def benchmark_structure(pdb_in, mon_lib_srv, ener_lib, verbose=False, w=1.0):
log = StringIO()
params = mmtbx.model.manager.get_default_pdb_interpretation_params()
params.pdb_interpretation.peptide_link.ramachandran_restraints = True
params.pdb_interpretation.ramachandran_plot_restraints.inject_emsley8k_into_oldfield_favored = False
model = mmtbx.model.manager(model_input=pdb_in,
pdb_interpretation_params=params,
log=null_out(),
build_grm=True)
grm = model.get_restraints_manager().geometry
pdb_hierarchy = model.get_hierarchy()
r0 = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
atoms = pdb_hierarchy.atoms()
sites_cart_1 = atoms.extract_xyz().deep_copy()
sites_cart_2 = sites_cart_1.deep_copy()
assert (grm is not None)
e = grm.energies_sites(sites_cart=sites_cart_1)
b0 = e.bond_deviations()[-1]
a0 = e.angle_deviations()[-1]
flags = cctbx.geometry_restraints.flags.flags(default=True)
lbfgs = geometry_minimization.lbfgs(
sites_cart=sites_cart_1,
correct_special_position_tolerance=1.0,
geometry_restraints_manager=grm,
geometry_restraints_flags=flags,
lbfgs_termination_params=scitbx.lbfgs.termination_parameters(
max_iterations=500))
a1 = lbfgs.rmsd_angles
b1 = lbfgs.rmsd_bonds
atoms.set_xyz(sites_cart_1)
r1 = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
rama_params = ramachandran.master_phil.fetch().extract(
).ramachandran_plot_restraints
rama_manager = ramachandran.ramachandran_manager(pdb_hierarchy,
rama_params, log)
grm.set_ramachandran_restraints(rama_manager)
lbfgs = geometry_minimization.lbfgs(
sites_cart=sites_cart_2,
correct_special_position_tolerance=1.0,
geometry_restraints_manager=grm,
geometry_restraints_flags=flags,
lbfgs_termination_params=scitbx.lbfgs.termination_parameters(
max_iterations=500))
a2 = lbfgs.rmsd_angles
b2 = lbfgs.rmsd_bonds
atoms.set_xyz(sites_cart_2)
r2 = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
return group_args(a0=a0,
a1=a1,
a2=a2,
b0=b0,
b1=b1,
b2=b2,
r0=r0,
r1=r1,
r2=r2)
def benchmark_structure (pdb_in, mon_lib_srv, ener_lib, verbose=False, w=1.0) :
params = pdb_interpretation.master_params.extract()
processed_pdb_file = pdb_interpretation.process(
mon_lib_srv=mon_lib_srv,
ener_lib=ener_lib,
params=params,
pdb_inp=pdb_in,
log=StringIO())
log = StringIO()
pdb_hierarchy = processed_pdb_file.all_chain_proxies.pdb_hierarchy
r0 = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
atoms = pdb_hierarchy.atoms()
sites_cart_1 = atoms.extract_xyz().deep_copy()
sites_cart_2 = sites_cart_1.deep_copy()
grm = processed_pdb_file.geometry_restraints_manager()
assert (grm is not None)
e = grm.energies_sites(sites_cart=sites_cart_1)
b0 = e.bond_deviations()[-1]
a0 = e.angle_deviations()[-1]
flags = cctbx.geometry_restraints.flags.flags(default=True)
lbfgs = geometry_minimization.lbfgs(
sites_cart=sites_cart_1,
correct_special_position_tolerance=1.0,
geometry_restraints_manager=grm,
geometry_restraints_flags=flags,
lbfgs_termination_params=scitbx.lbfgs.termination_parameters(
max_iterations=500))
a1 = lbfgs.rmsd_angles
b1 = lbfgs.rmsd_bonds
atoms.set_xyz(sites_cart_1)
r1 = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
rama_params = ramachandran.master_phil.fetch().extract()
rama_manager = ramachandran.ramachandran_manager(
pdb_hierarchy, None, rama_params, log)
grm.set_ramachandran_restraints(rama_manager)
lbfgs = geometry_minimization.lbfgs(
sites_cart=sites_cart_2,
correct_special_position_tolerance=1.0,
geometry_restraints_manager=grm,
geometry_restraints_flags=flags,
lbfgs_termination_params=scitbx.lbfgs.termination_parameters(
max_iterations=500))
a2 = lbfgs.rmsd_angles
b2 = lbfgs.rmsd_bonds
atoms.set_xyz(sites_cart_2)
r2 = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
return group_args(
a0=a0,
a1=a1,
a2=a2,
b0=b0,
b1=b1,
b2=b2,
r0=r0,
r1=r1,
r2=r2)
def benchmark_structure(pdb_in, mon_lib_srv, ener_lib, verbose=False, w=1.0):
params = pdb_interpretation.master_params.extract()
processed_pdb_file = pdb_interpretation.process(mon_lib_srv=mon_lib_srv,
ener_lib=ener_lib,
params=params,
pdb_inp=pdb_in,
log=StringIO())
log = StringIO()
pdb_hierarchy = processed_pdb_file.all_chain_proxies.pdb_hierarchy
r0 = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
atoms = pdb_hierarchy.atoms()
sites_cart_1 = atoms.extract_xyz().deep_copy()
sites_cart_2 = sites_cart_1.deep_copy()
grm = processed_pdb_file.geometry_restraints_manager()
assert (grm is not None)
e = grm.energies_sites(sites_cart=sites_cart_1)
b0 = e.bond_deviations()[-1]
a0 = e.angle_deviations()[-1]
flags = cctbx.geometry_restraints.flags.flags(default=True)
lbfgs = geometry_minimization.lbfgs(
sites_cart=sites_cart_1,
correct_special_position_tolerance=1.0,
geometry_restraints_manager=grm,
geometry_restraints_flags=flags,
lbfgs_termination_params=scitbx.lbfgs.termination_parameters(
max_iterations=500))
a1 = lbfgs.rmsd_angles
b1 = lbfgs.rmsd_bonds
atoms.set_xyz(sites_cart_1)
r1 = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
rama_params = ramachandran.master_phil.fetch().extract()
rama_manager = ramachandran.ramachandran_manager(pdb_hierarchy, None,
rama_params, log)
grm.set_ramachandran_restraints(rama_manager)
lbfgs = geometry_minimization.lbfgs(
sites_cart=sites_cart_2,
correct_special_position_tolerance=1.0,
geometry_restraints_manager=grm,
geometry_restraints_flags=flags,
lbfgs_termination_params=scitbx.lbfgs.termination_parameters(
max_iterations=500))
a2 = lbfgs.rmsd_angles
b2 = lbfgs.rmsd_bonds
atoms.set_xyz(sites_cart_2)
r2 = ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
return group_args(a0=a0,
a1=a1,
a2=a2,
b0=b0,
b1=b1,
b2=b2,
r0=r0,
r1=r1,
r2=r2)
def __init__(self, model1, model2, params=None, log=null_out()):
self.plots = None
self.params = params
if self.params is None:
self.params = rcompare.get_default_params().comparama
self.rama1 = ramalyze(model1.get_hierarchy(), out=null_out())
self.rama2 = ramalyze(model2.get_hierarchy(), out=null_out())
self.results = []
# looping technique trying to recover when 1 or several residues are
# missing
i1 = i2 = 0
self.skipped_1 = []
self.skipped_2 = []
while i1 < len(self.rama1.results) and i2 < len(self.rama2.results):
r1 = self.rama1.results[i1]
r2 = self.rama2.results[i2]
if r1.id_str() == r2.id_str():
# regular calculations
diff = math.sqrt((r1.phi - r2.phi)**2 + (r1.psi - r2.psi)**2)
v = determine_validation_change_text(r1, r2)
diff2 = math.sqrt(
get_distance(r1.phi, r2.phi)**2 +
get_distance(r1.psi, r2.psi)**2)
diff3 = two_rama_points((r1.phi, r1.psi),
(r2.phi, r2.psi)).min_length()
assert approx_equal(diff2,
diff3), "%s, %s" % ((r1.phi, r1.psi),
(r2.phi, r2.psi))
self.results.append(
(r1.id_str(), diff2, r1.phi, r1.psi, r2.phi, r2.psi, v,
r2.res_type, r1.score / 100, r2.score / 100))
i1 += 1
i2 += 1
else:
skip_1 = False
# figure out what to skip
if r1.chain_id == r2.chain_id:
if r1.resseq_as_int() < r2.resseq_as_int():
skip_1 = True
else:
if r1.resseq_as_int() > r2.resseq_as_int():
skip_1 = True
if skip_1:
i1 += 1
self.skipped_1.append(r1)
else:
i2 += 1
self.skipped_2.append(r2)
self.res_columns = None
if len(self.results) > 0:
self.res_columns = list(zip(*self.get_results()))
def __init__(self, pdb_hierarchy, params=None, log=null_out(), verbose=True):
if len(pdb_hierarchy.models()) > 1:
raise Sorry("Multi-model files are not supported")
self.original_pdb_h = pdb_hierarchy
xrs = pdb_hierarchy.extract_xray_structure()
asc = pdb_hierarchy.atom_selection_cache()
self.resulting_pdb_h = pdb_hierarchy.deep_copy()
self.params = self.process_params(params)
self.log = log
self.verbose = verbose
self.r = ramachandran_eval.RamachandranEval()
ram = ramalyze.ramalyze(pdb_hierarchy=pdb_hierarchy)
self.p_initial_rama_outliers = ram.out_percent
self.p_before_minimization_rama_outliers = None
self.p_after_minimiaztion_rama_outliers = None
self.ref_exclusion_selection = ""
for chain in pdb_hierarchy.only_model().chains():
print >>self.log, "Idealizing chain %s" % chain.id
selection = "protein and chain %s and (name N or name CA or name C or name O)" % chain.id
sel = asc.selection("chain %s" % chain.id)
chain_h = self.original_pdb_h.select(sel)
m = chain_h.only_model()
i = 0
cutted_chain_h = None
for c in m.chains():
if i == 0:
cutted_chain_h = iotbx.pdb.hierarchy.new_hierarchy_from_chain(c)
else:
print >>self.log, "WARNING!!! Duplicating chain ids! Only the first chain will be processed."
print >>self.log, " Removing chain %s with %d residues" % (c.id, len(c.residues()))
m.remove_chain(c)
i += 1
exclusions, ch_h = self.idealize_chain(hierarchy=(cutted_chain_h if cutted_chain_h else chain_h))
if ch_h is not None:
set_xyz_smart(self.resulting_pdb_h, ch_h)
for resnum in exclusions:
selection += " and not resseq %s" % resnum
self.ref_exclusion_selection += "(%s) or " % selection
if len(self.ref_exclusion_selection) > 0:
self.ref_exclusion_selection = self.ref_exclusion_selection[:-3]
self.resulting_pdb_h.write_pdb_file(file_name="%s_before_minization.pdb" % self.params.output_prefix)
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_before_minimization_rama_outliers = ram.out_percent
if self.params.minimize_whole:
print >>self.log, "minimizing whole thing..."
print >>self.log, "self.ref_exclusion_selection", self.ref_exclusion_selection
minimize_hierarchy(self.resulting_pdb_h, xrs, self.original_pdb_h, self.ref_exclusion_selection, log=None)
# self.resulting_pdb_h.write_pdb_file(file_name="%s_all_minized.pdb" % self.params.output_prefix)
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_after_minimiaztion_rama_outliers = ram.out_percent
def exclude_outliers_from_reference_restraints_selection(
pdb_hierarchy, restraints_selection):
from mmtbx.validation.ramalyze import ramalyze
# the import below is SLOW!!!
from mmtbx.rotamer.rotamer_eval import RotamerEval
assert restraints_selection is not None
# ramachandran plot outliers
rama_outlier_selection = ramalyze(pdb_hierarchy=pdb_hierarchy,
outliers_only=False).outlier_selection()
rama_outlier_selection = flex.bool(restraints_selection.size(),
rama_outlier_selection)
# rotamer outliers
rota_outlier_selection = flex.size_t()
rotamer_manager = RotamerEval() # SLOW!!!
for model in pdb_hierarchy.models():
for chain in model.chains():
for residue_group in chain.residue_groups():
conformers = residue_group.conformers()
if (len(conformers) > 1): continue
for conformer in residue_group.conformers():
residue = conformer.only_residue()
if (rotamer_manager.evaluate_residue(residue) == "OUTLIER"
):
rota_outlier_selection.extend(
residue.atoms().extract_i_seq())
rota_outlier_selection = flex.bool(restraints_selection.size(),
rota_outlier_selection)
outlier_selection = rama_outlier_selection | rota_outlier_selection
return restraints_selection & (~outlier_selection)
def exclude_outliers_from_reference_restraints_selection(
pdb_hierarchy,
restraints_selection):
from mmtbx.validation.ramalyze import ramalyze
# the import below is SLOW!!!
from mmtbx.rotamer.rotamer_eval import RotamerEval
assert restraints_selection is not None
# ramachandran plot outliers
rama_outlier_selection = ramalyze(pdb_hierarchy=pdb_hierarchy,
outliers_only=False).outlier_selection()
rama_outlier_selection = flex.bool(restraints_selection.size(),
rama_outlier_selection)
# rotamer outliers
rota_outlier_selection = flex.size_t()
rotamer_manager = RotamerEval() # SLOW!!!
for model in pdb_hierarchy.models():
for chain in model.chains():
for residue_group in chain.residue_groups():
conformers = residue_group.conformers()
if(len(conformers)>1): continue
for conformer in residue_group.conformers():
residue = conformer.only_residue()
if(rotamer_manager.evaluate_residue(residue)=="OUTLIER"):
rota_outlier_selection.extend(residue.atoms().extract_i_seq())
rota_outlier_selection = flex.bool(restraints_selection.size(),
rota_outlier_selection)
outlier_selection = rama_outlier_selection | rota_outlier_selection
return restraints_selection & (~outlier_selection)
def _write_plots_if_needed(self, model, label, type_of_plot='whole'):
write_plot = getattr(self.params, "write_%s_plot" % type_of_plot)
write_general_only = getattr(self.params,
"write_%s_general_only" % type_of_plot)
if write_plot:
self.rama = ramalyze(model.get_hierarchy(), out=null_out())
self.plots = self.rama.get_plots(show_labels=False,
point_style='.',
markersize=3,
markeredgecolor="red",
dpi=300,
markerfacecolor="yellow")
plots_to_write = range(6)
if write_general_only:
plots_to_write = [0]
for i in plots_to_write:
file_label = res_type_labels[i].replace("/", "_")
fn = "%s.png" % self.get_default_output_filename(
prefix='%s_%s_' % (self.inp_fn, label),
suffix=file_label,
serial=Auto)
if os.path.isfile(fn) and not self.params.output.overwrite:
raise Sorry(
"%s already exists and overwrite is set to False." %
fn)
print("Saving:", fn, file=self.logger)
self.plots[i].save_image(fn, dpi=300)
def validate_single_model(self, i_model):
hierarchy = self.pdb_hierarchies[i_model]
rama_validation = ramalyze.ramalyze(pdb_hierarchy=hierarchy,
outliers_only=False)
rota_validation = rotalyze.rotalyze(pdb_hierarchy=hierarchy,
outliers_only=False)
return rama_validation, rota_validation
def validate_single_model (self, i_model) :
hierarchy = self.pdb_hierarchies[i_model]
rama_validation = ramalyze.ramalyze(
pdb_hierarchy=hierarchy,
outliers_only=False)
rota_validation = rotalyze.rotalyze(
pdb_hierarchy=hierarchy,
outliers_only=False)
return rama_validation, rota_validation
def ramachandran(self):
if self.cached_rama is None:
self.cached_rama = ramalyze(pdb_hierarchy=self.pdb_hierarchy,
outliers_only=False)
return group_args(
outliers=self.cached_rama.percent_outliers,
allowed=self.cached_rama.percent_allowed,
favored=self.cached_rama.percent_favored,
ramalyze=self.cached_rama #XXX Bulky object -- REMOVE!
)
def exercise():
pdb_inp = iotbx.pdb.input(lines=pdb_str.split("\n"), source_info=None)
model = mmtbx.model.manager(model_input=pdb_inp)
pdb_hierarchy = model.get_hierarchy()
r = ramalyze.ramalyze(pdb_hierarchy=pdb_hierarchy, outliers_only=False)
assert (
len(r.results) == 3
), 'Supposed to fail until fixed. Ramalyze results not available for all models.'
def exercise_1(prefix="tst_add_arrows_on_plot_1"):
model = mmtbx.model.manager(
model_input=iotbx.pdb.input(source_info=None, lines=pdb_str))
rama = ramalyze(model.get_hierarchy(), out=null_out())
plots = rama.get_plots(show_labels=True,
point_style='bo',
markersize=1,
markeredgecolor="black",
dpi=300,
markerfacecolor="white")
ad_testing = []
ad_testing.append(((60, -120), (120, -120)))
ad_testing.append(((-125, 120), (-125, 179)))
ad_testing.append(((-120, 120), (-120, -120))) # wrapping up
ad_testing.append(((-115, -120), (-115, 120))) # wrapping down
ad_testing.append(((120, -60), (-120, -60))) # wrapping right
ad_testing.append(((-120, -65), (120, -65))) # wrapping left
ad_testing.append(((120, 0), (-120, 60))) # diag right
ad_testing.append(((-120, 55), (120, -5))) # diag left
ad_testing.append(((-60, 120), (0, -120))) # diag up
ad_testing.append(((5, -120), (-55, 120))) # diag up
ad_testing.append(
((150, 150), (-150, -150))) # going to top right corner straight
ad_testing.append(
((140, 155), (-130, -140))) # going to top right corner not straight
ad_testing.append(
((150, -150), (-150, 150))) # going to bottom right corner straight
ad_testing.append(
((140, -155), (-130,
140))) # going to bottom right corner not straight
ad_testing.append(
((-150, 150), (150, -150))) # going to top left corner straight
ad_testing.append(
((-140, 155), (130, -140))) # going to top left corner not straight
ad_testing.append(
((-150, -150), (150, 150))) # going to bottom left corner straight
ad_testing.append(
((-140, -155), (130, 140))) # going to bottom left corner not straight
plot = plots[0]
add_arrows_on_plot(plot, ad_testing, color="red")
plot_file_name = "%s.png" % prefix
plot.save_image(plot_file_name, dpi=300)
img = Image.open(plot_file_name)
hist = img.histogram()
# print(hist)
hist_ok = True
for ref, res in zip(reference_hist, hist):
if (ref != res) and not (0.98 < ref / res < 1.02):
hist_ok = False
# print (ref, res, ref/res)
assert hist_ok
def run_ramalyze(self) :
from mmtbx.validation import ramalyze
ramalyze_result = ramalyze.ramalyze(self.hierarchy)
for result in ramalyze_result.results :
resd = mdb_utils.get_resd(self.pdb_code,result)
MDBRes = mdb_utils.MDBResidue(**resd)
reskey = MDBRes.get_residue_key()
if reskey not in self.residues.keys(): # alternates likely exist
reskeys = self.get_alternate_keys(resd)
for k in reskeys :
self.residues[k].add_ramalyze_result(result)
else : # No alternates
self.residues[reskey].add_ramalyze_result(result)
def exercise_1(prefix="tst_add_arrows_on_plot_1"):
model = mmtbx.model.manager(
model_input=iotbx.pdb.input(source_info=None, lines=pdb_str))
rama = ramalyze(model.get_hierarchy(), out=null_out())
plots = rama.get_plots(show_labels=True,
point_style='bo',
markersize=1,
markeredgecolor="black",
dpi=300,
markerfacecolor="white")
ad_testing = []
ad_testing.append(((60, -120), (120, -120)))
ad_testing.append(((-125, 120), (-125, 179)))
ad_testing.append(((-120, 120), (-120, -120))) # wrapping up
ad_testing.append(((-115, -120), (-115, 120))) # wrapping down
ad_testing.append(((120, -60), (-120, -60))) # wrapping right
ad_testing.append(((-120, -65), (120, -65))) # wrapping left
ad_testing.append(((120, 0), (-120, 60))) # diag right
ad_testing.append(((-120, 55), (120, -5))) # diag left
ad_testing.append(((-60, 120), (0, -120))) # diag up
ad_testing.append(((5, -120), (-55, 120))) # diag up
ad_testing.append(
((150, 150), (-150, -150))) # going to top right corner straight
ad_testing.append(
((140, 155), (-130, -140))) # going to top right corner not straight
ad_testing.append(
((150, -150), (-150, 150))) # going to bottom right corner straight
ad_testing.append(
((140, -155), (-130,
140))) # going to bottom right corner not straight
ad_testing.append(
((-150, 150), (150, -150))) # going to top left corner straight
ad_testing.append(
((-140, 155), (130, -140))) # going to top left corner not straight
ad_testing.append(
((-150, -150), (150, 150))) # going to bottom left corner straight
ad_testing.append(
((-140, -155), (130, 140))) # going to bottom left corner not straight
plot = plots[0]
add_arrows_on_plot(plot, ad_testing, color="black")
plot_file_name = "%s.png" % prefix
plot.save_image(plot_file_name, dpi=300)
hasher = hashlib.md5()
with open(plot_file_name, 'rb') as afile:
buf = afile.read()
hasher.update(buf)
fhash = hasher.hexdigest()
assert fhash == reference_md5, "%s != %s" % (fhash, reference_md5)
def worker(in_queue, out_queue):
while True:
if in_queue.empty():
exit(0)
pdb_id = in_queue.get()
decompress_pdb_redo_dir(pdb_id, suffixes={0})
pdb_path = os.path.join(PDB_REDO_DATA_DIR, pdb_id,
pdb_id + '_0cyc.pdb')
pdb_in = file_reader.any_file(file_name=pdb_path)
hierarchy = pdb_in.file_object.hierarchy
rama_analysis = ramalyze.ramalyze(pdb_hierarchy=hierarchy,
outliers_only=False)
out = StringIO()
rama_analysis.show_old_output(out=out, verbose=False)
output = out.getvalue()
class_results = {}
for pair in CLASSIFICATION_PAIRS:
class_results[pair] = 0
score_results = {}
for clf in CLASSIFICATIONS:
score_results[clf] = []
for line in output.split('\n'):
if len(line) == 0:
continue
chain_id = line[:2].strip()
seqnum = int(line[2:6].strip())
splitline = [x.strip() for x in line[6:].split(':')]
code = splitline[0].strip()
if code not in utils.THREE_LETTER_CODES[0]:
continue
if code == 'MSE':
code = 'MET'
phi, psi = [float(x) for x in splitline[2:4]]
phi *= pi / 180
psi *= pi / 180
iris_score = utils.calculate_ramachandran_score(
None, code, phi, psi)
iris_class = CLASSIFICATIONS[
0] if iris_score < THRESHOLDS[1] else CLASSIFICATIONS[
1] if iris_score < THRESHOLDS[0] else CLASSIFICATIONS[2]
mp_class = splitline[-2][0].upper() + splitline[-2][1:].lower()
class_results[(iris_class, mp_class)] += 1
score_results[mp_class].append(iris_score)
cleanup_pdb_redo_dir(pdb_id)
out_queue.put((pdb_id, class_results, score_results))
def run(self):
results = []
for model_name in self.data_manager.get_model_names():
hierarchy = self.data_manager.get_model(model_name).get_hierarchy()
hierarchy.atoms().reset_i_seq()
result = ramalyze(pdb_hierarchy=hierarchy,
show_errors=None,
outliers_only=self.params.outliers_only,
out=self.logger,
quiet=False)
results.append(result)
if len(self.data_manager.get_model_names()) > 1:
self.params.verbose = False
print('\nmodel : %s' % model_name, file=self.logger)
# combine models
result = results[0]
for i in range(1, len(results)):
result += results[i]
if self.params.verbose:
result.show_old_output(out=self.logger, verbose=True)
if self.params.plot:
plot_file_base = self.params.output_prefix
if plot_file_base is None:
plot_file_base = os.path.splitext(
os.path.basename(
self.data_manager.get_model_names()[0]))[0]
result.write_plots(plot_file_base=plot_file_base,
out=self.logger,
show_labels=self.params.show_labels,
point_style=self.params.point_style,
markerfacecolor=self.params.markerfacecolor,
show_filling=self.params.show_filling,
show_contours=self.params.show_contours,
dpi=self.params.dpi,
markeredgecolor=self.params.markeredgecolor,
markersize=self.params.markersize)
if self.params.wxplot:
try:
import wxtbx.app
except ImportError as e:
raise Sorry("wxPython not available.")
else:
app = wxtbx.app.CCTBXApp(0)
result.display_wx_plots()
app.MainLoop()
def compute(hierarchies,
params,
log,
quiet=False,
plot_file_base_default=None):
results = []
for hierarchy in hierarchies:
result = ramalyze(pdb_hierarchy=hierarchy,
show_errors=None,
outliers_only=params.outliers_only,
out=log,
quiet=quiet)
results.append(result)
# combine models
result = results[0]
for i in range(1, len(results)):
result += results[i]
if params.verbose:
result.show_old_output(out=log, verbose=True)
if params.plot:
plot_file_base = params.output_prefix
if plot_file_base is None:
plot_file_base = plot_file_base_default
result.write_plots(plot_file_base=plot_file_base,
out=log,
show_labels=params.show_labels,
point_style=params.point_style,
markerfacecolor=params.markerfacecolor,
show_filling=params.show_filling,
show_contours=params.show_contours,
dpi=params.dpi,
markeredgecolor=params.markeredgecolor,
markersize=params.markersize)
if params.wxplot:
try:
import wxtbx.app
except ImportError as e:
raise Sorry("wxPython not available.")
else:
app = wxtbx.app.CCTBXApp(0)
result.display_wx_plots()
app.MainLoop()
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 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_ramalyze():
from mmtbx.rotamer.rotamer_eval import find_rotarama_data_dir
regression_pdb = libtbx.env.find_in_repositories(
relative_path="phenix_regression/pdb/jcm.pdb", test=os.path.isfile)
if (regression_pdb is None):
print "Skipping exercise_ramalyze(): input pdb (jcm.pdb) not available"
return
if (find_rotarama_data_dir(optional=True) is None):
print "Skipping exercise_ramalyze(): rotarama_data directory not available"
return
from iotbx import file_reader
# Exercise 1
pdb_in = file_reader.any_file(file_name=regression_pdb)
hierarchy = pdb_in.file_object.hierarchy
pdb_io = pdb.input(file_name=regression_pdb)
hierarchy.atoms().reset_i_seq()
r = ramalyze.ramalyze(pdb_hierarchy=hierarchy, outliers_only=True)
out = StringIO()
r.show_old_output(out=out)
output = out.getvalue()
assert output.count("OUTLIER") == 100
assert output.count("Favored") == 0
assert output.count("Allowed") == 0
assert output.count("General") == 64
assert output.count("Glycine") == 6
assert output.count("Trans-proline") == 1
assert output.count("Cis-proline") == 0
assert output.count("Pre-proline") == 4
assert output.count("Isoleucine or valine") == 25
assert (len(r.outlier_selection()) == 494)
outlier_ids = set([])
atoms = hierarchy.atoms()
for i_seq in r.outlier_selection():
atom = atoms[i_seq]
atom_group = atoms[i_seq].parent()
outlier_ids.add(atom_group.id_str())
outliers1 = sorted([o.atom_group_id_str() for o in r.results])
outliers2 = sorted(list(outlier_ids))
assert (outliers1 == outliers2)
r = ramalyze.ramalyze(pdb_hierarchy=hierarchy, outliers_only=False)
for unpickle in [False, True]:
if unpickle:
r = loads(dumps(r))
for outlier in r.results:
assert (len(outlier.xyz) == 3)
out = StringIO()
r.show_old_output(out=out, verbose=False)
output = out.getvalue()
assert output.count("OUTLIER") == 100
assert output.count("Favored") == 463
assert output.count("Allowed") == 162
assert output.count("General") == 514
assert output.count("Glycine") == 39
assert output.count("Trans-proline") == 23
assert output.count("Cis-proline") == 0
assert output.count("Pre-proline") == 21
assert output.count("Isoleucine or valine") == 128
numtotal = r.get_phi_psi_residues_count()
assert r.get_outliers_count_and_fraction() == (100, 100. / numtotal)
assert r.get_allowed_count_and_fraction() == (162, 162. / numtotal)
assert r.get_favored_count_and_fraction() == (463, 463. / numtotal)
assert r.get_general_count_and_fraction() == (514, 514. / numtotal)
assert r.get_gly_count_and_fraction() == (39, 39. / numtotal)
assert r.get_trans_pro_count_and_fraction() == (23, 23. / numtotal)
assert r.get_cis_pro_count_and_fraction() == (0, 0. / numtotal)
assert r.get_prepro_count_and_fraction() == (21, 21. / numtotal)
assert r.get_ileval_count_and_fraction() == (128, 128. / numtotal)
#assert numtotal == 75+154+494 #reasons for this math unclear
assert numtotal == 725
output_lines = output.splitlines()
assert len(output_lines) == 725
selected_lines = []
for x in [
0, 1, 168, 169, 715, 716, 717, 718, 719, 720, 721, 722, 723,
724
]:
selected_lines.append(output_lines[x])
assert not show_diff(
"\n".join(selected_lines), """\
A 15 SER:35.07:-83.26:131.88:Favored:General
A 16 SER:0.74:-111.53:71.36:Allowed:General
A 191 ASP:2.66:-42.39:121.87:Favored:Pre-proline
A 192 PRO:0.31:-39.12:-31.84:Allowed:Trans-proline
B 368 LYS:56.44:-62.97:-53.28:Favored:General
B 369 GLU:8.89:-44.36:-45.50:Favored:General
B 370 LYS:40.00:-50.00:-39.06:Favored:General
B 371 VAL:68.24:-60.38:-51.85:Favored:Isoleucine or valine
B 372 LEU:0.02:-61.13:-170.23:OUTLIER:General
B 373 ARG:0.02:60.09:-80.26:OUTLIER:General
B 374 ALA:0.13:-37.21:-36.12:Allowed:General
B 375 LEU:11.84:-89.81:-41.45:Favored:General
B 376 ASN:84.33:-58.30:-41.39:Favored:General
B 377 GLU:30.88:-56.79:-21.74:Favored:General""")
assert (len(r.outlier_selection()) == 494)
# Exercise 2
regression_pdb = libtbx.env.find_in_repositories(
relative_path="phenix_regression/pdb/pdb1jxt.ent", test=os.path.isfile)
pdb_in = file_reader.any_file(file_name=regression_pdb)
hierarchy = pdb_in.file_object.hierarchy
hierarchy.atoms().reset_i_seq()
r = ramalyze.ramalyze(pdb_hierarchy=hierarchy, outliers_only=True)
out = StringIO()
r.show_old_output(out=out)
output = out.getvalue()
assert output.count("Favored") == 0
assert output.count("Allowed") == 0
assert output.count("OUTLIER") == 0
r = ramalyze.ramalyze(pdb_hierarchy=hierarchy, outliers_only=False)
for unpickle in [False, True]:
if unpickle:
r = loads(dumps(r))
out = StringIO()
r.show_old_output(out=out, verbose=False)
output = out.getvalue()
assert output.count("Favored") == 50
assert output.count("Allowed") == 1
assert output.count("OUTLIER") == 0
assert output.count("General") == 29
assert output.count("Glycine") == 4
assert output.count("Trans-proline") == 5
assert output.count("Cis-proline") == 0
assert output.count("Pre-proline") == 5
assert output.count("Isoleucine or valine") == 8
numtotal = r.get_phi_psi_residues_count()
assert r.get_outliers_count_and_fraction() == (0, 0. / numtotal)
assert r.get_allowed_count_and_fraction() == (1, 1. / numtotal)
assert r.get_favored_count_and_fraction() == (43, 43. / numtotal)
#print r.get_general_count_and_fraction()
assert r.get_general_count_and_fraction() == (25, 25. / numtotal)
assert r.get_gly_count_and_fraction() == (4, 4. / numtotal)
assert r.get_trans_pro_count_and_fraction() == (5, 5. / numtotal)
assert r.get_cis_pro_count_and_fraction() == (0, 0. / numtotal)
assert r.get_prepro_count_and_fraction() == (5, 5. / numtotal)
assert r.get_ileval_count_and_fraction() == (5, 5. / numtotal)
output_lines = output.splitlines()
assert len(output_lines) == 51
selected_lines = []
for x in [0, 1, 5, 6, 7, 8, 9, 47, 48, 49, 50]:
selected_lines.append(output_lines[x])
assert not show_diff(
"\n".join(selected_lines), """\
A 2 ATHR:33.85:-106.92:144.23:Favored:General
A 3 ACYS:47.07:-132.54:137.26:Favored:General
A 7 AILE:98.76:-61.91:-44.35:Favored:Isoleucine or valine
A 7 BILE:61.50:-56.21:-51.56:Favored:Isoleucine or valine
A 8 AVAL:23.11:-50.35:-49.64:Favored:Isoleucine or valine
A 8 BVAL:12.01:-83.20:-12.14:Favored:Isoleucine or valine
A 8 CVAL:73.11:-61.22:-36.49:Favored:Isoleucine or valine
A 43 AASP:51.81:-94.64:5.45:Favored:General
A 43 BASP:56.98:-88.69:-0.12:Favored:General
A 44 TYR:1.76:-133.10:58.75:Allowed:General
A 45 ALA:57.37:-86.61:-8.57:Favored:General""")
# Exercise 3: 2plx excerpt (unusual icode usage)
import iotbx.pdb.hierarchy
pdb_io = iotbx.pdb.hierarchy.input(pdb_string="""\
ATOM 1468 N GLY A 219 3.721 21.322 10.752 1.00 14.12 N
ATOM 1469 CA GLY A 219 3.586 21.486 12.188 1.00 14.85 C
ATOM 1470 C GLY A 219 4.462 20.538 12.995 1.00 15.63 C
ATOM 1471 O GLY A 219 5.513 20.090 12.512 1.00 14.55 O
ATOM 1472 N CYS A 220 4.036 20.213 14.235 1.00 15.02 N
ATOM 1473 CA CYS A 220 4.776 19.228 15.068 1.00 15.56 C
ATOM 1474 C CYS A 220 3.773 18.322 15.741 1.00 14.69 C
ATOM 1475 O CYS A 220 2.799 18.828 16.338 1.00 15.54 O
ATOM 1476 CB CYS A 220 5.620 19.906 16.174 1.00 15.72 C
ATOM 1477 SG CYS A 220 6.762 21.133 15.448 1.00 15.45 S
ATOM 1478 N ALA A 221A 4.054 17.017 15.707 1.00 14.77 N
ATOM 1479 CA ALA A 221A 3.274 16.015 16.507 1.00 14.01 C
ATOM 1480 C ALA A 221A 1.774 15.992 16.099 1.00 14.50 C
ATOM 1481 O ALA A 221A 0.875 15.575 16.881 1.00 14.46 O
ATOM 1482 CB ALA A 221A 3.440 16.318 17.935 1.00 12.28 C
ATOM 1483 N GLN A 221 1.523 16.390 14.848 1.00 14.52 N
ATOM 1484 CA GLN A 221 0.159 16.391 14.325 1.00 15.19 C
ATOM 1485 C GLN A 221 -0.229 15.044 13.717 1.00 14.43 C
ATOM 1486 O GLN A 221 0.641 14.280 13.307 1.00 16.88 O
ATOM 1487 CB GLN A 221 0.002 17.491 13.272 1.00 16.41 C
ATOM 1488 CG GLN A 221 0.253 18.906 13.805 1.00 16.52 C
ATOM 1489 CD GLN A 221 -0.640 19.181 14.995 1.00 17.87 C
ATOM 1490 OE1 GLN A 221 -1.857 19.399 14.826 1.00 13.54 O
ATOM 1491 NE2 GLN A 221 -0.050 19.149 16.228 1.00 16.18 N
ATOM 1492 N LYS A 222 -1.537 14.773 13.694 1.00 14.34 N
ATOM 1493 CA LYS A 222 -2.053 13.536 13.125 1.00 15.07 C
ATOM 1494 C LYS A 222 -1.679 13.455 11.655 1.00 14.88 C
ATOM 1495 O LYS A 222 -1.856 14.424 10.883 1.00 14.32 O
""")
r = ramalyze.ramalyze(pdb_hierarchy=pdb_io.hierarchy, outliers_only=False)
assert (len(r.results) == 3)
def __init__(self,
pdb_hierarchy,
params=None,
secondary_structure_annotation=None,
reference_map=None,
crystal_symmetry=None,
grm=None,
rama_manager=None,
rotamer_manager=None,
log=null_out(),
verbose=False,
tried_rama_angles={},
tried_final_rama_angles={},
n_run=0):
if len(pdb_hierarchy.models()) > 1:
raise Sorry("Multi-model files are not supported")
self.original_pdb_h = pdb_hierarchy
self.secondary_structure_annotation=secondary_structure_annotation
asc = pdb_hierarchy.atom_selection_cache()
self.xrs = pdb_hierarchy.extract_xray_structure(crystal_symmetry=crystal_symmetry)
self.reference_map = reference_map
self.resulting_pdb_h = pdb_hierarchy.deep_copy()
self.resulting_pdb_h.reset_atom_i_seqs()
self.params = self.process_params(params)
self.log = log
self.verbose = verbose
self.grm = grm
self.r = rama_manager
self.ideal_res_dict = idealized_aa.residue_dict()
self.n_run = n_run
if self.r is None:
self.r = rama_eval()
self.rotamer_manager = rotamer_manager
if self.rotamer_manager is None:
self.rotamer_manager = RotamerEval()
ram = ramalyze.ramalyze(pdb_hierarchy=pdb_hierarchy)
self.p_initial_rama_outliers = ram.out_percent
self.p_before_minimization_rama_outliers = None
self.p_after_minimiaztion_rama_outliers = None
n_inputs = [reference_map, crystal_symmetry].count(None)
if not (n_inputs == 0 or n_inputs == 2):
print >> log, "Need to have both map and symmetry info. Not using map."
self.reference_map = None
# here we are recording what CCD solutions were used to fix particular
# outliers to not use the same in the next CCD try.
# Nested dict. First level:
# key: chain id, value: dict
# key: resid (string), value: list of tried variants.
self.tried_rama_angles = tried_rama_angles
self.tried_final_rama_angles = tried_final_rama_angles
berkeley_count = utils.list_rama_outliers_h(self.resulting_pdb_h).count("\n")
self.berkeley_p_before_minimization_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
n_bad_omegas = utils.n_bad_omegas(self.resulting_pdb_h)
self.berkeley_p_after_minimiaztion_rama_outliers = self.berkeley_p_before_minimization_rama_outliers
self.ref_exclusion_selection = ""
self.number_of_ccd_trials = 0
# print "logic expr outcome:", (self.number_of_ccd_trials < 10 and self.berkeley_p_before_minimization_rama_outliers > 0.001)
# print self.number_of_ccd_trials < 10
# print "berkeley before rama out:", self.berkeley_p_before_minimization_rama_outliers
if (self.berkeley_p_before_minimization_rama_outliers <= 0.001 and
(n_bad_omegas<1 and self.params.make_all_trans)):
print >> self.log, "No ramachandran outliers, skipping CCD step."
print "n_bad_omegas", n_bad_omegas
print "self.params.make_all_trans",self.params.make_all_trans
if not self.params.enabled:
print >> self.log, "Loop idealization is not enabled, use 'enabled=True'."
while (self.number_of_ccd_trials < self.params.number_of_ccd_trials
and (self.berkeley_p_after_minimiaztion_rama_outliers > 0.001 or
(n_bad_omegas>=1 and self.params.make_all_trans))
and self.params.enabled):
print >> self.log, "CCD try number, outliers:", self.number_of_ccd_trials, self.berkeley_p_before_minimization_rama_outliers
processed_chain_ids = []
for chain in self.resulting_pdb_h.only_model().chains():
if chain.id not in self.tried_rama_angles.keys():
self.tried_rama_angles[chain.id] = {}
if chain.id not in self.tried_final_rama_angles.keys():
self.tried_final_rama_angles[chain.id] = {}
print >> self.log, "Idealizing chain %s" % chain.id
if chain.id not in processed_chain_ids:
processed_chain_ids.append(chain.id)
else:
continue
selection = "protein and chain %s and (name N or name CA or name C or name O)" % chain.id
sel = asc.selection("chain %s" % chain.id)
chain_h = self.resulting_pdb_h.select(sel)
m = chain_h.only_model()
i = 0
cutted_chain_h = None
for c in m.chains():
if i == 0:
cutted_chain_h = iotbx.pdb.hierarchy.new_hierarchy_from_chain(c)
else:
print >> self.log, "WARNING!!! Duplicating chain ids! Only the first chain will be processed."
print >> self.log, " Removing chain %s with %d residues" % (c.id, len(c.residues()))
m.remove_chain(c)
i += 1
exclusions, ch_h = self.idealize_chain(
hierarchy=(cutted_chain_h if cutted_chain_h else chain_h),
tried_rama_angles_for_chain=self.tried_rama_angles[chain.id],
tried_final_rama_angles_for_chain=self.tried_final_rama_angles[chain.id])
if ch_h is not None:
set_xyz_smart(
# dest_h=self.resulting_pdb_h,
dest_h=chain,
source_h=ch_h)
for resnum in exclusions:
selection += " and not resseq %s" % resnum
self.ref_exclusion_selection += "(%s) or " % selection
print "self.tried_rama_angles", self.tried_rama_angles
print "self.tried_final_rama_angles", self.tried_final_rama_angles
#
# dumping and reloading hierarchy to do proper rounding of coordinates
self.resulting_pdb_h = iotbx.pdb.input(
source_info=None,
lines=self.resulting_pdb_h.as_pdb_string()).construct_hierarchy()
berkeley_count = utils.list_rama_outliers_h(self.resulting_pdb_h).count("\n")
self.berkeley_p_before_minimization_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
if len(self.ref_exclusion_selection) > 0:
self.ref_exclusion_selection = self.ref_exclusion_selection[:-3]
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_before_minimization_rama_outliers = ram.out_percent
duke_count = ram.get_outliers_count_and_fraction()[0]
if berkeley_count != duke_count:
print >> self.log, "Discrepancy between berkeley and duke after ccd:", berkeley_count, duke_count
self.resulting_pdb_h.write_pdb_file(file_name="%d%s_discrepancy.pdb" % (self.number_of_ccd_trials, self.params.output_prefix))
if self.params.debug:
self.resulting_pdb_h.write_pdb_file(
file_name="%d%s_all_not_minized.pdb" % (self.number_of_ccd_trials,
self.params.output_prefix))
if self.params.minimize_whole:
print >> self.log, "minimizing whole chain..."
print >> self.log, "self.ref_exclusion_selection", self.ref_exclusion_selection
# print >> sel
# XXX but first let's check and fix rotamers...
print >> self.log, "Fixing/checking rotamers in loop idealization..."
excl_sel = self.ref_exclusion_selection
if len(excl_sel) == 0:
excl_sel = None
non_outliers_for_check = asc.selection("(%s)" % self.ref_exclusion_selection)
pre_result_h = mmtbx.utils.fix_rotamer_outliers(
pdb_hierarchy=self.resulting_pdb_h,
grm=self.grm.geometry,
xrs=self.xrs,
map_data=self.reference_map,
radius=5,
mon_lib_srv=None,
rotamer_manager=self.rotamer_manager,
backrub_range=None, # don't sample backrub at this point
non_outliers_to_check=non_outliers_for_check, # bool selection
asc=asc,
verbose=True,
log=self.log)
if self.reference_map is None:
minimize_wrapper_for_ramachandran(
hierarchy=self.resulting_pdb_h,
xrs=self.xrs,
original_pdb_h=self.original_pdb_h,
excl_string_selection=self.ref_exclusion_selection,
grm=self.grm,
log=None,
ss_annotation=self.secondary_structure_annotation)
else:
mwwm = minimize_wrapper_with_map(
pdb_h=self.resulting_pdb_h,
xrs=self.xrs,
target_map=self.reference_map,
grm=self.grm,
ss_annotation=self.secondary_structure_annotation,
number_of_cycles=Auto,
log=self.log)
if self.params.debug:
self.resulting_pdb_h.write_pdb_file(
file_name="%d%s_all_minized.pdb" % (self.number_of_ccd_trials,
self.params.output_prefix))
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_after_minimiaztion_rama_outliers = ram.out_percent
berkeley_count = utils.list_rama_outliers_h(self.resulting_pdb_h).count("\n")
duke_count = ram.get_outliers_count_and_fraction()[0]
n_bad_omegas = utils.n_bad_omegas(self.resulting_pdb_h)
self.berkeley_p_after_minimiaztion_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
if berkeley_count != duke_count:
print >> self.log, "Discrepancy between berkeley and duke after min:", berkeley_count, duke_count
else:
print >> self.log, "Number of Rama outliers after min:", berkeley_count
print >> self.log, "Number of bad omegas:", n_bad_omegas
self.number_of_ccd_trials += 1
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,
params=None,
secondary_structure_annotation=None,
log=null_out(),
verbose=True):
if len(pdb_hierarchy.models()) > 1:
raise Sorry("Multi-model files are not supported")
self.original_pdb_h = pdb_hierarchy
self.secondary_structure_annotation=secondary_structure_annotation
xrs = pdb_hierarchy.extract_xray_structure()
asc = pdb_hierarchy.atom_selection_cache()
self.resulting_pdb_h = pdb_hierarchy.deep_copy()
self.resulting_pdb_h.reset_atom_i_seqs()
self.params = self.process_params(params)
self.log = log
self.verbose = verbose
self.r = rama_eval()
self.rotamer_manager = RotamerEval()
ram = ramalyze.ramalyze(pdb_hierarchy=pdb_hierarchy)
self.p_initial_rama_outliers = ram.out_percent
self.p_before_minimization_rama_outliers = None
self.p_after_minimiaztion_rama_outliers = None
berkeley_count = utils.list_rama_outliers_h(self.resulting_pdb_h).count("\n")
self.berkeley_p_before_minimization_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
# self.berkeley_p_before_minimization_rama_outliers = None
self.berkeley_p_after_minimiaztion_rama_outliers = None
self.ref_exclusion_selection = ""
number_of_ccd_trials = 0
# print "logic expr outcome:", (number_of_ccd_trials < 10 and self.berkeley_p_before_minimization_rama_outliers > 0.001)
# print number_of_ccd_trials < 10
# print "berkeley before rama out:", self.berkeley_p_before_minimization_rama_outliers
if self.berkeley_p_before_minimization_rama_outliers <= 0.001:
print >> self.log, "No ramachandran outliers, skipping CCD step."
if not self.params.enabled:
print >> self.log, "Loop idealization is not enabled, use 'enabled=True'."
while (number_of_ccd_trials < self.params.number_of_ccd_trials
and self.berkeley_p_before_minimization_rama_outliers > 0.001
and self.params.enabled):
print "CCD try number, outliers:", number_of_ccd_trials, self.berkeley_p_before_minimization_rama_outliers
number_of_ccd_trials += 1
processed_chain_ids = []
for chain in self.resulting_pdb_h.only_model().chains():
print >> self.log, "Idealizing chain %s" % chain.id
if chain.id not in processed_chain_ids:
processed_chain_ids.append(chain.id)
else:
continue
selection = "protein and chain %s and (name N or name CA or name C or name O)" % chain.id
sel = asc.selection("chain %s" % chain.id)
chain_h = self.resulting_pdb_h.select(sel)
m = chain_h.only_model()
i = 0
cutted_chain_h = None
for c in m.chains():
if i == 0:
cutted_chain_h = iotbx.pdb.hierarchy.new_hierarchy_from_chain(c)
else:
print >> self.log, "WARNING!!! Duplicating chain ids! Only the first chain will be processed."
print >> self.log, " Removing chain %s with %d residues" % (c.id, len(c.residues()))
m.remove_chain(c)
i += 1
exclusions, ch_h = self.idealize_chain(
hierarchy=(cutted_chain_h if cutted_chain_h else chain_h))
if ch_h is not None:
set_xyz_smart(
# dest_h=self.resulting_pdb_h,
dest_h=chain,
source_h=ch_h)
for resnum in exclusions:
selection += " and not resseq %s" % resnum
self.ref_exclusion_selection += "(%s) or " % selection
#
# dumping and reloading hierarchy to do proper rounding of coordinates
self.resulting_pdb_h = iotbx.pdb.input(
source_info=None,
lines=self.resulting_pdb_h.as_pdb_string()).construct_hierarchy()
berkeley_count = utils.list_rama_outliers_h(self.resulting_pdb_h).count("\n")
self.berkeley_p_before_minimization_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
if len(self.ref_exclusion_selection) > 0:
self.ref_exclusion_selection = self.ref_exclusion_selection[:-3]
self.resulting_pdb_h.write_pdb_file(file_name="%s_before_minimization.pdb" % self.params.output_prefix)
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_before_minimization_rama_outliers = ram.out_percent
duke_count = ram.get_outliers_count_and_fraction()[0]
if berkeley_count != duke_count:
print >> self.log, "Discrepancy between berkeley and duke after ccd:", berkeley_count, duke_count
if self.params.minimize_whole:
print >> self.log, "minimizing whole thing..."
print >> self.log, "self.ref_exclusion_selection", self.ref_exclusion_selection
# print >> sel
minimize_wrapper_for_ramachandran(
hierarchy=self.resulting_pdb_h,
xrs=xrs,
original_pdb_h=self.original_pdb_h,
excl_string_selection=self.ref_exclusion_selection,
log=None,
ss_annotation=self.secondary_structure_annotation)
# self.resulting_pdb_h.write_pdb_file(file_name="%s_all_minized.pdb" % self.params.output_prefix)
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_after_minimiaztion_rama_outliers = ram.out_percent
berkeley_count = utils.list_rama_outliers_h(self.resulting_pdb_h).count("\n")
duke_count = ram.get_outliers_count_and_fraction()[0]
self.berkeley_p_after_minimiaztion_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
if berkeley_count != duke_count:
print >> self.log, "Discrepancy between berkeley and duke after min:", berkeley_count, duke_count
else:
print >> self.log, "Number of Rama outliers after min:", berkeley_count
def exercise_ramalyze():
from mmtbx.rotamer.rotamer_eval import find_rotarama_data_dir
regression_pdb = libtbx.env.find_in_repositories(
relative_path="phenix_regression/pdb/jcm.pdb",
test=os.path.isfile)
if (regression_pdb is None):
print "Skipping exercise_ramalyze(): input pdb (jcm.pdb) not available"
return
if (find_rotarama_data_dir(optional=True) is None):
print "Skipping exercise_ramalyze(): rotarama_data directory not available"
return
from iotbx import file_reader
# Exercise 1
pdb_in = file_reader.any_file(file_name=regression_pdb)
hierarchy = pdb_in.file_object.hierarchy
pdb_io = pdb.input(file_name=regression_pdb)
hierarchy.atoms().reset_i_seq()
r = ramalyze.ramalyze(
pdb_hierarchy=hierarchy,
outliers_only=True)
out = StringIO()
r.show_old_output(out=out)
output = out.getvalue()
assert output.count("OUTLIER") == 100
assert output.count("Favored") == 0
assert output.count("Allowed") == 0
assert output.count("General") == 64
assert output.count("Glycine") == 6
assert output.count("Trans-proline") == 1
assert output.count("Cis-proline") == 0
assert output.count("Pre-proline") == 4
assert output.count("Isoleucine or valine") == 25
assert (len(r.outlier_selection()) == 788)
outlier_ids = set([])
atoms = hierarchy.atoms()
for i_seq in r.outlier_selection() :
atom = atoms[i_seq]
atom_group = atoms[i_seq].parent()
outlier_ids.add(atom_group.id_str())
outliers1 = sorted([ o.atom_group_id_str() for o in r.results ])
outliers2 = sorted(list(outlier_ids))
assert (outliers1 == outliers2)
r = ramalyze.ramalyze(
pdb_hierarchy=hierarchy,
outliers_only=False)
for unpickle in [False, True] :
if unpickle :
r = loads(dumps(r))
for outlier in r.results :
assert (len(outlier.xyz) == 3)
out = StringIO()
r.show_old_output(out=out, verbose=False)
output = out.getvalue()
assert output.count("OUTLIER") == 100
assert output.count("Favored") == 461
assert output.count("Allowed") == 162
assert output.count("General") == 513
assert output.count("Glycine") == 39
assert output.count("Trans-proline") == 23
assert output.count("Cis-proline") == 0
assert output.count("Pre-proline") == 21
assert output.count("Isoleucine or valine") == 127
numtotal = r.get_phi_psi_residues_count()
assert r.get_outliers_count_and_fraction() == (100, 100./numtotal)
assert r.get_allowed_count_and_fraction() == (162, 162./numtotal)
assert r.get_favored_count_and_fraction() == (461, 461./numtotal)
assert r.get_general_count_and_fraction() == (513, 513./numtotal)
assert r.get_gly_count_and_fraction() == (39, 39./numtotal)
assert r.get_trans_pro_count_and_fraction() == (23, 23./numtotal)
assert r.get_cis_pro_count_and_fraction() == (0, 0./numtotal)
assert r.get_prepro_count_and_fraction() == (21, 21./numtotal)
assert r.get_ileval_count_and_fraction() == (127, 127./numtotal)
assert numtotal == 75+154+494
output_lines = output.splitlines()
assert len(output_lines) == 723
selected_lines = []
for x in [0, 1, 168, 169, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722]:
selected_lines.append(output_lines[x])
assert not show_diff("\n".join(selected_lines), """\
A 15 SER:35.07:-83.26:131.88:Favored:General
A 16 SER:0.74:-111.53:71.36:Allowed:General
A 191 ASP:2.66:-42.39:121.87:Favored:Pre-proline
A 192 PRO:0.31:-39.12:-31.84:Allowed:Trans-proline
B 368 LYS:56.44:-62.97:-53.28:Favored:General
B 369 GLU:8.89:-44.36:-45.50:Favored:General
B 370 LYS:40.00:-50.00:-39.06:Favored:General
B 371 VAL:68.24:-60.38:-51.85:Favored:Isoleucine or valine
B 372 LEU:0.02:-61.13:-170.23:OUTLIER:General
B 373 ARG:0.02:60.09:-80.26:OUTLIER:General
B 374 ALA:0.13:-37.21:-36.12:Allowed:General
B 375 LEU:11.84:-89.81:-41.45:Favored:General
B 376 ASN:84.33:-58.30:-41.39:Favored:General
B 377 GLU:30.88:-56.79:-21.74:Favored:General""")
assert (len(r.outlier_selection()) == 788)
# Exercise 2
regression_pdb = libtbx.env.find_in_repositories(
relative_path="phenix_regression/pdb/pdb1jxt.ent",
test=os.path.isfile)
pdb_in = file_reader.any_file(file_name=regression_pdb)
hierarchy = pdb_in.file_object.hierarchy
hierarchy.atoms().reset_i_seq()
r = ramalyze.ramalyze(
pdb_hierarchy=hierarchy,
outliers_only=True)
out = StringIO()
r.show_old_output(out=out)
output = out.getvalue()
assert output.count("Favored") == 0
assert output.count("Allowed") == 0
assert output.count("OUTLIER") == 0
r = ramalyze.ramalyze(
pdb_hierarchy=hierarchy,
outliers_only=False)
for unpickle in [False, True] :
if unpickle :
r = loads(dumps(r))
out = StringIO()
r.show_old_output(out=out, verbose=False)
output = out.getvalue()
assert output.count("Favored") == 47
assert output.count("Allowed") == 1
assert output.count("OUTLIER") == 0
assert output.count("General") == 27
assert output.count("Glycine") == 4
assert output.count("Trans-proline") == 4
assert output.count("Cis-proline") == 0
assert output.count("Pre-proline") == 5
assert output.count("Isoleucine or valine") == 8
numtotal = r.get_phi_psi_residues_count()
assert r.get_outliers_count_and_fraction() == (0, 0./numtotal)
assert r.get_allowed_count_and_fraction() == (1, 1./numtotal)
assert r.get_favored_count_and_fraction() == (47, 47./numtotal)
assert r.get_general_count_and_fraction() == (27, 27./numtotal)
assert r.get_gly_count_and_fraction() == (4, 4./numtotal)
assert r.get_trans_pro_count_and_fraction() == (4, 4./numtotal)
assert r.get_cis_pro_count_and_fraction() == (0, 0./numtotal)
assert r.get_prepro_count_and_fraction() == (5, 5./numtotal)
assert r.get_ileval_count_and_fraction() == (8, 8./numtotal)
output_lines = output.splitlines()
assert len(output_lines) == 48
selected_lines = []
for x in [0, 1, 6, 7, 8, 9, 10, 44, 45, 46, 47]:
selected_lines.append(output_lines[x])
assert not show_diff("\n".join(selected_lines), """\
A 2 ATHR:33.85:-106.92:144.23:Favored:General
A 2 BTHR:37.07:-97.44:137.00:Favored:General
A 7 AILE:98.76:-61.91:-44.35:Favored:Isoleucine or valine
A 7 BILE:61.50:-56.21:-51.56:Favored:Isoleucine or valine
A 8 AVAL:23.11:-50.35:-49.64:Favored:Isoleucine or valine
A 8 BVAL:12.01:-83.20:-12.14:Favored:Isoleucine or valine
A 8 CVAL:73.11:-61.22:-36.49:Favored:Isoleucine or valine
A 43 AASP:51.81:-94.64:5.45:Favored:General
A 43 BASP:56.98:-88.69:-0.12:Favored:General
A 44 TYR:1.76:-133.10:58.75:Allowed:General
A 45 ALA:57.37:-86.61:-8.57:Favored:General""")
# Exercise 3: 2plx excerpt (unusual icode usage)
import iotbx.pdb.hierarchy
pdb_io = iotbx.pdb.hierarchy.input(pdb_string="""\
ATOM 1468 N GLY A 219 3.721 21.322 10.752 1.00 14.12 N
ATOM 1469 CA GLY A 219 3.586 21.486 12.188 1.00 14.85 C
ATOM 1470 C GLY A 219 4.462 20.538 12.995 1.00 15.63 C
ATOM 1471 O GLY A 219 5.513 20.090 12.512 1.00 14.55 O
ATOM 1472 N CYS A 220 4.036 20.213 14.235 1.00 15.02 N
ATOM 1473 CA CYS A 220 4.776 19.228 15.068 1.00 15.56 C
ATOM 1474 C CYS A 220 3.773 18.322 15.741 1.00 14.69 C
ATOM 1475 O CYS A 220 2.799 18.828 16.338 1.00 15.54 O
ATOM 1476 CB CYS A 220 5.620 19.906 16.174 1.00 15.72 C
ATOM 1477 SG CYS A 220 6.762 21.133 15.448 1.00 15.45 S
ATOM 1478 N ALA A 221A 4.054 17.017 15.707 1.00 14.77 N
ATOM 1479 CA ALA A 221A 3.274 16.015 16.507 1.00 14.01 C
ATOM 1480 C ALA A 221A 1.774 15.992 16.099 1.00 14.50 C
ATOM 1481 O ALA A 221A 0.875 15.575 16.881 1.00 14.46 O
ATOM 1482 CB ALA A 221A 3.440 16.318 17.935 1.00 12.28 C
ATOM 1483 N GLN A 221 1.523 16.390 14.848 1.00 14.52 N
ATOM 1484 CA GLN A 221 0.159 16.391 14.325 1.00 15.19 C
ATOM 1485 C GLN A 221 -0.229 15.044 13.717 1.00 14.43 C
ATOM 1486 O GLN A 221 0.641 14.280 13.307 1.00 16.88 O
ATOM 1487 CB GLN A 221 0.002 17.491 13.272 1.00 16.41 C
ATOM 1488 CG GLN A 221 0.253 18.906 13.805 1.00 16.52 C
ATOM 1489 CD GLN A 221 -0.640 19.181 14.995 1.00 17.87 C
ATOM 1490 OE1 GLN A 221 -1.857 19.399 14.826 1.00 13.54 O
ATOM 1491 NE2 GLN A 221 -0.050 19.149 16.228 1.00 16.18 N
ATOM 1492 N LYS A 222 -1.537 14.773 13.694 1.00 14.34 N
ATOM 1493 CA LYS A 222 -2.053 13.536 13.125 1.00 15.07 C
ATOM 1494 C LYS A 222 -1.679 13.455 11.655 1.00 14.88 C
ATOM 1495 O LYS A 222 -1.856 14.424 10.883 1.00 14.32 O
""")
r = ramalyze.ramalyze(
pdb_hierarchy=pdb_io.hierarchy,
outliers_only=False)
assert (len(r.results) == 3)
def __init__(self,
pdb_hierarchy,
params=None,
secondary_structure_annotation=None,
reference_map=None,
crystal_symmetry=None,
grm=None,
rama_manager=None,
rotamer_manager=None,
log=null_out(),
verbose=False):
if len(pdb_hierarchy.models()) > 1:
raise Sorry("Multi-model files are not supported")
self.original_pdb_h = pdb_hierarchy
self.secondary_structure_annotation = secondary_structure_annotation
asc = pdb_hierarchy.atom_selection_cache()
self.xrs = pdb_hierarchy.extract_xray_structure(
crystal_symmetry=crystal_symmetry)
self.reference_map = reference_map
self.resulting_pdb_h = pdb_hierarchy.deep_copy()
self.resulting_pdb_h.reset_atom_i_seqs()
self.params = self.process_params(params)
self.log = log
self.verbose = verbose
self.grm = grm
self.r = rama_manager
if self.r is None:
self.r = rama_eval()
self.rotamer_manager = rotamer_manager
if self.rotamer_manager is None:
self.rotamer_manager = RotamerEval()
ram = ramalyze.ramalyze(pdb_hierarchy=pdb_hierarchy)
self.p_initial_rama_outliers = ram.out_percent
self.p_before_minimization_rama_outliers = None
self.p_after_minimiaztion_rama_outliers = None
n_inputs = [reference_map, crystal_symmetry].count(None)
if not (n_inputs == 0 or n_inputs == 2):
print >> log, "Need to have both map and symmetry info. Not using map."
self.reference_map = None
berkeley_count = utils.list_rama_outliers_h(
self.resulting_pdb_h).count("\n")
self.berkeley_p_before_minimization_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
# self.berkeley_p_before_minimization_rama_outliers = None
self.berkeley_p_after_minimiaztion_rama_outliers = self.berkeley_p_before_minimization_rama_outliers
self.ref_exclusion_selection = ""
number_of_ccd_trials = 0
# print "logic expr outcome:", (number_of_ccd_trials < 10 and self.berkeley_p_before_minimization_rama_outliers > 0.001)
# print number_of_ccd_trials < 10
# print "berkeley before rama out:", self.berkeley_p_before_minimization_rama_outliers
if self.berkeley_p_before_minimization_rama_outliers <= 0.001:
print >> self.log, "No ramachandran outliers, skipping CCD step."
if not self.params.enabled:
print >> self.log, "Loop idealization is not enabled, use 'enabled=True'."
while (number_of_ccd_trials < self.params.number_of_ccd_trials
and self.berkeley_p_after_minimiaztion_rama_outliers > 0.001
and self.params.enabled):
print "CCD try number, outliers:", number_of_ccd_trials, self.berkeley_p_before_minimization_rama_outliers
number_of_ccd_trials += 1
processed_chain_ids = []
for chain in self.resulting_pdb_h.only_model().chains():
print >> self.log, "Idealizing chain %s" % chain.id
if chain.id not in processed_chain_ids:
processed_chain_ids.append(chain.id)
else:
continue
selection = "protein and chain %s and (name N or name CA or name C or name O)" % chain.id
sel = asc.selection("chain %s" % chain.id)
chain_h = self.resulting_pdb_h.select(sel)
m = chain_h.only_model()
i = 0
cutted_chain_h = None
for c in m.chains():
if i == 0:
cutted_chain_h = iotbx.pdb.hierarchy.new_hierarchy_from_chain(
c)
else:
print >> self.log, "WARNING!!! Duplicating chain ids! Only the first chain will be processed."
print >> self.log, " Removing chain %s with %d residues" % (
c.id, len(c.residues()))
m.remove_chain(c)
i += 1
exclusions, ch_h = self.idealize_chain(
hierarchy=(cutted_chain_h if cutted_chain_h else chain_h))
if ch_h is not None:
set_xyz_smart(
# dest_h=self.resulting_pdb_h,
dest_h=chain,
source_h=ch_h)
for resnum in exclusions:
selection += " and not resseq %s" % resnum
self.ref_exclusion_selection += "(%s) or " % selection
#
# dumping and reloading hierarchy to do proper rounding of coordinates
self.resulting_pdb_h = iotbx.pdb.input(
source_info=None, lines=self.resulting_pdb_h.as_pdb_string(
)).construct_hierarchy()
berkeley_count = utils.list_rama_outliers_h(
self.resulting_pdb_h).count("\n")
self.berkeley_p_before_minimization_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
if len(self.ref_exclusion_selection) > 0:
self.ref_exclusion_selection = self.ref_exclusion_selection[:
-3]
# self.resulting_pdb_h.write_pdb_file(file_name="%s_before_minimization.pdb" % self.params.output_prefix)
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_before_minimization_rama_outliers = ram.out_percent
duke_count = ram.get_outliers_count_and_fraction()[0]
if berkeley_count != duke_count:
print >> self.log, "Discrepancy between berkeley and duke after ccd:", berkeley_count, duke_count
if self.params.minimize_whole:
print >> self.log, "minimizing whole thing..."
print >> self.log, "self.ref_exclusion_selection", self.ref_exclusion_selection
# print >> sel
if self.reference_map is None:
minimize_wrapper_for_ramachandran(
hierarchy=self.resulting_pdb_h,
xrs=self.xrs,
original_pdb_h=self.original_pdb_h,
excl_string_selection=self.ref_exclusion_selection,
grm=self.grm,
log=None,
ss_annotation=self.secondary_structure_annotation)
else:
mwwm = minimize_wrapper_with_map(
pdb_h=self.resulting_pdb_h,
xrs=self.xrs,
target_map=self.reference_map,
grm=self.grm,
ss_annotation=self.secondary_structure_annotation,
log=self.log)
# self.resulting_pdb_h.write_pdb_file(file_name="%s_all_minized.pdb" % self.params.output_prefix)
ram = ramalyze.ramalyze(pdb_hierarchy=self.resulting_pdb_h)
self.p_after_minimiaztion_rama_outliers = ram.out_percent
berkeley_count = utils.list_rama_outliers_h(
self.resulting_pdb_h).count("\n")
duke_count = ram.get_outliers_count_and_fraction()[0]
self.berkeley_p_after_minimiaztion_rama_outliers = \
berkeley_count/float(self.resulting_pdb_h.overall_counts().n_residues)*100
if berkeley_count != duke_count:
print >> self.log, "Discrepancy between berkeley and duke after min:", berkeley_count, duke_count
else:
print >> self.log, "Number of Rama outliers after min:", berkeley_count
def ramalyze_parallel(hierarchy):
return ramalyze(hierarchy, out=null_out())
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 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,
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
