def getLigandNbrs(resids: List[Residue],
struct: Structure) -> List[ResidueDict]:
"""KDTree search the neighbors of a given list of residues(which constitue a ligand)
and return unique having tagged them with a ban identifier proteins within 5 angstrom of these residues. """
ns = NeighborSearch(list(struct.get_atoms()))
nbrs = []
for r in resids:
# a ligand consists of residues
resatoms = r.child_list[0]
# each residue has an atom plucked at random
for nbrresidues in ns.search(resatoms.get_coord(), 5, level='R'):
# we grab all residues in radius around that atom and extend the list of neighbors with those
nbrs.extend([nbrresidues])
# Filter out the residues that constitute the ligand itself
filtered = []
for neighbor in nbrs:
present = 0
for constit in resids:
if ResidueDict(constit) == ResidueDict(neighbor):
present = 1
if present == 0:
filtered.append(ResidueDict(neighbor))
return [*map(lambda x: addBanClass(x), set(filtered))]
def merge_structure(self, new_st: Structure, mod_id: str, ch_id: str,
brk_list: Union[Sequence[Atom],
Sequence[Sequence[Atom]]],
offset: int) -> Union[str, List[str]]:
spimp = Superimposer()
fixed_ats = [
atm for atm in self.st[mod_id][ch_id].get_atoms() if atm.id == 'CA'
]
moving_ats = []
for atm in fixed_ats:
moving_ats.append(new_st[0][' '][atm.get_parent().id[1] - offset +
1]['CA'])
spimp.set_atoms(fixed_ats, moving_ats)
spimp.apply(new_st.get_atoms())
list_res = self.st[mod_id][ch_id].get_list()
fixed_gaps = []
for i in range(0,
len(self.sequence_data.data[ch_id]['pdb'][mod_id]) - 1):
gap_start = self.sequence_data.data[ch_id]['pdb'][mod_id][
i].features[0].location.end
gap_end = self.sequence_data.data[ch_id]['pdb'][mod_id][
i + 1].features[0].location.start
if [
self.st[mod_id][ch_id][gap_start],
self.st[mod_id][ch_id][gap_end]
] not in brk_list:
continue
pos = 0
while pos < len(list_res) and self.st[mod_id][ch_id].child_list[
pos].id[1] != gap_start:
pos += 1
self.remove_residue(self.st[mod_id][ch_id][gap_start],
update_int=False)
self.remove_residue(self.st[mod_id][ch_id][gap_end],
update_int=False)
for nres in range(gap_start, gap_end + 1):
res = new_st[0][' '][nres - offset + 1].copy()
res.id = (' ', nres, ' ')
self.st[mod_id][ch_id].insert(pos, res)
pos += 1
print("Adding " + mu.residue_id(res))
fixed_gaps.append('{}{}-{}{}/{}'.format(ch_id, gap_start, ch_id,
gap_end, mod_id + 1))
print()
return fixed_gaps
def getLigandNbrs(resids: List[Residue], struct:Structure):
ns = NeighborSearch(list( struct.get_atoms() ))
nbrs = []
for r in resids:
# a ligand consists of residues
resatom = r.child_list[0]
# each residue has an atom plucked at random
for nbr in ns.search(resatom.get_coord(), 5,level='R'):
# we grab all residues in radius around that atom and extend the list with those
nbrs.extend([* nbr])
filtered = []
for neighor in nbrs:
present = 0
for constit in resids:
if ResidueId( constit ) == ResidueId( neighor ):
present = 1
if present == 0:
filtered.append(ResidueId(neighor))
return [ * map(lambda x: addBanClass(x) , set(filtered) ) ]
def getResidueNeighbors(struct:Structure, resid: ResidueFullIdDict, radius:float, level:str = 'R', all_levels=False)-> List[Residue] or List[Chain] or List[Chain or Residue]:
"""
struct: opened cif structure
resid: A dictionary containing the residue's identifiers (see associated class)
radiue: radius of neighborhood to check
level: Atom, Residue, Chain : one of A/R/C
"""
if level.upper() not in ['A', 'R', 'C']:
print('Level has to be one of A R C')
raise Error
parentStrand :Chain = struct[resid.model][resid.strand_id]
residuesOfInterest:List[Residue] = list(filter(lambda x : x.get_full_id()[3][1] == resid.residue_id, parentStrand.child_list))
ligandAtoms = residuesOfInterest[0].get_atoms()
coords = list( map(lambda atom: atom.get_coord(), ligandAtoms) )
ns = NeighborSearch(list( struct.get_atoms() ))
yield ns.search(coords[0],radius,level.upper())
