def test_basic_with_epitope(self):
"""
epitope is specified
"""
path1 = DIRNAME + '/data/sample1.pdb'
path2 = DIRNAME + '/data/sample2.pdb'
p = PDBParser(PERMISSIVE=1)
query_struct = p.get_structure(os.path.basename(path1), path1)
against_struct = p.get_structure(os.path.basename(path2), path2)
query_complex = Complex(
query_struct,
epitope=[211, 213, 214, 224, 225, 226, 227, 228, 229])
against_complex = Complex(against_struct,
epitope=[216, 217, 218, 219, 220, 221])
query_complex.get_fp()
against_complex.get_fp()
query_fp_string = query_complex.fp2str()
against_fp_string = against_complex.fp2str()
query = FPWithComplex(query_complex, query_fp_string)
against = FPWithComplex(against_complex, against_fp_string)
score1, score2, score3 = similarity_between(query, against)
expected = {'score1': 34.705754203703862, 'score3': 0, 'score2': 6}
actual = {"score1": score1, "score2": score2, "score3": score3}
self.assertEqual(actual, expected)
def test_with_epitope_another_cutoff(self):
"""
the similarity calculation cutoff is set to 5
"""
path1 = DIRNAME + '/data/sample1.pdb'
path2 = DIRNAME + '/data/sample2.pdb'
p = PDBParser(PERMISSIVE=1)
query_struct = p.get_structure(os.path.basename(path1), path1)
against_struct = p.get_structure(os.path.basename(path2), path2)
query_complex = Complex(query_struct)
against_complex = Complex(against_struct)
query_complex.get_fp()
against_complex.get_fp()
query_fp_string = query_complex.fp2str()
against_fp_string = against_complex.fp2str()
query = FPWithComplex(query_complex, query_fp_string)
against = FPWithComplex(against_complex, against_fp_string)
score1, score2, score3 = similarity_between(query, against, cutoff=5)
expected = {"score1": 119.75339423551459, "score3": -8, "score2": 20}
actual = {"score1": score1, "score2": score2, "score3": score3}
self.assertEqual(actual, expected)
def test_basic_with_another_spinimage(self):
"""
non-default spinimage
"""
path1 = DIRNAME + '/data/sample1.pdb'
path2 = DIRNAME + '/data/sample2.pdb'
p = PDBParser(PERMISSIVE=1)
query_struct = p.get_structure(os.path.basename(path1), path1)
against_struct = p.get_structure(os.path.basename(path2), path2)
query_complex = Complex(query_struct)
against_complex = Complex(against_struct)
query_complex.get_fp(spin_image_radius_step=2,
spin_image_height_step=2,
sphere_radius_step=2)
against_complex.get_fp(spin_image_radius_step=2,
spin_image_height_step=2,
sphere_radius_step=2)
query_fp_string = query_complex.fp2str()
against_fp_string = against_complex.fp2str()
query = FPWithComplex(query_complex, query_fp_string)
against = FPWithComplex(against_complex, against_fp_string)
score1, score2, score3 = similarity_between(query, against)
expected = {'score1': 129.68169758476202, 'score3': 5, 'score2': 20}
actual = {"score1": score1, "score2": score2, "score3": score3}
self.assertEqual(actual, expected)
def test_basic(self):
"""
nothing is specified
"""
path1 = DIRNAME + '/data/sample1.pdb'
path2 = DIRNAME + '/data/sample2.pdb'
p = PDBParser(PERMISSIVE=1)
query_struct = p.get_structure(os.path.basename(path1), path1)
against_struct = p.get_structure(os.path.basename(path2), path2)
query_complex = Complex(query_struct)
against_complex = Complex(against_struct)
query_complex.get_fp()
against_complex.get_fp()
query_fp_string = query_complex.fp2str()
against_fp_string = against_complex.fp2str()
query = FPWithComplex(query_complex, query_fp_string)
against = FPWithComplex(against_complex, against_fp_string)
score1, score2, score3 = similarity_between(query, against)
expected = {"score1": 118.00269647021572, "score3": 20, "score2": -8}
actual = {"score1": score1, "score3": score2, "score2": score3}
self.assertEqual(actual, expected)
def get_structure(self, *args):
if len(args) == 2:
pdbId, fileName = args
elif len(args) == 1:
fileName = args[0]
pdbId, fileName = str(fileName), fileName
else:
raise ValueError(
"Error, input should be (id, fileName) or (fileName))")
if re.match("http(s?)://", fileName):
r = requests.get(fileName)
if r.ok:
fileName = StringIO(r.text)
else:
raise Exception("Error downloading pdb")
try:
if not isinstance(fileName, str) or not fileName.endswith(".gz"):
structure = PDBParser.get_structure(self, pdbId, fileName)
else:
with gzip.open(fileName) as f:
structure = PDBParser.get_structure(self, pdbId, f)
except Exception as e:
print(e)
structure = MMCIFParser.get_structure(self, pdbId, fileName)
if self.removeHeteroDuplicated:
structure = self.filterOutDuplicated(structure)
return structure
def test_bad_charge(self):
"""Test if missing or malformed charge case is handled correctly."""
# Test Entries
malformed = "ATOM 1 N PRO 1 000001 02.000 3.0000 -0.W000 1.0000 N\n"
missing = "ATOM 1 N PRO 1 000001 02.000 3.0000 1.0000 N\n"
# Malformed
parser = PDBParser(PERMISSIVE=True,
is_pqr=True) # default initialization
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", PDBConstructionWarning)
structure = parser.get_structure("test", StringIO(malformed))
atom = next(structure.get_atoms())
self.assertEqual(atom.get_charge(), None)
# Missing
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", PDBConstructionWarning)
structure = parser.get_structure("test", StringIO(missing))
atom = next(structure.get_atoms())
self.assertEqual(atom.get_charge(), None)
# Test PERMISSIVE mode behaviour
parser = PDBParser(PERMISSIVE=False,
is_pqr=True) # default initialization
self.assertRaises(
PDBConstructionException,
parser.get_structure,
"example",
StringIO(malformed),
)
def score(query_pdb_path,
against_pdb_path,
query_fp_path=None,
against_fp_path=None,
query_epitope=[],
against_epitope=[],
spin_image_height_step=5,
spin_image_radius_step=2,
sphere_radius_step=2,
cutoff=20.0,
spin_image_radius_range=(0, 20),
spin_image_height_range=(-30, 10),
sphere_radius_range=(0, 20),
callback=write_score_to_file,
cbargs=[]):
p = PDBParser(PERMISSIVE=1)
query_struct = p.get_structure(os.path.basename(query_pdb_path),
query_pdb_path)
against_struct = p.get_structure(os.path.basename(against_pdb_path),
against_pdb_path)
query_complex = Complex(query_struct, query_epitope)
against_complex = Complex(against_struct, against_epitope)
if query_fp_path is None or against_fp_path is None: #if fp is not given
query_complex.get_fp(spin_image_radius_step=spin_image_radius_step,
spin_image_height_step=spin_image_height_step,
sphere_radius_step=sphere_radius_step)
against_complex.get_fp(spin_image_radius_step=spin_image_radius_step,
spin_image_height_step=spin_image_height_step,
sphere_radius_step=sphere_radius_step)
query_fp_string = query_complex.fp2str()
against_fp_string = against_complex.fp2str()
else:
#if fp is given, read them
with open(query_fp_path, 'r') as f1, open(against_fp_path, 'r') as f2:
query_fp_string = f1.read()
against_fp_string = f2.read()
query = FPWithComplex(query_complex, query_fp_string)
against = FPWithComplex(against_complex, against_fp_string)
score1, score2, score3 = similarity_between(query, against, cutoff=cutoff)
#z1, z2, z3 = similarity_between (query, query, cutoff = cutoff) #the normalization constant
#print score1, score2, score3
if callback is not None:
callback((score1, score2, score3), *cbargs)
return score1, score2, score3
def save_to_csv(input_path, output_path):
parser = PDBParser(PERMISSIVE=True)
pdb_files = glob(input_path + '*.pdb')
str_id = []
for filename in pdb_files:
base = os.path.basename(filename)
structure_id = os.path.splitext(base)[0]
parser.get_structure(structure_id, filename)
str_id.append(structure_id)
data_total = data_processing(input_path)
data_total[0].to_csv(output_path + 'coordinate_' + structure_id + '.csv',
index=False)
data_total[1].to_csv(output_path + 'missing_seq_' + structure_id + '.csv',
index=False)
def getPdbSequance(pdb_file, chain_id):
pdb_indexes = []
pdb_sequance = []
p = PDBParser(PERMISSIVE=1, QUIET=True)
s = p.get_structure("", pdb_file)
pdb_id = pdb_file[0:-4]
if not s[0].has_id(chain_id):
print("PDB " + pdb_id + " doesn't have chain with id " + chain_id)
print()
exit()
chain = s[0][chain_id]
ires = 0
for res in chain:
is_regular_res = res.has_id('N') and res.has_id('CA') and res.has_id(
'C') and (res.get_resname() == 'GLY' or res.has_id('CB'))
res_id = res.get_id()[0]
if (res_id == ' ' or res_id == 'H_MSE' or res_id == 'H_M3L'
or res_id == 'H_CAS') and is_regular_res:
ires = ires + 1
res_name = res.get_resname()
residue_no = res.get_id()[1]
pdb_sequance.append(res_name)
pdb_indexes.append(residue_no)
elif res_id != 'W':
print("Unknown residue in " + pdb_id + " with res_id " + res_id)
pdb_seq = three2one(pdb_sequance)
return pdb_seq, pdb_indexes
def get_CA_coordinates(filename, my_set):
"""
Given a pdb file, it creates a dictionary with the CA (alpha-carbon) coordinates
of those residues that are in the surface (set).
"""
p = PDBParser(PERMISSIVE=1)
s = p.get_structure("code.pdb", filename)
model = s[0]
CA_coordinates = {}
sys.stderr.write("Calculating CA coordinates of residues...\n")
for chain in model:
for residue in chain:
residue_name = str(
residue.get_full_id()[3][1]) + residue.get_full_id()[2]
if residue.get_id()[0] == " " and residue_name in my_set:
residue_number = str(residue.get_resname()) + str(
residue.get_id()[1])
for atom in residue:
if atom.get_name() == "CA":
# get CA coordinates, will be the values
CA = atom.get_coord()
CA_coordinates[residue_number] = tuple(CA)
return CA_coordinates
def getrange(pdbfile):
p = PDBParser(PERMISSIVE=1)
s = p.get_structure("", pdbfile)
chnames = []
pf, pt = 0, 1000000
for c in s[0]: # iterate over chains
cl = list(c) # list of all residues in a chain
#if len(cl)<25: continue # skip short chains
chnames.append(c.get_id())
c = cl
pos = 0
assert (c[0].get_full_id()[3][0]
) == ' ' # first residue is not an HET atom
while c[pos].get_full_id()[3][0] == ' ' and pos < len(c) - 1:
pos += 1
pos -= 1
f, t = c[0].get_id()[1], c[pos].get_id()[1]
#print f,t
if f > pf: pf = f
if t < pt: pt = t
return pf, pt, ' '.join(chnames)
def get_best_res(target_dist, label=None):
minimal_score = 1000000000000000000
best_part = None
ava = 0
for subdir, dirs, files in os.walk("top500H"):
for file in files:
try:
ava += 1
filepath = subdir + os.sep + file
p = PDBParser(PERMISSIVE=1, QUIET=True)
structure = p.get_structure('file', filepath)
residues = [
residue for model in structure for chain in model
for residue in chain
]
atoms = [atom for residue in residues for atom in residue]
print("Processing ", file, ava / 5, "%")
for i in range(0, len(residues)):
res = residues[i]
if label == None or label == three_to_one(
res.get_resname()):
first, last = get_first_last_atoms(res, i)
dist = get_distance(atoms, first, last)
if getScore(dist, target_dist) < minimal_score:
minimal_score = getScore(dist, target_dist)
best_part = res
except Exception:
pass
return best_part
def get_surface_residues(filename, my_acc_array, my_threshold):
"""
Given a pdb file, finds the residues exposed to the solvent (not buried)
according to the ASA (accessible surface area) value given by DSSP module.
The user can select a threshold of ASA. Default is 0.2.
"""
p = PDBParser(PERMISSIVE=1)
s = p.get_structure("code.pdb", filename)
model = s[0]
d = DSSP(model, filename, dssp='mkdssp', acc_array=my_acc_array)
sys.stderr.write("\nHandled %i residues\n" % len(d))
residue_number = set()
for element in sorted(d):
if type(element[3]) is not str: #Sometimes the element[3] is NA
if element[3] >= my_threshold:
# foreach aa in the surface (according to threshold) store residue_number
try:
residue_number.add(
str(list(d.keys())[element[0] - 1][1][1]) +
list(d.keys())[element[0] - 1][0])
except IndexError:
sys.stderr.write("Element " + str(d.keys()[0]) +
" index out of range\n")
return residue_number
def main(file, atom, CAd=15, CBd=12, mind=6):
"""Analyze the pdb using distance between atom and minimum distances."""
logging.info("Analyzing %s using %s", file, atom)
dist = {"CA": CAd, "CB": CBd, "min": mind}
base = os.path.basename(args.file)
name_f = os.path.splitext(base)[0]
parser = PDBParser(PERMISSIVE=1)
logging.captureWarnings(True)
structure = parser.get_structure("test", file)
residues = filter_residues(structure)
dist_matrix = calc_dist_matrix(residues, atom)
title_dist = 'Distances of the file {}'.format(name_f)
name_heatmap = plots.plot_heatmap(dist_matrix, name_f, title_dist, atom)
logging.info("Heatmap %s created", name_heatmap)
cont_matrix = contact_map(dist_matrix, atom, dist)
title_bin = 'Distance contacts of the file {}'.format(name_f)
name_bin = plots.plot_matrix_binary(cont_matrix, name_f, title_bin, atom)
logging.info("Contact map %s created", name_bin)
logging.captureWarnings(False)
return(dist_matrix, cont_matrix)
def load_model(model_fname, model_fmt):
"""
Load a transformation model from the file of the specified format.
If something goes wrong, the function returns None, otherwise the loaded
transformation model is returned.
"""
result = None
if model_fmt == 'pdb':
parser = PDBParser(PERMISSIVE=True)
struct = parser.get_structure('PROMPTPY', model_fname)
if not pdb.is_transformation(struct):
logger.error('specified PDB file is not a transformation')
return None
x = pdb.get_atom_coordinates(struct)
w = pdb.get_atom_masses(pdb.extract_model(struct, 0), True)
result = model.from_conf_coords(x, w)
elif model_fmt == 'json':
with open(model_fname) as f:
result = model.Transformation.from_dict(json.load(f))
elif model_fmt == 'hdf5':
result = model.read_hdf5(model_fname)
else:
logger.error('unknown model format %s', model_fmt)
return result
def distance_alpha_c(centers, draw, unit_dir):
"""
compute the distance between center of mass and each alpha carbon
in the corresponding unit
:param centers: list of coordinates of the centers of mass for each unit
:param draw: boolean, if True pymol is used to draw the respective
geometric element
:return: dictionary of the distaces between center of mass and alpha carbon
"""
unit_dir_list = os.listdir(unit_dir)
unit_dir_list.sort(key=utils.natural_keys)
distances = {}
for unit in unit_dir_list:
pdb_parser = PDBParser()
structure = pdb_parser.get_structure(unit, unit_dir + unit)
alpha_c = alpha_carbon(structure)
center = centers[unit]
distances[unit] = {}
for i, ca in enumerate(alpha_c):
distances[unit][ca.get_id() + str(i)] = distance(
ca.get_coord(), center)
# drawing distances
if draw:
utils.draw_distance_center_mass_alpha(unit, center, alpha_c)
return distances
def compute_localQ_init(MAX_OFFSET=4, DISTANCE_CUTOFF=9.5):
from pathlib import Path
home = str(Path.home())
struct_id = '2xov'
filename = os.path.join(home, "opt/pulling/2xov.pdb")
p = PDBParser(PERMISSIVE=1)
s = p.get_structure(struct_id, filename)
chains = s[0].get_list()
# import pdb file
native_coords = []
for chain in chains:
dis = []
all_res = []
for res in chain:
is_regular_res = res.has_id('CA') and res.has_id('O')
res_id = res.get_id()[0]
if (res.get_resname() == 'GLY'):
native_coords.append(res['CA'].get_coord())
elif (res_id == ' ' or res_id == 'H_MSE' or res_id == 'H_M3L'
or res_id == 'H_CAS') and is_regular_res:
native_coords.append(res['CB'].get_coord())
else:
print('ERROR: irregular residue at %s!' % res)
exit()
native_contacts_table = compute_native_contacts(native_coords, MAX_OFFSET,
DISTANCE_CUTOFF)
return native_contacts_table
def deleteChain():# Delete a complete chain from a pdb and save the new structure in pdbname_free.pdb
parser = PDBParser()
nameStruct=pdb_name.partition('.')[0]
structure = parser.get_structure(nameStruct, pdb_name)
header = parser.get_header()
trailer = parser.get_trailer()
seq=''
nb_chain=input('How many chain do you want to delete : ')
for i in range(nb_chain):
rm_chain=raw_input('What chain you want to delete : ')
for model in structure:
for chain in model:
if(chain.id==rm_chain):
model.detach_child(chain.id)
pept = raw_input('Do you want to get a pdb with the sequence in its name : ')
if(pept == 'y'):
ppb=PPBuilder()
for pp in ppb.build_peptides(structure):
seq = seq + pp.get_sequence()
seq=seq.lower()
seq=str(seq)
w = PDBIO()
w.set_structure(structure)
w.save(seq+'_bound.pdb')
else:
w = PDBIO()
w.set_structure(structure)
w.save(nameStruct+'_without'+rm_chain+'.pdb')
def load_chains(raw, pdb_id, pdb_type, known):
parser = PDBParser()
structure = parser.get_structure(pdb_id, raw)
data = {'ordering': []}
for model in structure:
for chain in model:
chain_id = chain.get_id()
data[chain_id] = {'residues': [], 'sequence': []}
for residue in chain:
name = residue.resname.strip()
if name in known:
res_id = residue.get_id()
id_data = [
structure.get_id(), pdb_type,
model.get_id(), chain_id, res_id[1], residue.resname,
res_id[2]
]
id_data = [str(part).strip() for part in id_data]
unit_id = '_'.join(id_data)
data[chain_id]['residues'].append(unit_id)
data[chain_id]['sequence'].append(known[name])
data['ordering'].append(unit_id)
if not data[chain_id]['residues']:
del data[chain_id]
else:
data[chain_id]['sequence'] = ''.join(
data[chain_id]['sequence'])
return data
def get_aa_residues(pdb, chain):
"""
pdb: Protein Data Bank file.
chain: Chain of the PDB file.
Get the amino acids from a protein.
returns: List of Biopython PDB Residue objects representing the amino acids
of the specified protein.
"""
parser = PDBParser()
structure = parser.get_structure("prot", pdb)
model = structure[0]
chain = model[chain]
# Get a list of all residues in the specified protein model.
residue_list = list(chain.get_residues())
to_remove_list = []
for res in residue_list:
# Store non-amino acid residues in PDB in another list.
if res.get_id()[0] != " ":
to_remove_list.append(res)
# Remove non-amino acid residues from original list.
for res in to_remove_list:
residue_list.remove(res)
return residue_list
def residue_depth(pdbName,
ReaderAtomsInput,
filename,
UseInterfaceAtoms=False):
parser = PDBParser(PERMISSIVE=1)
structure = parser.get_structure(pdbName, filename)
model = structure[0]
BioAtoms = []
for chain in model:
for residue in chain:
for atom in residue:
BioAtoms.append(atom)
if UseInterfaceAtoms:
BioAtoms = pdbReader_to_BioPyth(ReaderAtomsInput, BioAtoms)
surface = get_surface(model)
BioDepthDistances = []
for atom in BioAtoms:
dist = min_dist(atom.get_coord(), surface)
BioDepthDistances.append([atom, dist])
pdbReaderDistances = BioPyth_to_pdbReader(BioDepthDistances,
ReaderAtomsInput)
return pdbReaderDistances
def free_cys_tyr(pdb_utils):
parser = PDBParser(PERMISSIVE=1, QUIET=1)
_log.debug("procesing free cys/tyr")
total = ExperimentalStructure.objects(residue_sets__name__ne = "free_tyr").count()
for strdoc in tqdm(ExperimentalStructure.objects(residue_sets__name__ne = "free_tyr").no_cache().timeout(False), total=total):
if not (strdoc.residue_set("free_cys") or strdoc.residue_set("free_tyr")):
if not os.path.exists(pdb_utils.pdb_path(strdoc.name)):
pdb_utils.update_pdb(strdoc.name)
if not os.path.exists(pdb_utils.pdb_path(strdoc.name)):
continue
try:
bp_pdb = list(parser.get_structure(strdoc.name, pdb_utils.pdb_path(strdoc.name) ))[0]
except PDBConstructionException:
continue
except TypeError:
continue
free = {"CYS": [], "TYR": []}
codes = {"CYS": "SG", "TYR": "OH"}
for x in bp_pdb.get_residues():
if x.resname in codes:
neighbor_atoms = set(list(bp_pdb.get_atoms())) - set(list(x))
if (codes[x.resname] in x) and (
not any(map(lambda atom: (x[codes[x.resname]] - atom) <= 3, neighbor_atoms))):
free[x.resname].append(x.parent.id + "_" + str(x.id[1]))
if free["CYS"]:
rs = ResidueSet(name="free_cys", residues=free["CYS"])
strdoc.residue_sets.append(rs)
if free["TYR"]:
rs = ResidueSet(name="free_tyr", residues=free["TYR"])
strdoc.residue_sets.append(rs)
if free["CYS"] or free["TYR"]:
strdoc.save()
def getPDBSequence(pdb_name, pdb_path, chain):
logging.info("getPDBSequence pdb " + pdb_name + " cadena " + chain)
from Bio.PDB.PDBParser import PDBParser
from Bio.PDB.Polypeptide import three_to_one
from Bio.PDB.Polypeptide import is_aa
residue_position = []
residue_name = list()
try:
parser = PDBParser(PERMISSIVE=1)
structure = parser.get_structure(pdb_name, pdb_path)
model = structure[0]
chain = model[chain]
for residue in chain:
if is_aa(residue.get_resname(), standard=True):
residue_name.append(three_to_one(residue.get_resname()))
residue_position.append(residue.get_full_id()[3][1])
#else:
#residue_name.append("X")
#residue_position.append(residue.get_full_id()[3][1])
#raise Exception("Secuencia no valida, error en la posicion: " + str(residue.get_full_id()[3][1]))
except Exception as inst:
print inst
logging.error(
"Error no controlado intentando leer la sequencia del pdb " +
pdb_name + " cadena " + chain + " path " + pdb_path)
raise Exception("PDB Invalido pdb " + pdb_name + " cadena " + chain +
" path " + pdb_path)
return residue_position, residue_name
'''
def parse_pdb_local(code):
code = code.lower()
path = '%s/%s/pdb%s.ent.gz' % (LOCAL_PDB_DIR, code[1:3], code)
f = gzip.open(path, 'rb')
p = PDBParser()
structure = p.get_structure(code, f)
return structure
def update_entry_data(self, code, pdb_path):
pdb_model = PDB.objects.get(code=code)
p = PDBParser(PERMISSIVE=True, QUIET=True)
chains = list(p.get_structure(code, pdb_path)[0].get_chains())
for chain in tqdm(chains):
self._process_chain_residues(pdb_model, chain)
self._process_chain_atoms(pdb_model, chain)
def load_chains(raw, pdb_id, pdb_type, known):
parser = PDBParser()
structure = parser.get_structure(pdb_id, raw)
data = {'ordering': []}
for model in structure:
for chain in model:
chain_id = chain.get_id()
data[chain_id] = {'residues': [], 'sequence': []}
for residue in chain:
name = residue.resname.strip()
if name in known:
res_id = residue.get_id()
id_data = [structure.get_id(), pdb_type, model.get_id(),
chain_id, res_id[1], residue.resname, res_id[2]]
id_data = [str(part).strip() for part in id_data]
unit_id = '_'.join(id_data)
data[chain_id]['residues'].append(unit_id)
data[chain_id]['sequence'].append(known[name])
data['ordering'].append(unit_id)
if not data[chain_id]['residues']:
del data[chain_id]
else:
data[chain_id]['sequence'] = ''.join(data[chain_id]['sequence'])
return data
def getPdbSequance(pdb_file, chain_id):
pdb_indexes = []
pdb_sequance = []
p = PDBParser(PERMISSIVE=1)
s = p.get_structure("", pdb_file)
pdb_id = pdb_file[0:-4]
if not s[0].has_id(chain_id):
print "PDB "+pdb_id+" doesn't have chain with id "+chain_id
print
exit()
chain = s[0][chain_id]
ires = 0
for res in chain:
is_regular_res = res.has_id('N') and res.has_id('CA') and res.has_id('C') and (res.get_resname()=='GLY' or res.has_id('CB'))
res_id = res.get_id()[0]
if (res_id ==' ' or res_id =='H_MSE' or res_id =='H_M3L' or res_id =='H_CAS') and is_regular_res:
ires = ires + 1
res_name = res.get_resname()
residue_no = res.get_id()[1]
pdb_sequance.append(res_name)
pdb_indexes.append(residue_no)
elif res_id !='W':
print "Unknown residue in "+pdb_id+" with res_id "+res_id
pdb_seq = three2one(pdb_sequance)
return pdb_seq, pdb_indexes
def get_biopython_structure(path, model_id=None):
structure = None
path = path.strip()
parser = PDBParser()
if not model_id:
model_id = os.path.basename(path)
if os.path.basename(path).split('.')[-1] == "pdb":
structure = parser.get_structure(model_id, path)
elif os.path.basename(path).split('.')[-1] == 'gz':
GZ = gzip.open(path, 'rb')
structure = parser.get_structure(model_id, GZ)
GZ.close()
else:
sys.exit("Unknown extension to read PDB: " + path)
return structure
def read_pdbs(directory, verbose=False):
"""Reads the input directory and generates pdb models"""
if verbose:
print("Reading pdb input files from %s" % directory)
parser = PDBParser(PERMISSIVE=1, QUIET=True)
if os.path.isdir(directory) and directory.endswith("/"):
try:
pdbmodels = [
parser.get_structure("Model_pair", directory + f)[0]
for f in listdir(directory) if f.endswith(".pdb")
] # Generates pdb objects for files that end with .pdb
except:
sys.stderr.write(
"PDB files couldn't be opened. Please, revise that their format is correct."
)
sys.exit(1)
else:
sys.stderr.write(
"Directory %s doesn't exists, please select a valid directory." %
directory)
sys.exit(1)
if not bool(pdbmodels): # If no pdb instance is generated
sys.stderr.write(
"No pdb files where read. Please make sure the given directory contains pdb files. "
)
sys.exit(1)
for model in pdbmodels:
if len(model.child_list) != 2:
sys.stderr.write(
"A pdb input file doesn't contains two chains. Please, all input pdbs must only contain "
"two chains.")
sys.exit(1)
if verbose:
print("Pdb objects stored")
return pdbmodels
def get_template_stech_dict(template, seq_dict, verbose=False):
"""Generates a stechometry dictionary for a given pdb template"""
template_stech_dict = {
} # Format: { "A": 2, "B": 3, ...}, where key is chain id and value
# is the number of repetitions
parser = PDBParser(PERMISSIVE=1, QUIET=True)
template_object = parser.get_structure(
"template", template)[0] # Generates pdb template object
for chain in template_object:
chain = CustomChain(
chain) # Transforms pdb chain object to CustomChain instance
chain.parent = None # Removes previous parent to evade biopython errors of id repetitions
chain_seq = chain.get_sequence()
if chain_seq in seq_dict:
chain.id = seq_dict[
chain_seq] # Updates the template chain id to the corresponding by its sequence
template_stech_dict.setdefault(chain.id, 0)
template_stech_dict[chain.id] += 1 # Adds to chain id counter
if verbose: # Transforms the stech_dict to a string to be printed
stechometry_string = ""
for key in sorted(template_stech_dict.keys()):
stechometry_string += key + ":" + str(
template_stech_dict[key]) + ","
stechometry_string = stechometry_string[:-1]
print("Template's Stoichiometry is: " + stechometry_string)
return template_stech_dict
def Init():
ptask = open("task.input","r")
para = {}
jobs = []
for line in ptask.readlines():
if(line[0]=='/' or line[0]=='\n'):
continue
[a,b] = line.split("=")
if a=='angle':
jobs.append([float(x) for x in b.strip().split(',')])
else:
para[a]=b.strip()
ptask.close()
filename = para['protein_file']
protein_name = filename.strip().split('.')[0]
file_type = filename.strip().split('.')[1]
if file_type == 'cif':
mt = MMCIF2Dict(filename)
xlist = [float(x) for x in mt['_atom_site.Cartn_x']]
ylist = [float(x) for x in mt['_atom_site.Cartn_y']]
zlist = [float(x) for x in mt['_atom_site.Cartn_z']]
allarr = numpy.vstack((xlist,ylist,zlist)).T
elif file_type == 'pdb':
parser = PDBParser()
structure = parser.get_structure("test", filename)
atoms = structure.get_atoms()
alllist = []
xlist = []
ylist = []
zlist = []
for atom in atoms:
xlist.append(atom.get_coord()[0])
ylist.append(atom.get_coord()[1])
zlist.append(atom.get_coord()[2])
alllist.append(atom.get_coord())
allarr = numpy.array(alllist)
if para['CENTER'] == 'ON':
x_ave = allarr.mean(axis=0)[0]
y_ave = allarr.mean(axis=0)[1]
z_ave = allarr.mean(axis=0)[2]
allarr[:,0] = allarr[:,0]-x_ave;
allarr[:,1] = allarr[:,1]-y_ave;
allarr[:,2] = allarr[:,2]-z_ave
scr_size = int(para['scr_size'])
pix_size = float(para['pix_size'])
distance = float(para['distance'])
wavenum = 1.0/float(para['lambda'])
ssc = scr_size/2.0-0.5
s = numpy.zeros((scr_size,scr_size,3))
for i in range(scr_size):
for j in range(scr_size):
x = (i-ssc)*pix_size
y = (j-ssc)*pix_size
z = distance
sr = numpy.sqrt(x*x+y*y+z*z)
s[i,j,:] = numpy.array([x*wavenum/sr,y*wavenum/sr,z*wavenum/sr-wavenum])
return s,allarr
def get_pdb_sequence(input_pdb_file,
chain_id,
mapping_output=False,
with_gaps=False):
"""Gets the PDB sequence in a dictionary"""
mapping = {}
pdb_parser = PDBParser(PERMISSIVE=True, QUIET=True)
structure = pdb_parser.get_structure(input_pdb_file, input_pdb_file)
model = structure[0]
chain = model[chain_id]
residues = list(chain)
for res in residues:
# Remove alternative location residues
if "CA" in res.child_dict and is_aa(res) and res.id[2] == ' ':
try:
mapping[res.id[1]] = three_to_one(res.get_resname())
except KeyError:
# Ignore non standard residues such as HIC, MSE, etc.
pass
if with_gaps:
# Add missing gap residues by their residue number
res_numbers = sorted(mapping.keys())
start, end = res_numbers[0], res_numbers[-1]
missing = sorted(set(range(start, end + 1)).difference(res_numbers))
for m in missing:
mapping[m] = '-'
if mapping_output:
return mapping
else:
return ''.join([mapping[k] for k in sorted(mapping.keys())])
def collect_1(self, checkboard = []):
def getChains(s):
ret = s.split('___')[:2]
assert(len(ret) == 2)
return ret
parser = PDBParser()
io = PDBIO()
for f in self.files:
if not checkboard:
break
if f not in checkboard:
continue
try:
os.mkdir(os.path.join( self.outpath, f))
except OSError as e:
if e.errno != errno.EEXIST:
raise
structure = parser.get_structure(f, os.path.join(self.inpath, f))
chain_A, chain_B = getChains(f)
io.set_structure(structure[0]['A'])
io.save(os.path.join(self.outpath,f,chain_A + '.pdb'))
io.set_structure(structure[0]['B'])
io.save(os.path.join(self.outpath,f,chain_B + '.pdb'))
#make this module can be reuse to other application
self.then_do(f)
#remove the finished file from checkboard
checkboard.remove(f)
def scwrl(self, altseq):
""" Repacks sidechains using SCWRL4 and returns a copy """
io = PDBIO()
seqfname = "temp/%d.txt" % multidigit_rand(10)
with open(seqfname, 'wb') as seqfile:
structfile = "temp/%d.pdb" % multidigit_rand(10)
seqfile.write(altseq)
scwrlfile = structfile + ".scwrl"
io.set_structure(self.structure)
io.save(structfile)
cmd = [
"scwrl", "-0", "-i", structfile, '-s', seqfname, '-o', scwrlfile
]
print "\n%s" % ' '.join(cmd)
sp.Popen(cmd, stdout=sp.PIPE, stderr=sp.PIPE).communicate()
p = PDBParser()
with open(scwrlfile, 'rb') as fin:
filterwarnings('ignore', category=PDBConstructionWarning)
s = p.get_structure(self.id, scwrlfile)
resetwarnings()
s = PDBMapStructure(s, pdb2pose={}, refseq=self.refseq)
os.remove(structfile)
os.remove(scwrlfile)
os.remove(seqfname)
return s
def main(args):
if not os.path.exists(args.out_folder):
os.mkdir(args.out_folder)
proteins = []
for file in os.listdir(args.in_folder):
if file.endswith('.pdb'):
proteins.append(file)
if args.join == 'yes':
result_joined = pd.DataFrame()
for protein in proteins:
ID = protein.replace('.pdb', '')
parser = PDBParser()
protein_path = args.in_folder + '/' + protein
structure = parser.get_structure(ID, protein_path)
result = combine(structure)
if result is None:
print('No ligands and/or water present in ', ID)
continue
if args.join == 'no':
result_path = args.out_folder + '/' + ID + '.csv'
result.to_csv(result_path)
elif args.join == 'yes':
result_joined = pd.concat([result_joined, result])
if args.join == 'yes':
result_joined_path = args.out_folder + '/' + 'AALI_contacts.csv'
result_joined.to_csv(result_joined_path)
def __pdb_ordering__(self, raw, pdb_id, pdb_type):
"""Generate a dict of the form: { unit_id: {index: index, pdb: pdb }
for all nucleotides in the given structure. Nucleotides are identified
by being in the list of known units in self.known.
"""
parser = PDBParser(QUIET=True)
structure = parser.get_structure(pdb_id, raw)
data = {}
index = 0
for model in structure:
model_id = model.get_id() + 1
for chain in model:
chain_id = chain.get_id()
for residue in chain:
name = residue.resname.strip()
if name in self.known:
res_id = residue.get_id()
id_data = [structure.get_id(), pdb_type, model_id,
chain_id, res_id[1], name, res_id[2]]
id_data = [str(part).strip() for part in id_data]
unit_id = '_'.join(id_data)
data[unit_id] = {'index': index, 'pdb': pdb_id}
index += 1
return data
def old_residue_ids(raw, filename):
parser = PDBParser()
path, ext = os.path.splitext(filename)
pdb_id = os.path.basename(path)
structure = parser.get_structure(pdb_id, raw)
data = []
pdb_type = 'AU'
if ext != '.pdb':
pdb_type = 'BA' + filename[-1]
for model in structure:
# BioPython seems to start number models at 0, but it should start
# at 1.
model_id = str(model.get_id() + 1)
for chain in model:
chain_id = chain.get_id()
for residue in chain:
res_id = residue.get_id()
data.append({
'pdb': pdb_id,
'type': pdb_type,
'model': model_id,
'chain': chain_id,
'number': str(res_id[1]),
'unit': residue.resname.strip(),
'insertion': res_id[2].rstrip()
})
return data
def prepare_virtual_sites(pdb_file, use_cis_proline=False):
parser = PDBParser(QUIET=True)
structure=parser.get_structure('X',pdb_file,)
for model in structure:
for chain in model:
r_im={}
r_i={}
for residue in chain:
r_im=r_i
r_i={}
for atom in residue:
r_i[atom.get_name()]=atom
if use_cis_proline and residue.get_resname() == "IPR":
if 'N' in r_i:
r_i['N'].set_coord(-0.2094*r_im['CA'].get_coord()+ 0.6908*r_i['CA'].get_coord() + 0.5190*r_im['O'].get_coord())
if 'C' in r_im:
r_im['C'].set_coord(0.2196*r_im['CA'].get_coord()+ 0.2300*r_i['CA'].get_coord() + 0.5507*r_im['O'].get_coord())
if 'H' in r_i:
r_i['H'].set_coord(-0.9871*r_im['CA'].get_coord()+ 0.9326*r_i['CA'].get_coord() + 1.0604*r_im['O'].get_coord())
else:
if 'N' in r_i:
r_i['N'].set_coord(0.48318*r_im['CA'].get_coord()+ 0.70328*r_i['CA'].get_coord()- 0.18643 *r_im['O'].get_coord())
if 'C' in r_im:
r_im['C'].set_coord(0.44365*r_im['CA'].get_coord()+ 0.23520*r_i['CA'].get_coord()+ 0.32115 *r_im['O'].get_coord())
if 'H' in r_i:
r_i['H'].set_coord(0.84100*r_im['CA'].get_coord()+ 0.89296*r_i['CA'].get_coord()- 0.73389 *r_im['O'].get_coord())
io = PDBIO()
io.set_structure(structure)
io.save(pdb_file)
def get_normalized_pairs(n):
'''Return a dictionary with keys corresponding to the pairs of residues found
within a radius n, and the values to the number of times found in a set of pdb files.\
This dictionary sets the knowledge of pair-residues at a given frequency found naturally\
in nature. It is based in 1.110 sequences with known structure with <40% of homology in\
order to avoid family redundancy. Not necessary for the package.'''
p = PDBParser(PERMISSIVE=1)
pdb = glob.glob('./pdbfiles/*.ent')
pairs = []
file_list = []
###### Parsing through PDB files #######
for filename in pdb:
s = p.get_structure('X', filename)
atom_list = np.array([atom for atom in s.get_atoms() if atom.name == 'CB'])
if len(atom_list)>2:
#creates a list containing all atom pairs within a n radius
ns = Bio.PDB.NeighborSearch(atom_list)
neighbors = ns.search_all(n)
file_list.append(filename)
sys.stderr.write(filename+' processed.\n') #check-point
else:
sys.stderr.write(filename+' could not be processed.\n') #check-point
pass
pairs = [(x.get_parent().get_resname(),y.get_parent().get_resname()) for x,y in neighbors]
outfile = open( 'normalized_pairs8.py', 'w' )
counter = dict(Counter(pairs))
sys.stderr.write(str(len(file_list))+' files processed.\n') #check-point
sys.stderr.write('Dictionary length: '+str(len(counter))+'.\n') #check-point
outfile.write('\nNormalized_pairs_'+str(n)+'='+str(counter))
outfile.close()
def score(PDBfile):
"""
Calculates the m-score for a given PDB file
arguments:
PDBfile - the PDB file to score
hidden arguments:
aas.scr, pro.scr, gly.scr - the scoring tables
need to be present in working directory
"""
from pro_angle import find_residue
from Bio.PDB.PDBParser import PDBParser
from pro_length import length
(aas, gly, pro) = load_scores() ##define global tables
score = 0 #initialize
pars = PDBParser(PERMISSIVE = 1)
struct = pars.get_structure(PDBfile.rstrip('.pdb'), PDBfile)
model = struct.child_list[0]
chain = model.child_list[0]
pro_list = find_residue(chain, 'PRO')
gly_list = find_residue(chain, 'GLY')
aas_list = range(chain.child_list[1].id[1],
chain.child_list[len(chain)-1].id[1])
#need to remove pro/gly indices in first/last position
if pro_list.count(1) > 0:
pro_list.remove(1)
if pro_list.count(len(chain)-1) > 0:
pro_list.remove(len(chain)-1)
if gly_list.count(1) > 0:
gly_list.remove(1)
if gly_list.count(len(chain)-1) > 0:
gly_list.remove(len(chain)-1)
try:
for index in pro_list:
aas_list.remove(index) #remove pros from aas_list
for index in gly_list:
aas_list.remove(index) #remove glys from aas_list
except ValueError:
print 'incosistency in PDB file - will return score = 0'
return 0
else:
proscore = score_help(chain, pro_list, pro)
glyscore = score_help(chain, gly_list, gly)
aasscore = score_help(chain, aas_list, aas)
score = proscore+glyscore+aasscore
size=length(chain)
try:
score = (score/size)*1000 #normalize score
return score
except ZeroDivisionError:
print "calculated protein length 0 -> returning score 0"
score = 0
return score
def score (query_pdb_path,
against_pdb_path,
query_fp_path = None,
against_fp_path = None,
query_epitope = [],
against_epitope = [],
spin_image_height_step = 5,
spin_image_radius_step = 2,
sphere_radius_step = 2,
cutoff = 20.0,
spin_image_radius_range = (0, 20),
spin_image_height_range = (-30, 10),
sphere_radius_range = (0, 20),
callback = write_score_to_file, cbargs=[]):
p = PDBParser(PERMISSIVE=1)
query_struct = p.get_structure(os.path.basename (query_pdb_path), query_pdb_path)
against_struct = p.get_structure(os.path.basename (against_pdb_path), against_pdb_path)
query_complex = Complex (query_struct, query_epitope)
against_complex = Complex (against_struct, against_epitope)
if query_fp_path is None or against_fp_path is None:#if fp is not given
query_complex.get_fp(spin_image_radius_step = spin_image_radius_step, spin_image_height_step = spin_image_height_step, sphere_radius_step = sphere_radius_step)
against_complex.get_fp(spin_image_radius_step = spin_image_radius_step, spin_image_height_step = spin_image_height_step, sphere_radius_step = sphere_radius_step)
query_fp_string = query_complex.fp2str ()
against_fp_string = against_complex.fp2str ()
else:
#if fp is given, read them
with open (query_fp_path, 'r') as f1, open(against_fp_path, 'r') as f2:
query_fp_string = f1.read ()
against_fp_string = f2.read ()
query = FPWithComplex (query_complex, query_fp_string)
against = FPWithComplex (against_complex, against_fp_string)
score1, score2, score3 = similarity_between (query, against, cutoff = cutoff)
#z1, z2, z3 = similarity_between (query, query, cutoff = cutoff) #the normalization constant
#print score1, score2, score3
if callback is not None:
callback ((score1, score2, score3), *cbargs)
return score1, score2, score3
def get_structure(pdb_id):
'''Returns a PDB structure.'''
source_url = 'http://www.rcsb.org/pdb/files/' + pdb_id + '.pdb'
target_filename = os.path.join(os.path.expanduser('~'), _DIR, _PDB_DIR,
pdb_id + '.pdb')
with open(io_utils.get_file(source_url, target_filename)) as pdb_file:
parser = PDBParser(QUIET=True)
return parser.get_structure(pdb_id, pdb_file.name)
def main():
if len(sys.argv) < 2:
sys.exit("Usage: %s input_pdb_file" % sys.argv[0])
pdb_name = sys.argv[1]
parser = PDBParser(PERMISSIVE=1)
structure_id = "temp"
structure = parser.get_structure(structure_id, pdb_name)
model = structure[0]
calculate_ss(model)
def parse(self, *pdb_filenames):
"""
REQUIRED. Adds the protein PDB files. You can specify as many as you want, but only two will be used for the superimposition.
"""
self.proteins = [] # reset proteins to an empty array
parser = PDBParser(QUIET=True)
for filename in pdb_filenames:
# use file name as PDB id
pdb_id = self.__get_pdb_id_from_filename(filename)
# get PDB contents
self.proteins.append(parser.get_structure(pdb_id, filename))
def removeDoubleAtoms():# Remove all double atoms defined in a pdb and save the new structure in pdbname_noDouble.pdb
parser = PDBParser()
nameStruct=pdb_name.partition('.')[0]
structure = parser.get_structure(nameStruct, pdb_name)
header = parser.get_header()
trailer = parser.get_trailer()
structure.remove_disordered_atoms()
w = PDBIO()
w.set_structure(structure)
w.save(nameStruct+'_noDouble.pdb')
def get_pdb_structure(pdb_file, pdb_id=None, quiet=True):
"""Set QUIET to False to output warnings like incomplete chains etc."""
if pdb_id is None:
pdb_id = get_pdb_id(pdb_file)
parser = PDBParser(get_header=True, QUIET=quiet)
if pdb_file.endswith('.gz'):
with gzip.open(pdb_file, 'rt') as ifh:
structure = parser.get_structure(pdb_id, ifh)
else:
structure = parser.get_structure(pdb_id, pdb_file)
# Rename empty chains (i.e. chain.id == ' ')
model = structure[0]
chain_ids = {chain.id for chain in model.child_list}
for chain in model.child_list:
if chain.id in [' ', 'Z']:
chain_ids.remove(chain.id)
chain.id = next(c for c in string.ascii_uppercase if c not in chain_ids)
chain_ids.add(chain.id)
model.child_dict = {chain.id: chain for chain in model.child_list}
return structure
def Draw(self, parent, filename):
p = PDBParser(PERMISSIVE=1)
# structure_id = Rec[1]
structure = p.get_structure("WHYY", filename)
self.pdbMat = structure.get_list()
rx = []
ry = []
rz = []
bx = []
by = []
bz = []
gx = []
gy = []
gz = []
for chain in self.pdbMat[0].get_list():
for resnum, residue in enumerate(chain.get_list()):
atom = residue.get_list()
if len(atom) > 3:
if resnum > 1:
bx[resnum - 2].append(npos[0])
by[resnum - 2].append(npos[1])
bz[resnum - 2].append(npos[2])
npos = atom[0].get_coord()
capos = atom[1].get_coord()
cpos = atom[2].get_coord()
opos = atom[3].get_coord()
rx.append([npos[0], capos[0]])
ry.append([npos[1], capos[1]])
rz.append([npos[2], capos[2]])
bx.append([capos[0], cpos[0]])
by.append([capos[1], cpos[1]])
bz.append([capos[2], cpos[2]])
gx.append([cpos[0], opos[0]])
gy.append([cpos[1], opos[1]])
gz.append([cpos[2], opos[2]])
for n, line in enumerate(rx):
x = np.array(line)
y = np.array(ry[n])
z = np.array(rz[n])
parent.ax2.plot(x, y, z, "r-", linewidth=5)
for n, line in enumerate(bx):
x = np.array(line)
y = np.array(by[n])
z = np.array(bz[n])
parent.ax2.plot(x, y, z, "b-", linewidth=5)
for n, line in enumerate(gx):
x = np.array(line)
y = np.array(gy[n])
z = np.array(gz[n])
parent.ax2.plot(x, y, z, "g-", linewidth=5)
def Pdb2Gro(pdb_file, gro_file, ch_name):
from Bio.PDB.PDBParser import PDBParser
p = PDBParser(PERMISSIVE=1)
pdb_id = pdb_file
if pdb_file[-4:].lower()!=".pdb":
pdb_file = pdb_file + ".pdb"
if pdb_id[-4:].lower()==".pdb":
pdb_id = pdb_id[:-4]
output = gro_file
s = p.get_structure(pdb_id, pdb_file)
chains = s[0].get_list()
if ch_name=='':
ch_name = 'A'
for chain in chains:
if chain.get_id()==ch_name:
ires = 0
iatom = 0
res_name = ""
atoms = []
for res in chain:
is_regular_res = res.has_id('N') and res.has_id('CA') and res.has_id('C')
res_id = res.get_id()[0]
if (res_id ==' ' or res_id =='H_MSE' or res_id =='H_M3L' or res_id=='H_CAS') and is_regular_res:
ires = ires + 1
res_name = res.get_resname()
residue_no = res.get_id()[1]
for atom in res:
iatom = iatom + 1
atom_name = atom.get_name()
xyz = atom.get_coord()
# residue_no = atom.get_full_id()[3][1]
atoms.append( Atom(iatom, atom_name, residue_no, res_name, xyz) )
out = open(output, 'w')
out.write(" Structure-Based gro file\n")
out.write( (" "+str(len(atoms)))[-12:] )
out.write("\n")
for iatom in atoms:
iatom.write_(out)
out.close()
def get_ca(pdbfile):
p=PDBParser(PERMISSIVE=1)
ca_atoms = []
s = p.get_structure(pdbfile,pdbfile)
chains = s[0].get_list()
for chain in chains:
for res in chain:
is_regular_res = res.has_id('CA') and res.has_id('O')
res_id = res.get_id()[0]
if (res_id==' ' or res_id=='H_MSE' or res_id=='H_M3L' or res_id=='H_CAS' ) and is_regular_res:
resname = res.get_resname();
ca_atoms.append(res['CA'].get_coord())
else :
print "Pdb file contains irregular residue names or missing CA / O atoms! Fix it and run again! Exit with error."
print "res_id :", res_id
sys.exit()
return ca_atoms
def __init__(self, filename):
self.spheredata = ''
E2C = {}
E2R = {}
exec elements # Read the color mappings at the bottom of this file
# Read the file
atoms = []
parser = PDBParser()
structure = parser.get_structure('test',filename)
for model in structure.get_list():
for chain in model.get_list():
for residue in chain.get_list():
for atom in residue.get_list():
atoms += [atom]
# Look up colors and radius
spheres = []
for atom in atoms:
s = Sphere()
s.x, s.y, s.z = atom.get_coord()
element = atom.get_name().strip(string.digits)
s.radius = E2R[element] if E2R.has_key(element) else 1.5
color = E2C[element] if E2C.has_key(element) else 0xFF1493
s.r = (color & 0xff) / 255.0
s.g = ((color & 0xff00) >> 8) / 255.0
s.b = ((color & 0xff0000) >> 16) / 255.0
spheres += [s]
self.spheredata += struct.pack('fff f ffff', s.x,s.y,s.z, s.radius, s.r,s.g,s.b,1.0)
self.spheres = spheres
# Figure out the total radius
xs, ys, zs = [s.x for s in spheres], [s.y for s in spheres], [s.z for s in spheres]
dx = max(xs) - min(xs)
dy = max(ys) - min(ys)
dz = max(zs) - min(zs)
self.radius = np.sqrt(dx*dx + dy*dy + dz*dz) / 2 + 1.5
self.x = (max(xs) + min(xs)) / 2
self.y = (max(ys) + min(ys)) / 2
self.z = (max(zs) + min(zs)) / 2
def renameChain():
parser = PDBParser()
nameStruct=pdb_name.partition('.')[0]
structure = parser.get_structure(nameStruct, pdb_name)
header = parser.get_header()
trailer = parser.get_trailer()
what_chain=raw_input('What is the chain you want to rename : ')
what_chain2=raw_input('What is the new name of this chain : ')
for model in structure:
for chain in model:
if chain.id == what_chain:
chain.id = what_chain2
w = PDBIO()
w.set_structure(structure)
w.save(nameStruct+'_rename.pdb')
def removeHetero():# Remove all heteroatoms from a pdb and save the new structure in pdbname_noHetero.pdb
parser = PDBParser()
nameStruct=pdb_name.partition('.')[0]
structure = parser.get_structure(nameStruct, pdb_name)
header = parser.get_header()
trailer = parser.get_trailer()
for model in structure:
for chain in model:
for residue in chain:
id = residue.id
if id[0] != ' ':
chain.detach_child(residue.id)
if len(chain) == 0:
model.detach_child(chain.id)
w = PDBIO()
w.set_structure(structure)
w.save(nameStruct+'_noHetero.pdb')
def deleteResidue():# Delete a residue from a pdb and save the new structure in pdbname_noResidue.pdb
parser = PDBParser()
nameStruct=pdb_name.partition('.')[0]
structure = parser.get_structure(nameStruct, pdb_name)
header = parser.get_header()
trailer = parser.get_trailer()
rm_residue=raw_input('What residue you want to delete : ')
for model in structure:
for chain in model:
for residue in chain:
print residue.id
if(residue.id[1]==rm_residue):
print 'HELLO'
chain.detach_child(residue.id)
w = PDBIO()
w.set_structure(structure)
w.save(nameStruct+'_noResidue.pdb')
def Draw(self, parent, filename):
p = PDBParser(PERMISSIVE=1)
structure = p.get_structure('WHYY', filename)
self.pdbMat = structure.get_list()
rx = []
ry = []
rz = []
bx = []
by = []
bz = []
gx = []
gy = []
gz = []
for chain in self.pdbMat[0].get_list():
for residue in chain.get_list():
for atom in residue.get_list():
if atom.get_id()[0][0] not in ["H","W"]:
pos = atom.get_coord()
if atom.get_name() == 'CA':
bx.append(pos[0])
by.append(pos[1])
bz.append(pos[2])
elif atom.get_name() == 'N':
rx.append(pos[0])
ry.append(pos[1])
rz.append(pos[2])
elif atom.get_name() == 'O':
gx.append(pos[0])
gy.append(pos[1])
gz.append(pos[2])
x = np.array(bx)
y = np.array(by)
z = np.array(bz)
parent.ax2.scatter(x, y, z, zdir='z', marker='o', s=385, c='b') #385 is the radius of carbon times 5
x = np.array(rx)
y = np.array(ry)
z = np.array(rz)
parent.ax2.scatter(x, y, z, zdir='z', marker='o', s=350, c='r') #350 is the radius of Nitrogen times 5
x = np.array(gx)
y = np.array(gy)
z = np.array(gz)
parent.ax2.scatter(x, y, z, zdir='z', marker='o', s=330, c='g') #330 is the radius of oxygen times 5
def getSequence(): # Get the sequence of a specific chain
parser = PDBParser()
nameStruct=pdb_name.partition('.')[0]
structure = parser.get_structure(nameStruct, pdb_name)
header = parser.get_header()
trailer = parser.get_trailer()
seq=''
what_chain=raw_input('For what chain do you want the sequence : ')
for model in structure:
for chain in model:
if chain.id != what_chain:
model.detach_child(chain.id)
ppb=PPBuilder()
for pp in ppb.build_peptides(structure):
seq = seq + pp.get_sequence()
seq=seq.upper()
print seq
def Draw(self, parent, filename):
p = PDBParser(PERMISSIVE=1)
#structure_id = Rec[1]
structure = p.get_structure('WHYY', filename)
self.pdbMat = structure.get_list()
x = []
y = []
z = []
for chain in self.pdbMat[0].get_list():
for residue in chain.get_list():
for atom in residue.get_list():
if atom.get_name() == 'CA':
pos = atom.get_coord()
x.append(pos[0])
y.append(pos[1])
z.append(pos[2])
x = np.array(x)
y = np.array(y)
z = np.array(z)
parent.ax2.plot(x,y,z)
def GetExec():
Recs = os.listdir(os.getcwd())
newList = []
j = 0
listdata=dict()
k = 0
p = PDBParser(PERMISSIVE=1)
ftime = open('lastChecked.txt','r')
pT = float(ftime.readline())
ftime.close()
f = open('lastChecked.txt','w')
f.write(str(time.time()))
f.close()
while k < len(Recs):
try:
(name, ext) = os.path.splitext(Recs[k])
if ext=='':
2+2
elif ext==".pdb":
f = name + ".pickle"
newList.append([Recs[k],os.getcwd()])
if not os.path.isfile(f) or float(fmt.filemtime(Recs[k])) > pT:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
pdbRec = p.get_structure(name, Recs[k])
models = pdbRec.get_list()
listdata[j] = str(name), len(models), os.getcwd()+'/'+str(name) + str(ext)
rHoward = [str(name), len(models), str(name) + str(ext)]
mP.spickle(f, rHoward)
else:
rHoward = mP.opickle(f)
listdata[j] = str(rHoward[0]), rHoward[1], rHoward[2]
j += 1
except IOError, e:
print e
k += 1
def assembleChain(): # Allow to assemble 2 chains together
parser = PDBParser()
nameStruct=pdb_name.partition('.')[0]
structure = parser.get_structure(nameStruct, pdb_name)
header = parser.get_header()
trailer = parser.get_trailer()
what_chain=raw_input('What is the 1st chain you want to assemble : ')
what_chain2=raw_input('What is the 2nd chain you want to assemble : ')
for model in structure:
for chain in model:
if chain.id == what_chain:
parent=chain;
elif chain.id == what_chain2:
for residue in chain:
residue.get_parent().id=what_chain
w = PDBIO()
w.set_structure(structure)
w.save(nameStruct+'_assemble.pdb')
def parsePDBInformation(self, file):
"""
Parses a single pdb file and counts the residues in
helices, sheets and the total length of the protein
"""
helices = []
helixSequences = []
f = open(self.dir + "/" + file)
line = f.readline()
while line:
#If HELIX, check for type and add length and positions
if line.startswith("HELIX"):
start = int(line[21:25].replace(" " ,""))
end = int(line[33:37].replace(" ", ""))
type = int(line[39:40])
chain = line[19:20].replace(" ", "")
currentHelix = (start, end, chain)
if type == 1:
helices.append(currentHelix)
line = f.readline()
else:
line = f.readline()
f.close()
# Parse the structure with a PDBParser object
pdbParser = PDBParser()
structure = pdbParser.get_structure("currentFile", self.dir+"/"+file)
# For every helix tuple, extract the residues and store them in helixSequences
for helix in helices:
if helix[2] == "":
residues = structure.get_residues()
helixSequences.append(self.getResiduesFromList(residues, helix[0], helix[1]))
chains = structure.get_chains()
for chain in chains:
if (chain.get_id() == helix[2]):
helixSequences.append(self.getResiduesFromChain(chain, helix[0], helix[1]))
return helixSequences
def parse_atoms_infile(filename):
'''
Parse a PDB file and return atom list.\n
parse_atoms_infile(filename):\n
File needs to be a PDB file format (*.ent or *.pdb)
'''
p = PDBParser(QUIET=True)
s = p.get_structure("X", filename)
atom_list = [atom for atom in s.get_atoms() if atom.name == 'CB']
return atom_list