def create_construct_data(self, filenames=False):
self.logger.info('ADDING EXPERIMENTAL CONSTRUCT DATA')
# read source files
if not filenames:
filenames = os.listdir(self.construct_data_dir)
for filename in filenames:
if filename[-4:]!='json':
continue
filepath = os.sep.join([self.construct_data_dir, filename])
with open(filepath) as json_file:
d = json.load(json_file)
add_construct(d)
structures = Structure.objects.all()
for s in structures:
pdbname = str(s)
try:
protein = Protein.objects.filter(entry_name=pdbname.lower()).get()
d = fetch_pdb_info(pdbname,protein)
add_construct(d)
except:
print(pdbname,'failed')
self.logger.info('COMPLETED CREATING EXPERIMENTAL CONSTRUCT DATA')
def create_construct_data(self, filenames=False):
self.logger.info('ADDING EXPERIMENTAL CONSTRUCT DATA')
# read source files
if not filenames:
filenames = os.listdir(self.construct_data_dir)
for filename in filenames:
if filename[-4:] != 'json':
continue
filepath = os.sep.join([self.construct_data_dir, filename])
with open(filepath) as json_file:
d = json.load(json_file)
add_construct(d)
structures = Structure.objects.all()
for s in structures:
pdbname = str(s)
try:
protein = Protein.objects.filter(
entry_name=pdbname.lower()).get()
d = fetch_pdb_info(pdbname, protein)
add_construct(d)
except:
print(pdbname, 'failed')
self.logger.info('COMPLETED CREATING EXPERIMENTAL CONSTRUCT DATA')
def replace_deletions(self):
# delete alle deletions
# ConstructDeletion.objects.all().delete()
for c in Construct.objects.all():
pdbname = c.structure.pdb_code.index
# if not pdbname in ['5F8U','2VT4']:
# continue
# print(pdbname)
#reset caches
c.schematics = None
c.snakecache = None
c.save()
c.deletions.all().delete()
pdbname = c.structure.pdb_code.index
cname = c.name
protein = Protein.objects.filter(entry_name=pdbname.lower()).get()
uniprot = protein.parent.entry_name
d = cache.get(pdbname+"_auto_d")
# d = None
if not d and 'deletions' in d:
d = fetch_pdb_info(pdbname,protein)
cache.set(pdbname+"_auto_d",d,60*60*24)
if 'deletions' in d:
for d in d['deletions']:
dele, created = ConstructDeletion.objects.get_or_create(construct=c, start=d['start'],end=d['end'])
else:
print('No deletions in d[]',pdbname)
def replace_deletions(self):
# delete alle deletions
# ConstructDeletion.objects.all().delete()
for c in Construct.objects.all():
pdbname = c.structure.pdb_code.index
# if not pdbname in ['5F8U','2VT4']:
# continue
# print(pdbname)
#reset caches
c.schematics = None
c.snakecache = None
c.save()
c.deletions.all().delete()
pdbname = c.structure.pdb_code.index
cname = c.name
protein = Protein.objects.filter(entry_name=pdbname.lower()).get()
uniprot = protein.parent.entry_name
d = cache.get(pdbname + "_deletions")
# d = None
if not d:
d = fetch_pdb_info(pdbname, protein)
cache.set(pdbname + "_deletions", d, 60 * 60 * 24)
for d in d['deletions']:
dele, created = ConstructDeletion.objects.get_or_create(
construct=c, start=d['start'], end=d['end'])
def check_deletions(self):
constructs = Construct.objects.all()
csv_rows = []
for c in constructs:
issues = []
pdbname = c.structure.pdb_code.index
cname = c.name
# if pdbname!='4GPO':
# continue
protein = Protein.objects.filter(entry_name=pdbname.lower()).get()
uniprot = protein.parent.entry_name
d = cache.get(pdbname + "_deletions")
# d = None
if not d:
d = fetch_pdb_info(pdbname, protein)
cache.set(pdbname + "_deletions", d, 60 * 60 * 24)
pdb_deletions = []
for d in d['deletions']:
pdb_deletions += range(d['start'], d['end'] + 1)
cons_dels = c.deletions.all()
db_deletions = []
for d in cons_dels:
db_deletions += range(d.start, d.end + 1)
present_in_pdb_only = set(pdb_deletions) - set(db_deletions)
present_in_pdb_only_list = []
for k, g in groupby(enumerate(present_in_pdb_only),
lambda x: x[0] - x[1]):
group = list(map(itemgetter(1), g))
present_in_pdb_only_list.append([group[0], group[-1]])
present_in_db_only = set(db_deletions) - set(pdb_deletions)
present_in_db_only_list = []
for k, g in groupby(enumerate(present_in_db_only),
lambda x: x[0] - x[1]):
group = list(map(itemgetter(1), g))
present_in_db_only_list.append([group[0], group[-1]])
if present_in_pdb_only or present_in_db_only:
print(pdbname)
if present_in_pdb_only: print("PDBONLY", present_in_pdb_only)
if present_in_db_only: print("DBONLY", present_in_db_only)
csv_rows.append([
pdbname, uniprot, cname, present_in_db_only_list,
present_in_pdb_only_list, ''
])
import csv
with open('construct_del_issues.csv', 'w') as f:
writer = csv.writer(f, delimiter='\t')
writer.writerows(csv_rows)
def create_construct_local_data(self, filenames=False):
self.logger.info('ADDING EXPERIMENTAL CONSTRUCT DATA')
#delete existing
self.purge_construct_data()
# read source files
if not filenames:
filenames = os.listdir(self.construct_data_dir)
for filename in sorted(filenames):
print('dealing with', filename)
if filename[-4:] != 'json':
continue
filepath = os.sep.join([self.construct_data_dir, filename])
with open(filepath) as json_file:
d = json.load(json_file)
add_construct(d)
filenames = os.listdir(self.construct_data_local_dir)
for filename in sorted(filenames):
print('dealing with', filename)
if filename[-4:] != 'json':
continue
filepath = os.sep.join([self.construct_data_local_dir, filename])
with open(filepath) as json_file:
d = json.load(json_file)
add_construct(d)
structures = Structure.objects.all()
for s in structures:
pdbname = str(s)
try:
exists = Construct.objects.filter(
structure__pdb_code__index=pdbname).exists()
if not exists:
print(pdbname)
protein = Protein.objects.filter(
entry_name=pdbname.lower()).get()
d = fetch_pdb_info(pdbname, protein)
add_construct(d)
else:
print("Entry for", pdbname, "already there")
except:
print(pdbname, 'failed')
def create_construct_local_data(self, filenames=False):
self.logger.info('ADDING EXPERIMENTAL CONSTRUCT DATA')
#delete existing
self.purge_construct_data()
# read source files
if not filenames:
filenames = os.listdir(self.construct_data_dir)
for filename in sorted(filenames):
print('dealing with',filename)
if filename[-4:]!='json':
continue
filepath = os.sep.join([self.construct_data_dir, filename])
with open(filepath) as json_file:
d = json.load(json_file)
add_construct(d)
filenames = os.listdir(self.construct_data_local_dir)
for filename in sorted(filenames):
print('dealing with',filename)
if filename[-4:]!='json':
continue
filepath = os.sep.join([self.construct_data_local_dir, filename])
with open(filepath) as json_file:
d = json.load(json_file)
add_construct(d)
structures = Structure.objects.all()
for s in structures:
pdbname = str(s)
try:
exists = Construct.objects.filter(structure__pdb_code__index=pdbname).exists()
if not exists:
print(pdbname)
protein = Protein.objects.filter(entry_name=pdbname.lower()).get()
d = fetch_pdb_info(pdbname,protein)
add_construct(d)
else:
print("Entry for",pdbname,"already there")
except:
print(pdbname,'failed')
def check_deletions(self):
constructs = Construct.objects.all()
csv_rows = []
for c in constructs:
issues = []
pdbname = c.structure.pdb_code.index
cname = c.name
# if pdbname!='4GPO':
# continue
protein = Protein.objects.filter(entry_name=pdbname.lower()).get()
uniprot = protein.parent.entry_name
d = cache.get(pdbname+"_auto_d")
# d = None
if not d:
d = fetch_pdb_info(pdbname,protein)
cache.set(pdbname+"_auto_d",d,60*60*24)
pdb_deletions = []
for d in d['deletions']:
pdb_deletions += range(d['start'],d['end']+1)
cons_dels = c.deletions.all()
db_deletions = []
for d in cons_dels:
db_deletions += range(d.start,d.end+1)
present_in_pdb_only = set(pdb_deletions)-set(db_deletions)
present_in_pdb_only_list = []
for k, g in groupby(enumerate(present_in_pdb_only), lambda x:x[0]-x[1]):
group = list(map(itemgetter(1), g))
present_in_pdb_only_list.append([group[0], group[-1]])
present_in_db_only = set(db_deletions)-set(pdb_deletions)
present_in_db_only_list = []
for k, g in groupby(enumerate(present_in_db_only), lambda x:x[0]-x[1]):
group = list(map(itemgetter(1), g))
present_in_db_only_list.append([group[0], group[-1]])
if present_in_pdb_only or present_in_db_only:
print(pdbname)
if present_in_pdb_only: print("PDBONLY",present_in_pdb_only)
if present_in_db_only: print("DBONLY",present_in_db_only)
csv_rows.append([pdbname,uniprot,cname,present_in_db_only_list,present_in_pdb_only_list,''])
import csv
with open('construct_del_issues.csv', 'w') as f:
writer = csv.writer(f, delimiter = '\t')
writer.writerows(csv_rows)
def create_construct_data(self, filenames=False):
self.logger.info('ADDING EXPERIMENTAL CONSTRUCT DATA')
# read source files
do_all = False
if not filenames:
do_all = True
# self.purge_construct_data()
# filenames = os.listdir(self.construct_data_dir)
if filenames:
for filename in filenames:
if filename[-4:] != 'json':
continue
filepath = os.sep.join([self.construct_data_dir, filename])
print('Adding ' + filepath)
with open(filepath) as json_file:
d = json.load(json_file)
add_construct(d)
if do_all:
structures = Structure.objects.all().exclude(refined=True)
for s in structures:
pdbname = str(s)
try:
exists = Construct.objects.filter(
structure__pdb_code__index=pdbname).exists()
if not exists:
print(pdbname)
protein = Protein.objects.filter(
entry_name=pdbname.lower()).get()
d = fetch_pdb_info(pdbname, protein)
add_construct(d)
else:
# pass
print("Entry for", pdbname, "already there")
except:
print(pdbname, 'failed')
self.logger.info('COMPLETED CREATING EXPERIMENTAL CONSTRUCT DATA')
def create_construct_data(self, filenames=False):
self.logger.info('ADDING EXPERIMENTAL CONSTRUCT DATA')
# read source files
do_all = False
if not filenames:
do_all = True
# self.purge_construct_data()
# filenames = os.listdir(self.construct_data_dir)
if filenames:
for filename in filenames:
if filename[-4:]!='json':
continue
filepath = os.sep.join([self.construct_data_dir, filename])
print('Adding '+filepath)
with open(filepath) as json_file:
d = json.load(json_file)
add_construct(d)
if do_all:
structures = Structure.objects.all().exclude(refined=True)
for s in structures:
pdbname = str(s)
try:
exists = Construct.objects.filter(structure__pdb_code__index=pdbname).exists()
if not exists:
# print(pdbname)
protein = Protein.objects.filter(entry_name=pdbname.lower()).get()
d = fetch_pdb_info(pdbname,protein)
add_construct(d)
else:
# pass
print("Entry for",pdbname,"already there")
except:
print(pdbname,'failed')
self.logger.info('COMPLETED CREATING EXPERIMENTAL CONSTRUCT DATA')
def check_mutations(self):
track_annotated_mutations = []
cached_mutations = {}
for i, mut in enumerate(self.excel_mutations):
# print("Progress ",i,len(self.excel_mutations))
# continue
#print(mut)
m = {}
m['gn'] = mut[8]
m['mut_aa'] = mut[11]
m['wt_aa'] = mut[10]
m['entry_name'] = mut[6]
m['pos'] = int(mut[9])
m['pdb'] = mut[1]
m['thermo_effect'] = mut[12]
m['expression_effect'] = mut[13]
m['site_effect'] = mut[14]
m['site_effect_type'] = mut[15]
m['other_effect'] = mut[16]
# if m['entry_name']!='glp1r_human':
# continue
# print(m)
if m['pdb'] and m['pdb'][0] != '%':
pdbs = m['pdb'].split(',')
# print(pdbs)
if len(pdbs) == 0:
cons = Construct.objects.filter(
structure__pdb_code__index=m['pdb'])
else:
cons = Construct.objects.filter(
structure__pdb_code__index__in=pdbs)
else:
cons = Construct.objects.filter(
structure__protein_conformation__protein__parent__entry_name
=m['entry_name'])
pdbs_has = []
pdbs_hasnot = []
for c in cons:
c_pdb = c.structure.pdb_code.index
not_to_check = None
if m['pdb'] and m['pdb'][0] == '%':
# if there are some pdbs not to check on this uniport
not_to_check = m['pdb'].replace("%", "").split(",")
if c_pdb in not_to_check:
continue
# if not_to_check:
# print(c_pdb,not_to_check,m['pdb'][0])
protein = Protein.objects.filter(
entry_name=c_pdb.lower()).get()
if c_pdb in cached_mutations:
d = cached_mutations[c_pdb]
else:
d = cache.get(c_pdb + "_mutations")
if not d:
d = fetch_pdb_info(c_pdb, protein)
cache.set(c_pdb + "_mutations", d, 60 * 60 * 24)
cached_mutations[c_pdb] = d
# Find construct mutation
cons_muts = ConstructMutation.objects.filter(
construct=c,
sequence_number=m['pos'],
mutated_amino_acid=m['mut_aa'],
wild_type_amino_acid=m['wt_aa'])
if not cons_muts.exists(
) and m['other_effect'] != 'Non-receptor' and m[
'other_effect'] != 'Wrong annotation - remove!':
# If no hits something is odd
# print(c.structure.pdb_code.index,' do not have following mutation:',mut)
found = False
for pdb_m in d['mutations']:
if int(pdb_m['pos']) == m['pos']:
found = True
break
if found:
# print('It was however found in pdb! ADDING')
res_wt = Residue.objects.get(
protein_conformation__protein=protein.parent,
sequence_number=m['pos'])
mut = ConstructMutation.objects.create(
construct=c,
sequence_number=m['pos'],
wild_type_amino_acid=m['wt_aa'],
mutated_amino_acid=m['mut_aa'],
residue=res_wt)
pdbs_has.append(c_pdb)
else:
# print('Was also not found in pdb!')
pdbs_hasnot.append("%" + c_pdb)
cons_muts_odd = ConstructMutation.objects.filter(
construct=c, sequence_number=m['pos'])
for cons_mut in cons_muts_odd:
print(c_pdb, cons_mut)
else:
# print(c.structure.pdb_code.index,' HAS following mutation:',mut)
pdbs_has.append(c_pdb)
cons_muts = ConstructMutation.objects.filter(
construct=c,
sequence_number=m['pos'],
mutated_amino_acid=m['mut_aa'],
wild_type_amino_acid=m['wt_aa'])
for cons_mut in cons_muts:
if m['other_effect'] == 'Non-receptor' or m[
'other_effect'] == 'Wrong annotation - remove!':
# print('Delete!',cons_mut.construct.structure.pdb_code.index,cons_mut)
cons_mut.delete()
continue
# Clear existing to replace with current
cons_mut.effects.clear()
if m['thermo_effect']:
mutation_type, created = ConstructMutationType.objects.get_or_create(
slug=slugify('Thermostabilising'),
name='Thermostabilising',
effect=m['thermo_effect'])
cons_mut.effects.add(mutation_type)
if m['expression_effect']:
mutation_type, created = ConstructMutationType.objects.get_or_create(
slug=slugify('Receptor Expression'),
name='Receptor Expression',
effect=m['expression_effect'])
cons_mut.effects.add(mutation_type)
if m['site_effect']:
mutation_type, created = ConstructMutationType.objects.get_or_create(
slug=slugify(m['site_effect']),
name=m['site_effect'],
effect=m['site_effect_type'])
cons_mut.effects.add(mutation_type)
if m['other_effect']:
# print(m['other_effect'])
mutation_type, created = ConstructMutationType.objects.get_or_create(
slug=slugify('Other effect'),
name='Other effect',
effect=m['other_effect'])
cons_mut.effects.add(mutation_type)
track_annotated_mutations.append(cons_mut.pk)
# if not m['pdb'] and len(pdbs_hasnot) and len(pdbs_has):
# print(m['entry_name'],m['wt_aa']+str(m['pos'])+m['mut_aa'])
# print("has",",".join(pdbs_has))
# print("hasnot",",".join(pdbs_hasnot))
# if not len(pdbs_has):
# print('NOONE HAS',m['entry_name'],m['wt_aa']+str(m['pos'])+m['mut_aa'])
print(len(track_annotated_mutations), 'annotated mutations')
non_annotated_muts = ConstructMutation.objects.all().exclude(
pk__in=track_annotated_mutations).order_by(
'construct__protein__entry_name', 'sequence_number')
print(len(non_annotated_muts), 'non-annotated mutations')
csv_rows = [[
'reference', 'pdb', 'construct name', 'class', 'lig type',
'rec fam', 'uniprot', 'segment', 'gpcrdb#', 'AA no.', 'WT aa',
'Mut aa', '', '', '', '', '', 'Remark'
]]
for mut in non_annotated_muts:
pdb = mut.construct.structure.pdb_code.index
uniprot = mut.construct.protein.entry_name
seg = mut.residue.protein_segment.slug
if mut.residue.generic_number:
gn = mut.residue.generic_number.label
else:
gn = ''
pos = mut.sequence_number
wt_aa = mut.wild_type_amino_acid
mut_aa = mut.mutated_amino_acid
annotated_effect = [e.slug for e in mut.effects.all()]
# print(annotated_effect)
csv_rows.append([
'', pdb, '', '', '', '', uniprot, seg, gn, pos, wt_aa, mut_aa,
'', '', '', '', '', ','.join(annotated_effect)
])
# print(csv_rows[-1])
# print(csv_rows)
import csv
with open('construct_mut_missing.csv', 'w') as f:
writer = csv.writer(f, delimiter='\t')
writer.writerows(csv_rows)
def match_all_with_uniprot_mutations(self):
constructs = Construct.objects.all()
csv_rows = [[
'reference', 'pdb', 'construct name', 'class', 'lig type',
'rec fam', 'uniprot', 'segment', 'gpcrdb#', 'AA no.', 'WT aa',
'Mut aa', '', '', '', '', '', 'Remark'
]]
for c in constructs:
issues = []
pdbname = c.structure.pdb_code.index
# if pdbname!='4GPO':
# continue
protein = Protein.objects.filter(entry_name=pdbname.lower()).get()
uniprot = protein.parent.entry_name
# if uniprot !='glp1r_human':
# continue
d = cache.get(pdbname + "_mutations")
# d = None
if not d:
d = fetch_pdb_info(pdbname, protein)
cache.set(pdbname + "_mutations", d, 60 * 60 * 24)
# print('pdb',d['mutations'])
cons_muts = ConstructMutation.objects.filter(construct=c)
for m in cons_muts:
seq_pos = m.sequence_number
found = False
for pdb_m in d['mutations']:
if int(pdb_m['pos']) == seq_pos and pdb_m[
'wt'] == m.wild_type_amino_acid and (
pdb_m['mut'] == m.mutated_amino_acid):
found = True
break
if not found:
ignore = False
for m_xlx in self.excel_mutations:
if m_xlx[6] == uniprot and int(
m_xlx[9]) == seq_pos and m_xlx[
11] == m.mutated_amino_acid and m_xlx[
10] == m.wild_type_amino_acid:
found = False
if pdbname in m_xlx[1] or m_xlx[1] == '':
found = True
if '%' + pdbname in m_xlx[1]:
found = False
if found:
if m_xlx[16] != 'Non-receptor' and m_xlx[
16] != 'Wrong annotation - remove!':
ignore = True
if ignore:
issues.append(('In excel but missing in pdb?', seq_pos,
m.mutated_amino_acid))
else:
issues.append(('Not in excel nor pdb, deleting',
seq_pos, m.mutated_amino_acid))
m.delete()
print(issues)
continue
issues.append(
('missing in pdb?', seq_pos, m.mutated_amino_acid))
mut_aa = m.mutated_amino_acid
pos = m.sequence_number
wt_aa = m.wild_type_amino_acid
annotated_effect = "Not identified in PDB -- perhaps delete?"
res = Residue.objects.get(
protein_conformation__protein=protein.parent,
sequence_number=pos)
seg = res.protein_segment.slug
if res.generic_number:
gn = res.generic_number.label
else:
gn = ''
csv_rows.append([
'', pdbname, '', '', '', '', protein.parent.entry_name,
seg, gn, pos, wt_aa, mut_aa, '', '', '', '', '',
annotated_effect
])
for m in d['mutations']:
cons_muts = ConstructMutation.objects.filter(
construct=c,
sequence_number=m['pos'],
mutated_amino_acid=m['mut'],
wild_type_amino_acid=m['wt'])
if not cons_muts.exists():
# print('missing',m)
ignore = False
for m_xlx in self.excel_mutations:
if m_xlx[6] == uniprot and int(m_xlx[9]) == m['pos']:
found = False
if pdbname in m_xlx[1] or m_xlx[1] == '':
found = True
if '%' + pdbname in m_xlx[1]:
found = False
if found:
if m_xlx[16] == 'Non-receptor' or m_xlx[
16] == 'Wrong annotation - remove!':
ignore = True
if ignore:
continue
issues.append(
('{}{}{} ({})'.format(m['wt'], m['pos'], m['mut'],
m['type']),
' not in db, nor to be ignored in excel'))
mut_aa = m['mut']
pos = m['pos']
wt_aa = m['wt']
annotated_effect = m['type']
res = Residue.objects.get(
protein_conformation__protein=protein.parent,
sequence_number=pos)
seg = res.protein_segment.slug
if res.generic_number:
gn = res.generic_number.label
else:
gn = ''
csv_rows.append([
'', pdbname, '', '', '', '', protein.parent.entry_name,
seg, gn, pos, wt_aa, mut_aa, '', '', '', '', '',
annotated_effect
])
if issues:
print(pdbname)
for i in issues:
print(i)
import csv
with open('construct_mut_issues.csv', 'w') as f:
writer = csv.writer(f, delimiter='\t')
writer.writerows(csv_rows)
def check_mutations(self):
track_annotated_mutations = []
cached_mutations = {}
mut_pdb_list = {}
for i,mut in enumerate(self.excel_mutations):
# print("Progress ",i,len(self.excel_mutations))
# continue
#print(mut)
m = {}
m['gn'] = mut[8]
m['mut_aa'] = mut[11]
m['wt_aa'] = mut[10]
m['entry_name'] = mut[6]
m['pos'] = int(mut[9])
m['pdb'] = mut[1]
m['thermo_effect'] = mut[12]
m['expression_effect'] = mut[13]
m['site_effect'] = mut[14]
m['site_effect_type'] = mut[15]
m['other_effect'] = mut[16]
# if m['entry_name']!='glp1r_human':
# continue
# print(m)
if m['pdb'] and m['pdb'][0]!='%':
pdbs = m['pdb'].split(',')
# print(pdbs)
for pdb in pdbs:
if pdb not in mut_pdb_list:
mut_pdb_list[pdb] = []
if len(pdbs)==0:
cons = Construct.objects.filter(structure__pdb_code__index = m['pdb'])
else:
cons = Construct.objects.filter(structure__pdb_code__index__in = pdbs)
else:
cons = Construct.objects.filter(structure__protein_conformation__protein__parent__entry_name = m['entry_name'])
pdbs_has = []
pdbs_hasnot = []
for c in cons:
c_pdb = c.structure.pdb_code.index
if c_pdb not in mut_pdb_list:
mut_pdb_list[c_pdb] = []
not_to_check = None
if m['pdb'] and m['pdb'][0]=='%':
# if there are some pdbs not to check on this uniport
not_to_check = m['pdb'].replace("%","").split(",")
for pdb in not_to_check:
if pdb not in mut_pdb_list:
mut_pdb_list[pdb] = []
if c_pdb in not_to_check:
continue
# if not_to_check:
# print(c_pdb,not_to_check,m['pdb'][0])
protein = Protein.objects.filter(entry_name=c_pdb.lower()).get()
# print(c_pdb,protein)
if c_pdb in cached_mutations:
d = cached_mutations[c_pdb]
else:
d = cache.get(c_pdb+"_auto_d")
if not d:
d = fetch_pdb_info(c_pdb,protein)
cache.set(c_pdb+"_auto_d",d,60*60*24)
cached_mutations[c_pdb] = d
# Find construct mutation
cons_muts = ConstructMutation.objects.filter(construct=c, sequence_number = m['pos'], mutated_amino_acid = m['mut_aa'], wild_type_amino_acid = m['wt_aa'])
if not cons_muts.exists() and m['other_effect']!='Non-receptor' and m['other_effect']!='Wrong annotation - remove!':
# If no hits something is odd
# print(c.structure.pdb_code.index,' do not have following mutation:',mut)
found = False
for pdb_m in d['mutations']:
if int(pdb_m['pos']) == m['pos']:
found = True
break
if found:
# print('It was however found in pdb! ADDING')
res_wt = Residue.objects.get(protein_conformation__protein=protein.parent, sequence_number=m['pos'])
mut = ConstructMutation.objects.create(construct=c, sequence_number=m['pos'],wild_type_amino_acid= m['wt_aa'],mutated_amino_acid=m['mut_aa'], residue=res_wt)
pdbs_has.append(c_pdb)
else:
# print('Was also not found in pdb!')
pdbs_hasnot.append("%"+c_pdb)
cons_muts_odd = ConstructMutation.objects.filter(construct=c, sequence_number = m['pos'])
for cons_mut in cons_muts_odd:
print(c_pdb,cons_mut)
else:
# print(c.structure.pdb_code.index,' HAS following mutation:',mut)
pdbs_has.append(c_pdb)
cons_muts = ConstructMutation.objects.filter(construct=c, sequence_number = m['pos'], mutated_amino_acid = m['mut_aa'], wild_type_amino_acid = m['wt_aa'])
for cons_mut in cons_muts:
if m['other_effect']=='Non-receptor' or m['other_effect']=='Wrong annotation - remove!':
# print('Delete!',cons_mut.construct.structure.pdb_code.index,cons_mut)
cons_mut.delete()
continue
# Clear existing to replace with current
cons_mut.effects.clear()
if m['thermo_effect']:
mutation_type, created = ConstructMutationType.objects.get_or_create(slug=slugify('Thermostabilising'),name='Thermostabilising', effect=m['thermo_effect'])
cons_mut.effects.add(mutation_type)
if m['expression_effect']:
mutation_type, created = ConstructMutationType.objects.get_or_create(slug=slugify('Receptor Expression'),name='Receptor Expression', effect=m['expression_effect'])
cons_mut.effects.add(mutation_type)
if m['site_effect']:
mutation_type, created = ConstructMutationType.objects.get_or_create(slug=slugify(m['site_effect']),name=m['site_effect'], effect=m['site_effect_type'])
cons_mut.effects.add(mutation_type)
if m['other_effect']:
# print(m['other_effect'])
mutation_type, created = ConstructMutationType.objects.get_or_create(slug=slugify('Other effect'),name='Other effect', effect=m['other_effect'])
cons_mut.effects.add(mutation_type)
track_annotated_mutations.append(cons_mut.pk)
# if not m['pdb'] and len(pdbs_hasnot) and len(pdbs_has):
# print(m['entry_name'],m['wt_aa']+str(m['pos'])+m['mut_aa'])
# print("has",",".join(pdbs_has))
# print("hasnot",",".join(pdbs_hasnot))
# if not len(pdbs_has):
# print('NOONE HAS',m['entry_name'],m['wt_aa']+str(m['pos'])+m['mut_aa'])
print(len(track_annotated_mutations),'annotated mutations')
non_annotated_muts = ConstructMutation.objects.all().exclude(pk__in=track_annotated_mutations).order_by('construct__protein__entry_name','sequence_number')
print(len(non_annotated_muts),'non-annotated mutations')
csv_rows = [['reference','pdb','construct name','class','lig type','rec fam','uniprot','segment','gpcrdb#','AA no.','WT aa','Mut aa','','','','','','Remark']]
missing = list(set(self.all_pdbs) - set(mut_pdb_list.keys()))
print(sorted(missing)," do not have any mutations annotated -- add them to sheet with NONE if they have none")
missing2 = []
for c_pdb in missing:
d = cache.get(c_pdb+"_auto_d")
if not d:
protein = Protein.objects.filter(entry_name=c_pdb.lower()).get()
d = fetch_pdb_info(c_pdb,protein)
cache.set(c_pdb+"_auto_d",d,60*60*24)
if len(d['mutations']):
missing2.append(c_pdb)
print(sorted(missing2)," do not have any mutations annotated (but auto has them having) -- add them to sheet with NONE if they have none")
for mut in non_annotated_muts:
pdb = mut.construct.structure.pdb_code.index
uniprot = mut.construct.protein.entry_name
seg = mut.residue.protein_segment.slug
if mut.residue.generic_number:
gn = mut.residue.generic_number.label
else:
gn = ''
pos = mut.sequence_number
wt_aa = mut.wild_type_amino_acid
mut_aa = mut.mutated_amino_acid
annotated_effect = [e.slug for e in mut.effects.all()]
# print(annotated_effect)
csv_rows.append(['',pdb,'','','','',uniprot,seg,gn,pos,wt_aa,mut_aa,'','','','','',','.join(annotated_effect)])
# print(csv_rows[-1])
# print(csv_rows)
import csv
with open('construct_mut_missing.csv', 'w') as f:
writer = csv.writer(f, delimiter = '\t')
writer.writerows(csv_rows)
def match_all_with_uniprot_mutations(self):
constructs = Construct.objects.all()
csv_rows = [['reference','pdb','construct name','class','lig type','rec fam','uniprot','segment','gpcrdb#','AA no.','WT aa','Mut aa','','','','','','Remark']]
for c in constructs:
issues = []
pdbname = c.structure.pdb_code.index
# if pdbname!='4GPO':
# continue
protein = Protein.objects.filter(entry_name=pdbname.lower()).get()
uniprot = protein.parent.entry_name
# if uniprot !='glp1r_human':
# continue
d = cache.get(pdbname+"_auto_d")
# d = None
if not d:
d = fetch_pdb_info(pdbname,protein)
cache.set(pdbname+"_auto_d",d,60*60*24)
# print('pdb',d['mutations'])
cons_muts = ConstructMutation.objects.filter(construct = c)
for m in cons_muts:
seq_pos = m.sequence_number
found = False
for pdb_m in d['mutations']:
if int(pdb_m['pos']) == seq_pos and pdb_m['wt']==m.wild_type_amino_acid and (pdb_m['mut']==m.mutated_amino_acid):
found = True
break
if not found:
ignore = False
for m_xlx in self.excel_mutations:
if m_xlx[6]==uniprot and int(m_xlx[9])==seq_pos and m_xlx[11]==m.mutated_amino_acid and m_xlx[10]==m.wild_type_amino_acid:
found = False
if pdbname in m_xlx[1] or m_xlx[1]=='':
found = True
if '%'+pdbname in m_xlx[1]:
found = False
if found:
if m_xlx[16]!='Non-receptor' and m_xlx[16]!='Wrong annotation - remove!':
ignore = True
if ignore:
issues.append(('In excel but missing in pdb?',seq_pos,m.mutated_amino_acid))
else:
issues.append(('Not in excel nor pdb, deleting',seq_pos,m.mutated_amino_acid))
m.delete()
print(issues)
continue
issues.append(('missing in pdb?',seq_pos,m.mutated_amino_acid))
mut_aa = m.mutated_amino_acid
pos = m.sequence_number
wt_aa = m.wild_type_amino_acid
annotated_effect = "Not identified in PDB -- perhaps delete?"
res = Residue.objects.get(protein_conformation__protein=protein.parent, sequence_number=pos)
seg = res.protein_segment.slug
if res.generic_number:
gn = res.generic_number.label
else:
gn = ''
csv_rows.append(['',pdbname,'','','','',protein.parent.entry_name,seg,gn,pos,wt_aa,mut_aa,'','','','','',annotated_effect])
for m in d['mutations']:
cons_muts = ConstructMutation.objects.filter(construct = c, sequence_number = m['pos'], mutated_amino_acid = m['mut'], wild_type_amino_acid = m['wt'])
if not cons_muts.exists():
# print('missing',m)
ignore = False
for m_xlx in self.excel_mutations:
if m_xlx[6]==uniprot and int(m_xlx[9])==m['pos']:
found = False
if pdbname in m_xlx[1] or m_xlx[1]=='':
found = True
if '%'+pdbname in m_xlx[1]:
found = False
if found:
if m_xlx[16]=='Non-receptor' or m_xlx[16]=='Wrong annotation - remove!':
ignore = True
if ignore:
continue
issues.append(('{}{}{} ({})'.format(m['wt'],m['pos'],m['mut'],m['type']),' not in db, nor to be ignored in excel'))
mut_aa = m['mut']
pos = m['pos']
wt_aa = m['wt']
annotated_effect = m['type']
res = Residue.objects.get(protein_conformation__protein=protein.parent, sequence_number=pos)
seg = res.protein_segment.slug
if res.generic_number:
gn = res.generic_number.label
else:
gn = ''
csv_rows.append(['',pdbname,'','','','',protein.parent.entry_name,seg,gn,pos,wt_aa,mut_aa,'','','','','',annotated_effect])
if issues:
print(pdbname)
for i in issues:
print(i)
import csv
with open('construct_mut_issues.csv', 'w') as f:
writer = csv.writer(f, delimiter = '\t')
writer.writerows(csv_rows)