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 get_pharmacophore_model(self, identifier="id_01", threshold=5):
"""
Generates a :class:`hotspots.hotspot_pharmacophore.PharmacophoreModel` instance from peaks in the hotspot maps
TODO: investigate using feature recognition to go from grids to features.
:param str identifier: Identifier for displaying multiple models at once
:param float cutoff: The score cutoff used to identify islands in the maps. One peak will be identified per island
:return: a :class:`hotspots.hotspot_pharmacophore.PharmacophoreModel` instance
"""
return PharmacophoreModel.from_hotspot(self,
identifier=identifier,
threshold=threshold)
def run(self):
# generate representatives
ref = PharmacophoreModel.from_pdb(pdb_code=self.pdb,
chain=self.chain,
identifier=self.pdb)
if len(ref.representatives) > 2:
ligands = ref.representatives
else:
ligands = ref.all_ligands
with open(self.output().path, "w") as f:
for l in ligands:
f.write("{},{},{}\n".format(l.structure_id, l.chemical_id,
l.smiles))
def to_grid(target, pdb):
out_dir = "Z:/patel_set/{}/{}".format(target, pdb)
mols = MoleculeReader(
join(out_dir, "reference_pharmacophore", "aligned_mols.mol2"))
p = PharmacophoreModel.from_ligands(ligands=mols, identifier="test")
result = Results(super_grids=p.dic,
protein=Protein.from_file(
join(out_dir, "hs", "{}.pdb".format(pdb))))
out = Helper.get_out_dir(join(out_dir, "reference_pharmacophore", "grids"))
settings = HotspotWriter.Settings()
settings.isosurface_threshold = [2, 5, 10]
with HotspotWriter(path=out, zip_results=True, settings=settings) as w:
w.write(result)
}
for target, pdbs in targets.items():
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)
PDBResult(identifier=pdb).download(out_dir=dirname)
if os.path.exists(reps):
representatives = reps
else:
representatives = None
try:
result = HotspotReader(path=os.path.join(dirname, "out.zip")).read()
pharmacophore = result.get_pharmacophore_model()
pharmacophore.rank_features(max_features=5)
except:
pharmacophore = PharmacophoreModel.from_pdb(
pdb_code=pdb,
chain="H",
out_dir=dirname,
representatives=representatives)
pharmacophore.rank_features(max_features=5)
result = Results(super_grids=pharmacophore.dic,
protein=Protein.from_file(
os.path.join(dirname, pdb + ".pdb")))
pharmacophore.write(os.path.join(dirname, "crossminer.cm"))
pharmacophore.write(os.path.join(dirname, "pharmit.json"))
# write out Results object
settings = HotspotWriter.Settings()
settings.isosurface_threshold = [2, 5, 10]
with HotspotWriter(dirname, settings=settings) as w:
w.write(result)
tmp = tempfile.mkdtemp()
out_dir = "/home/pcurran/patel/CDK2/1aq1_ensemble"
centre_pdb = PDBResult("1aq1")
centre_pdb.download(out_dir)
ensemble_members = [("3QTU", "X44"), ("2VTL", "LZ5"), ("1OIT", "HDT"),
("3R6X", "X84")]
pdbs = [PDBResult(x[0]) for x in ensemble_members]
for i, pdb in enumerate(pdbs):
pdb.download(tmp)
pdb.clustered_ligand = ensemble_members[i][1]
#prots = [Protein.from_file(os.path.join(tmp, member[0] + ".pdb")) for member in ensemble_members]
proteins, ligands = PharmacophoreModel._align_proteins(centre_pdb, "A", pdbs)
# create ensemble
for p in proteins:
print p.identifier
p.remove_all_metals()
for l in p.ligands:
p.remove_ligand(l.identifier)
p.add_hydrogens()
h = Runner()
# hotspot calculation settings
s = h.Settings()
s.apolar_translation_threshold = 15
s.polar_translation_threshold = 15