def shrink_to_mols(self, mols):
"""Reduces the grid size to be just large enough to contain all mol objects in mol"""
blank_grd = Grid.initalise_grid(
[a.coordinates for l in mols for a in l.atoms])
for probe, g in self.super_grids.items():
self.super_grids[probe] = Grid.shrink(blank_grd, g)
def augmentation(hr, hits):
# create a grid which can contain all pharmacophore poses
small_blank = Grid.initalise_grid(coords={
atm.coordinates
for mol in hits.hits for atm in mol.molecule.heavy_atoms
},
padding=3)
# dilate the grids
for p, g in hr.super_grids.items():
hr.super_grids[p] = g.dilate_by_atom()
# inflate
prot_g = Grid.initalise_grid(
[a.coordinates for a in hr.protein.heavy_atoms], padding=1)
for p, g in hr.super_grids.items():
hr.super_grids[p] = prot_g.common_boundaries(g)
# shrink hotspot maps to save time
sub_grids = {
p: Grid.shrink(small=small_blank, big=g)
for p, g in hr.super_grids.items()
}
# create single grid
mask_dic, sg = Grid.get_single_grid(sub_grids)
hr.super_grids = mask_dic
# set background to 1
hr.set_background()
hr.normalize_to_max()
return hr
def testscore_atoms_as_spheres(self):
with PushDir("testdata/result/data"):
mols = [m for m in MoleculeReader("gold_docking_poses.sdf")]
# create a grid which can contain all docking poses
small_blank = Grid.initalise_grid(coords={
atm.coordinates
for mol in mols for atm in mol.heavy_atoms
},
padding=2)
# read hotspot maps
with HotspotReader(path="out.zip") as r:
self.result = r.read()
# dilate the grids
for p, g in self.result.super_grids.items():
self.result.super_grids[p] = g.dilate_by_atom()
# shrink hotspot maps to save time
sub_grids = {
p: Grid.shrink(small=small_blank, big=g)
for p, g in self.result.super_grids.items()
}
# create single grid
mask_dic, sg = Grid.get_single_grid(sub_grids)
self.result.super_grids = mask_dic
# set background to 1
self.result.set_background()
self.result.normalize_to_max()
print([g.extrema for p, g in self.result.super_grids.items()])
for m in mols[:1]:
s = self.result.score_atoms_as_spheres(m, small_blank)
print(s)
def augmentation(hr, entries):
# create a grid which can contain all docking poses
coords = set()
for i, entry in enumerate(entries):
for atm in entry.molecule.heavy_atoms:
coords.add(atm.coordinates)
if i > 100:
break
small_blank = Grid.initalise_grid(coords=coords, padding=12)
# dilate the grids
# for p, g in hr.super_grids.items():
# hr.super_grids[p] = g.dilate_by_atom()
# inflate
prot_g = Grid.initalise_grid(
[a.coordinates for a in hr.protein.heavy_atoms], padding=1)
for p, g in hr.super_grids.items():
hr.super_grids[p] = prot_g.common_boundaries(g)
# shrink hotspot maps to save time
sub_grids = {
p: Grid.shrink(small=small_blank, big=g)
for p, g in hr.super_grids.items()
}
# create single grid
mask_dic, sg = Grid.get_single_grid(sub_grids)
hr.super_grids = mask_dic
# set background to 1
hr.set_background()
hr.normalize_to_max()
return hr
