def assign_secondary_structure(pdb):
ppdb = PandasPdb().read_pdb(pdb)
secondary_structure = {}
helices_from_pdb = ppdb.df['OTHERS'][ppdb.df['OTHERS']['record_name'] ==
'HELIX']['entry']
for helix in helices_from_pdb:
identifier_h = helix[5:8].strip()
initial_chain_h = helix[13].strip()
initial_pos_h = helix[16:19].strip()
final_pos_h = helix[28:31].strip()
for i in range(int(initial_pos_h), int(final_pos_h) + 1):
secondary_structure[
initial_chain_h +
str(i)] = 'helix' + identifier_h + '-' + initial_chain_h
sheets_from_pdb = ppdb.df['OTHERS'][ppdb.df['OTHERS']['record_name'] ==
'SHEET']['entry']
for sheet in sheets_from_pdb:
identifier_s = sheet[6:8].strip()
initial_chain_s = sheet[15].strip()
initial_pos_s = sheet[17:20].strip()
final_pos_s = sheet[28:31].strip()
for i in range(int(initial_pos_s), int(final_pos_s) + 1):
secondary_structure[
initial_chain_s +
str(i)] = 'sheet' + identifier_s + '-' + initial_chain_s
mol = bg.Pmolecule(pdb)
net = mol.network()
residues_type = {}
for residue in mol.model.get_residues():
res_type = residue.resname
res_pos = residue.parent.id + str(residue.id[1])
residues_type[res_pos] = res_type
residues = list(net.nodes) #assume they are ordered
last_structure = None
last_chain = None
i = 0
for residue in residues:
chain = residue[0]
try:
structure = secondary_structure[residue]
if structure != last_structure:
i += 1
except KeyError:
if chain != last_chain:
i += 1
structure = 'loop' + str(i)
secondary_structure[residue] = structure
last_structure = structure
last_chain = chain
return secondary_structure
def create_id2name(self, pdb):
mol = bg.Pmolecule(pdb)
net = mol.network(cutoff=self.cutoff)
self.structure = PDBParser().get_structure('X', pdb)[0]
residues = []
for residue in self.structure.get_residues():
residues.append(self.three2one[residue.resname])
old_labels = net.nodes
labels = [a + b[1:] + ':' + b[0] for a, b in zip(residues, old_labels)]
self.id2name = dict(zip(old_labels, labels))
def create(self, pdb):
""" Creates the amino acid network using biographs"""
mol = bg.Pmolecule(pdb)
self.net = mol.network(cutoff=self.cutoff, weight=True)
self.structure = PDBParser().get_structure('X', pdb)[0]
# if self.pos1 and self.pos2:
# for node in list(self.net.nodes):
# pos = int(node[1::])
# if pos not in range(self.pos1, self.pos2):
# self.net.remove_node(node)
residues = []
for residue in self.structure.get_residues():
if residue.resname in self.three2one:
residues.append(self.three2one[residue.resname])
else:
residues.append(residue.resname)
old_labels = self.net.nodes
labels = [a + b[1:] + ':' + b[0] for a, b in zip(residues, old_labels)]
mapping = dict(zip(old_labels, labels))
self.net = nx.relabel_nodes(self.net, mapping)
return self.net
def create_network(pdb_id,
database=None,
net=None,
pdbs_path=None,
database_path=None,
pathogenic=[],
non_pathogenic=[],
both=[],
colors='mutations',
sizes='neighborhood_watch_sharp'):
if pdbs_path and database_path:
if 'pdb' + pdb_id + '.pdb' in os.listdir(pdbs_path):
pdb = os.path.join(pdbs_path, 'pdb' + pdb_id + '.pdb')
else:
pdb = os.path.join(pdbs_path, 'pdb' + pdb_id + '.ent')
mol = bg.Pmolecule(pdb)
net = mol.network()
database = pd.DataFrame(pd.read_csv(database_path))
node_labels = {}
for node in net.nodes:
info = database[database['Residue name'] == 'pdb' + pdb_id + node]
if len(info) > 1: info = info.iloc[0] # check why more than one
type_aa = amino_acids_conversion.three2one(
info["Type of residue"].item())
label = type_aa + node[1::] + ":" + node[0]
node_labels[node] = label
mutation_type = []
neighborhood_watch_sharp = []
neighborhood_watch_smooth = []
degree = []
pairwise_sharp = []
for node in net.nodes:
if colors == 'mutations' or sizes == 'mutations':
seq_pos = node[1::]
if seq_pos in pathogenic:
mutation_type.append('tomato')
elif seq_pos in non_pathogenic:
mutation_type.append('limegreen')
elif seq_pos in both:
mutation_type.append('gold')
else:
mutation_type.append('lightgrey')
elif colors or sizes:
k = nx.degree(net, node)
degree.append(k)
weight = nx.degree(net, node, weight='weight')
if colors == 'neighborhood_watch_sharp':
if weight / k < 5: neighborhood_watch_sharp.append('blue')
elif weight / k < 10: neighborhood_watch_sharp.append('cyan')
elif weight / k < 15:
neighborhood_watch_sharp.append('greenyellow')
elif weight / k < 20:
neighborhood_watch_sharp.append('yellow')
elif weight / k < 25:
neighborhood_watch_sharp.append('orange')
else:
neighborhood_watch_sharp.append('red')
elif sizes == 'neighborhood_watch_sharp':
if weight / k < 5: neighborhood_watch_sharp.append(1000)
elif weight / k < 10: neighborhood_watch_sharp.append(4000)
elif weight / k < 15: neighborhood_watch_sharp.append(7000)
elif weight / k < 20: neighborhood_watch_sharp.append(10000)
elif weight / k < 25: neighborhood_watch_sharp.append(13000)
else: neighborhood_watch_sharp.append(16000)
elif colors == 'neighborhood_watch_smooth' or sizes == 'neighborhood_watch_smooth':
neighborhood_watch_smooth.append(weight / k)
if colors == 'pairwise_sharp' or sizes == 'pairwise_sharp':
for u, v in net.edges:
wij = net.get_edge_data(u, v)['weight']
if wij < 10: pairwise_sharp.append('blue')
elif wij < 20: pairwise_sharp.append('cyan')
elif wij < 30: pairwise_sharp.append('greenyellow')
elif wij < 40: pairwise_sharp.append('yellow')
elif wij < 50: pairwise_sharp.append('orange')
else: pairwise_sharp.append('red')
color_map = []
edge_color_map = []
if colors == 'mutations': color_map = mutation_type
elif colors == 'degree': color_map = degree
elif colors == 'neighborhood_watch_sharp':
color_map = neighborhood_watch_sharp
elif colors == 'neighborhood_watch_smooth':
color_map = neighborhood_watch_smooth
elif colors == 'pairwise_sharp':
edge_color_map = pairwise_sharp
size_map = []
# edge_size_map = []
if sizes == 'mutations': size_map = mutation_type
elif sizes == 'degree': size_map = degree
elif sizes == 'neighborhood_watch_sharp':
size_map = neighborhood_watch_sharp
elif sizes == 'neighborhood_watch_smooth':
size_map = neighborhood_watch_smooth
# elif sizes == 'pairwise_sharp': edge_size_map = pairwise_sharp
return net, node_labels, size_map, color_map, edge_color_map
def create_database_nodes(pdbs,
folder_path,
pdbs_path,
cutoff=5,
save_csv=True,
db_name=None):
"""Creates a database of node properties from a list of pdbs.
Parameters
----------
pdbs : list
list of pdb id's
folder_path: str
path of the output folder
pdbs_path: str
path of the pdb files folder
cutoff: int, optional
cutoff threshold for the connection of nodes in the amino acid network (dafault is 5).
save_csv: boolean, optional
if True, saves the database as a csv file in the directory specified by folder_path (default is True).
db_name: str, optional
name of the database. If None and save_csv is True, saves the database as database_nodes.csv (default is None).
Returns
-------
DataFrame
pandas DataFrame object
"""
amino_acids = list_aa()
# initialize database to report
database = []
for pdb_id in pdbs:
pdb, downloaded = get_pdb_path(pdb_id, pdbs_path)
mol = bg.Pmolecule(pdb)
net = mol.network(cutoff=cutoff)
secondary_structure = assign_secondary_structure(pdb)
residues_dict = {}
for residue in mol.model.get_residues():
res_type = residue.resname.strip()
if len(res_type) < 3:
res_type = aaconv.one2three(res_type)
res_pos = residue.parent.id + str(residue.id[1])
residues_dict[res_pos] = res_type
for residue in mol.model.get_residues():
node_name = residue.parent.id + str(residue.id[1])
deg = nx.degree(net, residue.parent.id + str(residue.id[1]))
if deg == 0:
net.remove_node(residue.parent.id + str(residue.id[1]))
else:
weight = nx.degree(net,
residue.parent.id + str(residue.id[1]),
weight="weight")
restype = residue.resname
resname = (os.path.split(pdb)[1][:-4] + residue.parent.id +
str(residue.id[1]))
size = len(residue)
structure = secondary_structure[node_name]
if structure[0] == "h":
structure = "helix"
elif structure[0] == "s":
structure = "sheet"
else:
structure = "loop"
line = [
resname, restype, deg, weight, weight / deg, size,
structure
]
# divide 1D from other neighbors
w1D = 0
k1D = 0 # can be 1 or 2
wOTH = 0
kOTH = 0
for neighbor in list(nx.neighbors(net, node_name)):
edge_weight = nx.edges(net)[(node_name,
neighbor)]["weight"]
relation = get_neighbor_structure_relation(
secondary_structure, node_name, neighbor)
if relation == "1D":
w1D += edge_weight
k1D += 1
else:
wOTH += edge_weight
kOTH += 1
if k1D == 0 or kOTH == 0:
# print(resname)
continue
nw1D = w1D / k1D
nwOTH = wOTH / kOTH
not_terminal = k1D == 2
line += [nw1D, nwOTH, not_terminal]
database.append(line)
if not db_name:
db_name = "database_nodes.csv"
elif db_name[-3::] != ".csv":
db_name += ".csv"
columns = [
"Residue name",
"Type of residue",
"Degree",
"Weight",
"Weight/Degree",
"Atomic number",
"Secondary structure",
"NW1D",
"NWothers",
"Not terminal",
]
if save_csv:
db_path = os.path.join(folder_path, db_name)
with open(db_path, "w", newline="") as f:
writer = csv.writer(f)
writer.writerow(columns)
writer.writerows(database)
db = pd.DataFrame(database, columns=columns)
return db
def create_aa_network(pdb_id,
rel_list,
folder_path,
selected_positions=None,
cutoff=5,
kw_reprod=False,
k_w=None,
db_1_name=None,
db_2_name=None,
separate_jaccard=False,
separate_weights=False,
pdbs_path="pdbs",
save_csv=True,
remove_hydrogen_atoms=False):
"""Creates the amino acid network from a pdb id.
Parameters
----------
pdb_id : str
pdb id of the protein
rel_list: list
list of relation (1D, 2D, 3D, 4D) to consider.
folder_path: str
path of the output folder
selected_positions: None or list, optional
list of sequence positions to consider. If None, all positions are considered (default is None)
cutoff: int, optional
cutoff threshold for the connection of nodes in the amino acid network (dafault is 5).
kw_reprod: boolean, optional
if True, adds a column that checks if (k, w) of a node exist in a database of nodes of robust proteins (default is False).
k_w: dict or None, optional
dictionary of weight values associated to each k value in the database of nodes of robust proteins (default is None).
db_1_name: str, optional
name of the database of type 1. If None and save_csv is True, saves the database as database_pos_1.csv (default is None).
db_2_name: str, optional
name of the database of type 2. If None and save_csv is True, saves the database as database_pos_1.csv (default is None).
separate_jaccard: boolean, optional
if True, separates the Jaccard vector in the database of type 2 based on the size of he neighbors (small, medium, large) (default is False).
separate_weights: boolean, optional
if True, separates the weight in the database of type 2 based on the size of he neighbors (small, medium, large) (default is False).
pdbs_path: str, optional
path of the pdb files folder (default is "pdbs")
save_csv: boolean, optional
if True, saves the database as a csv file in the directory specified by folder_path (default is True).
remove_hydrogen_atoms: boolean, optional
if True, saves removes the hydrogen atoms from the pdb file (default is True).
Returns
-------
Graph
NetworkX Graph object
DataFrame
pandas DataFrame object
DataFrame
pandas DataFrame object
Pmolecule
Biograph Pmolecule object
boolean
True if the pdb file was downloaded
"""
amino_acids = list_aa()
if separate_jaccard:
amino_acids_schl = dict_aa_schl(amino_acids)
if not separate_jaccard: # DO I WANT TO LEAVE IT LIKE THIS?
separate_weights = False
pdb, downloaded = get_pdb_path(pdb_id, pdbs_path)
if remove_hydrogen_atoms:
remove_hydrogens(pdb)
# initialize databases to report
database_1 = []
database_2 = []
mol = bg.Pmolecule(pdb)
net = mol.network(cutoff=cutoff)
# take only selected positions:
if selected_positions:
for node in list(net.nodes):
pos = int(node[1::])
if pos not in selected_positions:
net.remove_node(node)
else:
positions = [int(node[1::]) for node in list(net.nodes)]
pos_min = min(positions)
pos_max = max(positions)
selected_positions = range(pos_min, pos_max + 1)
secondary_structure = assign_secondary_structure(pdb)
residues_dict = {}
for residue in mol.model.get_residues():
res_type = residue.resname.strip()
if len(res_type) < 3:
res_type = aaconv.one2three(res_type)
res_pos = residue.parent.id + str(residue.id[1])
residues_dict[res_pos] = res_type
for residue in mol.model.get_residues():
adj_vector = [0] * 20
weight_vector = [0] * 20
node_name = residue.parent.id + str(residue.id[1])
deg = nx.degree(net, residue.parent.id + str(residue.id[1]))
if deg == 0:
net.remove_node(residue.parent.id + str(residue.id[1]))
else:
weight = nx.degree(net,
residue.parent.id + str(residue.id[1]),
weight="weight")
restype = residue.resname
resname = (os.path.split(pdb)[1][:-4] + residue.parent.id +
str(residue.id[1]))
size = len(residue)
seqpos = residue.id[1]
if seqpos not in selected_positions:
continue
structure = secondary_structure[node_name]
# check how many other aas can have the same k and w in the database
if kw_reprod:
n_others = 0
for aa in amino_acids:
if aa != restype:
try:
[w_min, w_max] = k_w[aa][deg]
if w_min <= weight and w_max >= weight:
n_others += 1
except KeyError:
pass
if separate_weights:
w_separated = {"s": 0, "m": 0, "l": 0}
line_neigh = []
for neighbor in list(nx.neighbors(net, node_name)):
neighbor_type = residues_dict[neighbor]
edge_weight = nx.edges(net)[(node_name, neighbor)]["weight"]
aa_num = amino_acids.index(neighbor_type)
relation = get_neighbor_structure_relation(
secondary_structure, node_name, neighbor)
# select only edges of desired relations
if relation in rel_list:
adj_vector[aa_num] += 1
weight_vector[aa_num] += edge_weight
line_neigh.append(neighbor)
line_neigh.append(neighbor_type)
line_neigh.append(edge_weight)
line_neigh.append(relation)
else:
net.remove_edge(neighbor, node_name)
# separate weights
if separate_weights:
neigh_size = scl.dict_classif[aaconv.three2one(
neighbor_type)]
w_separated[neigh_size] += edge_weight
# check if the residue became of degree zero:
deg = nx.degree(net, residue.parent.id + str(residue.id[1]))
if deg == 0:
net.remove_node(residue.parent.id + str(residue.id[1]))
else:
weight = nx.degree(net,
residue.parent.id + str(residue.id[1]),
weight="weight")
line = [
resname,
node_name,
seqpos,
restype,
deg,
weight,
weight / deg,
size,
structure,
]
line_2 = [
resname,
node_name,
seqpos,
restype,
deg,
weight,
weight / deg,
size,
structure,
]
if kw_reprod:
line.append(n_others)
line_2.append(n_others)
line += line_neigh
database_1.append(line)
line_2 += adj_vector
if separate_jaccard:
sep_jaccard_dict = {"s": 0, "m": 0, "l": 0}
for k in range(len(amino_acids)):
num = adj_vector[k]
size = amino_acids_schl[k]
sep_jaccard_dict[size] += num
sep_jacc = [
sep_jaccard_dict["s"],
sep_jaccard_dict["m"],
sep_jaccard_dict["l"],
]
line_2 += sep_jacc
if separate_weights:
w_separated = list(w_separated.values())
line_2 += w_separated
database_2.append(line_2)
sortedlist_pos = sorted(database_1, key=lambda row: row[2])
sortedlist_pos_2 = sorted(database_2, key=lambda row: row[2])
if not db_1_name:
db_1_name = "database_pos_1.csv"
if not db_2_name:
db_2_name = "database_pos_2.csv"
if kw_reprod:
columns1 = [
"Residue name",
"Position",
"Sequence position",
"Type of residue",
"Degree",
"Weight",
"Weight/Degree",
"Atomic number",
"Secondary structure",
"N. others",
"Neighbor position",
"Neighbor type",
"Pairwise weight",
"Relation",
]
columns2 = [
"Residue name",
"Position",
"Sequence position",
"Type of residue",
"Degree",
"Weight",
"Weight/Degree",
"Atomic number",
"Secondary structure",
"N. others",
] + amino_acids
else:
columns1 = [
"Residue name",
"Position",
"Sequence position",
"Type of residue",
"Degree",
"Weight",
"Weight/Degree",
"Atomic number",
"Secondary structure",
"Neighbor position",
"Neighbor type",
"Pairwise weight",
"Relation",
]
columns2 = [
"Residue name", "Position", "Sequence position", "Type of residue",
"Degree", "Weight", "Weight/Degree", "Atomic number",
"Secondary structure"
] + amino_acids
if separate_jaccard:
columns2 += ["small neighbors", "medium neighbors", "large neighbors"]
if separate_weights:
columns2 += ["w_small", "w_medium", "w_large"]
if save_csv:
db_1_path = os.path.join(folder_path, db_1_name)
with open(db_1_path, "w", newline="") as f:
writer = csv.writer(f)
writer.writerow(columns1)
writer.writerows(sortedlist_pos)
db_2_path = os.path.join(folder_path, db_2_name)
with open(db_2_path, "w", newline="") as f:
writer = csv.writer(f)
writer.writerow(columns2)
writer.writerows(sortedlist_pos_2)
# database 1 has to have all rows of the same lenght to be read as a dataframe
lengths = [len(row) for row in database_1]
max_length = max(lengths)
db_1 = []
missing_header = max_length - len(columns1)
for i in range(int(missing_header / 4)):
columns1.append("Neighbor position")
columns1.append("Neighbor type")
columns1.append("Pairwise weight")
columns1.append("Relation")
for row in database_1:
missing = max_length - len(row)
for i in range(int(missing)):
row.append("-")
db_1.append(row)
db_1 = pd.DataFrame(db_1, columns=columns1)
db_2 = pd.DataFrame(database_2, columns=columns2)
return net, db_1, db_2, mol, downloaded
def assign_secondary_structure(pdb):
"""Returns the secondary structure elements of a pdb
Parameters
----------
pdb : str
pdb file path
Returns
-------
dict
dictionary of secondary structure elements
"""
ppdb = PandasPdb().read_pdb(pdb)
secondary_structure = {}
helices_from_pdb = ppdb.df["OTHERS"][ppdb.df["OTHERS"]["record_name"] ==
"HELIX"]["entry"]
for helix in helices_from_pdb:
identifier_h = helix[5:8].strip()
initial_chain_h = helix[13].strip()
initial_pos_h = helix[16:19].strip()
final_pos_h = helix[28:31].strip()
for i in range(int(initial_pos_h), int(final_pos_h) + 1):
secondary_structure[initial_chain_h +
str(i)] = ("helix" + identifier_h + "-" +
initial_chain_h)
sheets_from_pdb = ppdb.df["OTHERS"][ppdb.df["OTHERS"]["record_name"] ==
"SHEET"]["entry"]
for sheet in sheets_from_pdb:
identifier_s = sheet[6:8].strip()
initial_chain_s = sheet[15].strip()
initial_pos_s = sheet[17:20].strip()
final_pos_s = sheet[28:31].strip()
for i in range(int(initial_pos_s), int(final_pos_s) + 1):
secondary_structure[initial_chain_s +
str(i)] = ("sheet" + identifier_s + "-" +
initial_chain_s)
mol = bg.Pmolecule(pdb)
net = mol.network()
residues_type = {}
for residue in mol.model.get_residues():
res_type = residue.resname
res_pos = residue.parent.id + str(residue.id[1])
residues_type[res_pos] = res_type
residues = list(net.nodes) # assume they are ordered
last_structure = None
last_chain = None
i = 0
for residue in residues:
chain = residue[0]
try:
structure = secondary_structure[residue]
if structure != last_structure:
i += 1
except KeyError:
if chain != last_chain:
i += 1
structure = "loop" + str(i)
secondary_structure[residue] = structure
last_structure = structure
last_chain = chain
return secondary_structure
def create_adj(self, pdb):
if not self.id2name:
self.create_id2name(pdb)
mol = bg.Pmolecule(pdb)
net = mol.network(cutoff=self.cutoff)
return nx.to_numpy_array(net)
else:
pdb_id = current_path.rsplit('/', 1)[1]
pdbl = PDBList(obsolete_pdb=True)
if not glob(os.path.join(pdbs_path, '*')):
pdbl.download_pdb_files(pdbs, file_format='pdb', pdir=pdbs_path)
#initialize databases to report
database_1 = []
database_2 = []
pdbs = glob(os.path.join(pdbs_path, '*'))
for pdb in pdbs:
mol = bg.Pmolecule(pdb)
net = mol.network()
# take only selected positions:
if selected_positions:
for node in list(net.nodes):
pos = int(node[1::])
if pos not in selected_positions:
net.remove_node(node)
secondary_structure = assign_secondary_structure(pdb)
residues_dict = {}
for residue in mol.model.get_residues():
res_type = residue.resname
res_pos = residue.parent.id + str(residue.id[1])
def create_aa_network(pdb_id, rel_list, selected_positions, cutoff, pdbs_path):
"""Creates the amino acid network from a pdb id.
Parameters
----------
pdb_id : str
pdb id of the protein
rel_list: list
list of relation (1D, 2D, 3D, 4D) to consider.
folder_path: str
path of the output folder
selected_positions: None or list, optional
list of sequence positions to consider. If None, all positions are considered (default is None)
cutoff: int, optional
cutoff threshold for the connection of nodes in the amino acid network (dafault is 5).
pdbs_path: str, optional
path of the pdb files folder (default is "pdbs")
Returns
-------
Graph
NetworkX Graph object
dict
labels dictionary
"""
pdb, downloaded = get_pdb_path(pdb_id, pdbs_path)
# initialize database to report
database = []
mol = bg.Pmolecule(pdb)
net = mol.network(cutoff=cutoff)
# take only selected positions:
if selected_positions:
for node in list(net.nodes):
pos = int(node[1::])
if pos not in selected_positions:
net.remove_node(node)
else:
positions = [int(node[1::]) for node in list(net.nodes)]
pos_min = min(positions)
pos_max = max(positions)
selected_positions = range(pos_min, pos_max + 1)
secondary_structure = assign_secondary_structure(pdb)
labels = {}
residues_dict = {}
for residue in mol.model.get_residues():
res_type = residue.resname.strip()
if len(res_type) > 1:
res_type = aaconv.three2one(res_type)
res_chain = residue.parent.id
res_pos = str(residue.id[1])
labels[res_chain + res_pos] = f"{res_type}{res_pos}:{res_chain}"
residues_dict[res_chain + res_pos] = res_type
for residue in mol.model.get_residues():
node_name = residue.parent.id + str(residue.id[1])
deg = nx.degree(net, node_name)
if deg == 0:
net.remove_node(node_name)
_ = labels.pop(node_name)
else:
seqpos = residue.id[1]
if seqpos not in selected_positions:
_ = labels.pop(node_name)
continue
structure = secondary_structure[node_name]
for neighbor in list(nx.neighbors(net, node_name)):
edge_weight = nx.edges(net)[(node_name, neighbor)]["weight"]
relation = get_neighbor_structure_relation(
secondary_structure, node_name, neighbor
)
# select only edges of desired relations
if relation not in rel_list:
net.remove_edge(neighbor, node_name)
# check if the residue became of degree zero:
deg = nx.degree(net, residue.parent.id + str(residue.id[1]))
if deg == 0:
net.remove_node(node_name)
_ = labels.pop(node_name)
return net, labels
def GetData(path, prot, mutations, csv_path=None):
"""Get data from amino acid mutation perturbation networks as CSV files.
Parameters:
path (string): path where original and mutated pdb files are located
prot (string): name of original pdb file
mutations (dict): keys are strings representing positions to mutate
(amino acid, chain and index), each contains a list of
mutations performed (original aa, chain, index and
mutated aa). Mutated pdb files should be found in path,
as prot_mutation.pdb according to mutations in said list
csv_path (string): default None, path where CSV files will be saved,
if None, a dir named "perturbation_network_data" will
be created in 'path'
Returns:
None
"""
# Sorted list of one letter amino acids
AA = list(Bio.PDB.Polypeptide.aa1)
N = len(AA) # Number of amino acids
M = len(mutations.keys()) # Number of mutated positions
cols = list(mutations.keys()) # List of mutated positions
# Generate molecule of original pdb file
original_prot = bg.Pmolecule(os.path.join(path, f"{prot}.pdb"))
# The range of thresholds will define the networks
thresholds = [round(i, 1) for i in np.linspace(3, 10, 71)]
# Create dir to save resulting csv files if not specified
if csv_path is None:
csv_path = os.path.join(path, "perturbation_network_data")
if not os.path.exists(csv_path):
os.makedirs(csv_path)
# Check if path and csv_path exist
assert os.path.exists(path), f"Directory {path} doesn't exist."
assert os.path.exists(csv_path), f"Directory {csv_path} doesn't exist."
# Array to save data from original protein network
original_data = np.zeros((4, len(thresholds)))
# For each threshold we iterate over all mutations
for i, threshold in enumerate(thresholds):
nodes = np.zeros((N, M))
edges = np.zeros((N, M))
weight = np.zeros((N, M))
distance = np.zeros((N, M))
# Generate network for original graph with threshold
original = original_prot.network(cutoff=threshold)
original_matrix = nx.adjacency_matrix(original).toarray()
# Saving data from original network
original_data[0][i] = GetNodes(original)
original_data[1][i] = GetEdges(original)
original_data[2][i] = GetWeight(original)
original_data[3][i] = GetDistance(original)
for index, position in enumerate(mutations.keys()):
for mutation in mutations[position]:
# Generate network for current mutation
current_path = os.path.join(path, f"{prot}_{mutation}.pdb")
current_prot = bg.Pmolecule(current_path)
current = current_prot.network(cutoff=threshold)
# Obtain the absolute difference in terms of adjacency
# matrices: the perturbation network.
current_matrix = nx.adjacency_matrix(current).toarray()
difference = np.abs(original_matrix - current_matrix)
perturbation_network = nx.from_numpy_array(difference)
# Remove isolates for accurate perturbation network node count
perturbation_network.remove_nodes_from(
list(nx.isolates(perturbation_network)))
# Corresponding row in array according to mutation
assert mutation[-1] in AA, \
f"{mutation[-1]} not one of {Bio.PDB.Polypeptide.aa1}"
aa_index = AA.index(mutation[-1])
# Information obtained from perturbation network
nodes[aa_index][index] = GetNodes(perturbation_network)
edges[aa_index][index] = GetEdges(perturbation_network)
weight[aa_index][index] = GetWeight(perturbation_network)
distance[aa_index][index] = GetDistance(perturbation_network)
# Save data arrays as csv files in csv_path
WriteCSV(csv_path, nodes, cols, f"{prot}_{threshold}_nodes.csv")
WriteCSV(csv_path, edges, cols, f"{prot}_{threshold}_edges.csv")
WriteCSV(csv_path, weight, cols, f"{prot}_{threshold}_weight.csv")
WriteCSV(csv_path, distance, cols, f"{prot}_{threshold}_distance.csv")
# Save array from original data
original_data = np.vstack(
[thresholds, original_data]) # add thresholds
original_data = np.transpose(original_data) # to add names as header
header = ['threshold', 'nodes', 'edges', 'weight', 'distance']
# Write CSV of original data
WriteCSV(csv_path, original_data, header, f"{prot}_original.csv")
return None