Tutorial: Protein Graph Mining

This tutorial can help you start working with the pyemap.graph_mining module of PyeMap.

Parsing

The first step of graph mining with PyeMap is to create a PDBGroup object, and populate it with emap objects for the PDBs of interest. Here, we’ll fetch and parse a small set of flavoprotein PDBs.

import pyemap
from pyemap.graph_mining import PDBGroup
pdb_ids = ['1X0P', '1DNP', '1EFP', '1G28', '1IQR', '1IQU',
'1NP7', '1O96', '1O97', '1QNF', '1U3C', '1U3D', '2IYG', '2J4D',
'2WB2', '2Z6C', '3FY4', '3ZXS', '4EER', '4GU5', '4I6G', '4U63',
'6FN2', '6KII', '6LZ3', '6PU0', '6RKF']
pg = PDBGroup('My Group')
for pdb in pdb_ids:
   pg.add_emap(pyemap.fetch_and_parse(pdb))

Generating Protein Graphs

The next step is to generate the graphs for each PDB. One can specify the chains, ET active moieties, residues, and any other kwargs from process(). If no arguments are specified, the first chain from each PDB will be chosen, and all ET active moieties on those chains will be included. See process_emaps() for more details.

pg.process_emaps()

Generate graph database

The next step is to classify the nodes and edges. One can define “substitutions” for nodes, and thresholds for edges. See generate_graph_database() for more details.

# W and Y would be interchangeable and given the label 'X'
substitutions = ['W','Y']
# edges with weights > 12 are distinguished from those with weights < 12
edge_thresholds = [12]
pg.generate_graph_database(sub=[],edge_thresh=edge_thresholds)

Mine for subgraphs

There are two types of searches available in PyeMap.

mine for all possible subgraphs:

pg.run_gspan(19)

search for a specific pattern:

pg.find_subgraph('WWW#')

Analysis: Subgraph patterns

The identified subgraphs are stored in the subgraph_patterns dictionary.

>>> pg.subgraph_patterns
{'1_WWW#_18': <pyemap.graph_mining.frequent_subgraph.SubgraphPattern at 0x10d652430>,
'2_WWW#_14': <pyemap.graph_mining.frequent_subgraph.SubgraphPattern at 0x183c3a1f0>,
'3_WWW#_4': <pyemap.graph_mining.frequent_subgraph.SubgraphPattern at 0x183c3aca0>,
'4_WWW#_2': <pyemap.graph_mining.frequent_subgraph.SubgraphPattern at 0x183c2de50>,
'5_WWW#_2': <pyemap.graph_mining.frequent_subgraph.SubgraphPattern at 0x183c24a30>,
'6_WWW#_2': <pyemap.graph_mining.frequent_subgraph.SubgraphPattern at 0x183c24730>,
'7_WWW#_1': <pyemap.graph_mining.frequent_subgraph.SubgraphPattern at 0x12c680f10>}

The subgraph pattern can be visualized using pyemap.graph_mining.SubgraphPattern.subgraph_to_Image() or pyemap.graph_mining.SubgraphPattern.subgraph_to_file().

sg = pg.subgraph_patterns['1_WWW#_18']
sg.subgraph_to_Image()

Analysis: Protein subgraphs

To identify the specific residues in each PDB involved in the identified patterns, one should first call pyemap.graph_mining.SubgraphPattern.find_protein_subgraphs(). The identified protein subgraphs are stored in the protein_subgraphs dictionary. Each protein subgraph has a unique ID.

sg.find_protein_subgraphs()
sg.protein_subgraphs
{'4U63_1': <networkx.classes.graph.Graph at 0x183d58fa0>,
'4U63_2': <networkx.classes.graph.Graph at 0x183b66be0>,
'4U63_3': <networkx.classes.graph.Graph at 0x183cf7040>,
'4U63_4': <networkx.classes.graph.Graph at 0x183d53eb0>,
'4U63_5': <networkx.classes.graph.Graph at 0x183d51220> ...

To visualize a protein subgraph, use pyemap.graph_mining.SubgraphPattern.subgraph_to_Image() or pyemap.graph_mining.SubgraphPattern.subgraph_to_file() and pass the ID as a keyword argument.

sg.subgraph_to_Image('1U3D_51')

Clustering

Protein subgraphs are clustered into groups based on sequence or structural similarity. By default, structural clustering is used. To switch to sequence clustering, call pyemap.graph_mining.SubgraphPattern.set_clustering().

print(sg.groups)
{1: ['4U63_33', '1DNP_34', '2J4D_40', '1IQR_43', '1IQU_47'], ...
44: ['3ZXS_60'] ... }

Visualize in NGLView

Protein subgraphs can be visualized in the crystal structure using the NGLView Jupyter Widget. Pass the pathway ID of interest along with a nglview.widget.NGLWidget object to the visualize_subgraph_in_nglview() function.

import nglview as nv
view = nv.show_file(sg.support['1U3D'].file_path)
view.clear_representations()
view.add_cartoon(color="lightgray")
sg.visualize_subgraph_in_nglview('1U3D_50',view)
view