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_perturbation_network(net1, net2, db1, db2):
# check which edges are only in net1 or only in net2 and which are in common:
edges1 = set(net1.edges)
edges2 = set(net2.edges)
common_edges = edges1.intersection(edges2)
edges_1only = edges1.difference(edges2)
edges_2only = edges2.difference(edges1)
net = nx.Graph()
edge_colors = {}
size_map = []
node_borders = []
color_map = []
node_labels = {}
for u, v in common_edges:
wij1 = net1.get_edge_data(u, v)['weight']
wij2 = net2.get_edge_data(u, v)['weight']
deltawij = wij2 - wij1
if deltawij > 0:
color = 'green'
net.add_edge(u, v, weight=abs(deltawij))
edge_colors[(u, v)] = color
elif deltawij < 0:
color = 'red'
net.add_edge(u, v, weight=abs(deltawij))
edge_colors[(u, v)] = color
for u, v in edges_1only:
wij = net1.get_edge_data(u, v)['weight']
color = 'red'
net.add_edge(u, v, weight=wij)
edge_colors[(u, v)] = color
for u, v in edges_2only:
wij = net2.get_edge_data(u, v)['weight']
color = 'green'
net.add_edge(u, v, weight=wij)
edge_colors[(u, v)] = color
for node in net.nodes:
k1 = net1.degree(node)
k2 = net2.degree(node)
w1 = net1.degree(node, weight='weight')
w2 = net2.degree(node, weight='weight')
if isinstance(k1, int) and isinstance(k2, int):
nw1 = w1 / k1
nw2 = w2 / k2
info1 = db1[db1['Position'] == node]
if len(info1) > 1: info1 = info1.iloc[0] # check why more than one
type_aa_1 = amino_acids_conversion.three2one(
info1["Type of residue"].item())
info2 = db2[db2['Position'] == node]
if len(info2) > 1: info2 = info2.iloc[0] # check why more than one
type_aa_2 = amino_acids_conversion.three2one(
info2["Type of residue"].item())
if type_aa_1 == type_aa_2:
color_map.append('gray')
label = type_aa_1 + node[1::] + ":" + node[0]
node_labels[node] = label
else:
color_map.append('blue')
label = type_aa_1 + '-' + type_aa_2 + node[1::] + ":" + node[0]
node_labels[node] = label
elif isinstance(k2, int):
nw1 = 0
nw2 = w2 / k2
color_map.append('green')
info2 = db2[db2['Position'] == node]
if len(info2) > 1: info2 = info2.iloc[0] # check why more than one
type_aa_2 = amino_acids_conversion.three2one(
info2["Type of residue"].item())
label = type_aa_2 + node[1::] + ":" + node[0]
node_labels[node] = label
else:
nw1 = w1 / k1
nw2 = 0
color_map.append('red')
info1 = db1[db1['Position'] == node]
if len(info1) > 1: info1 = info1.iloc[0] # check why more than one
type_aa_1 = amino_acids_conversion.three2one(
info1["Type of residue"].item())
label = type_aa_1 + node[1::] + ":" + node[0]
node_labels[node] = label
deltanw = nw2 - nw1
size_map.append(abs(deltanw) * 100 + 500)
if deltanw > 0:
node_borders.append('green')
elif deltanw < 0:
node_borders.append('red')
else:
node_borders.append('gray')
#
# node_labels = {node: node for node in net.nodes} #TO DO: labels as in normal networks, but with mutations
edge_color_map = [color for color in edge_colors.values()]
return net, node_labels, size_map, color_map, edge_color_map, node_borders
def create_perturbation_network(net1, net2, db1, db2, threshold=4):
# check which edges are only in net1 or only in net2 and which are in common:
edges1 = set(net1.edges)
edges2 = set(net2.edges)
common_edges = edges1.intersection(edges2)
edges_1only = edges1.difference(edges2)
edges_2only = edges2.difference(edges1)
net = nx.Graph()
edge_colors = {}
size_map = []
node_borders = []
color_map = []
node_labels = {}
added_edges = []
for u, v in common_edges:
wij1 = net1.get_edge_data(u, v)['weight']
wij2 = net2.get_edge_data(u, v)['weight']
deltawij = wij2 - wij1
if deltawij > threshold:
color = 'green'
net.add_edge(u, v, weight=abs(deltawij))
edge_colors[(u, v)] = color
added_edges.append((u, v))
elif deltawij < -threshold:
color = 'red'
net.add_edge(u, v, weight=abs(deltawij))
edge_colors[(u, v)] = color
added_edges.append((u, v))
for u, v in edges_1only:
wij = net1.get_edge_data(u, v)['weight']
color = 'red'
if wij > threshold:
net.add_edge(u, v, weight=wij)
edge_colors[(u, v)] = color
added_edges.append((u, v))
for u, v in edges_2only:
wij = net2.get_edge_data(u, v)['weight']
color = 'green'
if wij > threshold:
net.add_edge(u, v, weight=wij)
edge_colors[(u, v)] = color
added_edges.append((u, v))
sec_str = {}
for node in net.nodes:
k1 = net1.degree(node)
k2 = net2.degree(node)
w1 = net1.degree(node, weight='weight')
w2 = net2.degree(node, weight='weight')
if isinstance(k1, int) and isinstance(k2, int):
nw1 = w1 / k1
nw2 = w2 / k2
info1 = db1[db1['Position'] == node]
if len(info1) > 1: info1 = info1.iloc[0] # check why more than one
type_aa_1 = amino_acids_conversion.three2one(info1["Type of residue"].item())
sec_str_1 = info1["Secondary structure"].item()
chain1 = info1["Position"].item()[0]
pos1 = info1["Sequence position"].item()
info2 = db2[db2['Position'] == node]
if len(info2) > 1: info2 = info2.iloc[0] # check why more than one
type_aa_2 = amino_acids_conversion.three2one(info2["Type of residue"].item())
sec_str_2 = info2["Secondary structure"].item()
chain2 = info2["Position"].item()[0]
pos2 = info2["Sequence position"].item()
if type_aa_1 == type_aa_2:
color_map.append('gray')
label = type_aa_1 + node[1::] + ":" + node[0]
node_labels[node] = label
else:
color_map.append('blue')
label = type_aa_1 + '-' + type_aa_2 + node[1::] + ":" + node[0]
node_labels[node] = label
elif isinstance(k2, int):
nw1 = 0
nw2 = w2 / k2
color_map.append('green')
info2 = db2[db2['Position'] == node]
if len(info2) > 1: info2 = info2.iloc[0] # check why more than one
type_aa_2 = amino_acids_conversion.three2one(info2["Type of residue"].item())
label = type_aa_2 + node[1::] + ":" + node[0]
node_labels[node] = label
sec_str_2 = info2["Secondary structure"].item()
chain2 = info2["Position"].item()[0]
pos2 = info2["Sequence position"].item()
sec_str_1 = None
chain1 = None
pos1 = None
else:
nw1 = w1 / k1
nw2 = 0
color_map.append('red')
info1 = db1[db1['Position'] == node]
if len(info1) > 1: info1 = info1.iloc[0] # check why more than one
type_aa_1 = amino_acids_conversion.three2one(info1["Type of residue"].item())
label = type_aa_1 + node[1::] + ":" + node[0]
node_labels[node] = label
sec_str_1 = info1["Secondary structure"].item()
chain1 = info1["Position"].item()[0]
pos1 = info1["Sequence position"].item()
sec_str_2 = None
chain2 = None
pos2 = None
deltanw = nw2 - nw1
size_map.append(abs(deltanw)* 100 + 500)
if deltanw > 0:
node_borders.append('green')
elif deltanw < 0:
node_borders.append('red')
else:
node_borders.append('gray')
if chain1 == chain2 and sec_str_1[0] == sec_str_2[0]:
sec_str[node] = (chain1, pos1, sec_str_1)
else:
sec_str[node] = ('extra', 'extra', 'extra')
w1D = []
w2D = []
w3D = []
w4D = []
relations = {}
for u, v in net.edges:
chain_u, pos_u, sec_str_u = sec_str[u]
chain_v, pos_v, sec_str_v = sec_str[v]
wij = net.get_edge_data(u, v)['weight']
if chain_u == 'extra' or chain_v == 'extra':
relation = 'extra'
elif chain_u != chain_v:
relation = '4D'
w4D.append(wij)
elif sec_str_u != sec_str_v:
relation = '3D'
w3D.append(wij)
elif abs(pos2 - pos1) != 1:
relation = '2D'
w2D.append(wij)
else:
relation = '1D'
w1D.append(wij)
relations[(u, v)] = relation
#
# node_labels = {node: node for node in net.nodes} #TO DO: labels as in normal networks, but with mutations
# edge_color_map = [edge_colors[edge] for edge in added_edges]
return net, node_labels, size_map, color_map, edge_colors, node_borders, w1D, w2D, w3D, w4D, relations