#!/user/bin/env python
'''interactionFingerprinter.py
This class creates dataset of ligand - macromolecule and macromolecule -
macromolecule interaction information. Criteria to select interactions are
specified by the InteractionFilter.
'''
__author__ = "Mars (Shih-Cheng) Huang"
__maintainer__ = "Mars (Shih-Cheng) Huang"
__email__ = "marshuang80@gmail.com"
__version__ = "0.2.0"
__status__ = "done"
from pyspark.sql import SparkSession
from pyspark.sql.types import *
from pyspark import SparkContext
from mmtfPyspark.interactions import LigandInteractionFingerprint, PolymerInteractionFingerprint
[docs]class InteractionFingerprinter(object):
[docs] def get_ligand_polymer_interactions(structures, interactionFilter):
'''Returns a dataset of ligand - macromolecule interacting residues.
The dataset contains the following columns:
- structureChainId - pdbId.chainName of chain that interacts with ligand
- queryLigandId - id of ligand from PDB chemical component dictionary
- queryLigandNumber - group number of ligand including insetion code
- queryLigandChainId - chain name of ligand
- targetChainId - name of chain for which the interaction data are listed
- groupNumbers - array of residue number of interacting groups including insertion code (e.g. 101A)
- sequenceIndices - array of zero-based index of interaction groups (residues) mapped onto target sequence
- sequence - interacting polymer sequence
- interactingChains - total number of chains that interact with ligand
Parameters
----------
structures : PythonRDD
a set of PDB structures
interactionFilter : InteractionFilter
interaction criteria
Returns
-------
dataset
dataset with interacting residue information
'''
# find sll interactions
row = structures.flatMap(LigandInteractionFingerprint(interactionFilter))
# convert RDD to a Dataset with the following columns
nullable = False
fields = [StructField("structureChainId", StringType(), nullable),
StructField("queryLigandId", StringType(), nullable),
StructField("queryLigandNumber", StringType(), nullable),
StructField("queryLigandChainId", StringType(), nullable),
StructField("targetChainId", StringType(), nullable),
StructField("groupNumbers", ArrayType(StringType(), nullable), nullable),
StructField("sequenceIndices", ArrayType(IntegerType(), nullable), nullable),
StructField("sequence", StringType(), nullable),
StructField("interactingChains", IntegerType(), nullable)
]
schema = StructType(fields)
spark = SparkSession.builder.getOrCreate()
return spark.createDataFrame(row, schema)
[docs] def get_polymer_interactions(structures, interactionFilter):
'''Returns a dataset of ligand - macromolecule interacting information.
The dataset contains the following columns:
structureChainId - pdbId.chainName for which the interaction data are listed
queryChainId - name of chain that interacts with target chain
targetChainId - name of chain for which the interaction data are listed
groupNumbers - array of residue number of interacting groups including insertion code (e.g. 101A)
sequenceIndices - array of zero-based index of interaction groups (residues) mapped onto target sequence
sequence - target polymer sequence
Parameters
----------
structures : PythonRDD
a set of PDB structures
interactionFilter : InteractionFilter
interaction criteria
Returns
-------
dataset
dataset with interacting residue information
'''
# find all interactions
row = structures.flatMap(PolymerInteractionFingerprint(interactionFilter))
# convert RDD to a Dataset with the following columns
nullable = False
fields = [StructField("structureChainId", StringType(), nullable),
StructField("queryChainId", StringType(), nullable),
StructField("targetChainId", StringType(), nullable),
StructField("groupNumbers", ArrayType(StringType(),nullable), nullable),
StructField("sequenceIndices", ArrayType(IntegerType(), nullable), nullable),
StructField("sequence", StringType(), nullable),
]
schema = StructType(fields)
spark = SparkSession.builder.getOrCreate()
return spark.createDataFrame(row, schema)
