def _processPDB(self):
""" Processes the PDB file, i.e. adds all relevant atoms to a dataframe and determines the peptide an phosphate chains"""
self._logger.info("Processing PDB")
ppb=CaPPBuilder()
d = []
peptide_chains = {}
phosphate_chains = set()
# Loop over all chains
for chain_idx, chain in enumerate(self._pdb[0]):
isPeptideChain = False
isPhosphateChain = False
# try to create peptide sequence
pp = ppb.build_peptides(chain)
if pp:
# tag chain as peptide chain
isPeptideChain = True
peptide_chains[chain.get_id()] = pp[0].get_sequence().tostring()
# loop over residues in chain
for residue in chain:
resn = residue.get_resname()
if resn in ['PTR', 'TPO', 'SEP']:
# Chain contains a phospho-residue; tag as phosphateChain
isPhosphateChain = True
phosphate_chains.add(chain.get_id())
# process atoms only if residue is not water and is part of a peptide or phospho chain
if residue.get_id()[0] != 'W' and (isPeptideChain or isPhosphateChain):
resi = residue.get_id()[1]
inscode = residue.get_id()[2].strip()
hasPhosphate = False
for atom in residue:
vdw = -1
if isPeptideChain:
elem = atom.element
if elem:
elem = elem if len(elem)==1 else elem[0]+elem[1].lower()
vdw = self._periodicTable.GetRvdw(self._periodicTable.GetAtomicNumber(elem))
coords = atom.get_coord()
sn = atom.get_serial_number()
# append to dataframe
d.append((chain.get_id().strip(), chain_idx, resn, resi, inscode, sn, atom.get_name(), isPeptideChain, isPhosphateChain, coords[0], coords[1], coords[2], vdw))
if resn in ['PTR', 'TPO', 'SEP'] and atom.get_name().strip() == 'P' and len(coords)==3:
hasPhosphate = True
if resn in ['PTR', 'TPO', 'SEP'] and not hasPhosphate:
# del residue because no annotated phosphate
d = d[:-len(residue)]
if len(d) == 0:
raise Exception('No amino acids found.')
# save list to dataframe
data = np.zeros((len(d), ), dtype=[('chain', 'a1'), ('chain_idx', 'a1'), ('resn', 'a3'), ('resi', 'i4'), ('inscode', 'a1'), ('sn', 'i4'), ('an', 'a4'), ('peptideChain', 'b'), ('phosphateChain', 'b'), ('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('vdw', 'f4')])
data[:] = d
idf = pd.DataFrame(data)
idf[['peptideChain', 'phosphateChain']] = idf[['peptideChain', 'phosphateChain']].astype('bool')
self._df = idf
self._peptideChains = peptide_chains
self._phosphateChains = phosphate_chains
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):
"""
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, offset=0, hse_up_key='HSE_U', hse_down_key='HSE_D', angle_key=None, check_chain_breaks=False,
check_knots=False, receptor=None, signprot=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=[]
### GP
if model.get_id()!=0:
model = model[0]
residues_in_pdb,residues_with_proper_CA=[],[]
if check_chain_breaks==True:
# for m in model:
for chain in model:
for res in chain:
# try:
if is_aa(res):
residues_in_pdb.append(res.get_id()[1])
# except:
# if is_aa(chain):
# residues_in_pdb.append(chain.get_id()[1])
# print('chain', chain, res)
# break
self.clash_pairs = []
self.chain_breaks = []
if check_knots:
possible_knots = PossibleKnots(receptor, signprot)
knot_resis = possible_knots.get_resnums()
self.remodel_resis = {}
for pp1 in ppl:
for i in range(0, len(pp1)):
residues_with_proper_CA.append(pp1[i].get_id()[1])
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()
residue_up=[] ### GP
residue_down=[] ### GP
for pp2 in ppl:
for j in range(0, len(pp2)):
try:
if r2.get_id()[1]-1!=r1.get_id()[1] or r2.get_id()[1]+1!=r3.get_id()[1]:
pass
else:
raise Exception
except:
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)<(math.pi/2):
hse_u+=1
### GP
# Puts residues' names in a list that were found in the upper half sphere
residue_up.append(ro)
### end of GP code
else:
hse_d+=1
### GP
# Puts residues' names in a list that were found in the lower half sphere
residue_down.append(ro)
### end of GP code
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, (residue_up, residue_down, hse_u, hse_d, angle)))
### GP residue_up and residue_down added to hse_list
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
### GP checking for knots
if check_knots:
for knot in knot_resis:
if knot[0][1]==pp1[i].get_id()[1] and knot[0][0]==pp1[i].get_parent().get_id():
print(pp1[i].get_parent().get_id(),pp1[i])
for r in residue_up:
if r.get_parent().get_id()==knot[1][0] and r.get_id()[1] in knot[1][1]:
print('close: ', r.get_parent().get_id(),r)
resi_range = [knot[1][1][0], knot[1][1][-1]]
if knot[1][0] not in self.remodel_resis:
self.remodel_resis[knot[1][0]] = [resi_range]
else:
if resi_range not in self.remodel_resis[knot[1][0]]:
self.remodel_resis[knot[1][0]].append(resi_range)
### GP checking for atom clashes
include_prev, include_next = False, False
try:
if pp1[i].get_id()[1]-1!=pp1[i-1].get_id()[1]:
include_prev = True
except:
include_prev = False
try:
if pp1[i].get_id()[1]+1!=pp1[i+1].get_id()[1]:
include_next = True
except:
include_next = False
for atom in pp1[i]:
ref_vector = atom.get_vector()
for other_res in residue_up:
try:
if other_res==pp1[i-1] and include_prev==False:
continue
elif len(pp1)>=i+1 and other_res==pp1[i+1] and include_next==False:
continue
else:
raise Exception
except:
for other_atom in other_res:
other_vector = other_atom.get_vector()
d = other_vector-ref_vector
if d.norm()<2:
if len(str(pp1[i]['CA'].get_bfactor()).split('.')[1])==1:
clash_res1 = float(str(pp1[i]['CA'].get_bfactor())+'0')
else:
clash_res1 = pp1[i]['CA'].get_bfactor()
if len(str(other_res['CA'].get_bfactor()).split('.')[1])==1:
clash_res2 = float(str(other_res['CA'].get_bfactor())+'0')
else:
clash_res2 = other_res['CA'].get_bfactor()
self.clash_pairs.append([(clash_res1, pp1[i].get_id()[1]), (clash_res2, other_res.get_id()[1])])
if check_chain_breaks==True:
for r in residues_in_pdb:
if r not in residues_with_proper_CA:
self.chain_breaks.append(r)
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 main(argv=None): # IGNORE:C0111
'''Command line options.'''
if argv is None:
argv = sys.argv
else:
sys.argv.extend(argv)
parser = ArgumentParser(formatter_class=RawDescriptionHelpFormatter)
parser.add_argument("-v",
"--verbose",
dest="verbose",
action="count",
help="set verbosity level [default: %(default)s]")
# parser.add_argument("-dir", "--structs_dir", required = True )
parser.add_argument("-db", "--database_name", default='pdb')
parser.add_argument("-host", "--db_host", default='127.0.0.1')
parser.add_argument("--procesados",
default='/tmp/pdbs_dist_procesados.txt')
parser.add_argument("--domains",
default='/data/databases/pdb/processed/dns_pdbs.tlb')
parser.add_argument(
"--seqs", default='/data/databases/pdb/processed/pdb_seq_res.fasta')
parser.add_argument("--pdbs", default='/data/databases/pdb/')
parser.add_argument(
"--distances",
default='/data/databases/pdb/processed/distances.tbl',
help=
"Final output: table with atom distances between residues and ligands. Only for distances less than 'dist' parameter"
)
parser.add_argument("--dist", default=5)
parser.add_argument(
"--pdbs_with_drug",
default='/data/databases/pdb/processed/pdbs_with_drug.txt',
help="Output: list of PDB codes with an associated ligand")
args = parser.parse_args()
if not os.path.exists(args.pdbs):
sys.stderr.write(
"%s not found. Specify where is pdbs/divided directory" %
(parser.pdbs))
sys.exit(1)
PDB_PATH = args.pdbs
CONTACT_DIST = args.dist
pdbs_with_drug_path = args.pdbs_with_drug
if not os.path.exists(os.path.dirname(args.pdbs_with_drug)):
sys.stderr.write("can't %s create %s. Set pdbs_with_drug correctly" %
(pdbs_with_drug_path))
sys.exit(1)
if not os.path.exists(os.path.dirname(args.distances)):
sys.stderr.write("can't %s create %s. Set distances correctly" %
(args.distances))
sys.exit(1)
pdbs_procesados_path = args.procesados
print(
"In %s the processed pdbs are kept, if the file is deleted, the process starts from scratch "
% pdbs_procesados_path)
print("Outputs: '%s' and '%s' " % (pdbs_with_drug_path, args.distances))
pdbs_procesados = []
if os.path.exists(pdbs_procesados_path):
with open(pdbs_procesados_path) as handle:
pdbs_procesados = [x.strip() for x in handle.readlines()]
pdbs_procesados = {x: 1 for x in pdbs_procesados}
pdbs_iterator = PDBsIterator(pdb_dir=args.pdbs)
def not_processed_iter():
for pdb, pdb_path in pdbs_iterator:
if pdb not in pdbs_procesados:
yield [pdb, pdb_path]
DNsPDBs = args.domains
if not os.path.exists(DNsPDBs):
seqs_from_pdb = args.seqs
if not os.path.exists(seqs_from_pdb):
sys.stderr.write(
"%s does not exists and %s not found. Specify where it is." %
(DNsPDBs, seqs_from_pdb))
sys.exit(1)
sys.stderr.write(
"%s not found. You can create it with the following command: \n" %
DNsPDBs)
sys.stderr.write(
"hmmscan --cut_tc --domtblout dns_pdbs.tlb --acc -o pdb_seq_res.hmm Pfam-A.hmm seqs_from_pdb.fasta"
)
sys.exit(1)
drugcompounds = [
x for x, y in compound_type.items() if y in ["DRUG", "COFACTOR"]
]
othercompounds = [
x for x, y in compound_type.items()
if y in ["METAL", "SUGAR", "NUCLEOTIDE", "LIPID"]
]
aminoacidcompounds = [
x for x, y in compound_type.items() if y in ["MODIFIED", "RESIDUE"]
]
drugcompounds = othercompounds + drugcompounds
pdbs_with_drug_path = "/data/databases/pdb/processed/pdbs_with_drug.txt"
_log.info("proceced pdbs: %i" % len(pdbs_procesados))
ppb = CaPPBuilder()
p = PDBParser(PERMISSIVE=1, QUIET=1)
pdbs_with_drug = []
if os.path.exists(pdbs_with_drug_path):
_log.info("pdbs with drugs already loaded")
with open(pdbs_with_drug_path) as handle:
for x in handle.readlines():
pdbs_with_drug.append(x.strip())
else:
with open(pdbs_with_drug_path, "a") as handle:
_log.info("pdbs with drugs will be loaded")
pdbs = list(pdbs_iterator)
for pdb, file_path in tqdm(pdbs):
try:
if pdb not in pdbs_with_drug:
structure = p.get_structure(pdb, file_path)
for res in structure.get_residues():
if res.resname in drugcompounds:
pdbs_with_drug.append(pdb)
handle.write(pdb + "\n")
handle.flush()
break
except Exception as ex:
print(str(ex))
# import re
# dns_table = re.sub(r" +", "\t","\n".join( [str(i) + "\t" + x for i,x in enumerate(open('/data/databases/pdb/processed/dns_pdbs.tlb').readlines()) if not x.startswith("#") ]) )
if not os.path.exists(DNsPDBs + "2"):
cols = [
"target_name", "accession", "tlen", "query_name", "accession2",
"qlen", "E-value", "score1", "bias1", "#", "of", "c-Evalue",
"i-Evalue", "score2", "bias2", "from1", "to1", "from2", "to2",
"from3", "to3", "acc"
]
_log.info("correcting hmmer-pdb output")
regexp = re.compile(" +")
items = []
for x in tqdm(open(DNsPDBs).readlines()):
if not x.startswith("#"):
line = regexp.split(x)
items.append(line[0:len(cols)])
# record = {c: line[i] for i, c in enumerate(cols)}
df_hmm = pd.DataFrame.from_records(items, columns=cols)
# df_hmm = df = pd.read_table('/data/databases/pdb/processed/dns_pdbs.tlb', index_col=None, header=None, delimiter=r"\s+",comment="#",names=cols)
# df_hmm = df_hmm.dropna()
df_hmm = df_hmm[["accession", "query_name", "from3", "to3"]]
df_hmm.to_csv(DNsPDBs + "2")
df_hmm["pdb"] = map(lambda x: x.split("_")[0].lower().strip(),
df_hmm["query_name"])
df_hmm["chain"] = map(lambda x: x.split("_")[1].upper().strip(),
df_hmm["query_name"])
df_hmm["start_res"] = map(lambda x: x.split("_")[2].upper().strip(),
df_hmm["query_name"])
df_hmm["end_res"] = map(lambda x: x.split("_")[3].upper().strip(),
df_hmm["query_name"])
else:
df_hmm = pd.read_csv(DNsPDBs + "2")
df_hmm["pdb"] = map(lambda x: x.split("_")[0].lower().strip(),
df_hmm["query_name"])
df_hmm["chain"] = map(lambda x: x.split("_")[1].upper().strip(),
df_hmm["query_name"])
df_hmm["start_res"] = map(lambda x: x.split("_")[2].upper().strip(),
df_hmm["query_name"])
df_hmm["end_res"] = map(lambda x: x.split("_")[3].upper().strip(),
df_hmm["query_name"])
print(len(df_hmm))
lock = Lock()
def centeroid(arr):
length = len(arr)
sum_x = np.sum([x.coord[0] for x in arr])
sum_y = np.sum([x.coord[1] for x in arr])
sum_z = np.sum([x.coord[2] for x in arr])
return sum_x / length, sum_y / length, sum_z / length
def residues_near_drug(drug_centroid, aa_residues):
residues_near = []
for r in aa_residues:
for a in list(r):
dist = a - Struct(coord=drug_centroid)
if dist > 20:
break
if dist < 10:
residues_near.append(r)
break
return residues_near
def juan(pdb_raw):
try:
pepe(pdb_raw)
except Exception:
traceback.print_exc()
finally:
with lock:
pdbs_procesados.append(pdb_raw)
with open(pdbs_procesados_path, "a") as handle:
handle.write(pdb_raw + "\n")
def pepe(pdb):
ppb = CaPPBuilder()
p = PDBParser(PERMISSIVE=1, QUIET=1)
path_dir = PDB_PATH + "/" + pdb[1:3].lower() + "/"
path = path_dir + "pdb" + pdb.lower() + ".ent"
model = list(p.get_structure('X', path))[0]
for chain_obj in list(model):
chain = chain_obj.id
hmm_residues = {}
pdb_seq = list(model[chain].get_residues())
if pdb_seq:
hmm_contacts = {}
hmm_residues = {}
hmms = df_hmm[(df_hmm["pdb"] == pdb)
& (df_hmm["chain"] == chain) &
(df_hmm["start_res"] == str(pdb_seq[0].id[1]))]
for j, hmm in hmms.iterrows():
try:
hmm_start = int(hmm["from3"]) - 1
hmm_end = int(hmm["to3"]) - 1
hmm_chain_name = "_".join(
map(str, [
hmm["accession"].split(".")[0], hmm["chain"],
pdb_seq[hmm_start].id[1],
pdb_seq[hmm_end].id[1]
]))
hmm_contacts[hmm_chain_name] = []
hmm_residues.update({
res.id[1]: hmm_chain_name
for res in pdb_seq[hmm_start:hmm_end]
})
except IndexError:
print(pdb, hmm["accession"], hmm["chain"], hmm_start,
hmm_end, pdb_seq)
aa_residues = []
drug_molecules = []
for res_obj in chain_obj.get_residues():
if res_obj.resname in drugcompounds:
drug_molecules.append(res_obj)
elif res_obj.resname in aminoacidcompounds:
aa_residues.append(res_obj)
for res_drug_obj in drug_molecules:
drug_centroid = centeroid(list(res_drug_obj))
near_residues = residues_near_drug(drug_centroid, aa_residues)
for drug_atom in list(res_drug_obj):
for near_residue in near_residues:
for residue_atom in list(near_residue):
distance = (residue_atom - drug_atom)
if distance > 20:
break
if distance < CONTACT_DIST:
with open(args.distances, "a") as handle:
hmm_name = hmm_residues[
near_residue.id[1]] if near_residue.id[
1] in hmm_residues else "NoDn"
fields = [
pdb, chain, hmm_name,
near_residue.id[1],
near_residue.resname,
residue_atom.serial_number,
res_drug_obj.id[1],
res_drug_obj.resname,
drug_atom.serial_number, distance
]
handle.write("\t".join(map(str, fields)) +
"\n")
_log.info("processing distances file")
for x in tqdm(set(pdbs_with_drug)):
if x not in pdbs_procesados:
juan(x)
# pool = ThreadPool(1)
# pool.map(juan, set(pdbs_with_drug) - set(pdbs_procesados))
print("Finished!!!")
def pepe(pdb):
ppb = CaPPBuilder()
p = PDBParser(PERMISSIVE=1, QUIET=1)
path_dir = PDB_PATH + "/" + pdb[1:3].lower() + "/"
path = path_dir + "pdb" + pdb.lower() + ".ent"
model = list(p.get_structure('X', path))[0]
for chain_obj in list(model):
chain = chain_obj.id
hmm_residues = {}
pdb_seq = list(model[chain].get_residues())
if pdb_seq:
hmm_contacts = {}
hmm_residues = {}
hmms = df_hmm[(df_hmm["pdb"] == pdb)
& (df_hmm["chain"] == chain) &
(df_hmm["start_res"] == str(pdb_seq[0].id[1]))]
for j, hmm in hmms.iterrows():
try:
hmm_start = int(hmm["from3"]) - 1
hmm_end = int(hmm["to3"]) - 1
hmm_chain_name = "_".join(
map(str, [
hmm["accession"].split(".")[0], hmm["chain"],
pdb_seq[hmm_start].id[1],
pdb_seq[hmm_end].id[1]
]))
hmm_contacts[hmm_chain_name] = []
hmm_residues.update({
res.id[1]: hmm_chain_name
for res in pdb_seq[hmm_start:hmm_end]
})
except IndexError:
print(pdb, hmm["accession"], hmm["chain"], hmm_start,
hmm_end, pdb_seq)
aa_residues = []
drug_molecules = []
for res_obj in chain_obj.get_residues():
if res_obj.resname in drugcompounds:
drug_molecules.append(res_obj)
elif res_obj.resname in aminoacidcompounds:
aa_residues.append(res_obj)
for res_drug_obj in drug_molecules:
drug_centroid = centeroid(list(res_drug_obj))
near_residues = residues_near_drug(drug_centroid, aa_residues)
for drug_atom in list(res_drug_obj):
for near_residue in near_residues:
for residue_atom in list(near_residue):
distance = (residue_atom - drug_atom)
if distance > 20:
break
if distance < CONTACT_DIST:
with open(args.distances, "a") as handle:
hmm_name = hmm_residues[
near_residue.id[1]] if near_residue.id[
1] in hmm_residues else "NoDn"
fields = [
pdb, chain, hmm_name,
near_residue.id[1],
near_residue.resname,
residue_atom.serial_number,
res_drug_obj.id[1],
res_drug_obj.resname,
drug_atom.serial_number, distance
]
handle.write("\t".join(map(str, fields)) +
"\n")
def __init__(self, model, radius, offset=0, hse_up_key='HSE_U', hse_down_key='HSE_D', angle_key=None, check_chain_breaks=False,
check_knots=False, receptor=None, signprot=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=[]
### GP
if model.get_id()!=0:
model = model[0]
residues_in_pdb,residues_with_proper_CA=[],[]
if check_chain_breaks==True:
# for m in model:
for chain in model:
for res in chain:
# try:
if is_aa(res):
residues_in_pdb.append(res.get_id()[1])
# except:
# if is_aa(chain):
# residues_in_pdb.append(chain.get_id()[1])
# print('chain', chain, res)
# break
self.clash_pairs = []
self.chain_breaks = []
if check_knots:
possible_knots = PossibleKnots(receptor, signprot)
knot_resis = possible_knots.get_resnums()
self.remodel_resis = {}
for pp1 in ppl:
for i in range(0, len(pp1)):
residues_with_proper_CA.append(pp1[i].get_id()[1])
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()
residue_up=[] ### GP
residue_down=[] ### GP
for pp2 in ppl:
for j in range(0, len(pp2)):
try:
if r2.get_id()[1]-1!=r1.get_id()[1] or r2.get_id()[1]+1!=r3.get_id()[1]:
pass
else:
raise Exception
except:
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)<(math.pi/2):
hse_u+=1
### GP
# Puts residues' names in a list that were found in the upper half sphere
residue_up.append(ro)
### end of GP code
else:
hse_d+=1
### GP
# Puts residues' names in a list that were found in the lower half sphere
residue_down.append(ro)
### end of GP code
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, (residue_up, residue_down, hse_u, hse_d, angle)))
### GP residue_up and residue_down added to hse_list
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
### GP checking for knots
if check_knots:
for knot in knot_resis:
if knot[0][1]==pp1[i].get_id()[1] and knot[0][0]==pp1[i].get_parent().get_id():
print(pp1[i].get_parent().get_id(),pp1[i])
for r in residue_up:
if r.get_parent().get_id()==knot[1][0] and r.get_id()[1] in knot[1][1]:
print('close: ', r.get_parent().get_id(),r)
resi_range = [knot[1][1][0], knot[1][1][-1]]
if knot[1][0] not in self.remodel_resis:
self.remodel_resis[knot[1][0]] = [resi_range]
else:
if resi_range not in self.remodel_resis[knot[1][0]]:
self.remodel_resis[knot[1][0]].append(resi_range)
### GP checking for atom clashes
include_prev, include_next = False, False
try:
if pp1[i].get_id()[1]-1!=pp1[i-1].get_id()[1]:
include_prev = True
except:
include_prev = False
try:
if pp1[i].get_id()[1]+1!=pp1[i+1].get_id()[1]:
include_next = True
except:
include_next = False
for atom in pp1[i]:
ref_vector = atom.get_vector()
for other_res in residue_up:
try:
if other_res==pp1[i-1] and include_prev==False:
continue
elif len(pp1)>=i+1 and other_res==pp1[i+1] and include_next==False:
continue
else:
raise Exception
except:
for other_atom in other_res:
other_vector = other_atom.get_vector()
d = other_vector-ref_vector
if d.norm()<2:
if len(str(pp1[i]['CA'].get_bfactor()).split('.')[1])==1:
clash_res1 = float(str(pp1[i]['CA'].get_bfactor())+'0')
else:
clash_res1 = pp1[i]['CA'].get_bfactor()
if len(str(other_res['CA'].get_bfactor()).split('.')[1])==1:
clash_res2 = float(str(other_res['CA'].get_bfactor())+'0')
else:
clash_res2 = other_res['CA'].get_bfactor()
self.clash_pairs.append([(clash_res1, pp1[i].get_id()[1]), (clash_res2, other_res.get_id()[1])])
if check_chain_breaks==True:
for r in residues_in_pdb:
if r not in residues_with_proper_CA:
self.chain_breaks.append(r)
def __init__(self,
model,
radius,
offset=0,
hse_up_key='HSE_U',
hse_down_key='HSE_D',
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 = []
### GP
self.clash_pairs = []
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()
residue_up = [] ### GP
residue_down = [] ### GP
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) < (math.pi / 2):
hse_u += 1
### GP
# Puts residues' names in a list that were found in the upper half sphere
residue_up.append(ro)
### end of GP code
else:
hse_d += 1
### GP
# Puts residues' names in a list that were found in the lower half sphere
residue_down.append(ro)
### end of GP code
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, (residue_up, residue_down, hse_u, hse_d, angle)))
### GP residue_up and residue_down added to hse_list
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
### GP checking for atom clashes
for atom in pp1[i]:
ref_vector = atom.get_vector()
for other_res in residue_up:
try:
if other_res != pp1[i - 1] and other_res != pp1[i +
1]:
for other_atom in other_res:
other_vector = other_atom.get_vector()
d = other_vector - ref_vector
if d.norm() < 2:
self.clash_pairs.append([
(pp1[i]['CA'].get_bfactor(),
pp1[i].get_id()[1]),
(other_res['CA'].get_bfactor(),
other_res.get_id()[1])
])
except:
pass
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 build_peptides(self, structure):
pp_list = self.ppb.build_peptides(structure, aa_only=False)
if len(pp_list) == 0: #case of failure
pp_list = CaPPBuilder().build_peptides(structure, aa_only=False)
return pp_list
class ContactMapper(FeaturesComputer):
'''
Extends FeaturesComputer class. Extracts res and chainIds for training and predicting and computes contact maps
for training for a given complex
'''
def __init__(self,
rFname,
lFname,
computedFeatsRootDir=None,
boundAvailable=True,
res2res_dist=6.0,
isForPrediction=False,
statusManager=None):
'''
@param rFname: str. path to receptor pdb file
@param lFname: str. path to ligand pdb file
@param computedFeatsRootDir: str. path where features will be stored
@param boundAvailable: bool. True if bound structures are available. False otherwise. Bound structures must be located
at the same path that unbound structures and need to be named as in the following example:
1A2K_l_u.pdb 1A2K_r_b.pdb
@param res2res_dist: float. max distance between any heavy atoms of 2 amino acids to be considered as interacting
(Amstrongs)
@param isForPrediction: bool. False to compute contacts between amino acids, True otherwise. Positive contacts will
be tag as 1, negative as -1. If True, all amino acids will have as tag np.nan
@param statusManager: class that implements .setStatus(msg) to communicate
'''
FeaturesComputer.__init__(self, rFname, lFname, computedFeatsRootDir)
self.prefixR = os.path.split(rFname)[1].split(".")[0].split("_")[0]
self.prefixL = os.path.split(lFname)[1].split(".")[0].split("_")[0]
if self.prefixR == self.prefixL:
self.prefix = self.prefixR
else:
if "<" in self.prefixL:
raise FeatureComputerException(
"Error. Ligand pdbFile name %s must not contain '<' or '>' character"
% lFname)
if ">" in self.prefixR:
raise FeatureComputerException(
"Error. Receptor pdbFile name %s must not contain '<' or'>' character"
% rFname)
self.prefixR = self.getExtendedPrefix(rFname)
self.prefixL = self.getExtendedPrefix(lFname)
self.prefix = self.prefixL + "<->" + self.prefixR
self.isForPrediction = isForPrediction
self.res2res_dist = res2res_dist
self.boundAvailable = boundAvailable
self.outPath = myMakeDir(self.computedFeatsRootDir,
"common/contactMaps")
self.outName = os.path.join(self.outPath, self.prefix + ".cMap.tab")
self.parser = PDBParser(QUIET=True)
# self.ppb=PPBuilder( radius= 200) # To not worry for broken chains
self.ppb = CaPPBuilder()
self.computeFun = self.contactMapOneComplex
def mapBoundToUnbound(self,
structureUnbound,
structureBound,
skipBoundChainsIds=set([])):
'''
Obtains correspondence between unbound structure and bound structure when available. Returns a dictionary
that maps bound_residue --> equivalent unbound_residue
@param structureUnbound: Bio.PDB.Structure. Structure in bound state
@param structureBound: Bio.PDB.Structure. Structure in unbound state
@param skipBoundChainsIds: Set of Chars. Set of chain ids that will be skipped for calculations.
@return bound2UnboundMapDict: Dict {Bio.PDB.Residue (from bound structure): Bio.PDB.Residue (from unbound structure)}
'''
bound2UnboundMapDict = {}
pp_list_unbound = self.ppb.build_peptides(structureUnbound,
aa_only=False)
if structureBound is None: # if there is no bound structure, use just unbound.
boundToUnboundMap = lambda x: x #For a given residue will return the same residue
pp_list_bound = pp_list_unbound
else:
pp_list_bound = self.ppb.build_peptides(structureBound,
aa_only=False)
mapper = BoundUnboundMapper(
pp_list_unbound,
pp_list_bound) # res_bound->res_unbound mapper object
mapper.build_correspondence()
boundToUnboundMap = mapper.mapBoundToUnbound #For a given bound residue will return its unbound equivalent
for pp in pp_list_bound:
for resBound in pp:
chainBound = resBound.get_full_id()[2] # str chainId
if chainBound in skipBoundChainsIds: continue
resUnbound = boundToUnboundMap(resBound)
if not resUnbound is None: #In case there is no equivalent unbound residue for a given bound residue
bound2UnboundMapDict[resBound] = resUnbound
return bound2UnboundMapDict
def fixHomooligomers(self, structureL, structureR, positiveContacts,
chainsInContactL, chainsInContactR):
'''
For each interacting pair of residues (resL_1, resR_2), it will add to positiveContacts (res_1L', resR_2) and/or
(resL_1, resR_2') where resL_1' is an equivalent residue in homooligomers of ligand
@param structureL: Bio.PDB.Structure. Structure of ligand
@param structureR: Bio.PDB.Structure. Structure of receptor
@param positiveContacts: [(ligandResId, receptorResId)]: ligandResId and receptorResIds are full_ids of Bio.PDB.Residue
@param chainsInContactL: [(ligandResId)]: ligandResId and receptorResIds are full_ids of Bio.PDB.Residue
@param chainsInContactR: [(receptorResId)]: ligandResId and receptorResIds are full_ids of Bio.PDB.Residue
@return positiveContacts, chainsInContactL, chainsInContactR. Updated with equivalent residues interactions added
'''
pp_list_l = self.ppb.build_peptides(structureL, aa_only=False)
equivalentLmapper = HomoOligomerFinder(pp_list_l,
positiveContacts,
chainType="l")
positiveContacts, chainsInContactL = equivalentLmapper.update_interactions(
)
pp_list_r = self.ppb.build_peptides(structureR, aa_only=False)
equivalentRmapper = HomoOligomerFinder(pp_list_r,
positiveContacts,
chainType="r")
positiveContacts, chainsInContactR = equivalentRmapper.update_interactions(
)
return positiveContacts, chainsInContactL, chainsInContactR
def getPairsOfResiduesInContact(self, structureL, structureR):
'''
Computes which amino acids of ligand are in contact with which amino acids of receptor
@param structureL: Bio.PDB.Structure. Structure of ligand (bound state if available)
@param structureR: Bio.PDB.Structure. Structure of receptor (bound state if available).
@return positiveContacts: Set {(Bio.PDB.Residue.fullResId (from bound structure structureL), Bio.PDB.Residue.fullResId (from bound structure structureR))}
@return chainsNotContactL: Set { str(chainId structureL)}
@return chainsNotContactR: Set { str(chainId structureR)}
'''
try:
atomListL = [
atom for atom in structureL.child_list[0].get_atoms()
if not atom.name.startswith("H")
]
except IndexError:
raise NoValidPDBFile("Problems parsing pdbFile 1")
try:
atomListR = [
atom for atom in structureR.child_list[0].get_atoms()
if not atom.name.startswith("H")
]
except IndexError:
raise NoValidPDBFile("Problems parsing pdbFile 2")
searcher = NeighborSearch(atomListL + atomListR)
allNeigs = searcher.search_all(self.res2res_dist, level="R")
lStructId = structureL.get_id()
rStructId = structureR.get_id()
positiveContacts = set([])
chainsInContactL = set([])
chainsInContactR = set([])
for res1, res2 in allNeigs:
pdbId1, modelId1, chainId1, resId1 = res1.get_full_id()
pdbId2, modelId2, chainId2, resId2 = res2.get_full_id()
fullResId1 = res1.get_full_id()
fullResId2 = res2.get_full_id()
if pdbId1 == lStructId and pdbId2 == rStructId:
positiveContacts.add((fullResId1, fullResId2))
chainsInContactL.add(fullResId1[2])
chainsInContactR.add(fullResId2[2])
elif pdbId1 == rStructId and pdbId2 == lStructId:
positiveContacts.add((fullResId2, fullResId1))
chainsInContactL.add(fullResId2[2])
chainsInContactR.add(fullResId1[2])
if CONSIDER_HOMOOLIG_AS_POS:
positiveContacts, chainsInContactL, chainsInContactR = self.fixHomooligomers(
structureL, structureR, positiveContacts, chainsInContactL,
chainsInContactR)
allChainsL = set([elem.get_id() for elem in structureL[0].get_list()])
allChainsR = set([elem.get_id() for elem in structureR[0].get_list()])
chainsNotContactL = allChainsL.difference(chainsInContactL)
chainsNotContactR = allChainsR.difference(chainsInContactR)
return positiveContacts, chainsNotContactL, chainsNotContactR
def contactMapOneComplex(self):
'''
Computes the contact map of a complex. Initial input for complex codification. Contact map is a file written at
self.computedFeatsRootDir/common/contactMaps/ with name prefix.cMap.tab where prefix is either the common name of
ligand and receptor pdb files or the concatenation of ligand and receptor names.
1A2K_l_u.pdb and 1A2K_r_u.pdb --> 1A2K.cMap.tab
1A2K_l_u.pdb and 1A22.pdb --> 1A2K-1A22.cMap.tab
'''
outName = self.outName
print(outName)
if os.path.isfile(outName):
print('Already computed contact map')
return 0
lStructId = self.prefixL + "_l_u.pdb"
rStructId = self.prefixR + "_r_u.pdb"
structureL_u = self.parser.get_structure(lStructId, self.lFname)
structureR_u = self.parser.get_structure(rStructId, self.rFname)
if self.boundAvailable == False or self.isForPrediction:
structureL_b = None
structureR_b = None
else:
try:
lStructId_b = self.prefix + "_l_b.pdb"
rStructId_b = self.prefix + "_r_b.pdb"
lFname_b = os.path.join(
os.path.split(self.lFname)[0], lStructId_b)
rFname_b = os.path.join(
os.path.split(self.rFname)[0], rStructId_b)
structureL_b = self.parser.get_structure(lStructId_b, lFname_b)
structureR_b = self.parser.get_structure(rStructId_b, rFname_b)
except IOError as e: # in this case there are just unbound pdbs available
structureL_b = None
structureR_b = None
if self.isForPrediction:
positiveContacts = None
chainsNotContactR = set([])
chainsNotContactL = set([])
elif structureL_b is None or structureR_b is None: #Compute contacs in bound structures
positiveContacts, chainsNotContactL, chainsNotContactR = self.getPairsOfResiduesInContact(
structureL_u, structureR_u)
else: #Compute contacs in unbound structures
positiveContacts, chainsNotContactL, chainsNotContactR = self.getPairsOfResiduesInContact(
structureL_b, structureR_b)
if JUST_INTERACTING_CHAINS == False:
chainsNotContactR = set([])
chainsNotContactL = set([])
rResDict = self.mapBoundToUnbound(structureR_u,
structureR_b,
skipBoundChainsIds=chainsNotContactR)
lResDict = self.mapBoundToUnbound(structureL_u,
structureL_b,
skipBoundChainsIds=chainsNotContactL)
nResiduesL = len(lResDict)
nResiduesR = len(rResDict)
if not (self.minNumResiduesPartner < nResiduesL <
self.maxNumResiduesPartner):
raise BadNumberOfResidues(nResiduesL, "1")
if not (self.minNumResiduesPartner < nResiduesR <
self.maxNumResiduesPartner):
raise BadNumberOfResidues(nResiduesR, "2")
outFile = open(outName, "w")
outFile.write(
"chainIdL structResIdL resNameL chainIdR structResIdR resNameR categ\n"
)
# print(sorted(lResDict, key= lambda x: x.get_id()))
# a= raw_input()
try:
for resL_bound in sorted(lResDict, key=lambda x: x.get_full_id()):
# print(resL_bound.get_full_id())
resL_unbound = lResDict[resL_bound]
pdbIdL, modelL, chainIdL, resIdL = resL_unbound.get_full_id()
resIdL = self.makeStrResId(resIdL)
try:
letraL = three_to_one(resL_unbound.resname)
if letraL != three_to_one(resL_bound.resname): continue
except KeyError:
continue
for resR_bound in sorted(rResDict,
key=lambda x: x.get_full_id()):
resR_unbound = rResDict[resR_bound]
pdbIdR, modelR, chainIdR, resIdR = resR_unbound.get_full_id(
)
try:
letraR = three_to_one(resR_unbound.resname)
if letraR != three_to_one(resR_bound.resname): continue
except KeyError:
continue
if self.isForPrediction:
categ = np.nan
elif (resL_bound.get_full_id(),
resR_bound.get_full_id()) in positiveContacts:
categ = 1
else:
categ = -1
resIdR = self.makeStrResId(resIdR)
if chainIdL == " ": chainIdL = "*"
if chainIdR == " ": chainIdR = "*"
# print("%s %s %s %s %s %s %s\n" %(chainIdL, resIdL, letraL, chainIdR, resIdR, letraR, categ) )
# raw_input("enter")
outFile.write("%s %s %s %s %s %s %s\n" %
(chainIdL, resIdL, letraL, chainIdR, resIdR,
letraR, categ))
outFile.close()
except (KeyboardInterrupt, Exception):
print("Exception happend computing %s" % outName)
tryToRemove(outName)
raise
def makeStrResId(self, resId):
valList = [str(elem) for elem in resId[1:]]
finalId = "".join(valList).strip()
return finalId