def __init__(self, sqlQuery):
self.chainLevel = False
self.pdbIds = []
dataset = pdbjMineDataset.get_dataset(sqlQuery)
if dataset == None:
raise Exception(
"Dataset empty. Either provide an sql query or a dataset")
# Check if there is a pdbID file
if 'structureId' in dataset.columns:
self.chainLevel = False
self.pdbIds = [
a[0] for a in dataset.select('structureId').collect()
]
self.structureIds = self.pdbIds
if 'structureChainId' in dataset.columns:
self.chainLevel = True
ids = [a[0] for a in dataset.select('structureChainId').collect()]
ids_sub = [i[:4] for i in ids]
self.pdbIds = ids + ids_sub
self.structureIds = ids_sub
def test1(self):
sql = "SELECT * FROM sifts.pdb_chain_uniprot LIMIT 10"
ds = pdbjMineDataset.get_dataset(sql)
count = ds.filter("structureChainId == '101M.A'").count()
self.assertTrue(count == 1)
def __get_entity_to_chain_id():
# get entityID to strandId mapping
query = "SELECT pdbid, entity_id, pdbx_strand_id FROM entity_poly"
mapping: DataFrame = pdbjMineDataset.get_dataset(query)
# split one-to-many relationship into multiple records: 'A,B -> [A, B] -> explode to separate rows
mapping = mapping.withColumn("chainId", split(mapping.pdbx_strand_id, ","))
mapping = mapping.withColumn("chainId", explode("chainId"))
# create a structureChainId file, e.g. 1XYZ + A -> 1XYZ.A
mapping = mapping.withColumn(
"pdbChainId", concat_ws(".", mapping.structureId, mapping.chainId))
return mapping.select(mapping.entity_id, mapping.structureId,
mapping.pdbChainId)
def test1(self):
sql = "select count(*) from brief_summary"
search = pdbjMineDataset.get_dataset(sql)
self.assertTrue(search.head()[0] > 100000)
# In[21]:
sql = "select pdbid from keyword_search('porin')"
pdb = pdb.filter(PdbjMineSearch(sql))
print(pdb.keys().collect())
print("\n")
print(f"Number of entries matching query: {pdb.count()}")
# ## Apply a SQL search on PDBj and get a dataset
# In[14]:
sql = "select pdbid, resolution, biol_species, db_uniprot, db_pfam, hit_score from keyword_search('porin') order by hit_score desc"
dataset = pdbjMineDataset.get_dataset(sql)
dataset.show(10)
# ## Terminate Spark Context
# In[15]:
sc.stop()
# ## Show one example per drug molecule
# In[7]:
ligands = ligands.dropDuplicates(["Commonname"])
ligands = ligands.select("structureChainId", "ligandId", "DrugBankID",
"Commonname", "ligandMolecularWeight",
"ligandFormula", "InChIKey", "ligandSmiles")
ligands.sort("Commonname").toPandas().head(10)
# ## Query structures with 2.7.11.1 EC number using PDBjMine
# In[8]:
enzymeQuery = "SELECT * FROM sifts.pdb_chain_enzyme WHERE ec_number = '2.7.11.1'"
enzyme = pdbjMineDataset.get_dataset(enzymeQuery)
print(f"First 10 results for query: {enzymeQuery}")
enzyme.show(10)
# ## Join ligand dataset with PDBjMine dataset with structureChainId
# In[9]:
ligands = ligands.join(enzyme,
ligands.structureChainId == enzyme.structureChainId)
print(f"Total number of structures: {ligands.count()}")
df = ligands.toPandas()
df.head()
# ## Configure Spark
# In[2]:
spark = SparkSession.builder .master("local[*]") .appName("SIFTSDataDemo") .getOrCreate()
# ## Get PDB entry to PubMed Id mappings
# In[4]:
pubmedQuery = "SELECT * FROM sifts.pdb_pubmed LIMIT 10"
pubmed = pdbjMineDataset.get_dataset(pubmedQuery)
print(f"First 10 results for query: {pubmedQuery}")
pubmed.show(10)
# ## Get PDB chain to InterPro mappings
# In[9]:
interproQuery = "SELECT * FROM sifts.pdb_chain_interpro LIMIT 10"
interpro = pdbjMineDataset.get_dataset(interproQuery)
print(f"First 10 results for query: {interproQuery}")
interpro.show(10)
# ## Query PDBj Mine
#
# Query the following fields from the \citation category using PDBj's Mine 2 web service:
# * journal_abbrev
# * pdbx_database_id_PubMed
# * year
#
# Note: mixed case column names must be quoted and escaped with \
#
# In[3]:
sqlQuery = "SELECT pdbid, journal_abbrev, \"pdbx_database_id_PubMed\", year from citation WHERE id = 'primary'"
ds = pdbjMineDataset.get_dataset(sqlQuery)
# ## Show first 10 results from query
# In[4]:
ds.show(10, False)
# ## Filter out unpublished entries
#
# Published entires contain the word "published" in various upper/lower case combinations
# In[5]:
ds = ds.filter("UPPER(journal_abbrev) NOT LIKE '%PUBLISHED%'")
