_separately=
count_anomalous_pairs_separately)
if (not use_maps): # if maps are used, keep previous results
if (flags.real_space):
self.real_space = experimental.real_space(
fmodel=fmodel,
pdb_hierarchy=pdb_hierarchy,
cc_min=min_cc_two_fofc)
if (flags.waters):
self.waters = waters.waters(pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
fmodel=fmodel,
collect_all=True)
if (unmerged_data is not None):
self.merging = experimental.merging_and_model_statistics(
f_obs=fmodel.f_obs(),
f_model=fmodel.f_model(),
r_free_flags=fmodel.r_free_flags(),
unmerged_i_obs=unmerged_data,
anomalous=count_anomalous_pairs_separately,
use_internal_variance=use_internal_variance,
n_bins=n_bins_data)
if (flags.xtriage):
import mmtbx.scaling.xtriage
f_model = abs(
fmodel.f_model()).set_observation_type_xray_amplitude()
if (raw_data is not None):
f_model, obs = f_model.common_sets(other=raw_data)
else:
obs = fmodel.f_obs()
self.xtriage = mmtbx.scaling.xtriage.xtriage_analyses(
miller_obs=obs,
miller_calc=f_model,
unmerged_obs=unmerged_data, # XXX some redundancy here...
text_out=null_out())
if (fmodel_neutron is not None) and (flags.rfactors):
self.neutron_stats = experimental.data_statistics(
fmodel_neutron,
n_bins=n_bins_data,
count_anomalous_pairs_separately=False)
if (pdb_hierarchy.models_size() == 1):
self._multi_criterion = multi_criterion_view(pdb_hierarchy)
def get_model_stat(file_name):
pdb_inp = iotbx.pdb.input(file_name=file_name)
atoms = pdb_inp.atoms()
box = uctbx.non_crystallographic_unit_cell_with_the_sites_in_its_center(
sites_cart=atoms.extract_xyz(), buffer_layer=5)
atoms.set_xyz(new_xyz=box.sites_cart)
ph = pdb_inp.construct_hierarchy()
if (all_single_atom_residues(ph=ph)): return None
raw_recs = ph.as_pdb_string(
crystal_symmetry=box.crystal_symmetry()).splitlines()
#
params = monomer_library.pdb_interpretation.master_params.extract()
params.clash_guard.nonbonded_distance_threshold = None
params.disable_uc_volume_vs_n_atoms_check = False
params.use_neutron_distances = True
params.restraints_library.cdl = False
processed_pdb_file = monomer_library.pdb_interpretation.process(
mon_lib_srv=mon_lib_srv,
ener_lib=ener_lib,
raw_records=raw_recs,
params=params,
log=null_out())
xrs = processed_pdb_file.xray_structure()
sctr_keys = xrs.scattering_type_registry().type_count_dict().keys()
has_hd = "H" in sctr_keys or "D" in sctr_keys
restraints_manager = processed_pdb_file.geometry_restraints_manager(
show_energies=False,
assume_hydrogens_all_missing=not has_hd,
plain_pairs_radius=5.0)
a_mean, b_mean = get_bonds_angles_rmsd(
restraints_manager=restraints_manager, xrs=xrs)
energies_sites = \
restraints_manager.energies_sites(
sites_cart = xrs.sites_cart(),
compute_gradients = False)
nonbonded_distances = energies_sites.nonbonded_distances()
number_of_worst_clashes = (nonbonded_distances < 0.5).count(True)
#
ramalyze_obj = ramalyze(pdb_hierarchy=ph, outliers_only=False)
ramachandran_outliers = ramalyze_obj.percent_outliers
rotamer_outliers = rotalyze(pdb_hierarchy=ph,
outliers_only=False).percent_outliers
c_beta_dev = cbetadev(pdb_hierarchy=ph, outliers_only=True,
out=null_out()).get_outlier_count()
omglz = omegalyze.omegalyze(pdb_hierarchy=ph, quiet=True)
n_cis_proline = omglz.n_cis_proline()
n_cis_general = omglz.n_cis_general()
n_twisted_proline = omglz.n_twisted_proline()
n_twisted_general = omglz.n_twisted_general()
#
clsc = clashscore(pdb_hierarchy=ph).get_clashscore()
mpscore = molprobity_score(clashscore=clsc,
rota_out=rotamer_outliers,
rama_fav=ramalyze_obj.percent_favored)
#
occ = atoms.extract_occ()
bs = atoms.extract_b()
#
return group_args(b_mean=b_mean,
a_mean=a_mean,
number_of_worst_clashes=number_of_worst_clashes,
ramachandran_outliers=ramachandran_outliers,
rotamer_outliers=rotamer_outliers,
c_beta_dev=c_beta_dev,
n_cis_proline=n_cis_proline,
n_cis_general=n_cis_general,
n_twisted_proline=n_twisted_proline,
n_twisted_general=n_twisted_general,
o=occ.min_max_mean().as_tuple(),
b=bs.min_max_mean().as_tuple(),
mpscore=mpscore,
clsc=clsc,
n_atoms=atoms.size())
def exercise_cbetadev():
regression_pdb = libtbx.env.find_in_repositories(
relative_path="phenix_regression/pdb/pdb1jxt.ent", test=os.path.isfile)
if (regression_pdb is None):
print "Skipping exercise_cbetadev(): input pdb (pdb1jxt.ent) not available"
return
from mmtbx.validation import cbetadev
from iotbx import file_reader
pdb_in = file_reader.any_file(file_name=regression_pdb)
hierarchy = pdb_in.file_object.hierarchy
validation = cbetadev.cbetadev(pdb_hierarchy=hierarchy, outliers_only=True)
assert approx_equal(validation.get_weighted_outlier_percent(),
4.40420846587)
for unpickle in [False, True]:
if unpickle:
validation = loads(dumps(validation))
assert (validation.n_outliers == len(validation.results) == 6)
assert ([cb.id_str() for cb in validation.results] == [
' A 7 AILE', ' A 8 BVAL', ' A 8 CVAL', ' A 30 BTHR',
' A 39 BTHR', ' A 43 BASP'
])
assert approx_equal([cb.deviation for cb in validation.results], [
0.25977096732623106, 0.2577218834868609, 0.6405578498280606,
0.81238828498566, 0.9239566035292618, 0.5001892640352836
])
out = StringIO()
validation.show_old_output(out=out, verbose=True)
assert not show_diff(
out.getvalue(), """\
pdb:alt:res:chainID:resnum:dev:dihedralNABB:Occ:ALT:
pdb :A:ile: A: 7 : 0.260: -46.47: 0.45:A:
pdb :B:val: A: 8 : 0.258: 80.92: 0.30:B:
pdb :C:val: A: 8 : 0.641: -53.98: 0.20:C:
pdb :B:thr: A: 30 : 0.812: -76.98: 0.30:B:
pdb :B:thr: A: 39 : 0.924: 56.41: 0.30:B:
pdb :B:asp: A: 43 : 0.500: 7.56: 0.25:B:
SUMMARY: 6 C-beta deviations >= 0.25 Angstrom (Goal: 0)
""")
# Now with all residues
validation = cbetadev.cbetadev(pdb_hierarchy=hierarchy,
outliers_only=False)
for unpickle in [False, True]:
if unpickle:
validation = loads(dumps(validation))
for outlier in validation.results:
assert (len(outlier.xyz) == 3)
assert (validation.n_outliers == 6)
assert (len(validation.results) == 51)
out = StringIO()
validation.show_old_output(out=out, verbose=True)
assert not show_diff(
out.getvalue(), """\
pdb:alt:res:chainID:resnum:dev:dihedralNABB:Occ:ALT:
pdb : :thr: A: 1 : 0.102: 11.27: 1.00: :
pdb :A:thr: A: 2 : 0.022: -49.31: 0.67:A:
pdb : :cys: A: 3 : 0.038: 103.68: 1.00: :
pdb : :cys: A: 4 : 0.047:-120.73: 1.00: :
pdb : :pro: A: 5 : 0.069:-121.41: 1.00: :
pdb : :ser: A: 6 : 0.052: 112.87: 1.00: :
pdb :A:ile: A: 7 : 0.260: -46.47: 0.45:A:
pdb :B:ile: A: 7 : 0.153: 122.97: 0.55:B:
pdb :A:val: A: 8 : 0.184:-155.36: 0.50:A:
pdb :B:val: A: 8 : 0.258: 80.92: 0.30:B:
pdb :C:val: A: 8 : 0.641: -53.98: 0.20:C:
pdb : :ala: A: 9 : 0.061: -82.84: 1.00: :
pdb :A:arg: A: 10 : 0.023: 172.25: 1.00:A:
pdb : :ser: A: 11 : 0.028:-129.11: 1.00: :
pdb :A:asn: A: 12 : 0.021: -80.80: 0.50:A:
pdb :B:asn: A: 12 : 0.199: 50.01: 0.50:B:
pdb :A:phe: A: 13 : 0.067: -37.32: 0.65:A:
pdb :B:phe: A: 13 : 0.138: 19.24: 0.35:B:
pdb : :asn: A: 14 : 0.065: -96.35: 1.00: :
pdb : :val: A: 15 : 0.138: -96.63: 1.00: :
pdb : :cys: A: 16 : 0.102: -28.64: 1.00: :
pdb : :arg: A: 17 : 0.053:-106.79: 1.00: :
pdb : :leu: A: 18 : 0.053:-141.51: 1.00: :
pdb : :pro: A: 19 : 0.065:-146.95: 1.00: :
pdb : :thr: A: 21 : 0.086: 53.80: 1.00: :
pdb :A:pro: A: 22 : 0.092: -83.39: 0.55:A:
pdb :A:glu: A: 23 : 0.014:-179.53: 0.50:A:
pdb :B:glu: A: 23 : 0.050:-179.78: 0.50:B:
pdb : :ala: A: 24 : 0.056: -88.96: 1.00: :
pdb : :leu: A: 25 : 0.084:-106.42: 1.00: :
pdb : :cys: A: 26 : 0.074: -94.70: 1.00: :
pdb : :ala: A: 27 : 0.056: -62.15: 1.00: :
pdb : :thr: A: 28 : 0.056:-114.82: 1.00: :
pdb :A:tyr: A: 29 : 0.068: 0.22: 0.65:A:
pdb :A:thr: A: 30 : 0.180: 103.27: 0.70:A:
pdb :B:thr: A: 30 : 0.812: -76.98: 0.30:B:
pdb : :cys: A: 32 : 0.029: -84.07: 1.00: :
pdb : :ile: A: 33 : 0.048:-119.17: 1.00: :
pdb : :ile: A: 34 : 0.045: 99.02: 1.00: :
pdb : :ile: A: 35 : 0.052:-128.24: 1.00: :
pdb : :pro: A: 36 : 0.084:-142.29: 1.00: :
pdb : :ala: A: 38 : 0.039: 50.01: 1.00: :
pdb :A:thr: A: 39 : 0.093: -96.63: 0.70:A:
pdb :B:thr: A: 39 : 0.924: 56.41: 0.30:B:
pdb : :cys: A: 40 : 0.013:-144.11: 1.00: :
pdb : :pro: A: 41 : 0.039: -97.09: 1.00: :
pdb :A:asp: A: 43 : 0.130:-146.91: 0.75:A:
pdb :B:asp: A: 43 : 0.500: 7.56: 0.25:B:
pdb : :tyr: A: 44 : 0.085:-143.63: 1.00: :
pdb : :ala: A: 45 : 0.055: 33.32: 1.00: :
pdb : :asn: A: 46 : 0.066: -50.46: 1.00: :
SUMMARY: 6 C-beta deviations >= 0.25 Angstrom (Goal: 0)
""")
# Auxilary function: extract_atoms_from_residue_group
from mmtbx.validation.cbetadev import extract_atoms_from_residue_group
from iotbx import pdb
pdb_1 = pdb.input(source_info=None,
lines="""\
ATOM 1185 N ASER A 146 24.734 37.097 16.303 0.50 16.64 N
ATOM 1186 N BSER A 146 24.758 37.100 16.337 0.50 16.79 N
ATOM 1187 CA ASER A 146 24.173 37.500 17.591 0.50 16.63 C
ATOM 1188 CA BSER A 146 24.237 37.427 17.662 0.50 16.87 C
ATOM 1189 C ASER A 146 22.765 36.938 17.768 0.50 15.77 C
ATOM 1190 C BSER A 146 22.792 36.945 17.783 0.50 15.94 C
ATOM 1191 O ASER A 146 22.052 36.688 16.781 0.50 14.91 O
ATOM 1192 O BSER A 146 22.091 36.741 16.779 0.50 15.17 O
ATOM 1193 CB ASER A 146 24.118 39.035 17.649 0.50 16.93 C
ATOM 1194 CB BSER A 146 24.321 38.940 17.904 0.50 17.48 C
ATOM 1195 OG ASER A 146 23.183 39.485 18.611 0.50 17.56 O
ATOM 1196 OG BSER A 146 23.468 39.645 17.028 0.50 18.32 O """
).construct_hierarchy()
pdb_2 = pdb.input(source_info=None,
lines="""\
ATOM 1185 N SER A 146 24.734 37.097 16.303 0.50 16.64 N
ATOM 1187 CA SER A 146 24.173 37.500 17.591 0.50 16.63 C
ATOM 1189 C SER A 146 22.765 36.938 17.768 0.50 15.77 C
ATOM 1191 O SER A 146 22.052 36.688 16.781 0.50 14.91 O
ATOM 1193 CB ASER A 146 24.118 39.035 17.649 0.50 16.93 C
ATOM 1194 CB BSER A 146 24.321 38.940 17.904 0.50 17.48 C
ATOM 1195 OG ASER A 146 23.183 39.485 18.611 0.50 17.56 O
ATOM 1196 OG BSER A 146 23.468 39.645 17.028 0.50 18.32 O """
).construct_hierarchy()
pdb_3 = pdb.input(source_info=None,
lines="""\
ATOM 1185 N SER A 146 24.734 37.097 16.303 0.50 16.64 N
ATOM 1187 CA SER A 146 24.173 37.500 17.591 0.50 16.63 C
ATOM 1189 C SER A 146 22.765 36.938 17.768 0.50 15.77 C
ATOM 1191 O SER A 146 22.052 36.688 16.781 0.50 14.91 O
ATOM 1193 CB SER A 146 24.118 39.035 17.649 0.50 16.93 C
ATOM 1195 OG ASER A 146 23.183 39.485 18.611 0.50 17.56 O
ATOM 1196 OG BSER A 146 23.468 39.645 17.028 0.50 18.32 O """
).construct_hierarchy()
rg1 = pdb_1.only_model().only_chain().only_residue_group()
rg2 = pdb_2.only_model().only_chain().only_residue_group()
rg3 = pdb_3.only_model().only_chain().only_residue_group()
all_relevant_atoms_1 = extract_atoms_from_residue_group(rg1)
all_relevant_atoms_2 = extract_atoms_from_residue_group(rg2)
all_relevant_atoms_3 = extract_atoms_from_residue_group(rg3)
keys_1 = [sorted([k for k in a.keys()]) for a in all_relevant_atoms_1]
keys_2 = [sorted([k for k in a.keys()]) for a in all_relevant_atoms_2]
keys_3 = [sorted([k for k in a.keys()]) for a in all_relevant_atoms_3]
assert keys_1 == [[' C ', ' CA ', ' CB ', ' N '],
[' C ', ' CA ', ' CB ', ' N ']]
assert keys_2 == [[' C ', ' CA ', ' CB ', ' N '],
[' C ', ' CA ', ' CB ', ' N ']]
assert keys_3 == [[' C ', ' CA ', ' CB ', ' N ']]
print "OK"
def make_multikin(f, processed_pdb_file, pdbID=None, keep_hydrogens=False):
if pdbID == None:
pdbID = "PDB"
hierarchy = processed_pdb_file.all_chain_proxies.pdb_hierarchy
i_seq_name_hash = build_name_hash(pdb_hierarchy=hierarchy)
sites_cart=processed_pdb_file.all_chain_proxies.sites_cart
geometry = processed_pdb_file.geometry_restraints_manager()
flags = geometry_restraints.flags.flags(default=True)
angle_proxies = geometry.angle_proxies
pair_proxies = geometry.pair_proxies(flags=flags,
sites_cart=sites_cart)
bond_proxies = pair_proxies.bond_proxies
quick_bond_hash = {}
for bp in bond_proxies.simple:
if (i_seq_name_hash[bp.i_seqs[0]][9:14] ==
i_seq_name_hash[bp.i_seqs[1]][9:14]):
if quick_bond_hash.get(bp.i_seqs[0]) is None:
quick_bond_hash[bp.i_seqs[0]] = []
quick_bond_hash[bp.i_seqs[0]].append(bp.i_seqs[1])
kin_out = get_default_header()
altid_controls = get_altid_controls(hierarchy=hierarchy)
if altid_controls != "":
kin_out += altid_controls
kin_out += "@group {%s} dominant animate\n" % pdbID
initiated_chains = []
rot_outliers = rotalyze(pdb_hierarchy=hierarchy, outliers_only=True)
cb = cbetadev(
pdb_hierarchy=hierarchy,
outliers_only=True)
rama = ramalyze(pdb_hierarchy=hierarchy, outliers_only=True)
counter = 0
for model in hierarchy.models():
for chain in model.chains():
if chain.id not in initiated_chains:
kin_out += "@subgroup {%s} dominant master= {chain %s}\n" % (
pdbID,
chain.id)
initiated_chains.append(chain.id)
kin_out += get_kin_lots(chain=chain,
bond_hash=quick_bond_hash,
i_seq_name_hash=i_seq_name_hash,
pdbID=pdbID,
index=counter)
if (chain.is_protein()) :
kin_out += rotamer_outliers(chain=chain, pdbID=pdbID,
rot_outliers=rot_outliers)
kin_out += rama_outliers(chain=chain, pdbID=pdbID, ram_outliers=rama)
# TODO use central methods in mmtbx.validation.restraints
kin_out += get_angle_outliers(angle_proxies=angle_proxies,
chain=chain,
sites_cart=sites_cart,
hierarchy=hierarchy)
kin_out += get_bond_outliers(bond_proxies=bond_proxies,
chain=chain,
sites_cart=sites_cart,
hierarchy=hierarchy)
if (chain.is_protein()) :
kin_out += cbeta_dev(chain_id=chain.id,
outliers=cb.results)
kin_out += pperp_outliers(hierarchy=hierarchy,
chain=chain)
counter += 1
kin_out += omegalyze.omegalyze(pdb_hierarchy=hierarchy,nontrans_only=True,
out=None,quiet=False).as_kinemage()
kin_out += make_probe_dots(hierarchy=hierarchy, keep_hydrogens=keep_hydrogens)
kin_out += get_footer()
outfile = file(f, 'w')
for line in kin_out:
outfile.write(line)
outfile.close()
return f
def __init__ (self,
pdb_hierarchy,
xray_structure=None,
fmodel=None,
fmodel_neutron=None,
geometry_restraints_manager=None,
crystal_symmetry=None,
sequences=None,
flags=None,
header_info=None,
raw_data=None,
unmerged_data=None,
all_chain_proxies=None,
keep_hydrogens=True,
nuclear=False,
save_probe_unformatted_file=None,
show_hydrogen_outliers=False,
min_cc_two_fofc=0.8,
n_bins_data=10,
count_anomalous_pairs_separately=False,
use_internal_variance=True,
outliers_only=True,
use_pdb_header_resolution_cutoffs=False,
file_name=None,
ligand_selection=None,
rotamer_library="8000",
map_params=None) :
assert rotamer_library == "8000", "data_version given to RotamerEval not recognized."
for name in self.__slots__ :
setattr(self, name, None)
# very important - the i_seq attributes may be extracted later
pdb_hierarchy.atoms().reset_i_seq()
self.pdb_hierarchy = pdb_hierarchy
if (xray_structure is None) :
if (fmodel is not None) :
xray_structure = fmodel.xray_structure
elif (crystal_symmetry is not None) :
xray_structure = pdb_hierarchy.extract_xray_structure(
crystal_symmetry=crystal_symmetry)
self.crystal_symmetry = crystal_symmetry
if (crystal_symmetry is None) and (fmodel is not None) :
self.crystal_symmetry = fmodel.f_obs().crystal_symmetry()
self.header_info = header_info
if (flags is None) :
flags = molprobity_flags()
if pdb_hierarchy.contains_protein() :
if (flags.ramalyze) :
self.ramalyze = ramalyze.ramalyze(
pdb_hierarchy=pdb_hierarchy,
outliers_only=outliers_only,
out=null_out(),
quiet=True)
##### omegalyze ################################################################
if (flags.omegalyze) :
self.omegalyze = omegalyze.omegalyze(
pdb_hierarchy=pdb_hierarchy,
nontrans_only=outliers_only,
out=null_out(),
quiet=True)
##### omegalyze ################################################################
if (flags.rotalyze) :
self.rotalyze = rotalyze.rotalyze(
pdb_hierarchy=pdb_hierarchy,
data_version=rotamer_library,
outliers_only=outliers_only,
out=null_out(),
quiet=True)
if (flags.cbetadev) :
self.cbetadev = cbetadev.cbetadev(
pdb_hierarchy=pdb_hierarchy,
outliers_only=outliers_only,
out=null_out(),
quiet=True)
if (flags.nqh) :
self.nqh_flips = clashscore.nqh_flips(
pdb_hierarchy=pdb_hierarchy)
if (pdb_hierarchy.contains_rna() and flags.rna and
libtbx.env.has_module(name="suitename")) :
if (geometry_restraints_manager is not None) :
self.rna = rna_validate.rna_validation(
pdb_hierarchy=pdb_hierarchy,
geometry_restraints_manager=geometry_restraints_manager,
outliers_only=outliers_only,
params=None)
if (flags.clashscore) :
self.clashes = clashscore.clashscore(
pdb_hierarchy=pdb_hierarchy,
save_probe_unformatted_file=save_probe_unformatted_file,
nuclear=nuclear,
keep_hydrogens=keep_hydrogens,
out=null_out(),
verbose=False)
if (flags.model_stats) and (xray_structure is not None) :
self.model_stats = model_properties.model_statistics(
pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
all_chain_proxies=all_chain_proxies,
ignore_hd=(not nuclear),
ligand_selection=ligand_selection)
if (geometry_restraints_manager is not None) and (flags.restraints) :
assert (xray_structure is not None)
self.restraints = restraints.combined(
pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
geometry_restraints_manager=geometry_restraints_manager,
ignore_hd=(not nuclear),
cdl=getattr(all_chain_proxies, "use_cdl", None))
if (sequences is not None) and (flags.seq) :
self.sequence = sequence.validation(
pdb_hierarchy=pdb_hierarchy,
sequences=sequences,
log=null_out(),
include_secondary_structure=True,
extract_coordinates=True)
# use maps (fmodel is not used)
use_maps = False
if (map_params is not None):
use_maps = ( (map_params.input.maps.map_file_name) or
( (map_params.input.maps.map_coefficients_file_name) and
(map_params.input.maps.map_coefficients_label) ) )
if (use_maps):
if (flags.real_space):
self.real_space = experimental.real_space(
fmodel=None,
pdb_hierarchy=pdb_hierarchy,
cc_min=min_cc_two_fofc,
molprobity_map_params=map_params.input.maps)
if (flags.waters):
self.waters = waters.waters(
pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
fmodel=None,
collect_all=True,
molprobity_map_params=map_params.input.maps)
if (fmodel is not None) :
if (use_pdb_header_resolution_cutoffs) and (header_info is not None) :
fmodel = fmodel.resolution_filter(
d_min=header_info.d_min,
d_max=header_info.d_max)
if (flags.rfactors) :
self.data_stats = experimental.data_statistics(fmodel,
raw_data=raw_data,
n_bins=n_bins_data,
count_anomalous_pairs_separately=count_anomalous_pairs_separately)
if (not use_maps): # if maps are used, keep previous results
if (flags.real_space):
self.real_space = experimental.real_space(
fmodel=fmodel,
pdb_hierarchy=pdb_hierarchy,
cc_min=min_cc_two_fofc)
if (flags.waters) :
self.waters = waters.waters(
pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
fmodel=fmodel,
collect_all=True)
if (unmerged_data is not None) :
self.merging = experimental.merging_and_model_statistics(
f_obs=fmodel.f_obs(),
f_model=fmodel.f_model(),
r_free_flags=fmodel.r_free_flags(),
unmerged_i_obs=unmerged_data,
anomalous=count_anomalous_pairs_separately,
use_internal_variance=use_internal_variance,
n_bins=n_bins_data)
if (flags.xtriage) :
import mmtbx.scaling.xtriage
f_model = abs(fmodel.f_model()).set_observation_type_xray_amplitude()
if (raw_data is not None) :
f_model, obs = f_model.common_sets(other=raw_data)
else :
obs = fmodel.f_obs()
self.xtriage = mmtbx.scaling.xtriage.xtriage_analyses(
miller_obs=obs,
miller_calc=f_model,
unmerged_obs=unmerged_data, # XXX some redundancy here...
text_out=null_out())
if (fmodel_neutron is not None) and (flags.rfactors) :
self.neutron_stats = experimental.data_statistics(fmodel_neutron,
n_bins=n_bins_data,
count_anomalous_pairs_separately=False)
if (pdb_hierarchy.models_size() == 1) :
self._multi_criterion = multi_criterion_view(pdb_hierarchy)
def __init__(
self,
pdb_hierarchy,
molprobity_scores=False,
):
""" This class is being pickled. Try not to introduce huge members, e.g.
self.hierarchy, etc. This is the reason ramalyze_obj, rotalyze_obj etc
are not members of the class (not self.ramalyze_obj). """
self.clashscore = None
self.ramachandran_outliers = None
self.ramachandran_allowed = None
self.ramachandran_favored = None
self.rotamer_outliers = None
self.c_beta_dev = None
self.mpscore = None
self.omglz = None
self.n_cis_proline = None
self.n_cis_general = None
self.n_twisted_proline = None
self.n_twisted_general = None
if(molprobity_scores):
ramalyze_obj = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
self.ramachandran_outliers = ramalyze_obj.percent_outliers
self.ramachandran_outliers_cf = ramalyze_obj.get_outliers_count_and_fraction()
self.ramachandran_allowed = ramalyze_obj.percent_allowed
self.ramachandran_allowed_cf = ramalyze_obj.get_allowed_count_and_fraction()
self.ramachandran_favored = ramalyze_obj.percent_favored
self.ramachandran_favored_cf = ramalyze_obj.get_favored_count_and_fraction()
rotalyze_obj = rotalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
self.rotamer_outliers = rotalyze_obj.percent_outliers
self.rotamer_cf = rotalyze_obj.get_outliers_count_and_fraction()
cbetadev_obj = cbetadev(
pdb_hierarchy = pdb_hierarchy,
outliers_only = True,
out = null_out())
self.c_beta_dev = cbetadev_obj.get_outlier_count()
self.c_beta_dev_percent = cbetadev_obj.get_weighted_outlier_percent()
self.clashscore = clashscore(pdb_hierarchy=pdb_hierarchy).get_clashscore()
self.mpscore = molprobity_score(
clashscore = self.clashscore,
rota_out = self.rotamer_outliers,
rama_fav = self.ramachandran_favored)
omglz = omegalyze.omegalyze(
pdb_hierarchy=pdb_hierarchy, quiet=True)
self.n_proline = omglz.n_proline()
self.n_general = omglz.n_general()
self.n_cis_proline = omglz.n_cis_proline()
self.n_cis_general = omglz.n_cis_general()
self.n_twisted_proline = omglz.n_twisted_proline()
self.n_twisted_general = omglz.n_twisted_general()
self.cis_general = 0
self.twisted_general = 0
self.cis_proline = 0
self.twisted_proline = 0
if self.n_proline != 0:
self.cis_proline = self.n_cis_proline*100./self.n_proline
self.twisted_proline = self.n_twisted_proline*100./self.n_proline
if self.n_general != 0:
self.cis_general = self.n_cis_general*100./self.n_general
self.twisted_general = self.n_twisted_general*100./self.n_general
self.cablam_outliers=None
self.cablam_disfavored=None
self.cablam_ca_outliers=None
try:
cablam_results = cablam.cablamalyze(pdb_hierarchy, outliers_only=False,
out=null_out(), quiet=True)
self.cablam_outliers = cablam_results.percent_outliers()
self.cablam_disfavored = cablam_results.percent_disfavored()
self.cablam_ca_outliers = cablam_results.percent_ca_outliers()
except Exception as e:
print "CaBLAM failed with exception:"
print " %s" % str(e)
pass
def __init__(
self,
pdb_hierarchy,
restraints_manager,
molprobity_scores=False,
n_histogram_slots=10,
cdl_restraints=False,
ignore_hydrogens=False, #only used by amber
):
self.cdl_restraints=cdl_restraints
sites_cart = pdb_hierarchy.atoms().extract_xyz()
energies_sites = \
restraints_manager.energies_sites(
sites_cart = sites_cart,
compute_gradients = False)
# molprobity scores
self.clashscore = None
self.ramachandran_outliers = None
self.ramachandran_allowed = None
self.ramachandran_favored = None
self.rotamer_outliers = None
self.c_beta_dev = None
self.mpscore = None
if(molprobity_scores):
self.ramalyze_obj = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
self.ramachandran_outliers = self.ramalyze_obj.percent_outliers
self.ramachandran_allowed = self.ramalyze_obj.percent_allowed
self.ramachandran_favored = self.ramalyze_obj.percent_favored
self.rotalyze_obj = rotalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
self.rotamer_outliers = self.rotalyze_obj.percent_outliers
self.cbetadev_obj = cbetadev(
pdb_hierarchy = pdb_hierarchy,
outliers_only = True,
out = null_out())
self.c_beta_dev = self.cbetadev_obj.get_outlier_count()
self.clashscore = clashscore(pdb_hierarchy=pdb_hierarchy).get_clashscore()
self.mpscore = molprobity_score(
clashscore = self.clashscore,
rota_out = self.rotamer_outliers,
rama_fav = self.ramachandran_favored)
#
if(hasattr(energies_sites, "geometry")):
esg = energies_sites.geometry
else: esg = energies_sites
self.a = None
self.b = None
if not hasattr(esg, "angle_deviations"): return
if hasattr(esg, "amber"):
amber_parm = restraints_manager.amber_structs.parm
self.a, angle_deltas = esg.angle_deviations(sites_cart, amber_parm,
ignore_hd=ignore_hydrogens,
get_deltas=True)
self.b, bond_deltas = esg.bond_deviations(sites_cart, amber_parm,
ignore_hd=ignore_hydrogens,
get_deltas=True)
self.a_number = esg.n_angle_proxies(amber_parm,
ignore_hd=ignore_hydrogens)
self.b_number = esg.n_bond_proxies(amber_parm,
ignore_hd=ignore_hydrogens)
self.c, self.p, self.ll, self.d, self.n = None, None, None, None, None
self.c_number=0
self.p_number=0
self.d_number=0
self.bond_deltas_histogram = \
flex.histogram(data = flex.abs(bond_deltas), n_slots = n_histogram_slots)
self.angle_deltas_histogram = \
flex.histogram(data = flex.abs(angle_deltas), n_slots = n_histogram_slots)
# nonbonded_distances = esg.nonbonded_distances()
# self.nonbonded_distances_histogram = flex.histogram(
# data = flex.abs(nonbonded_distances), n_slots = n_histogram_slots)
for restraint_type in ["b", "a", "c", "p", "ll", "d", "n"] :
for value_type in [("mean",2), ("max",1), ("min",0)] :
name = "%s_%s" % (restraint_type, value_type[0])
if getattr(self, restraint_type) is None:
setattr(self, name, None)
continue
setattr(self, name, getattr(self, restraint_type)[value_type[1]])
return
self.a = esg.angle_deviations()
self.b = esg.bond_deviations()
self.a_number = esg.get_filtered_n_angle_proxies()
self.b_number = esg.get_filtered_n_bond_proxies()
self.c = esg.chirality_deviations()
self.d = esg.dihedral_deviations()
self.p = esg.planarity_deviations()
self.ll = esg.parallelity_deviations()
self.n = esg.nonbonded_deviations()
self.c_number = esg.n_chirality_proxies
self.d_number = esg.n_dihedral_proxies
self.p_number = esg.n_planarity_proxies
self.n_number = esg.n_nonbonded_proxies
#
for restraint_type in ["b", "a", "c", "p", "ll", "d", "n"] :
for value_type in [("mean",2), ("max",1), ("min",0)] :
name = "%s_%s" % (restraint_type, value_type[0])
if getattr(self, restraint_type) is None: continue
setattr(self, name, getattr(self, restraint_type)[value_type[1]])
#
if(hasattr(restraints_manager, "geometry")):
rmg = restraints_manager.geometry
else: rmg = restraints_manager
bond_deltas = geometry_restraints.bond_deltas(
sites_cart = sites_cart,
sorted_asu_proxies = rmg.pair_proxies().bond_proxies)
angle_deltas = geometry_restraints.angle_deltas(
sites_cart = sites_cart,
proxies = rmg.angle_proxies)
nonbonded_distances = esg.nonbonded_distances()
self.bond_deltas_histogram = \
flex.histogram(data = flex.abs(bond_deltas), n_slots = n_histogram_slots)
self.angle_deltas_histogram = \
flex.histogram(data = flex.abs(angle_deltas), n_slots = n_histogram_slots)
self.nonbonded_distances_histogram = flex.histogram(
data = flex.abs(nonbonded_distances), n_slots = n_histogram_slots)
#
assert approx_equal(
esg.target,
esg.angle_residual_sum+
esg.bond_residual_sum+
esg.chirality_residual_sum+
esg.dihedral_residual_sum+
esg.nonbonded_residual_sum+
esg.planarity_residual_sum+
esg.parallelity_residual_sum+
esg.reference_coordinate_residual_sum+
esg.reference_dihedral_residual_sum+
esg.ncs_dihedral_residual_sum+
esg.den_residual_sum+
esg.ramachandran_residual_sum)
del energies_sites, esg # we accumulate this object, so make it clean asap
def load_refinement(self, ref):
self._pdb_file = ref.replace('.dat', '.pdb')
self._ins_file = ref.replace('.dat', '.ins')
self._lst_file = ref.replace('.dat', '.lst')
lst = LSTParser(self._lst_file)
pdb_io = pdb.input(file_name=self._pdb_file)
self._chain_lookup = PDBTools().get_chains(self._pdb_file)
r = clashscore()
self._clash_score, self._clashes = clashscore.analyze_clashes(r,pdb_io)# verbose=True)
self._clashes = self._clashes[''].split('\n')
rama = ramalyze()
output, self._rama_data = rama.analyze_pdb(pdb_io=pdb_io, outliers_only=False)
rota = rotalyze()
output, self._rotamer_data = rota.analyze_pdb(pdb_io, outliers_only=False)
r = cbetadev()
output, summary, self._cb_data = cbetadev.analyze_pdb(r,pdb_io=pdb_io,outliers_only=True)
self.sizer = wx.BoxSizer(wx.VERTICAL)
self._rmsds = lst.get_stats()
# Summary
self.stats_sizer = wx.FlexGridSizer(cols=3, rows=0, vgap=5, hgap=5)
self.stats_sizer.Add(wx.StaticText(self, -1, 'RMSD Bonds'))
self.stats_sizer.Add(wx.StaticText(self, -1, '%.3f' % (self._rmsds[0])))
self.stats_sizer.Add(wx.StaticText(self, -1, ''))
self.stats_sizer.Add(wx.StaticText(self, -1, 'RMSD Angles'))
self.stats_sizer.Add(wx.StaticText(self, -1, '%.3f' % (self._rmsds[1])))
self.stats_sizer.Add(wx.StaticText(self, -1, ''), 0, wx.EXPAND|wx.BOTTOM, 10)
self.stats_sizer.Add(wx.StaticText(self, -1, 'B Factor (Protein)'))
self.stats_sizer.Add(wx.StaticText(self, -1, '%.2f' % (self._residues['avg']['pro'])))
self.stats_sizer.Add(wx.StaticText(self, -1, ''), 0, wx.EXPAND|wx.BOTTOM)
self.stats_sizer.Add(wx.StaticText(self, -1, 'B Factor (Solvent)'))
self.stats_sizer.Add(wx.StaticText(self, -1, '%.2f' % (self._residues['avg']['sol'])))
self.stats_sizer.Add(wx.StaticText(self, -1, ''), 0, wx.EXPAND|wx.BOTTOM)
self.stats_sizer.Add(wx.StaticText(self, -1, 'B Factor (All)'))
self.stats_sizer.Add(wx.StaticText(self, -1, '%.2f' % (self._residues['avg']['all'])))
self.stats_sizer.Add(wx.StaticText(self, -1, ''), 0, wx.EXPAND|wx.BOTTOM, 10)
self.stats_sizer.Add(wx.StaticText(self, -1, 'Ramachandran Outliers'))
self.stats_sizer.Add(wx.StaticText(self, -1, '%.1f' % (rama.get_outliers_count_and_fraction()[1]*100) + '%'))
self.stats_sizer.Add(wx.StaticText(self, -1, '(Goal ' + rama.get_outliers_goal()+')'))
self.stats_sizer.Add(wx.StaticText(self, -1, 'Ramachandran Favoured'))
self.stats_sizer.Add(wx.StaticText(self, -1, '%.1f' % (rama.get_favored_count_and_fraction()[1]*100) + '%'))
self.stats_sizer.Add(wx.StaticText(self, -1, '(Goal ' + rama.get_favored_goal()+')'), 0, wx.EXPAND|wx.BOTTOM, 10)
self.stats_sizer.Add(wx.StaticText(self, -1, 'Rotamer Outliers'))
self.stats_sizer.Add(wx.StaticText(self, -1, '%.1f' % (rota.get_outliers_count_and_fraction()[1]*100) + '%'))
self.stats_sizer.Add(wx.StaticText(self, -1, '(Goal ' + rota.get_outliers_goal()+')'))
self.stats_sizer.Add(wx.StaticText(self, -1, 'C-beta Outliers'))
self.stats_sizer.Add(wx.StaticText(self, -1, '%d' % len(self._cb_data)))
self.stats_sizer.Add(wx.StaticText(self, -1, '(Goal 0)'))
self.stats_sizer.Add(wx.StaticText(self, -1, 'Clashscore'))
self.stats_sizer.Add(wx.StaticText(self, -1, '%d' % self._clash_score['']))
self.sizer.Add(self.stats_sizer, 0, wx.ALL, 10)
# Ramachandran Outliers
self.rama_sizer = wx.StaticBoxSizer(wx.StaticBox(self, -1, 'Ramachandran Outliers'),wx.VERTICAL)
if rama.get_outliers_count_and_fraction()[1] > 0:
rama_list = wx.ListCtrl(self, -1, style=wx.LC_REPORT)
self.Bind(wx.EVT_LIST_ITEM_SELECTED, self._show_rama, rama_list)
sizes = [50, 50, 150, 80, 80, 80]
for i, item in enumerate(['Chain', 'No', 'Residue', 'Score', 'Phi', 'Psi']):
rama_list.InsertColumn(i, item, width = sizes[i])
i = 0
self._rama_outliers = []
for r in self._rama_data:
(chain_id,resseq,resname,quality,phi,psi,status,pos_name,xyz) = r
if status == 'OUTLIER':
self._rama_outliers.append(r)
rama_list.InsertStringItem(i, str(chain_id))
rama_list.SetStringItem(i, 1, str(resseq))
rama_list.SetStringItem(i, 2, resname)
rama_list.SetStringItem(i, 3, '%.2f' % quality)
rama_list.SetStringItem(i, 4, '%.1f' % phi)
rama_list.SetStringItem(i, 5, '%.1f' % psi)
i += 1
self.rama_sizer.Add(wx.StaticText(self, -1, '%d Ramachandran outliers found' % i), 0)
self.rama_sizer.Add(rama_list, 0, wx.EXPAND|wx.ALL, 10)
self.rama_list = rama_list
else:
self.rama_sizer.Add(wx.StaticText(self, -1, 'No Ramachandran Outliers'), 0)
self.rama_sizer.Add(wx.Button(self, 0, 'Show Ramachandran Plot'), 0)
self.Bind(wx.EVT_BUTTON, self.show_ramachandran, id=0)
self.sizer.Add(self.rama_sizer, 0, wx.EXPAND|wx.ALL, 10)
# Rotamer Outliers
self.rota_sizer = wx.StaticBoxSizer(wx.StaticBox(self, -1, 'Rotamer Outliers'),wx.VERTICAL)
if rota.get_outliers_count_and_fraction()[1] > 0:
rota_list = wx.ListCtrl(self, -1, style=wx.LC_REPORT)
self.Bind(wx.EVT_LIST_ITEM_SELECTED, self._show_rota, rota_list)
sizes = [50, 50, 150, 80, 80, 80, 80, 80]
for i, item in enumerate(['Chain', 'No', 'Residue', 'Score', 'Chi1', 'Chi2', 'Chi3', 'Chi4']):
rota_list.InsertColumn(i, item, width = sizes[i])
i = 0
self._rota_outliers = []
for r in self._rotamer_data:
(chain_id,resseq,resname,quality,chi1,chi2,chi3,chi4,status,xyz) = r
if status == 'OUTLIER':
self._rota_outliers.append(r)
rota_list.InsertStringItem(i, str(chain_id))
rota_list.SetStringItem(i, 1, str(resseq))
rota_list.SetStringItem(i, 2, resname)
rota_list.SetStringItem(i, 3, '%.2f' % quality)
rota_list.SetStringItem(i, 4, '%.1f' % chi1)
rota_list.SetStringItem(i, 5, '%.1f' % chi2 if chi2 is not None else 'None')
rota_list.SetStringItem(i, 6, '%.1f' % chi3 if chi3 is not None else 'None')
rota_list.SetStringItem(i, 7, '%.1f' % chi4 if chi4 is not None else 'None')
i += 1
self.rota_sizer.Add(wx.StaticText(self, -1, '%d rotamer outliers found' % i), 0)
self.rota_sizer.Add(rota_list, 1, wx.EXPAND|wx.ALL, 5)
self.rota_list = rota_list
else:
self.rota_sizer.Add(wx.StaticText(self, -1, 'No Rotamer Outliers'))
self.rota_sizer.Add(wx.Button(self, 1, 'Show Chi1-Chi2 Plots'))
self.Bind(wx.EVT_BUTTON, self.show_rotamer, id=1)
self.sizer.Add(self.rota_sizer, 0, wx.EXPAND|wx.ALL, 10)
# C-beta Outliers
self.cb_sizer = wx.StaticBoxSizer(wx.StaticBox(self, -1, 'C-beta Outliers'),wx.VERTICAL)
if len(self._cb_data) > 0:
cb_list = wx.ListCtrl(self, -1, style=wx.LC_REPORT)
self.Bind(wx.EVT_LIST_ITEM_SELECTED, self._show_cb, cb_list)
sizes = [50, 50, 150, 100, 100]
for i, item in enumerate(['Chain', 'No', 'Residue', 'Deviation', 'Angle']):
cb_list.InsertColumn(i, item, width = sizes[i])
for i,r in enumerate(self._cb_data):
(pdbf, alt, resname, chain_id, resseq, resseq2, dev, diheral, occ, altchar, xyz) = r
cb_list.InsertStringItem(i, str(chain_id))
cb_list.SetStringItem(i, 1, str(resseq+resseq2))
cb_list.SetStringItem(i, 2, resname.upper())
cb_list.SetStringItem(i, 3, '%.3f' % dev)
cb_list.SetStringItem(i, 4, '%.2f' % diheral)
self.cb_sizer.Add(wx.StaticText(self, -1, '%d C-beta outliers found' % len(self._cb_data)), 0)
self.cb_sizer.Add(cb_list, 1, wx.EXPAND|wx.ALL, 5)
self.cb_list = cb_list
else:
self.cb_sizer.Add(wx.StaticText(self, -1, 'No C-beta Outliers'))
self.sizer.Add(self.cb_sizer, 0, wx.EXPAND|wx.ALL, 10)
# Bad Clashes
self.clash_sizer = wx.StaticBoxSizer(wx.StaticBox(self, -1, 'All Atom Contacts'), wx.VERTICAL)
if len(self._clashes) > 0:
clash_list = wx.ListCtrl(self, -1, style=wx.LC_REPORT)
self.Bind(wx.EVT_LIST_ITEM_SELECTED, self._show_clash, clash_list)
sizes = [50, 50, 80, 80, 50, 50, 80, 80, 100]
for i, item in enumerate(['Chain', 'No', 'Residue', 'Atom', 'Chain', 'No', 'Residue', 'Atom', 'Overlap']):
clash_list.InsertColumn(i, item, width = sizes[i])
for i,r in enumerate(self._clashes):
' 78 ILE CD1 83 LEU HD21 :-0.402'
clash_list.InsertStringItem(i, r[0:2].strip())
clash_list.SetStringItem(i, 1, r[2:6].strip())
clash_list.SetStringItem(i, 2, r[7:10].strip())
clash_list.SetStringItem(i, 3, r[11:15].strip())
clash_list.SetStringItem(i, 4, r[16:18].strip())
clash_list.SetStringItem(i, 5, r[19:22].strip())
clash_list.SetStringItem(i, 6, r[23:26].strip())
clash_list.SetStringItem(i, 7, r[28:32].strip())
clash_list.SetStringItem(i, 8, r[34:39].strip())
self.clash_sizer.Add(wx.StaticText(self, -1, '%d bad clashes found' % i), 0)
self.clash_sizer.Add(clash_list, 1, wx.EXPAND|wx.ALL, 5)
self.clash_list = clash_list
else:
self.clash_sizer.Add(wx.StaticText(self, -1, 'No Bad Clashes'))
self.sizer.Add(self.clash_sizer, 0, wx.EXPAND|wx.ALL, 10)
self._split_sites, self._npds = lst.get_site_info()
# Split Sites
self.split_sizer = wx.StaticBoxSizer(wx.StaticBox(self, -1, 'Split Sites'), wx.VERTICAL)
if len(self._split_sites) > 0:
split_list = wx.ListCtrl(self, -1, style=wx.LC_REPORT)
self.Bind(wx.EVT_LIST_ITEM_SELECTED, self._show_split, split_list)
sizes = [100,100,100,50,80,100]
for i, item in enumerate(['U1', 'U2', 'U3', 'No', 'Residue', 'Atom']):
split_list.InsertColumn(i, item, width=sizes[i])
i = 0
for j,s in enumerate(self._split_sites):
if '_' in s[3]:
split_list.InsertStringItem(i, str(s[0]))
split_list.SetStringItem(i, 1, str(s[1]))
split_list.SetStringItem(i, 2, str(s[2]))
atom,id = s[3].split('_')
split_list.SetStringItem(i, 3, str(id))
split_list.SetStringItem(i, 4, str())
split_list.SetStringItem(i, 5, str(atom))
i += 1
self.split_sizer.Add(wx.StaticText(self, -1, '%d possible split sites found' % len(self._split_sites)))
self.split_sizer.Add(split_list, 0, wx.EXPAND|wx.ALL, 5)
self.split_list = split_list
else:
self.split_sizer.Add(wx.StaticText(self, -1, 'No split sites found'))
self.sizer.Add(self.split_sizer, 0, wx.EXPAND|wx.ALL, 10)
# NPDs
self.npd_sizer = wx.StaticBoxSizer(wx.StaticBox(self, -1, 'Non Positive Definites'), wx.VERTICAL)
if len(self._npds) > 0:
npd_list = wx.ListCtrl(self, -1, style=wx.LC_REPORT)
self.Bind(wx.EVT_LIST_ITEM_SELECTED, self._show_npd, npd_list)
sizes = [100,100,100,50,80,100]
for i, item in enumerate(['U1', 'U2', 'U3', 'No', 'Residue', 'Atom']):
npd_list.InsertColumn(i, item, width=sizes[i])
for i, s in enumerate(self._npds):
npd_list.InsertStringItem(i, str(s[0]))
npd_list.SetStringItem(i, 1, str(s[1]))
npd_list.SetStringItem(i, 2, str(s[2]))
atom, id = s[3].split('_')
npd_list.SetStringItem(i, 3, str(id))
npd_list.SetStringItem(i, 4, str())
npd_list.SetStringItem(i, 5, str(atom))
self.npd_sizer.Add(wx.StaticText(self, -1, '%d non positive definite sites found' % len(self._npds)))
self.npd_sizer.Add(npd_list, 0, wx.EXPAND|wx.ALL, 5)
self.npd_list = npd_list
else:
self.npd_sizer.Add(wx.StaticText(self, -1, 'No non positive definite sites found'))
self.sizer.Add(self.npd_sizer, 0, wx.EXPAND|wx.ALL, 10)
self.SetSizer(self.sizer)
self.SetAutoLayout(1)
self.SetupScrolling()
def exercise_cbetadev():
regression_pdb = libtbx.env.find_in_repositories(
relative_path="phenix_regression/pdb/pdb1jxt.ent",
test=os.path.isfile)
if (regression_pdb is None):
print "Skipping exercise_cbetadev(): input pdb (pdb1jxt.ent) not available"
return
from mmtbx.validation import cbetadev
from iotbx import file_reader
pdb_in = file_reader.any_file(file_name=regression_pdb)
hierarchy = pdb_in.file_object.hierarchy
validation = cbetadev.cbetadev(
pdb_hierarchy=hierarchy,
outliers_only=True)
for unpickle in [False, True] :
if unpickle :
validation = loads(dumps(validation))
assert (validation.n_outliers == len(validation.results) == 6)
assert ([ cb.id_str() for cb in validation.results ] ==
[' A 7 AILE', ' A 8 BVAL', ' A 8 CVAL', ' A 30 BTHR',
' A 39 BTHR', ' A 43 BASP'])
assert approx_equal([ cb.deviation for cb in validation.results ],
[0.25977096732623106, 0.2577218834868609, 0.6405578498280606,
0.81238828498566, 0.9239566035292618, 0.5001892640352836])
out = StringIO()
validation.show_old_output(out=out, verbose=True)
assert not show_diff(out.getvalue(),
"""\
pdb:alt:res:chainID:resnum:dev:dihedralNABB:Occ:ALT:
pdb :A:ile: A: 7 : 0.260: -46.47: 0.45:A:
pdb :B:val: A: 8 : 0.258: 80.92: 0.30:B:
pdb :C:val: A: 8 : 0.641: -53.98: 0.20:C:
pdb :B:thr: A: 30 : 0.812: -76.98: 0.30:B:
pdb :B:thr: A: 39 : 0.924: 56.41: 0.30:B:
pdb :B:asp: A: 43 : 0.500: 7.56: 0.25:B:
SUMMARY: 6 C-beta deviations >= 0.25 Angstrom (Goal: 0)
""")
# Now with all residues
validation = cbetadev.cbetadev(
pdb_hierarchy=hierarchy,
outliers_only=False)
for unpickle in [False, True] :
if unpickle :
validation = loads(dumps(validation))
for outlier in validation.results :
assert (len(outlier.xyz) == 3)
assert (validation.n_outliers == 6)
assert (len(validation.results) == 51)
out = StringIO()
validation.show_old_output(out=out, verbose=True)
assert not show_diff(out.getvalue(), """\
pdb:alt:res:chainID:resnum:dev:dihedralNABB:Occ:ALT:
pdb : :thr: A: 1 : 0.102: 11.27: 1.00: :
pdb :A:thr: A: 2 : 0.022: -49.31: 0.67:A:
pdb : :cys: A: 3 : 0.038: 103.68: 1.00: :
pdb : :cys: A: 4 : 0.047:-120.73: 1.00: :
pdb : :pro: A: 5 : 0.069:-121.41: 1.00: :
pdb : :ser: A: 6 : 0.052: 112.87: 1.00: :
pdb :A:ile: A: 7 : 0.260: -46.47: 0.45:A:
pdb :B:ile: A: 7 : 0.153: 122.97: 0.55:B:
pdb :A:val: A: 8 : 0.184:-155.36: 0.50:A:
pdb :B:val: A: 8 : 0.258: 80.92: 0.30:B:
pdb :C:val: A: 8 : 0.641: -53.98: 0.20:C:
pdb : :ala: A: 9 : 0.061: -82.84: 1.00: :
pdb :A:arg: A: 10 : 0.023: 172.25: 1.00:A:
pdb : :ser: A: 11 : 0.028:-129.11: 1.00: :
pdb :A:asn: A: 12 : 0.021: -80.80: 0.50:A:
pdb :B:asn: A: 12 : 0.199: 50.01: 0.50:B:
pdb :A:phe: A: 13 : 0.067: -37.32: 0.65:A:
pdb :B:phe: A: 13 : 0.138: 19.24: 0.35:B:
pdb : :asn: A: 14 : 0.065: -96.35: 1.00: :
pdb : :val: A: 15 : 0.138: -96.63: 1.00: :
pdb : :cys: A: 16 : 0.102: -28.64: 1.00: :
pdb : :arg: A: 17 : 0.053:-106.79: 1.00: :
pdb : :leu: A: 18 : 0.053:-141.51: 1.00: :
pdb : :pro: A: 19 : 0.065:-146.95: 1.00: :
pdb : :thr: A: 21 : 0.086: 53.80: 1.00: :
pdb :A:pro: A: 22 : 0.092: -83.39: 0.55:A:
pdb :A:glu: A: 23 : 0.014:-179.53: 0.50:A:
pdb :B:glu: A: 23 : 0.050:-179.78: 0.50:B:
pdb : :ala: A: 24 : 0.056: -88.96: 1.00: :
pdb : :leu: A: 25 : 0.084:-106.42: 1.00: :
pdb : :cys: A: 26 : 0.074: -94.70: 1.00: :
pdb : :ala: A: 27 : 0.056: -62.15: 1.00: :
pdb : :thr: A: 28 : 0.056:-114.82: 1.00: :
pdb :A:tyr: A: 29 : 0.068: 0.22: 0.65:A:
pdb :A:thr: A: 30 : 0.180: 103.27: 0.70:A:
pdb :B:thr: A: 30 : 0.812: -76.98: 0.30:B:
pdb : :cys: A: 32 : 0.029: -84.07: 1.00: :
pdb : :ile: A: 33 : 0.048:-119.17: 1.00: :
pdb : :ile: A: 34 : 0.045: 99.02: 1.00: :
pdb : :ile: A: 35 : 0.052:-128.24: 1.00: :
pdb : :pro: A: 36 : 0.084:-142.29: 1.00: :
pdb : :ala: A: 38 : 0.039: 50.01: 1.00: :
pdb :A:thr: A: 39 : 0.093: -96.63: 0.70:A:
pdb :B:thr: A: 39 : 0.924: 56.41: 0.30:B:
pdb : :cys: A: 40 : 0.013:-144.11: 1.00: :
pdb : :pro: A: 41 : 0.039: -97.09: 1.00: :
pdb :A:asp: A: 43 : 0.130:-146.91: 0.75:A:
pdb :B:asp: A: 43 : 0.500: 7.56: 0.25:B:
pdb : :tyr: A: 44 : 0.085:-143.63: 1.00: :
pdb : :ala: A: 45 : 0.055: 33.32: 1.00: :
pdb : :asn: A: 46 : 0.066: -50.46: 1.00: :
SUMMARY: 6 C-beta deviations >= 0.25 Angstrom (Goal: 0)
""")
# Auxilary function: extract_atoms_from_residue_group
from mmtbx.validation.cbetadev import extract_atoms_from_residue_group
from iotbx import pdb
pdb_1 = pdb.input(source_info=None, lines="""\
ATOM 1185 N ASER A 146 24.734 37.097 16.303 0.50 16.64 N
ATOM 1186 N BSER A 146 24.758 37.100 16.337 0.50 16.79 N
ATOM 1187 CA ASER A 146 24.173 37.500 17.591 0.50 16.63 C
ATOM 1188 CA BSER A 146 24.237 37.427 17.662 0.50 16.87 C
ATOM 1189 C ASER A 146 22.765 36.938 17.768 0.50 15.77 C
ATOM 1190 C BSER A 146 22.792 36.945 17.783 0.50 15.94 C
ATOM 1191 O ASER A 146 22.052 36.688 16.781 0.50 14.91 O
ATOM 1192 O BSER A 146 22.091 36.741 16.779 0.50 15.17 O
ATOM 1193 CB ASER A 146 24.118 39.035 17.649 0.50 16.93 C
ATOM 1194 CB BSER A 146 24.321 38.940 17.904 0.50 17.48 C
ATOM 1195 OG ASER A 146 23.183 39.485 18.611 0.50 17.56 O
ATOM 1196 OG BSER A 146 23.468 39.645 17.028 0.50 18.32 O """).construct_hierarchy()
pdb_2 = pdb.input(source_info=None, lines="""\
ATOM 1185 N SER A 146 24.734 37.097 16.303 0.50 16.64 N
ATOM 1187 CA SER A 146 24.173 37.500 17.591 0.50 16.63 C
ATOM 1189 C SER A 146 22.765 36.938 17.768 0.50 15.77 C
ATOM 1191 O SER A 146 22.052 36.688 16.781 0.50 14.91 O
ATOM 1193 CB ASER A 146 24.118 39.035 17.649 0.50 16.93 C
ATOM 1194 CB BSER A 146 24.321 38.940 17.904 0.50 17.48 C
ATOM 1195 OG ASER A 146 23.183 39.485 18.611 0.50 17.56 O
ATOM 1196 OG BSER A 146 23.468 39.645 17.028 0.50 18.32 O """).construct_hierarchy()
pdb_3 = pdb.input(source_info=None, lines="""\
ATOM 1185 N SER A 146 24.734 37.097 16.303 0.50 16.64 N
ATOM 1187 CA SER A 146 24.173 37.500 17.591 0.50 16.63 C
ATOM 1189 C SER A 146 22.765 36.938 17.768 0.50 15.77 C
ATOM 1191 O SER A 146 22.052 36.688 16.781 0.50 14.91 O
ATOM 1193 CB SER A 146 24.118 39.035 17.649 0.50 16.93 C
ATOM 1195 OG ASER A 146 23.183 39.485 18.611 0.50 17.56 O
ATOM 1196 OG BSER A 146 23.468 39.645 17.028 0.50 18.32 O """).construct_hierarchy()
rg1 = pdb_1.only_model().only_chain().only_residue_group()
rg2 = pdb_2.only_model().only_chain().only_residue_group()
rg3 = pdb_3.only_model().only_chain().only_residue_group()
all_relevant_atoms_1 = extract_atoms_from_residue_group(rg1)
all_relevant_atoms_2 = extract_atoms_from_residue_group(rg2)
all_relevant_atoms_3 = extract_atoms_from_residue_group(rg3)
keys_1 = [ sorted([ k for k in a.keys() ]) for a in all_relevant_atoms_1 ]
keys_2 = [ sorted([ k for k in a.keys() ]) for a in all_relevant_atoms_2 ]
keys_3 = [ sorted([ k for k in a.keys() ]) for a in all_relevant_atoms_3 ]
assert keys_1 == [[' C ',' CA ',' CB ',' N '],[' C ',' CA ',' CB ',' N ']]
assert keys_2 == [[' C ',' CA ',' CB ',' N '],[' C ',' CA ',' CB ',' N ']]
assert keys_3 == [[' C ', ' CA ', ' CB ', ' N ']]
print "OK"
