def _get_crossminer_pharmacophore(self):
"""
convert a PharmacophoreModel into a crossminer pharmacophore
"""
# TODO: UPDATE WITH CHARGED FEATURES
supported_features = {"acceptor_projected": "acceptor",
"donor_projected": "donor",
"ring": "apolar"}
try:
Pharmacophore.read_feature_definitions()
except:
raise ImportError("Crossminer is only available to CSD-Discovery")
feature_definitions = {supported_features[fd.identifier]: fd for fd in Pharmacophore.feature_definitions.values()
if fd.identifier in supported_features.keys()}
model_features = []
for feat in self._features:
if feat.feature_type == "negative" or feat.feature_type == "positive":
print("Charged feature not currently supported in CrossMiner: Its on the TODO list")
else:
sphere = GeometricDescriptors.Sphere(feat.feature_coordinates, self.settings.radius)
if feat.projected_coordinates:
projected = GeometricDescriptors.Sphere(feat.projected_coordinates, self.settings.radius)
p = Pharmacophore.Feature(feature_definitions[feat.feature_type], *[sphere, projected])
else:
p = Pharmacophore.Feature(feature_definitions[feat.feature_type], sphere)
model_features.append(p)
if self.settings.excluded_volume:
if not self.protein:
print("Pharmacophore Model must have protein to calculate excluded volume")
else:
bs = self._get_binding_site_residues()
for residue in bs.residues:
mol = None
mol = Molecule(identifier="temp_residue")
# for a in residue.backbone_atoms:
# ev = Pharmacophore.ExcludedVolume(GeometricDescriptors.Sphere(a.coordinates, 2))
# model_features.append(ev)
for a in residue.backbone_atoms:
mol.add_atom(a)
centre = mol.centre_of_geometry()
ev = Pharmacophore.ExcludedVolume(GeometricDescriptors.Sphere(centre, 2))
model_features.append(ev)
return Pharmacophore.Query(model_features)
def run(self):
if not os.path.exists(self.args.output_directory):
os.makedirs(self.args.output_directory)
Pharmacophore.read_feature_definitions()
self.crystals = list(io.CrystalReader(self.args.overlay_file))
if self.args.threshold <= 0.0:
self.args.threshold = (len(self.crystals)) / 2.0
if self.args.feature_definitions:
self.feature_definitions = [
v for k, v in Pharmacophore.feature_definitions.items()
if k in self.args.feature_definitions
]
else:
self.feature_definitions = [
fd for fd in Pharmacophore.feature_definitions.values()
if fd.identifier != 'exit_vector' and
fd.identifier != 'heavy_atom' and fd.identifier != 'hydrophobe'
]
complete_set_of_features = []
for fd in self.feature_definitions:
detected = [fd.detect_features(c) for c in self.crystals]
all_feats = [f for l in detected for f in l]
if not all_feats:
continue
minx = min(f.spheres[0].centre.x() for f in all_feats)
miny = min(f.spheres[0].centre.y() for f in all_feats)
minz = min(f.spheres[0].centre.z() for f in all_feats)
maxx = max(f.spheres[0].centre.x() for f in all_feats)
maxy = max(f.spheres[0].centre.y() for f in all_feats)
maxz = max(f.spheres[0].centre.z() for f in all_feats)
g = utilities.Grid((minx - 1., miny - 1., minz - 1.),
(maxx + 1, maxy + 1, maxz + 1), 0.2)
spheres = []
for f in all_feats:
if f.spheres[0] in spheres:
g.set_sphere(f.spheres[0].centre, f.spheres[0].radius, 0)
else:
spheres.append(f.spheres[0])
g.set_sphere(f.spheres[0].centre, f.spheres[0].radius, 1)
islands = g.islands(self.args.threshold)
print('Feature: %s, max value %.2f, n_features %d' %
(fd.identifier, g.extrema[1], len(islands)))
for island in islands:
# how do I make a feature from an island? Location of highest value
indices = island.indices_at_value(island.extrema[1])
centre = indices[0]
org = island.bounding_box[0]
centre = tuple(org[i] + island.spacing * centre[i]
for i in range(3))
radius = 1.0
# Any other spheres?
if len(all_feats[0].spheres) > 1:
# Pick all features which contain centre
feat_dists = {}
for f in all_feats:
dist, feat = (GeometricDescriptors.point_distance(
f.spheres[0].centre, centre), f)
if feat_dists.has_key(dist):
feat_dists[dist].append(feat)
else:
feat_dists.update({dist: [feat]})
feat_dists = collections.OrderedDict(
sorted(feat_dists.items()))
shortest_distance = feat_dists.keys()[0]
if len(feat_dists[shortest_distance]) > 1:
new_feat = [
Pharmacophore.Feature(
fd,
GeometricDescriptors.Sphere(centre, radius),
feat_dists[shortest_distance][i].spheres[1])
for i in range(len(feat_dists[shortest_distance]))
]
else:
new_feat = [
Pharmacophore.Feature(
fd,
GeometricDescriptors.Sphere(centre, radius),
feat_dists[shortest_distance][0].spheres[1])
]
else:
new_feat = [
Pharmacophore.Feature(
fd, GeometricDescriptors.Sphere(centre, radius))
]
complete_set_of_features.extend(new_feat)
model = Pharmacophore.Query(complete_set_of_features)
model.write(os.path.join(self.args.output_directory, 'model.cm'))