def get_dataset(structure, label, length=None):
'''Returns a dataset of continuous segments of protein sequence with the
specified DSSP secondary structure code (E, H, C) of a minimum length.
Attributes
----------
structure: structure data
label (char): DSSP secondary structure label (E, H, C)
length (int): minimum length of secondary structure segment
Returns
-------
dataset of continuous segments of protein sequence
'''
colNames = ["sequence", "label"]
if length == None:
rows = secondaryStructureExtractor.get_python_rdd(structure) \
.flatMap(StructureToSecondaryStructureElements(label))
return pythonRDDToDataset.get_dataset(rows, colNames)
else:
rows = secondaryStructureExtractor.get_python_rdd(structure) \
.flatMap(StructureToSecondaryStructureElements(label, length))
return pythonRDDToDataset.get_dataset(rows, colNames)
def get_dataset(structureRDD, length):
'''Returns a dataset of sequence segments of the specified length and
the DSSP Q8 and Q3 code of the center residue in a segment.
Parameters
----------
structureRDD : structure
length : int
segment length, must be an odd number
Returns
-------
dataset
dataset of segments
Raises
------
Exception
Segment length must be an odd number
'''
if length % 2 == 0:
raise Exception("Segment length must be an odd number %i" % length)
rows = secondaryStructureExtractor.get_python_rdd(structureRDD) \
.flatMap(StructureToSecondaryStructureSegments(length))
colNames = ["structureChainId", "sequence", "labelQ8", "labelQ3"]
return pythonRDDToDataset.get_dataset(rows, colNames)
def get_dataset(structure):
'''Returns a dataset with protein sequence and secondary structure assignments.
Parameters
----------
structure : mmtfStructure
single protein chain
Returns
-------
dataset
dataset with sequence and secondary structure assignments
'''
print("RUNNING")
rows = structure.flatMap(lambda x: _get_phi_psi(x)) # Map or flatMap
print("MAPPED")
# convert to dataset
colNames = ["pdbId", "chain", "resi", "resn", "phi",
"psi"] #+["is"+aa for aa in aa3]
#sc = SparkContext.getOrCreate()
#newdf = it.chain.from_iterable(rows)
#allrows = sc.union(rows)
#return reduce(DataFrame.unionAll, rows.collect())
return pythonRDDToDataset.get_dataset(rows, colNames)
def get_dataset():
'''Gets JPred 4/JNet (v.2.3.1) secondary structure dataset.
Returns
-------
dataset
secondaryStructure dataset
'''
URL = "http://www.compbio.dundee.ac.uk/jpred/downloads/retr231.tar.gz"
instream = urllib.request.urlopen(URL)
secondaryStructures, sequences, trained = {}, {}, {}
scopIds = set()
res = []
with tarfile.open(fileobj=instream, mode="r:gz") as tf:
for entry in tf:
if entry.isdir():
continue
br = tf.extractfile(entry)
if ".dssp" in entry.name:
scopID = str(br.readline())[3:-3] # Remove newline and byte
secondaryStructure = str(
br.readline())[2:-3] # Remove newline and byte
secondaryStructure = secondaryStructure.replace('-', 'C')
secondaryStructures[scopID] = secondaryStructure
if ".fasta" in entry.name:
scopID = str(br.readline())[3:-3] # Remove newline and byte
sequence = str(br.readline())[2:-3] # Remove newline and byte
scopIds.add(scopID)
sequences[scopID] = sequence
if "training/" in entry.name:
trained[scopID] = "true"
elif "blind/" in entry.name:
trained[scopID] = "false"
for scopId in scopIds:
row = Row(scopId, sequences[scopId], secondaryStructures[scopId],
trained[scopId])
res.append(row)
sc = SparkContext.getOrCreate()
data = sc.parallelize(res)
colNames = ["scopID", "sequence", "secondaryStructure", "trained"]
return pythonRDDToDataset.get_dataset(data, colNames)
def get_dataset(self, structures):
'''Returns a dataset of residues that interact with specified group
within a specified cutoff distance
Attricutes:
structure (pythonRdd): a set of PDB structures
Returns:
dataset with interacting residue and atom information
'''
# create a list of all residues with a threshold distance
rows = structures.flatMap(
StructureToAllInteractions(self.groupName, self.distance))
# convert to a dataset
colNames = [
"structureId", "residue1", "atom1", "element1", "index1",
"residue2", "atom2", "element2", "index2", "distance"
]
return pythonRDDToDataset.get_dataset(rows, colNames)
def get_dataset(structures):
'''Returns a dataset of polymer sequence contained in PDB entries
using the full sequence used in the experimnet
(i.e., the "SEQRES" record in PDB files)
Attributes
----------
structures (pythonRDD): a set of PDB structures
Returns
-------
dataset with interacting residue and atom information
'''
rows = structures.flatMap(StructureToPolymerSequences()) \
.map(lambda x: Row(x[0],x[1]))
colNames = ["structureChainId", "sequence"]
return pythonRDDToDataset.get_dataset(rows, colNames)
def get_dataset(structure, parameters=None, classifier=None, options=None):
'''Returns a dataset with protein sequence and secondary structure assignments.
Parameters
----------
structure : mmtfStructure
single protein chain
Returns
-------
dataset
dataset with sequence and secondary structure assignments
'''
rows = structure.map(lambda x: _get_free_sasa(x, parameters, classifier,
options)) # Map or flatMap
# convert to dataset
colNames = ["structureChainId", "totalArea"]
return pythonRDDToDataset.get_dataset(rows, colNames)
def get_dataset(structure):
'''Returns a dataset with protein sequence and secondary structure assignments.
Attributes
----------
structure (mmtfStructure): single protein chain
Returns
-------
dataset with sequence and secondary structure assignments
'''
rows = structure.map(
lambda x: _get_sec_struct_fractions(x)) # Map or flatMap
# convert to dataset
colNames = [
"structureChainId", "sequence", "alpha", "beta", "coil", "dsspQ8Code",
"dsspQ3Code"
]
return pythonRDDToDataset.get_dataset(rows, colNames)
