def cavities(self, obj):
"""
optional settings, cavities supplied to the calculation
:param list or Coordinate or `ccdc.molecule.Molecule` or `ccdc.cavity.Cavity`: cavity information provided
:return:
"""
if obj is not None:
if isinstance(obj, list) or isinstance(obj, tuple):
if isinstance(obj, Coordinates):
try:
print(obj.x)
self._cavities = [obj]
except AttributeError:
self._cavities = obj
self._cavities = [obj]
elif isinstance(obj[0], Molecule):
self._cavities = [m.centre_of_geometry() for m in obj]
elif isinstance(obj[0], Cavity):
self._cavities = [Helper.cavity_centroid(c) for c in obj]
else:
print("WARNING! Failed to detected cavity, Atomic Hotspot detection to run on whole protein")
self._cavities = None
elif isinstance(obj, Molecule):
self._cavities = [obj.centre_of_geometry()]
elif isinstance(obj, Cavity):
self._cavities = [Helper.cavity_centroid(obj)]
else:
print("WARNING! Failed to detected cavity, Atomic Hotspot detection to run on whole protein")
self._cavities = None
else:
self._cavities = None
def _get_cavities(self, min_vol):
"""
detect cavities using Cavity API, generate new directory for each cavity
:return: None
"""
# inputs
cavs = [c for c in Cavity.from_pdb_file(self.apo_prep) if c.volume > min_vol]
# task
for i in range(len(cavs)):
create_directory(path=os.path.join(self.working_dir, 'cavity_{}'.format(i)))
cav_dic = {os.path.join(self.working_dir, 'cavity_{}'.format(i)): Helper.cavity_centroid(c)
for i, c in enumerate(cavs)}
cav_volume_dic = {os.path.join(self.working_dir, 'cavity_{}'.format(i), "cavity.volume"): c.volume
for i, c in enumerate(cavs)}
cav_bb = {os.path.join(self.working_dir, 'cavity_{}'.format(i), "bounding_box.pkl"): c.bounding_box
for i, c in enumerate(cavs)}
# output
for path, origin in cav_dic.items():
with open(os.path.join(path, "cavity_origin.pkl"), 'wb') as handle:
pickle.dump(origin, handle)
for path, vol in cav_volume_dic.items():
with open(os.path.join(path), 'w') as f:
f.write(str(vol))
for path, bb in cav_bb.items():
with open(os.path.join(path), 'wb') as h:
pickle.dump(bb, h)
# update attr
self.runs += ["cavity_{}".format(i) for i in range(len(cavs))]
self.cavities = {
"cavity_{}".format(p): os.path.join(self.working_dir, 'cavity_{}'.format(p), "cavity_origin.pkl")
for p in range(len(cav_dic))}
self.cavities_volumes = {
"cavity_{}".format(p): os.path.join(self.working_dir, 'cavity_{}'.format(p), "cavity.volume")
for p in range(len(cav_dic))}
self.cavity_score = {
"cavity_{}".format(p): os.path.join(self.working_dir, "cavity_{}".format(p), "cavity.score")
for p in range(len(cav_dic))}
self.bounding_box = {
"cavity_{}".format(p): os.path.join(self.working_dir, 'cavity_{}'.format(p), "bounding_box.pkl")
for p in range(len(cav_dic))}
self.superstar = {p: os.path.join(self.working_dir, p, "superstar") for p in self.runs}
self.superstar_time = {k: os.path.join(v, "time.time") for k, v in self.superstar.items()}
self.hotspot = {p: os.path.join(self.working_dir, p, "hotspot") for p in self.runs}
self.hotspot_time = {k: os.path.join(v, "time.time") for k, v in self.hotspot.items()}
self.bcv = {i: {pid: {k: os.path.join(self.working_dir, i, "bcv", "volume_{}".format(k))
for k in self.ligand_id[j]}
for j, pid in enumerate(self.protein_id)}
for i in self.runs}
self.bcv_time = {i: {pid: {k: os.path.join(self.working_dir, i, "bcv", "volume_{}".format(k), "time.time")
for k in self.ligand_id[j]}
for j, pid in enumerate(self.protein_id)}
for i in self.runs}
self.bcv_threshold = {i: {pid: {k: os.path.join(self.working_dir, i, "bcv", "volume_{}".format(k),
"threshold.dat")
for k in self.ligand_id[j]}
for j, pid in enumerate(self.protein_id)}
for i in self.runs}
self.bcv_lig_overlaps = {
i: {pid: {k: os.path.join(self.working_dir, i, "bcv", "lig_overlap_{}.percentage".format(k))
for k in self.ligand_id[j]}
for j, pid in enumerate(self.protein_id)}
for i in self.runs}
self.bcv_hot_overlaps = {
i: {pid: {k: os.path.join(self.working_dir, i, "bcv", "hot_overlap_{}.percentage".format(k))
for k in self.ligand_id[j]}
for j, pid in enumerate(self.protein_id)}
for i in self.runs}
self.hot_lig_overlaps = {
i: {pid: {k: os.path.join(self.working_dir, i, "hotspot", "lig_overlap_{}.percentage".format(k))
for k in self.ligand_id[j]}
for j, pid in enumerate(self.protein_id)}
for i in self.runs}
self.hot_hot_overlaps = {
i: {pid: {k: os.path.join(self.working_dir, i, "hotspot", "hot_overlap_{}.percentage".format(k))
for k in self.ligand_id[j]}
for j, pid in enumerate(self.protein_id)}
for i in self.runs}
self.atomic_overlaps = {i: {pid: {k: os.path.join(self.working_dir, i, "bcv", "atomic_overlap_{}.dat".format(k))
for k in self.ligand_id[j]}
for j, pid in enumerate(self.protein_id)}
for i in self.runs}
self.matched = {i: {pid: {k: os.path.join(self.working_dir, i, "bcv", "atom_match_{}.percentage".format(k))
for k in self.ligand_id[j]}
for j, pid in enumerate(self.protein_id)}
for i in self.runs}