def run(self):
# create pharmacophore
ref = PharmacophoreModel.from_pdb(pdb_code=self.pdb,
chain=self.chain,
representatives=self.input().path,
identifier=self.pdb)
ref.rank_features(max_features=6, feature_threshold=5)
# write pymol file
ref.write(self.output()["pymol"].path)
# write Results file
temp = tempfile.mkdtemp()
PDBResult(self.pdb).download(temp)
result = Results(protein=Protein.from_file(
os.path.join(temp, "{}.pdb".format(self.pdb))),
super_grids=ref.dic)
out_settings = HotspotWriter.Settings()
out_settings.charged = False
with HotspotWriter(os.path.dirname(self.output()["grids"].path),
grid_extension=".grd",
zip_results=True,
settings=out_settings) as w:
w.write(result)
# write aligned molecules
with MoleculeWriter(self.output()['aligned_mols'].path) as w:
for l in ref.aligned_ligands:
w.write(l)
# points
points = ref._comparision_dict()
with open(self.output()['points'].path, 'wb') as w:
pickle.dump(points, w)
def from_protein(self,
protein,
charged_probes=False,
probe_size=7,
buriedness_method='ghecom',
cavities=None,
nprocesses=1,
settings=None,
buriedness_grid=None,
clear_tmp=False):
"""
generates a result from a protein
:param protein: a :class:`ccdc.protein.Protein` instance
:param bool charged_probes: If True include positive and negative probes
:param int probe_size: Size of probe in number of heavy atoms (3-8 atoms)
:param str buriedness_method: Either 'ghecom' or 'ligsite'
:param cavities: Coordinate or `ccdc.cavity.Cavity` or `ccdc.molecule.Molecule` or list specifying the cavity or cavities on which the calculation should be run
:param int nprocesses: number of CPU's used
:param `hotspots.calculation.Runner.Settings` settings: holds the sampler settings
:param `ccdc.utilities.Grid` buriedness_grid: pre-calculated buriedness grid
:return: a :class:`hotspots.result.Results` instance
>>> from ccdc.protein import Protein
>>> from hotspots.calculation import Runner
>>> protein = Protein.from_file(<path_to_protein>)
>>> runner = Runner()
>>> settings = Runner.Settings()
>>> settings.nrotations = 1000 # fewer rotations increase speed at the expense of accuracy
>>> runner.from_protein(protein, nprocesses=3, settings=settings)
Result()
"""
start = time.time()
self.super_grids = {}
self.buriedness = buriedness_grid
self.protein = protein
self.charged_probes = charged_probes
self.probe_size = probe_size
self.buriedness_method = buriedness_method
self.cavities = cavities
self.clear_tmp = clear_tmp
print(self.cavities)
self.nprocesses = nprocesses
if settings is None:
self.sampler_settings = self.Settings()
else:
self.sampler_settings = settings
self._calc_hotspots() # return probes = False by default
self.super_grids = {p: g[0] for p, g in self.out_grids.items()}
print("Runtime = {}seconds".format(time.time() - start))
return Results(super_grids=self.super_grids,
protein=self.protein,
buriedness=self.buriedness)
def _generate_result(self, path):
with PushDir(path):
files = set(listdir(path))
# fetch protein - this should always be protein.pdb
prot_name = [f for f in files if f.split(".")[1] == self.supported_protein_extensions][0]
prot = Protein.from_file(prot_name)
files.remove(prot_name)
# there should only be one grid extension in the directory, if there are more
# then you can manually read in your results
grid_extension = {f.split(".")[1] for f in files}.intersection(self.supported_grid_extensions)
if len(grid_extension) > 1:
raise IndexError("Too many grid types, create `hotspots.result.Results` manually")
elif len(grid_extension) < 1:
raise IndexError("No supported grid types found")
elif list(grid_extension)[0] == "dat":
raise NotImplementedError("Will put this in if requested")
else:
grid_extension = list(grid_extension)[0]
# read hotspot grids
stripped_files = {f.split(".")[0] for f in files}
hotspot_grids = stripped_files.intersection(self.supported_interactions)
super_grids = {p: Grid.from_file(f"{p}.{grid_extension}") for p in hotspot_grids}
# read superstar grids
if len([f.startswith("superstar") for f in files]) > 0 and self.read_superstar:
superstar_grids = {p: Grid.from_file(f"superstar_{p}.{grid_extension}") for p in hotspot_grids}
else:
superstar_grids = None
# read weighted_superstar grids
if len([f.startswith("weighted") for f in files]) > 0 and self.read_weighted:
weighted_grids = {p: Grid.from_file(f"weighted_{p}.{grid_extension}") for p in hotspot_grids}
else:
weighted_grids = None
# fetch buriedness grid
try:
buriedness_name = [f for f in files if f.startswith("buriedness")][0]
except IndexError:
buriedness_name = None
if buriedness_name and self.read_buriedness:
buriedness = Grid.from_file(buriedness_name)
else:
buriedness = None
return Results(super_grids=super_grids,
protein=prot,
buriedness=buriedness,
superstar=superstar_grids,
weighted_superstar=weighted_grids,
identifier=basename(path))
def read(self, identifier=None):
"""
creates a single or list of :class:`hotspots.result.Result` instance(s)
:param str identifier: for directories containing multiple Fragment Hotspot Map results,
identifier is the subdirectory for which a :class:`hotspots.result.Result` is requried
:return: `hotspots.result.Result` a Fragment Hotspot Map result
>>> from hotspots.hs_io import HotspotReader
>>> path = "<path_to_results_directory>"
>>> result = HotspotReader(path).read()
"""
if len(self.hs_dir) == 0:
self.grid_dic, self.buriedness = self._get_grids()
shutil.rmtree(self._base)
return Results(protein=self.protein,
super_grids=self.grid_dic,
buriedness=self.buriedness)
else:
hrs = []
if identifier:
self.grid_dic, self.buriedness = self._get_grids(
sub_dir=str(identifier))
return Results(protein=self.protein,
super_grids=self.grid_dic,
buriedness=self.buriedness)
else:
for dir in self.hs_dir:
self.grid_dic, self.buriedness = self._get_grids(
sub_dir=dir)
hrs.append(
Results(protein=self.protein,
super_grids=self.grid_dic,
buriedness=self.buriedness))
shutil.rmtree(self._base)
return hrs
def from_superstar(self,
protein,
superstar_grids,
buriedness,
charged_probes=False,
probe_size=7,
settings=None,
clear_tmp=False):
"""
calculate hotspot maps from precalculated superstar maps. This enables more effective parallelisation and reuse
of object such as the Buriedness grids
:param protein: a :class:`ccdc.protein.Protein` instance
:param superstar_grids: a :class:`hotspots.atomic_hotspot_calculation._AtomicHotspotResult` instance
:param buriedness: a :class:`hotspots.grid_extension.Grid` instance
:param bool charged_probes: If True, include positive and negative probes
:param int probe_size: Size of probe in number of heavy atoms (3-8 atoms)
:param settings: `hotspots.calculation.Runner.Settings` settings: holds the sampler settings
:param bool clear_tmp: If True, clear the temporary directory
:return:
"""
start = time.time()
self.super_grids = {}
self.superstar_grids = superstar_grids
self.probe_types = [p.identifier for p in self.superstar_grids]
self.buriedness = buriedness
self.protein = protein
self.charged_probes = charged_probes
self.probe_size = probe_size
self.clear_tmp = clear_tmp
if settings is None:
self.sampler_settings = self.Settings()
else:
self.sampler_settings = settings
self.weighted_grids = self._get_weighted_maps()
print("Start sampling")
grid_dict = {w.identifier: w.grid for w in self.weighted_grids}
for probe in self.probe_types:
self._get_out_maps(probe, grid_dict)
self.super_grids = {p: g[0] for p, g in self.out_grids.items()}
print("Sampling complete\n")
print("Runtime = {}seconds".format(time.time() - start))
return Results(super_grids=self.super_grids,
protein=self.protein,
buriedness=self.buriedness)
def generate_fake(self, buriedness=False, weighted=False, superstar=True):
"""
create a small set of grids for testing
:param buriedness:
:param weighted:
:param superstar:
:return:
"""
def populate_grid(template, num_spheres, radius=1, value=8, scaling='linear'):
h = template.copy_and_clear()
for i in range(1, num_spheres):
x, y, z = [np.random.randint(low=2, high=ax - 2, size=1) for ax in h.nsteps]
h.set_sphere(point=h.indices_to_point(x, y, z),
radius=radius,
value=value,
scaling=scaling)
return h
protein = Protein.from_file("testdata/6y2g_A/binding_site.pdb")
mol = MoleculeReader("testdata/6y2g_A/A_mol.mol2")[0]
g = Grid.initalise_grid([a.coordinates for a in mol.atoms])
if buriedness:
buriedness_grid = Grid.from_molecule(mol)
else:
buriedness_grid = None
interactions = ["apolar", "donor", "acceptor"]
super_grids = {p: populate_grid(template=g, num_spheres=3) for p in interactions}
if superstar:
superstar_grids = {p: populate_grid(template=g, num_spheres=3) for p in interactions}
else:
superstar_grids = None
if weighted:
weighted_superstar_grids = {p: populate_grid(template=g, num_spheres=3) for p in interactions}
else:
weighted_superstar_grids = None
return Results(super_grids=super_grids,
protein=protein,
buriedness=buriedness_grid,
superstar=superstar_grids,
weighted_superstar=weighted_superstar_grids)
def test_write_real_single(self):
base = "testdata/1hcl"
interactions = ["donor", "acceptor", "apolar"]
super_grids = {p: Grid.from_file(os.path.join(base, f"{p}.grd")) for p in interactions}
superstar_grids = {p: Grid.from_file(os.path.join(base, f"superstar_{p}.grd")) for p in interactions}
buriedness = Grid.from_file(os.path.join(base, "buriedness.grd"))
prot = Protein.from_file(os.path.join(base, "protein.pdb"))
hr = Results(super_grids=super_grids,
protein=prot,
buriedness=buriedness,
superstar=superstar_grids)
settings = HotspotWriter.Settings()
settings.output_superstar = True
with HotspotWriter("testdata/hs_io/minimal_all_grids_real", settings=settings) as w:
w.write(hr)
def test_write_pymol_isoslider(self):
# read in manually
path = "testdata/hs_io/minimal_all_grids/out.zip"
base = tempfile.mkdtemp()
with zipfile.ZipFile(path) as hs_zip:
hs_zip.extractall(base)
base = os.path.join(base, "hotspot")
interactions = ["donor", "acceptor", "apolar"]
super_grids = {p: Grid.from_file(os.path.join(base, f"{p}.grd")) for p in interactions}
superstar_grids = {p: Grid.from_file(os.path.join(base, f"superstar_{p}.grd")) for p in interactions}
prot = Protein.from_file(os.path.join(base, "protein.pdb"))
hr = Results(super_grids=super_grids,
protein=prot,
superstar=superstar_grids)
hr.identifier = "hotspot"
settings = HotspotWriter.Settings()
settings.output_superstar = True
writer = HotspotWriter("testdata/hs_io/minimal_all_grids", settings=settings) # we won't actually write
writer.pymol_out.commands += writer._write_pymol_isosurfaces(hr.super_grids,
"hotspot",
"hotspot",
"fhm")
writer.pymol_out.commands += writer._write_pymol_isosurfaces(hr.superstar,
"hotspot",
"hotspot",
"superstar")
writer._write_pymol_isoslider(hr)
writer.pymol_out.write("testdata/hs_io/minimal_all_grids/test_write_pymol_isoslider.py")
def masked_hotspot(base, pdb, hotspot_path):
assert os.path.exists(hotspot_path)
with HotspotReader(os.path.join(hotspot_path, "out.zip")) as r:
hr = [h for h in r.read() if h.identifier == "hotspot"][0]
b = (hr.buriedness > 3) * hr.buriedness
crystal_lig = MoleculeReader(os.path.join(base, pdb, f"{pdb}_ref.mol2"))[0]
g = hr.buriedness.copy_and_clear()
for atm in crystal_lig.heavy_atoms:
g.set_sphere(point=atm.coordinates,
radius=6,
value=1,
mode="replace",
scaling="None")
mol_buried = (g & b) * b
common_mol_buried = hr.super_grids["apolar"].common_boundaries(mol_buried)
apolar = (common_mol_buried
& hr.super_grids["apolar"]) * hr.super_grids["apolar"]
donor = (common_mol_buried
& hr.super_grids["donor"]) * hr.super_grids["donor"]
acceptor = (common_mol_buried
& hr.super_grids["acceptor"]) * hr.super_grids["acceptor"]
return Results(super_grids={
"apolar": apolar,
"donor": donor,
"acceptor": acceptor
},
protein=hr.protein,
buriedness=common_mol_buried)
def calc(args):
prot_file, hotspot_file = args
prot = Protein.from_file(prot_file)
# pre prepared
runner = Runner()
settings = Runner.Settings()
settings.apolar_translation_threshold = 8
settings.polar_translation_threshold = 10
# pdb = os.path.basename(prot_file)[0][:4]
#
# mol_path = os.path.join(os.path.dirname(prot_file))
hr = runner.from_protein(prot,
nprocesses=3,
settings=settings,
probe_size=3)
for p, g in hr.super_grids.items():
hr.super_grids[p] = g.dilate_by_atom()
try:
e = Extractor(hr)
bv = e.extract_volume(volume=250)
except:
bv = Results(
protein=hr.protein.copy(),
super_grids={p: g.copy()
for p, g in hr.super_grids.items()})
hr.identifier = "hotspot"
bv.identifier = "bcv"
with HotspotWriter(hotspot_file) as w:
w.write([hr, bv])
def generate_pharmacophore(ligands, ref_pdb, out_dir):
lig_pharms = []
for ligand in ligands:
ligand_pharmacophore = LigandPharmacophoreModel()
ligand_pharmacophore.feature_definitions = [
"ring", "acceptor_projected", "donor_projected"
]
ligand_pharmacophore.detect_from_ligand(ligand)
for feat in ligand_pharmacophore.detected_features:
ligand_pharmacophore.add_feature(feat)
lig_pharms.append(ligand_pharmacophore)
# 20 %
cutoff = len(ligands) * 0.2
feats, feat_point_grds = create_consensus(lig_pharms, cutoff=cutoff)
print(feats)
for feat in feats:
if feat.identifier == "ring":
p = feat.spheres[0].centre
feat.spheres = (GeometricDescriptors.Sphere((p[0], p[1], p[2]),
2.0), )
feat.point = feat.spheres[0]
ensemble_pharm = LigandPharmacophoreModel()
ensemble_pharm.detected_features = feats
ensemble_pharm.feature_point_grids = feat_point_grds
ensemble_pharm.ligands = ligands
ensemble_pharm.detected_features = ensemble_pharm.top_features(num=6)
pymol_o = os.path.join(out_dir, "pymol")
if not os.path.exists(pymol_o):
os.mkdir(pymol_o)
ensemble_pharm.pymol_visulisation(pymol_o)
# enable rescoring
tmp = tempfile.mkdtemp()
ftp_download([ref_pdb, tmp])
hr = Results(super_grids={
"apolar": feat_point_grds["ring"],
"donor": feat_point_grds["donor_projected"],
"acceptor": feat_point_grds["acceptor_projected"]
},
protein=Protein.from_file(
os.path.join(tmp, f"{ref_pdb}.pdb")))
hr_out = os.path.join(out_dir, "hr")
if not os.path.exists(hr_out):
os.mkdir(hr_out)
with HotspotWriter(hr_out) as w:
w.write(hr)
p_out = os.path.join(out_dir, "ligand_pharmacophores")
if not os.path.exists(p_out):
os.mkdir(p_out)
for n in [6, 5, 4, 3]:
lp = LigandPharmacophoreModel()
lp.detected_features = feats
lp.detected_features = lp.top_features(num=n)
for feat in lp.detected_features:
lp.add_feature(feat)
lp.intra_only = True
lp.write(os.path.join(p_out, f"{n}.cm"))
print target
for pdb in pdbs:
chain = chains[pdb]
ligand_id = ligands[pdb]
out_dir = os.path.join(base, target, pdb, "reference")
if not os.path.exists(out_dir):
os.mkdir(out_dir)
try:
p = PharmacophoreModel._from_siena(pdb,
ligand_id,
mode,
target,
out_dir=out_dir)
p.write(os.path.join(out_dir, "reference_pharmacophore.py"))
prot = hs_io.HotspotReader(
os.path.join(base, target, pdb, "out.zip")).read().protein
hs = Results(protein=prot, super_grids=p.dic)
with hs_io.HotspotWriter(out_dir) as wf:
wf.write(hs)
with io.MoleculeWriter(os.path.join(out_dir, "aligned.mol2")) as w:
for l in p.representatives:
w.write(l)
except RuntimeError:
print "skipped {}".format(target)
def make_selectivity_maps(self):
"""
Creates the selectivity maps for the polar and apolar probes.
:return:
"""
diff_maps = self.make_difference_maps()
probes_list = ['donor', 'acceptor', 'apolar', 'positive', 'negative']
polar_probes = ['donor', 'acceptor', 'positive', 'negative']
apolar_probes = ['apolar']
for probe in probes_list:
try:
dmap = diff_maps[probe]
if probe in polar_probes:
# Find the percentile threshold, if specified
perc = np.percentile(
dmap[dmap > 0],
self.settings.polar_percentile_threshold)
# Find clusters in the target and off-target maps
clust_map_on = _GridEnsemble.HDBSCAN_cluster(
dmap * (dmap > perc),
min_cluster_size=self.settings.min_points_cluster_polar
)
clust_map_off = _GridEnsemble.HDBSCAN_cluster(
dmap * (dmap < -perc),
min_cluster_size=self.settings.min_points_cluster_polar
)
elif probe in apolar_probes:
# Find the percentile threshold, if specified
perc = np.percentile(
dmap[dmap > 0],
self.settings.apolar_percentile_threshold)
# Find clusters in the target and off-target maps
clust_map_on = _GridEnsemble.HDBSCAN_cluster(
dmap * (dmap > perc),
min_cluster_size=self.settings.
min_points_cluster_apolar,
allow_single_cluster=True)
clust_map_off = _GridEnsemble.HDBSCAN_cluster(
dmap * (dmap < -perc),
min_cluster_size=self.settings.
min_points_cluster_apolar,
allow_single_cluster=True)
else:
print("Probe type {} not recognised as polar or apolar".
format(probe))
continue
#Get the center of mass coordinates for the target and off-target
coords = self.get_clusters_center_mass(dmap, clust_map_on)
minus_coords = self.get_clusters_center_mass(
dmap, clust_map_off)
for k in coords.keys():
for i in minus_coords.keys():
dist = self.get_distance(coords[k],
minus_coords[i]) * 0.5
# print("Plus clust: {}, minus_clust: {}, distance: {}".format(k, i, dist))
if dist < self.settings.cluster_distance_cutoff:
self.remove_cluster(clust_map_on, k)
self.remove_cluster(clust_map_off, i)
# Remove any clusters that don't make the medmian cutoff
for c in set(clust_map_on[clust_map_on > 0]):
med = np.median(dmap[clust_map_on == c])
if med < self.settings.minimal_cluster_score:
self.remove_cluster(clust_map_on, c)
for c in set(clust_map_off[clust_map_off > 0]):
min_med = np.median(dmap[clust_map_off == c])
if min_med > -self.settings.minimal_cluster_score:
self.remove_cluster(clust_map_off, c)
ge = _GridEnsemble(dimensions=self.common_grid_dimensions,
shape=self.common_grid_nsteps)
self.selectivity_maps[probe] = ge.as_grid(
(clust_map_on > 0) * dmap)
except KeyError:
continue
self.selectivity_result = Results(super_grids=self.selectivity_maps,
protein=self.target.protein)
def make_ensemble_maps(self, save_grid_ensembles=True):
"""
Creates summary maps for the ensemble based on the settings provided.
:return:
"""
probes_list = ['donor', 'acceptor', 'apolar']
polar_probes = ['donor', 'acceptor']
apolar_probes = ['apolar']
for probe in probes_list:
try:
# Don't need to create the ensemble array each time (eg if pickled GridEnsembles have been supplied)
if probe in self.grid_ensembles.keys():
ge = self.grid_ensembles[probe]
else:
#probe_grids = [hs.super_grids[probe].max_value_of_neighbours() for hs in self.hotspot_results]
probe_grids = [
hs.super_grids[probe] for hs in self.hotspot_results
]
ge = _GridEnsemble()
ge.make_ensemble_array(probe_grids)
if save_grid_ensembles:
self.grid_ensembles[probe] = ge
if probe in polar_probes:
if self.settings.combine_mode == 'median':
ens_grid = ge.as_grid(
ge.get_median_frequency_map(
threshold=self.settings.
polar_frequency_threshold))
# The mean and max modes don't currently take into account the frequency
elif self.settings.combine_mode in ['mean', 'max']:
ens_grid = ge.make_summary_grid(
mode=self.settings.combine_mode)
else:
print(
'Unrecognised mode for combining grids in {} {}: {}'
.format(self.ensemble_id, probe,
self.settings.combine_mode))
continue
elif probe in apolar_probes:
ens_grid = ge.make_summary_grid(
mode=self.settings.combine_mode)
else:
print(
"Probe type {} in ensemble {} not recognised as polar or apolar"
.format(probe, self.ensemble_id))
continue
print(probe, ens_grid.nsteps)
self.ensemble_maps[probe] = ens_grid
# In case of no charged probes
except KeyError:
continue
try:
self.ensemble_hotspot_result = Results(
super_grids=self.ensemble_maps,
protein=self.hotspot_results[0].protein,
buriedness=None,
pharmacophore=False)
except TypeError:
self.ensemble_hotspot_result = Results(
super_grids=self.ensemble_maps,
protein=None,
buriedness=None,
pharmacophore=False)
def _get_superstar(self, cav_id=None):
"""
calculate SuperStar for each cavity
if the buriedness method is ligsite, write out the grid for later
:param cav_id:
:return:
"""
# input
prot = Protein.from_file(self.apo_prep)
if cav_id is 'global':
cavity_origin = None
else:
with open(self.cavities[cav_id], 'rb') as handle:
cavity_origin = [pickle.load(handle)]
# tasks
start = time.time()
a = _AtomicHotspot()
a.settings.atomic_probes = {"apolar": "AROMATIC CH CARBON",
"donor": "UNCHARGED NH NITROGEN",
"acceptor": "CARBONYL OXYGEN"}
self.superstar_grids = a.calculate(prot, nthreads=None, cavity_origins=cavity_origin)
sr = Results(protein=prot,
super_grids={result.identifier: result.grid for result in self.superstar_grids})
finish = time.time()
# outputs
if not os.path.exists(self.superstar[cav_id]):
os.mkdir(self.superstar[cav_id])
if cav_id is not 'global':
out = os.path.join(a.settings.temp_dir, str(0))
else:
out = a.settings.temp_dir
for interaction in ["apolar", "acceptor", "donor"]:
shutil.copyfile(os.path.join(out, "{}.cavity.mol2".format(interaction)),
os.path.join(self.superstar[cav_id], "{}.cavity.mol2".format(interaction)))
shutil.make_archive(os.path.join(self.superstar[cav_id], "superstar"), 'zip', out)
with HotspotWriter(path=self.superstar[cav_id], zip_results=True) as w:
w.write(sr)
with open(self.superstar_time[cav_id], 'w') as t:
t.write(str(finish - start))
shutil.rmtree(a.settings.temp_dir)
if self.buriedness_method == 'ligsite':
# only write if it doesn't exist i.e. the first cavity run
if not os.path.exists(self.buriedness):
for ss in self.superstar_grids:
if ss.identifier == "apolar":
ss.buriedness.write(self.buriedness)
def from_pdb(self, pdb_code, charged_probes=False, probe_size=7, buriedness_method='ghecom', nprocesses=3,
cavities=False, settings=None, clear_tmp=False):
"""
generates a result from a pdb code
:param str pdb_code: PDB code
:param bool charged_probes: If True include positive and negative probes
:param int probe_size: Size of probe in number of heavy atoms (3-8 atoms)
:param str buriedness_method: Either 'ghecom' or 'ligsite'
:param int nprocesses: number of CPU's used
:param `hotspots.calculation.Runner.Settings` settings: holds the calculation settings
:return: a :class:`hotspots.result.Result` instance
>>> from hotspots.calculation import Runner
>>> runner = Runner()
>>> runner.from_pdb("1hcl")
Result()
"""
protoss = False
tmp = tempfile.mkdtemp()
# if protoss is True:
# protoss = Protoss(out_dir=tmp)
# self.protein = protoss.add_hydrogens(pdb_code).protein
#
# else:
PDBResult(identifier=pdb_code).download(out_dir=tmp)
fname = join(tmp, "{}.pdb".format(pdb_code))
self.protein = Protein.from_file(fname)
self._prepare_protein(protoss)
self.charged_probes = charged_probes
self.probe_size = probe_size
self.buriedness_method = buriedness_method
self.clear_tmp = clear_tmp
self.cavities = None
if cavities is True:
self.cavities = Cavity.from_pdb_file(fname)
self.nprocesses = nprocesses
if settings is None:
self.sampler_settings = self.Settings()
else:
self.sampler_settings = settings
if self.sampler_settings.return_probes is True:
print('here')
self._calc_hotspots(return_probes=True)
else:
self._calc_hotspots()
self.super_grids = {p: g[0] for p, g in self.out_grids.items()}
if clear_tmp == True:
shutil.rmtree(tmp)
return Results(super_grids=self.super_grids,
protein=self.protein,
buriedness=self.buriedness,
superstar={x.identifier: x.grid for x in self.superstar_grids},
weighted_superstar={x.identifier: x.grid for x in self.weighted_grids})