def split_proteins(self):
self.log("... Begin Applying Protein Annotations")
mod_table = modification.RestrictedModificationTable(
constant_modifications=self.constant_modifications,
variable_modifications=[])
const_modifications = [
mod_table[c] for c in self.constant_modifications
]
splitter = ProteinSplitter(constant_modifications=const_modifications,
variable_modifications=[])
i = 0
j = 0
protein_ids = self.retrieve_target_protein_ids()
n = len(protein_ids)
interval = min(n / 10., 100000)
acc = []
for protein_id in protein_ids:
i += 1
protein = self.query(Protein).get(protein_id)
if i % interval == 0:
self.log(
"... %0.3f%% Complete (%d/%d). %d Peptides Produced." %
(i * 100. / n, i, n, j))
for peptide in splitter.handle_protein(protein):
acc.append(peptide)
j += 1
if len(acc) > 100000:
self.session.bulk_save_objects(acc)
self.session.commit()
acc = []
self.session.bulk_save_objects(acc)
self.session.commit()
acc = []
def build_baseline_peptides(self):
mod_table = modification.RestrictedModificationTable(
constant_modifications=self.constant_modifications,
variable_modifications=[])
const_modifications = [mod_table[c] for c in self.constant_modifications]
digestor = ProteinDigestor(
self.enzymes[0], const_modifications,
max_missed_cleavages=self.enzymes[0].used_missed_cleavages,
min_length=self.peptide_length_range[0],
max_length=self.peptide_length_range[1])
accumulator = []
i = 0
for protein in self.get_target_proteins():
for peptide in digestor.process_protein(protein):
peptide.hypothesis_id = self.hypothesis_id
accumulator.append(peptide)
i += 1
if len(accumulator) > 5000:
self.session.bulk_save_objects(accumulator)
self.session.commit()
accumulator = []
if i % 1000 == 0:
self.log("... %d Baseline Peptides Created" % i)
self.session.bulk_save_objects(accumulator)
self.session.commit()
def split_proteins(self):
mod_table = modification.RestrictedModificationTable(
constant_modifications=self.constant_modifications,
variable_modifications=[])
const_modifications = [mod_table[c] for c in self.constant_modifications]
protein_ids = self.retrieve_target_protein_ids()
annotator = UniprotProteinAnnotator(self, protein_ids, const_modifications, [])
annotator.run()
decorin = """
>sp|P21793|PGS2_BOVIN Decorin
MKATIIFLLVAQVSWAGPFQQKGLFDFMLEDEASGIGPEEHFPEVPEIEPMGPVCPFRCQ
CHLRVVQCSDLGLEKVPKDLPPDTALLDLQNNKITEIKDGDFKNLKNLHTLILINNKISK
ISPGAFAPLVKLERLYLSKNQLKELPEKMPKTLQELRVHENEITKVRKSVFNGLNQMIVV
ELGTNPLKSSGIENGAFQGMKKLSYIRIADTNITTIPQGLPPSLTELHLDGNKITKVDAA
SLKGLNNLAKLGLSFNSISAVDNGSLANTPHLRELHLNNNKLVKVPGGLADHKYIQVVYL
HNNNISAIGSNDFCPPGYNTKKASYSGVSLFSNPVQYWEIQPSTFRCVYVRAAVQLGNYK
"""
constant_modifications = ["Carbamidomethyl (C)"]
variable_modifications = ["Deamidation (N)", "Pyro-glu from Q (Q@N-term)"]
mt = modification.RestrictedModificationTable(
constant_modifications=constant_modifications,
variable_modifications=variable_modifications)
variable_modifications = [mt[v] for v in variable_modifications]
constant_modifications = [mt[c] for c in constant_modifications]
class FastaGlycopeptideTests(unittest.TestCase):
def setup_tempfile(self, source):
file_name = tempfile.mktemp()
open(file_name, 'w').write(source)
return file_name
def clear_file(self, path):
open(path, 'wb')