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import os
from os.path import isfile
from enum import Enum, auto
import numpy as np
from scipy.spatial.distance import cdist
import networkx as nx
from biopandas.pdb import PandasPdb
class GraphType(Enum):
LINEAR = auto()
COMPLETE = auto()
DISCONNECTED = auto()
DIST_THRESH = auto()
DIST_THRESH_SHUFFLED = auto()
def save_graph(g, fn):
""" Saves graph to file """
nx.write_gexf(g, fn)
def load_graph(fn):
""" Loads graph from file """
g = nx.read_gexf(fn, node_type=int)
return g
def shuffle_nodes(g, seed=7):
""" Shuffles the nodes of the given graph and returns a copy of the shuffled graph """
# get the list of nodes in this graph
nodes = g.nodes()
# create a permuted list of nodes
np.random.seed(seed)
nodes_shuffled = np.random.permutation(nodes)
# create a dictionary mapping from old node label to new node label
mapping = {n: ns for n, ns in zip(nodes, nodes_shuffled)}
g_shuffled = nx.relabel_nodes(g, mapping, copy=True)
return g_shuffled
def linear_graph(num_residues):
""" Creates a linear graph where each node is connected to its sequence neighbor in order """
g = nx.Graph()
g.add_nodes_from(np.arange(0, num_residues))
for i in range(num_residues-1):
g.add_edge(i, i+1)
return g
def complete_graph(num_residues):
""" Creates a graph where each node is connected to all other nodes"""
g = nx.complete_graph(num_residues)
return g
def disconnected_graph(num_residues):
g = nx.Graph()
g.add_nodes_from(np.arange(0, num_residues))
return g
def dist_thresh_graph(dist_mtx, threshold):
""" Creates undirected graph based on a distance threshold """
g = nx.Graph()
g.add_nodes_from(np.arange(0, dist_mtx.shape[0]))
# loop through each residue
for rn1 in range(len(dist_mtx)):
# find all residues that are within threshold distance of current
rns_within_threshold = np.where(dist_mtx[rn1] < threshold)[0]
# add edges from current residue to those that are within threshold
for rn2 in rns_within_threshold:
# don't add self edges
if rn1 != rn2:
g.add_edge(rn1, rn2)
return g
def ordered_adjacency_matrix(g):
""" returns the adjacency matrix ordered by node label in increasing order as a numpy array """
node_order = sorted(g.nodes())
adj_mtx = nx.to_numpy_matrix(g, nodelist=node_order)
return np.asarray(adj_mtx).astype(np.float32)
def cbeta_distance_matrix(pdb_fn, start=0, end=None):
# note that start and end are not going by residue number
# they are going by whatever the listing in the pdb file is
# read the pdb file into a biopandas object
ppdb = PandasPdb().read_pdb(pdb_fn)
# group by residue number
# important to specify sort=True so that group keys (residue number) are in order
# the reason is we loop through group keys below, and assume that residues are in order
# the pandas function has sort=True by default, but we specify it anyway because it is important
grouped = ppdb.df["ATOM"].groupby("residue_number", sort=True)
# a list of coords for the cbeta or calpha of each residue
coords = []
# loop through each residue and find the coordinates of cbeta
for i, (residue_number, values) in enumerate(grouped):
# skip residues not in the range
end_index = (len(grouped) if end is None else end)
if i not in range(start, end_index):
continue
residue_group = grouped.get_group(residue_number)
atom_names = residue_group["atom_name"]
if "CB" in atom_names.values:
# print("Using CB...")
atom_name = "CB"
elif "CA" in atom_names.values:
# print("Using CA...")
atom_name = "CA"
else:
raise ValueError("Couldn't find CB or CA for residue {}".format(residue_number))
# get the coordinates of cbeta (or calpha)
coords.append(
residue_group[residue_group["atom_name"] == atom_name][["x_coord", "y_coord", "z_coord"]].values[0])
# stack the coords into a numpy array where each row has the x,y,z coords for a different residue
coords = np.stack(coords)
# compute pairwise euclidean distance between all cbetas
dist_mtx = cdist(coords, coords, metric="euclidean")
return dist_mtx
def get_neighbors(g, nodes):
""" returns a list (set) of neighbors of all given nodes """
neighbors = set()
for n in nodes:
neighbors.update(g.neighbors(n))
return sorted(list(neighbors))
def gen_graph(graph_type, res_dist_mtx, dist_thresh=7, shuffle_seed=7, graph_save_dir=None, save=False):
""" generate the specified structure graph using the specified residue distance matrix """
if graph_type is GraphType.LINEAR:
g = linear_graph(len(res_dist_mtx))
save_fn = None if not save else os.path.join(graph_save_dir, "linear.graph")
elif graph_type is GraphType.COMPLETE:
g = complete_graph(len(res_dist_mtx))
save_fn = None if not save else os.path.join(graph_save_dir, "complete.graph")
elif graph_type is GraphType.DISCONNECTED:
g = disconnected_graph(len(res_dist_mtx))
save_fn = None if not save else os.path.join(graph_save_dir, "disconnected.graph")
elif graph_type is GraphType.DIST_THRESH:
g = dist_thresh_graph(res_dist_mtx, dist_thresh)
save_fn = None if not save else os.path.join(graph_save_dir, "dist_thresh_{}.graph".format(dist_thresh))
elif graph_type is GraphType.DIST_THRESH_SHUFFLED:
g = dist_thresh_graph(res_dist_mtx, dist_thresh)
g = shuffle_nodes(g, seed=shuffle_seed)
save_fn = None if not save else \
os.path.join(graph_save_dir, "dist_thresh_{}_shuffled_r{}.graph".format(dist_thresh, shuffle_seed))
else:
raise ValueError("Graph type {} is not implemented".format(graph_type))
if save:
if isfile(save_fn):
print("err: graph already exists: {}. to overwrite, delete the existing file first".format(save_fn))
else:
os.makedirs(graph_save_dir, exist_ok=True)
save_graph(g, save_fn)
return g
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