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):
# 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 test_write_fake_multi(self):
a = self.generate_fake(buriedness=True, superstar=True)
b = self.generate_fake(buriedness=True, superstar=True)
settings = HotspotWriter.Settings()
settings.output_superstar = True
with HotspotWriter("testdata/hs_io/minimal_multi_all_grids", settings=settings) as w:
w.write([a, b])
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 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(self):
hr = HotspotReader(self.input().path).read()
bcv = hr.tractability_map(volume=self.volume)
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(bcv)
def _get_bcv(self, cav_id, other_id, lig_id):
"""
generate a BCV for each cavity, and each required volume
:param cav_id:
:return:
"""
# inputs
hr = HotspotReader(path=os.path.join(self.hotspot[cav_id], "out.zip")).read()
with open(self.ligand_volume[other_id][lig_id], 'r') as f:
target_volume = f.read()
# task
start = time.time()
extractor = Extractor(hr)
bcv = extractor.extract_volume(volume=int(float(target_volume)))
finish = time.time()
# output
out = self.bcv[cav_id][other_id][lig_id]
create_directory(os.path.dirname(out))
create_directory(out)
with HotspotWriter(path=out, grid_extension=".grd", zip_results=True) as writer:
writer.write(bcv)
with open(self.bcv_time[cav_id][other_id][lig_id], 'w') as t:
t.write(str(finish - start))
with open(self.bcv_threshold[cav_id][other_id][lig_id], 'w') as s:
s.write(str(bcv.step_threshold))
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)
def main():
"""
main
:return:
"""
base = "/local/pcurran/GOLD"
pdbs = set(pd.read_csv(os.path.join(base, "targets.csv"))['PDB'])
tmp = tempfile.mkdtemp()
pid = []
times = []
for pdb in pdbs:
prot = get_protein(pdb, base, tmp)
hs, time = get_hotspot(prot)
pid.append(pdb)
times.append(time)
with HotspotWriter(os.path.join(base, pdb), zip_results=True) as w:
w.write(hs)
df = pd.DataFrame({'PDB': pid,
'Time': times})
df.to_csv(os.path.join(base, "run_stats.csv"))
timer.report()
def run(self):
base = "/local/pcurran/leads_frag"
pdbs = [
p for p in os.listdir(base) if os.path.isdir(os.path.join(base, p))
]
fails = []
for pdb in tqdm(pdbs):
try:
hotspot_path = os.path.join(os.path.join(base, pdb, "hotspot"))
masked_path = os.path.join(hotspot_path, "masked_hotspot")
print(pdb)
if not os.path.exists(masked_path):
masked = masked_hotspot(base, pdb, hotspot_path)
with HotspotWriter(masked_path) as w:
w.write(masked)
fp_scheme(fpath=masked_path,
percentile=float(self.args.percentile),
low=float(self.args.low),
high=float(self.args.high),
id=self.args.id)
except:
print(f"{pdb} FAILED")
fails.append(pdb)
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 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_write_pymol_isosurfaces(self):
# test out.zip prepared, generate minimal pymol commands to test isosurface gen code
settings = HotspotWriter.Settings()
writer = HotspotWriter("testdata/hs_io/minimal_all_grids", settings=settings) # we won't actually write
# pymol file initialised in the writer init function, therefore the unzip code is already in place
writer.pymol_out.commands += writer._write_pymol_isosurfaces({"apolar": None, "donor": None, "acceptor": None},
"hotspot",
"hotspot",
"fhm")
writer.pymol_out.commands += writer._write_pymol_isosurfaces({"apolar": None, "donor": None, "acceptor": None},
"hotspot",
"hotspot",
"superstar")
writer.pymol_out.write("testdata/hs_io/minimal_all_grids/test_write_pymol_isosurfaces.py")
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 testconstruction(self):
extractor = Extractor(self.result)
# extractor.single_grid.write(os.path.join(self.out, "2vta_single_grid.grd"))
hr = extractor.extract_volume()
with HotspotWriter(self.bin) as w:
w.write(hr)
def run(self):
hs = HotspotReader(self.input().path).read()
settings = Extractor.Settings()
settings.cutoff = 12
settings.mvon = False
extractor = Extractor(hs, settings)
best = extractor.extract_best_volume(volume=100)[0]
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(best)
def shrink_hotspots(self, hotspot_paths, padding=2.0):
"""
Takes in the calculated hotspots on the aligned ensemble. Crops and saves only the area around the reference binding site.
Results are stored in the same parent directory as the fullsized hotspots, in dir called "binding_site_maps"
:param list hotspot_paths: Paths to the hotspot results we would like to shrink.
:param float padding: How many angstroms away from furthest binding site atom to look.
:return: list of the paths for all shrunk hotspots in the ensemble.
"""
# Get the area to truncate around the binding site:
print("Shrinking hotspots for ensemble...")
if not self.reference_binding_site:
self.reference_binding_site = self.get_binding_site(
self.reference_ID)
# Find the maximum and minimum coordinates of the reference binding site
dims = np.array([
a.coordinates for r in self.reference_binding_site.residues
for a in r.atoms
])
min_coords = np.array(
[np.min(dims[:, 0]),
np.min(dims[:, 1]),
np.min(dims[:, 2])])
max_coords = np.array(
[np.max(dims[:, 0]),
np.max(dims[:, 1]),
np.max(dims[:, 2])])
# Add some padding in both directions:
min_coords -= padding
max_coords += padding
h_out_dir_list = []
for p in hotspot_paths:
# Read in hotspot result
h_result = HotspotReader(p).read()
# Shrink the grids for each probe type
for probe, g in h_result.super_grids.items():
h_result.super_grids[probe] = self.shrink_to_binding_site(
g, min_coords, max_coords)
res_path = dirname(p)
# Save shrunk hotspot, assuming the directory it was previously in was named sensibly.
h_out_dir = join(res_path, "binding_site_maps")
h_out_dir_list.append(join(h_out_dir, "out"))
with HotspotWriter(h_out_dir,
visualisation="pymol",
grid_extension=".ccp4",
zip_results=False) as writer:
writer.write(h_result)
return h_out_dir_list
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 shrink_hotspot_maps(hs_result_paths, ligands, padding=4.0):
"""
Given the list of ligands in the ensemble and some hotspot maps, will shrink all the maps in the
:param hs_result_paths: a list of Paths to precalculated hotspot results. Should be all for the same target (or actually, targets that we are looking to compare.
:param ligands: a list of ccdc molecules corersponding to the ensmeble ligands. Needed to define the binding site of interest.
:return: a list of *shrunk* hotspot results
"""
# Find the largest ligand and use it to define the binding site
mws = [l.molecular_weight for l in ligands]
biggest_lig = ligands[mws.index(max(mws))]
# Get the dimensions in space of the largest ligand
dims = np.array([a.coordinates for a in biggest_lig.atoms])
min_coords = np.array(
[np.min(dims[:, 0]),
np.min(dims[:, 1]),
np.min(dims[:, 2])])
max_coords = np.array(
[np.max(dims[:, 0]),
np.max(dims[:, 1]),
np.max(dims[:, 2])])
# Add some padding in both directions:
min_coords -= padding
max_coords += padding
# Now shrink all the hotspot grids to the min and max dimensions
shrunk_hs_results = []
for hpath in hs_result_paths:
hs_res = HotspotReader(str(hpath.resolve())).read()
probes = hs_res.super_grids.keys()
# now to shrink the grids for each probe
for p in probes:
hs_res.super_grids[p] = EnsembleResult.shrink_to_binding_site(
in_grid=hs_res.super_grids[p],
new_origin=min_coords,
new_far_corner=max_coords)
shrunk_hs_results.append(hs_res)
h_out_dir = Path(hpath.parent, 'binding_site_maps')
if not h_out_dir.exists(): h_out_dir.mkdir()
with HotspotWriter(str(h_out_dir.resolve()),
visualisation="pymol",
grid_extension=".ccp4",
zip_results=False) as writer:
writer.write(hs_res)
return shrunk_hs_results
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)
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 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")
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)
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)
# 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)
out_settings = HotspotWriter.Settings()
out_settings.charged = False
out = os.path.join(out_dir, p.identifier)
if not os.path.exists(out):
os.mkdir(out)
with HotspotWriter(out,
grid_extension=".grd",
zip_results=True,
settings=out_settings) as w:
w.write(hr)
# read
# Mean map
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"))
from hotspots import calculation
from hotspots.hs_io import HotspotWriter
r = calculation.Runner()
result = r.from_pdb("3cqw", charged_probes=False, nprocesses=3)
with HotspotWriter("/home/pcurran/New folder/akt1/protoss") as w:
w.write(result)
print set([len(a.neighbours) for a in result.protein.atoms])