def __init__(self, model, pdb_file=None):
"""Initialize the class."""
# Issue warning if pdb_file is given
if pdb_file is not None:
warnings.warn(
"ResidueDepth no longer requires a pdb file. "
"This argument will be removed in a future release "
"of Biopython.", BiopythonDeprecationWarning)
depth_dict = {}
depth_list = []
depth_keys = []
# get_residue
residue_list = Selection.unfold_entities(model, 'R')
# make surface from PDB file using MSMS
surface = get_surface(model)
# calculate rdepth for each residue
for residue in residue_list:
if not is_aa(residue):
continue
rd = residue_depth(residue, surface)
ca_rd = ca_depth(residue, surface)
# Get the key
res_id = residue.get_id()
chain_id = residue.get_parent().get_id()
depth_dict[(chain_id, res_id)] = (rd, ca_rd)
depth_list.append((residue, (rd, ca_rd)))
depth_keys.append((chain_id, res_id))
# Update xtra information
residue.xtra['EXP_RD'] = rd
residue.xtra['EXP_RD_CA'] = ca_rd
AbstractPropertyMap.__init__(self, depth_dict, depth_keys, depth_list)
def __init__(self, model, pdb_file=None):
# Issue warning if pdb_file is given
if pdb_file is not None:
warnings.warn(("ResidueDepth no longer requires a pdb file."
" This argument will be removed in a future release"
" of Biopython."),
BiopythonDeprecationWarning)
depth_dict = {}
depth_list = []
depth_keys = []
# get_residue
residue_list = Selection.unfold_entities(model, 'R')
# make surface from PDB file using MSMS
surface = get_surface(model)
# calculate rdepth for each residue
for residue in residue_list:
if not is_aa(residue):
continue
rd = residue_depth(residue, surface)
ca_rd = ca_depth(residue, surface)
# Get the key
res_id = residue.get_id()
chain_id = residue.get_parent().get_id()
depth_dict[(chain_id, res_id)] = (rd, ca_rd)
depth_list.append((residue, (rd, ca_rd)))
depth_keys.append((chain_id, res_id))
# Update xtra information
residue.xtra['EXP_RD'] = rd
residue.xtra['EXP_RD_CA'] = ca_rd
AbstractPropertyMap.__init__(self, depth_dict, depth_keys, depth_list)
def __init__(self, model, msms_exec=None):
"""Initialize the class."""
if msms_exec is None:
msms_exec = "msms"
depth_dict = {}
depth_list = []
depth_keys = []
# get_residue
residue_list = Selection.unfold_entities(model, "R")
# make surface from PDB file using MSMS
surface = get_surface(model, MSMS=msms_exec)
# calculate rdepth for each residue
for residue in residue_list:
if not is_aa(residue):
continue
rd = residue_depth(residue, surface)
ca_rd = ca_depth(residue, surface)
# Get the key
res_id = residue.get_id()
chain_id = residue.get_parent().get_id()
depth_dict[(chain_id, res_id)] = (rd, ca_rd)
depth_list.append((residue, (rd, ca_rd)))
depth_keys.append((chain_id, res_id))
# Update xtra information
residue.xtra["EXP_RD"] = rd
residue.xtra["EXP_RD_CA"] = ca_rd
AbstractPropertyMap.__init__(self, depth_dict, depth_keys, depth_list)
def __init__(self, model, pdb_file):
depth_dict={}
depth_list=[]
depth_keys=[]
self.terminate=False
# get_residue
residue_list=Selection.unfold_entities(model, 'R')
# make surface from PDB file
surface=self.get_surface(pdb_file)
if not self.terminate:
# calculate rdepth for each residue
for residue in residue_list:
if not is_aa(residue):
continue
rd=residue_depth(residue, surface)
ca_rd=ca_depth(residue, surface)
# Get the key
res_id=residue.get_id()
chain_id=residue.get_parent().get_id()
depth_dict[(chain_id, res_id)]=(rd, ca_rd)
depth_list.append((residue, (rd, ca_rd)))
depth_keys.append((chain_id, res_id))
# Update xtra information
residue.xtra['EXP_RD']=rd
residue.xtra['EXP_RD_CA']=ca_rd
AbstractPropertyMap.__init__(self, depth_dict, depth_keys, depth_list)
else:
return None
def __init__(self, model, radius=12.0, offset=0):
"""Initialize.
A residue's exposure is defined as the number of CA atoms around
that residues CA atom. A dictionary is returned that uses a L{Residue}
object as key, and the residue exposure as corresponding value.
:param model: the model that contains the residues
:type model: L{Model}
:param radius: radius of the sphere (centred at the CA atom)
:type radius: float
:param offset: number of flanking residues that are ignored in
the calculation of the number of neighbors
:type offset: int
"""
assert (offset >= 0)
ppb = CaPPBuilder()
ppl = ppb.build_peptides(model)
fs_map = {}
fs_list = []
fs_keys = []
for pp1 in ppl:
for i in range(0, len(pp1)):
fs = 0
r1 = pp1[i]
if not is_aa(r1) or not r1.has_id('CA'):
continue
ca1 = r1['CA']
for pp2 in ppl:
for j in range(0, len(pp2)):
if pp1 is pp2 and abs(i - j) <= offset:
continue
r2 = pp2[j]
if not is_aa(r2) or not r2.has_id('CA'):
continue
ca2 = r2['CA']
d = (ca2 - ca1)
if d < radius:
fs += 1
res_id = r1.get_id()
chain_id = r1.get_parent().get_id()
# Fill the 3 data structures
fs_map[(chain_id, res_id)] = fs
fs_list.append((r1, fs))
fs_keys.append((chain_id, res_id))
# Add to xtra
r1.xtra['EXP_CN'] = fs
AbstractPropertyMap.__init__(self, fs_map, fs_keys, fs_list)
def __init__(self, model, radius=12.0, offset=0):
"""Initialize.
A residue's exposure is defined as the number of CA atoms around
that residues CA atom. A dictionary is returned that uses a L{Residue}
object as key, and the residue exposure as corresponding value.
:param model: the model that contains the residues
:type model: L{Model}
:param radius: radius of the sphere (centred at the CA atom)
:type radius: float
:param offset: number of flanking residues that are ignored in
the calculation of the number of neighbors
:type offset: int
"""
assert(offset >= 0)
ppb = CaPPBuilder()
ppl = ppb.build_peptides(model)
fs_map = {}
fs_list = []
fs_keys = []
for pp1 in ppl:
for i in range(0, len(pp1)):
fs = 0
r1 = pp1[i]
if not is_aa(r1) or not r1.has_id('CA'):
continue
ca1 = r1['CA']
for pp2 in ppl:
for j in range(0, len(pp2)):
if pp1 is pp2 and abs(i - j) <= offset:
continue
r2 = pp2[j]
if not is_aa(r2) or not r2.has_id('CA'):
continue
ca2 = r2['CA']
d = (ca2 - ca1)
if d < radius:
fs += 1
res_id = r1.get_id()
chain_id = r1.get_parent().get_id()
# Fill the 3 data structures
fs_map[(chain_id, res_id)] = fs
fs_list.append((r1, fs))
fs_keys.append((chain_id, res_id))
# Add to xtra
r1.xtra['EXP_CN'] = fs
AbstractPropertyMap.__init__(self, fs_map, fs_keys, fs_list)
def __init__(self, model, pdb_file):
depth_dict = {}
depth_list = []
depth_keys = []
# get_residue
residue_list = Selection.unfold_entities(model, 'R')
# make surface from PDB file
surface = get_surface(pdb_file)
# calculate rdepth for each residue
for residue in residue_list:
if not is_aa(residue):
continue
rd = residue_depth(residue, surface)
ca_rd = ca_depth(residue, surface)
# Get the key
res_id = residue.get_id()
chain_id = residue.get_parent().get_id()
depth_dict[(chain_id, res_id)] = (rd, ca_rd)
depth_list.append((residue, (rd, ca_rd)))
depth_keys.append((chain_id, res_id))
# Update xtra information
residue.xtra['EXP_RD'] = rd
residue.xtra['EXP_RD_CA'] = ca_rd
AbstractPropertyMap.__init__(self, depth_dict, depth_keys, depth_list)
def __init__(self,
model,
radius,
offset,
hse_up_key,
hse_down_key,
angle_key=None):
"""
@param model: model
@type model: L{Model}
@param radius: HSE radius
@type radius: float
@param offset: number of flanking residues that are ignored in the calculation
of the number of neighbors
@type offset: int
@param hse_up_key: key used to store HSEup in the entity.xtra attribute
@type hse_up_key: string
@param hse_down_key: key used to store HSEdown in the entity.xtra attribute
@type hse_down_key: string
@param angle_key: key used to store the angle between CA-CB and CA-pCB in
the entity.xtra attribute
@type angle_key: string
"""
assert (offset >= 0)
# For PyMOL visualization
self.ca_cb_list = []
ppb = CaPPBuilder()
ppl = ppb.build_peptides(model)
hse_map = {}
hse_list = []
hse_keys = []
for pp1 in ppl:
for i in range(0, len(pp1)):
if i == 0:
r1 = None
else:
r1 = pp1[i - 1]
r2 = pp1[i]
if i == len(pp1) - 1:
r3 = None
else:
r3 = pp1[i + 1]
# This method is provided by the subclasses to calculate HSE
result = self._get_cb(r1, r2, r3)
if result is None:
# Missing atoms, or i==0, or i==len(pp1)-1
continue
pcb, angle = result
hse_u = 0
hse_d = 0
ca2 = r2['CA'].get_vector()
for pp2 in ppl:
for j in range(0, len(pp2)):
if pp1 is pp2 and abs(i - j) <= offset:
# neighboring residues in the chain are ignored
continue
ro = pp2[j]
if not is_aa(ro) or not ro.has_id('CA'):
continue
cao = ro['CA'].get_vector()
d = (cao - ca2)
if d.norm() < radius:
if d.angle(pcb) < (pi / 2):
hse_u += 1
else:
hse_d += 1
res_id = r2.get_id()
chain_id = r2.get_parent().get_id()
# Fill the 3 data structures
hse_map[(chain_id, res_id)] = (hse_u, hse_d, angle)
hse_list.append((r2, (hse_u, hse_d, angle)))
hse_keys.append((chain_id, res_id))
# Add to xtra
r2.xtra[hse_up_key] = hse_u
r2.xtra[hse_down_key] = hse_d
if angle_key:
r2.xtra[angle_key] = angle
AbstractPropertyMap.__init__(self, hse_map, hse_keys, hse_list)
def __init__(self, model, radius, offset, hse_up_key, hse_down_key,
angle_key=None):
"""
@param model: model
@type model: L{Model}
@param radius: HSE radius
@type radius: float
@param offset: number of flanking residues that are ignored in the calculation
of the number of neighbors
@type offset: int
@param hse_up_key: key used to store HSEup in the entity.xtra attribute
@type hse_up_key: string
@param hse_down_key: key used to store HSEdown in the entity.xtra attribute
@type hse_down_key: string
@param angle_key: key used to store the angle between CA-CB and CA-pCB in
the entity.xtra attribute
@type angle_key: string
"""
assert(offset>=0)
# For PyMOL visualization
self.ca_cb_list=[]
ppb=CaPPBuilder()
ppl=ppb.build_peptides(model)
hse_map={}
hse_list=[]
hse_keys=[]
for pp1 in ppl:
for i in range(0, len(pp1)):
if i==0:
r1=None
else:
r1=pp1[i-1]
r2=pp1[i]
if i==len(pp1)-1:
r3=None
else:
r3=pp1[i+1]
# This method is provided by the subclasses to calculate HSE
result=self._get_cb(r1, r2, r3)
if result is None:
# Missing atoms, or i==0, or i==len(pp1)-1
continue
pcb, angle=result
hse_u=0
hse_d=0
ca2=r2['CA'].get_vector()
for pp2 in ppl:
for j in range(0, len(pp2)):
if pp1 is pp2 and abs(i-j)<=offset:
# neighboring residues in the chain are ignored
continue
ro=pp2[j]
if not is_aa(ro) or not ro.has_id('CA'):
continue
cao=ro['CA'].get_vector()
d=(cao-ca2)
if d.norm()<radius:
if d.angle(pcb)<(pi/2):
hse_u+=1
else:
hse_d+=1
res_id=r2.get_id()
chain_id=r2.get_parent().get_id()
# Fill the 3 data structures
hse_map[(chain_id, res_id)]=(hse_u, hse_d, angle)
hse_list.append((r2, (hse_u, hse_d, angle)))
hse_keys.append((chain_id, res_id))
# Add to xtra
r2.xtra[hse_up_key]=hse_u
r2.xtra[hse_down_key]=hse_d
if angle_key:
r2.xtra[angle_key]=angle
AbstractPropertyMap.__init__(self, hse_map, hse_keys, hse_list)
