def hot_calc(inputs):
pdb, het, pdir = inputs
p = Protein.from_file(os.path.join(pdir, f"{pdb}.pdb"))
mol = MoleculeReader(os.path.join(pdir, f"{pdb}_{het}.mol2"))[0]
runner = Runner()
hr = runner.from_protein(p, nprocesses=3, cavities=mol)
for p, g in hr.super_grids.items():
hr.super_grids[p] = g.max_value_of_neighbours()
# with HotspotReader(os.path.join(pdir, "out.zip")) as r:
# hr = [h for h in r.read() if h.identifier == "hotspot"][0]
e = Extractor(hr)
bv = e.extract_volume(volume=250)
# smoothing
for p, g in bv.super_grids.items():
bv.super_grids[p] = g.gaussian(sigma=0.5)
bv.identifier = "bestvol"
hr.identifier = "hotspot"
with HotspotWriter(pdir) as w:
w.write([hr, bv])
def calc_hotspot(path, prot_name, method, nrot=3000):
"""
param: path str, path to prepared protein
"""
protein = prepare_protein(path)
h = Runner()
settings = h.Settings()
settings.nrotations = nrot
settings.apolar_translation_threshold = 15
settings.polar_translation_threshold = 15
settings.sphere_maps = False
result = h.from_protein(protein=protein,
charged_probes=False,
probe_size=7,
buriedness_method=method,
cavities=None,
nprocesses=3,
settings=settings)
#out = make_savedir(prot_name)
out = os.getcwd()
with hs_io.HotspotWriter(out,
visualisation="pymol",
grid_extension=".ccp4",
zip_results=False) as writer:
writer.write(result)
return result
def run_hotspot_calculation(self, method="ghecom", sphere_maps=False):
"""
Runs the hotspots calculation on the specified PDB structure
:return:
"""
h = Runner()
settings = h.Settings()
settings.nrotations = self.number_rotations
settings.apolar_translation_threshold = 15
settings.polar_translation_threshold = 15
settings.sphere_maps = sphere_maps
result = h.from_protein(protein=self.prepare_protein(),
charged_probes=self.charged,
probe_size=7,
buriedness_method=method,
cavities=None,
nprocesses=1,
settings=settings)
#self.out_dir = self.make_savedir()
# Save and zip the SuperStar Grids:
self._save_superstar_grids(h)
# Save and zip the Results
with hs_io.HotspotWriter(self.out_dir, visualisation="pymol", grid_extension=".ccp4", zip_results=True) as writer:
writer.write(result)
def tractability_workflow(protein, tag):
"""
A very simple tractability workflow.
:param str protein: PDB identification code
:param str tag: Tractability tag: either 'druggable' or 'less-druggable'
:return: `pandas.DataFrame`
"""
# 1) calculate Fragment Hotspot Result
runner = Runner()
result = runner.from_pdb(pdb_code=protein,
nprocesses=1,
buriedness_method='ghecom')
# 2) calculate Best Continuous Volume
extractor = Extractor(hr=result)
bcv_result = extractor.extract_volume(volume=500)
# 3) find the median score
grid = Grid.get_single_grid(bcv_result.super_grids, mask=False)
values = grid.grid_values(threshold=5)
median = np.median(values)
# 4) return the data
return pd.DataFrame({
'scores': values,
'pdb': [protein] * len(values),
'median': [median] * len(values),
'tractability': [tag] * len(values),
})
def run(self):
prot = Protein.from_file(self.input().path)
mol = io.MoleculeReader('ligands/{}.sdf'.format(self.pdb))[0]
h = Runner()
s = h.Settings()
s.apolar_translation_threshold = 15
s.polar_translation_threshold = 15
s.polar_contributions = False
s.sphere_maps = True
s.nrotations = 3000
hr = h.from_protein(prot,
buriedness_method='ghecom',
nprocesses=1,
settings=s,
cavities=mol)
out_settings = HotspotWriter.Settings()
out_settings.charged = False
w = HotspotWriter(os.path.dirname(self.output().path),
grid_extension=".grd",
zip_results=True,
settings=out_settings)
w.write(hr)
def run(self):
h = Runner()
# hotspot calculation settings
s = h.Settings()
s.apolar_translation_threshold = 15
s.polar_translation_threshold = 15
s.polar_contributions = False
s.nrotations = 3000
s.sphere_maps = True
hr = h.from_pdb(pdb_code=self.pdb,
charged_probes=False,
buriedness_method='ghecom',
nprocesses=3,
settings=s,
cavities=None)
out_settings = HotspotWriter.Settings()
out_settings.charged = False
with HotspotWriter(os.path.dirname(self.output().path),
grid_extension=".grd",
zip_results=True,
settings=out_settings) as w:
w.write(hr)
def run(self, cavity=True):
"""from fragment hotspot calc from protein"""
h = Runner()
settings = Runner.Settings(sphere_maps=False)
if self.args.prepare is True:
self.prepare_protein()
else:
self.prot = Protein.from_file(self.args.prot_fname)
if cavity is True:
cavs = Cavity.from_pdb_file(self.args.prot_fname)
print(cavs)
else:
cavs = None
result = h.from_protein(protein=self.prot,
charged_probes=False,
buriedness_method=self.args.buriedness_method,
cavities=cavs,
nprocesses=5,
settings=settings)
with HotspotWriter(path=self.in_dir,
zip_results=self.args.zipped) as writer:
writer.write(result)
def test_generate_real(self):
runner = Runner()
hr = runner.from_pdb(pdb_code="2vta", buriedness_method='ghecom')
settings = HotspotWriter.Settings()
settings.output_superstar = True
parent = "testdata/2vta"
with HotspotWriter(parent) as w:
w.write(hr)
def run_hotspot_calculation(self, method="ghecom"):
"""
Runs the hotspots calculation on the specified PDB structure
:return:
"""
h = Runner()
settings = h.Settings()
settings.nrotations = self.number_rotations
settings.apolar_translation_threshold = 15
settings.polar_translation_threshold = 15
settings.sphere_maps = self.spheres
# Check if SuperStar and Ghecom have already been run.
super_archive_path = Path(self.out_dir.parent, "superstar_grids.zip")
if super_archive_path.exists():
super_tmp_path = Path(self.out_dir.parent, super_archive_path.stem)
if not super_tmp_path.exists(): super_tmp_path.mkdir()
unpack_archive(super_archive_path, super_tmp_path, 'zip')
b_grid = Grid.from_file(
str(Path(super_tmp_path, 'buriedness.ccp4').resolve()))
result = h.from_superstar(
protein=self.prepare_protein(),
superstar_grids=self.create_atomic_hotspots(super_tmp_path),
buriedness=b_grid,
charged_probes=self.charged,
settings=settings,
clear_tmp=True)
rmtree(super_tmp_path)
else:
result = h.from_protein(protein=self.prepare_protein(),
charged_probes=self.charged,
probe_size=7,
buriedness_method=method,
cavities=None,
nprocesses=1,
settings=settings)
# Save and zip the SuperStar Grids:
self._save_superstar_grids(h)
# Save and zip the Results
with hs_io.HotspotWriter(str(self.out_dir.resolve()),
visualisation="pymol",
grid_extension=".ccp4",
zip_results=True) as writer:
writer.write(result)
print(f"out_file: {str(Path(self.out_dir, 'out.zip').resolve())}")
return Path(self.out_dir, 'out.zip')
def run(self):
"""from fragment hotspot calc from protein"""
h = Runner()
settings = Runner.Settings(sphere_maps=False)
result = h.from_pdb(pdb_code=self.args.pdb,
charged_probes=True,
buriedness_method=self.args.buriedness_method,
nprocesses=5,
settings=settings)
with HotspotWriter(path=self.args.out_dir,
zip_results=self.args.zipped) as writer:
writer.write(result)
def run_hotspot_calculation(self,
nrot=100000,
method="ghecom",
charged=True,
sphere_maps=False,
save_ligand=True):
"""
Runs the hotspots calculation on the specified PDB structure
:return:
"""
if not self.out_dir:
self.out_dir = self.make_savedir()
if not self.protein_path:
self.protein_path = self.find_protein()
protein = self.prepare_protein()
else:
protein = Protein.from_file(self.protein_path)
if save_ligand:
self.extract_ligands()
# log the run parameters
self.log_runner(nrot)
h = Runner()
settings = h.Settings()
settings.nrotations = nrot
settings.apolar_translation_threshold = 15
settings.polar_translation_threshold = 15
settings.sphere_maps = sphere_maps
result = h.from_protein(protein=protein,
charged_probes=charged,
probe_size=7,
buriedness_method=method,
cavities=None,
nprocesses=5,
settings=settings)
#self.out_dir = self.make_savedir()
# Save and zip the SuperStar Grids:
self._save_superstar_grids(h)
# Save and zip the Results
with hs_io.HotspotWriter(self.out_dir,
visualisation="pymol",
grid_extension=".ccp4",
zip_results=True) as writer:
writer.write(result)
def get_hotspot(protein):
"""
generate hotspot result and time
:param protein:
:return:
"""
start = time.time()
r = Runner()
settings = Runner.Settings()
settings.rotations = 3000
settings.sphere_maps = False
result = r.from_protein(protein=protein,
charged_probes=False,
nprocesses=3,
cavities=None,
settings=settings
)
return result, time.time() - start
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 _get_hotspot(self, cav_id):
"""
calculate hotspot map from pre-calculated superstar and buriedness grids
:param cav_id:
:return:
"""
# inputs
prot = Protein.from_file(self.apo_prep)
sr = HotspotReader(path=os.path.join(self.superstar[cav_id], "out.zip")).read()
superstar = [_AtomicHotspotResult(identifier=ident, grid=grid, buriedness=None)
for ident, grid in sr.super_grids.items()]
buriedness = Grid.from_file(self.buriedness)
# tasks
start = time.time()
h = Runner()
s = h.Settings()
s.apolar_translation_threshold = 14
s.polar_translation_threshold = 14
s.polar_contributions = False
s.sphere_maps = False
s.nrotations = 3000
hr = h.from_superstar(prot, superstar, buriedness, settings=s, clear_tmp=True)
finish = time.time()
# output
if not os.path.exists(self.hotspot[cav_id]):
os.mkdir(self.hotspot[cav_id])
with open(self.hotspot_time[cav_id], 'w') as t:
t.write(str(finish - start))
with HotspotWriter(self.hotspot[cav_id], zip_results=True) as writer:
writer.write(hr)
def run(self):
prot = Protein.from_file(self.input().path)
cavs = Cavity.from_pdb_file(self.input().path)
h = Runner()
s = h.Settings()
s.apolar_translation_threshold = 15
s.polar_translation_threshold = 15
s.polar_contributions = False
s.nrotations = 1000
hr = h.from_protein(prot,
buriedness_method='ghecom',
nprocesses=1,
settings=s,
cavities=cavs)
out_settings = HotspotWriter.Settings()
out_settings.charged = False
w = HotspotWriter(os.path.dirname(self.output().path),
grid_extension=".grd",
zip_results=True,
settings=out_settings)
w.write(hr)
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
s.polar_contributions = False
s.nrotations = 3000
s.sphere_maps = True
hr = h.from_protein(protein=p,
charged_probes=False,
buriedness_method='ghecom',
nprocesses=3,
settings=s,
cavities=None)
