def post_process_protein(params, protein):
def has_tm_helix(protein):
for program in params['helix_programs']:
if dict_get(protein, '%s_helices' % program):
return True
return False
# these functions detect if and TM-containing IM proteins
# have large loops / terminal regions in the periplasm or cytoplasm
# that may be accessible / inaccessible in spheroplast shaving
# experiments.
def has_long_loops(protein, loop_str='_outer_loops', \
loop_length=params['internal_exposed_loop_min']):
for annot in protein:
if loop_str in annot:
for loop in protein[annot]:
l_len = loop[1]-loop[0]
if l_len >= loop_length:
return True
return False
def long_in_periplasm(protein, \
loop_length=params['internal_exposed_loop_min']):
return has_long_loops(protein, '_outer_loops', loop_length)
def long_in_cytoplasm(protein, \
loop_length=params['internal_exposed_loop_min']):
return has_long_loops(protein, '_inner_loops', loop_length)
details = []
category = "UNKNOWN"
is_hmm_profile_match = dict_get(protein, 'hmmsearch')
is_signalp = dict_get(protein, 'is_signalp')
is_tatfind = dict_get(protein, 'is_tatfind')
is_lipop = dict_get(protein, 'is_lipop')
# in terms of most sublocalization logic, a Tat signal is similar to a
# Sec (signalp) signal. We use has_signal_pept to denote that either
# is present.
has_signal_pept = False
if is_signalp or is_tatfind or \
(('hmmsearch' in protein) and "Tat_PS51318" in protein['hmmsearch']):
has_signal_pept = True
# annotate the barrels - high scoring bomp hits don't require a
# signal peptide, low scoring ones do
has_barrel = False
bomp_score = dict_get(protein, 'bomp')
if (bomp_score >= params['bomp_clearly_cutoff']) or \
(has_signal_pept and bomp_score >= params['bomp_maybe_cutoff']):
details += ['bomp(%i)' % (bomp_score)]
has_barrel = True
tmbhunt_prob = dict_get(protein, 'tmbhunt_prob')
if (tmbhunt_prob >= params['tmbhunt_clearly_cutoff']) or \
(has_signal_pept and tmbhunt_prob >= params['tmbhunt_maybe_cutoff']):
details += ['tmbhunt(%.2f)' % (tmbhunt_prob)]
has_barrel = True
if has_signal_pept and dict_get(protein, 'is_tmbetadisc_rbf'):
details += ['tmbetadisc-rbf']
has_barrel = True
if has_barrel:
category = 'OM(barrel)'
# we only regard the barrel prediction as a true positive
# if a signal peptide is also present
# is_barrel = False
# if has_signal_pept and has_barrel: # TODO and num_tms <= 1:
# category = 'OM(barrel)'
# is_barrel = True
# set number of predicted OM barrel strands in details
if has_barrel and \
dict_get(protein, 'tmbeta_strands'):
num_strands = len(protein['tmbeta_strands'])
details += ['tmbeta_strands(%i)' % (num_strands)]
if has_signal_pept and not is_lipop and \
(dict_get(protein, 'signalp_cleave_position')):
# we use the SignalP signal peptidase cleavage site for Tat signals
chop_nterminal_peptide(protein, protein['signalp_cleave_position'])
if is_tatfind:
details += ["tatfind"]
if is_signalp:
details += ["signalp"]
if is_lipop:
details += ["lipop"]
chop_nterminal_peptide(protein, protein['lipop_cleave_position'])
if is_hmm_profile_match:
details += ["hmm(%s)" % "|".join(protein['hmmsearch'])]
if has_tm_helix(protein) and not has_barrel:
for program in params['helix_programs']:
n = len(protein['%s_helices' % program])
details += [program + "(%d)" % n]
category = "IM"
if long_in_periplasm(protein):
category += "+peri"
if long_in_cytoplasm(protein):
category += "+cyto"
elif not has_barrel:
if is_lipop:
if dict_get(protein, 'lipop_im_retention_signal'):
category = "LIPOPROTEIN(IM)"
else:
category = "LIPOPROTEIN(OM)"
pass
elif (has_signal_pept):
category = "PERIPLASMIC/SECRETED"
else:
category = "CYTOPLASM"
if details == []:
details = ["."]
protein['details'] = details
protein['category'] = category
return details, category
def post_process_protein(params, protein):
"""
This is the main analysis of the protein, where theprotein
dictionary should contain all the necessary information
from the annotations. Thus post_process_protein contain
can determine the final analysis.
"""
def sequence_length(protein):
return protein['sequence_length']
def has_tm_helix(protein):
for program in params['helix_programs']:
if dict_get(protein, '%s_helices' % program):
return True
return False
def has_surface_exposed_loop(protein):
for program in params['helix_programs']:
if eval_surface_exposed_loop(
protein['sequence_length'],
len(protein['%s_helices' % (program)]),
protein['%s_outer_loops' % (program)],
params['terminal_exposed_loop_min'],
params['internal_exposed_loop_min']):
return True
return False
def exposed_loop_extent(protein):
extents = []
for program in params['helix_programs']:
if program+'_helices' in protein:
extents.append(max_exposed_loop(
protein['sequence_length'],
len(protein['%s_helices' % (program)]),
protein['%s_outer_loops' % (program)],
params['terminal_exposed_loop_min'],
params['internal_exposed_loop_min']))
if extents:
return max(extents)
else:
return 0
terminal_exposed_loop_min = \
params['terminal_exposed_loop_min']
is_hmm_profile_match = dict_get(protein, 'hmmsearch')
is_lipop = dict_get(protein, 'is_lipop')
if is_lipop:
i_lipop_cut = protein['lipop_cleave_position']
is_signalp = dict_get(protein, 'is_signalp')
if is_signalp:
i_signalp_cut = protein['signalp_cleave_position']
details = []
if is_hmm_profile_match:
details += ["hmm(%s)" % "|".join(protein['hmmsearch'])]
if is_lipop:
details += ["lipop"]
if is_signalp:
details += ["signalp"]
for program in params['helix_programs']:
if has_tm_helix(protein):
n = len(protein['%s_helices' % program])
details += [program + "(%d)" % n]
if is_lipop:
chop_nterminal_peptide(protein, i_lipop_cut)
elif is_signalp:
chop_nterminal_peptide(protein, i_signalp_cut)
if is_hmm_profile_match:
category = "PSE-Cellwall"
elif has_tm_helix(protein):
if has_surface_exposed_loop(protein):
category = "PSE-Membrane"
else:
category = "MEMBRANE(non-PSE)"
else:
if is_lipop:
# whole protein considered outer terminal loop
if sequence_length(protein) < terminal_exposed_loop_min:
category = "LIPOPROTEIN(non-PSE)"
else:
category = "PSE-Lipoprotein"
elif is_signalp:
category = "SECRETED"
else:
category = "CYTOPLASM(non-PSE)"
if details == []:
details = ["."]
protein['details'] = details
protein['category'] = category
if 'CYTOPLASM' not in category and 'SECRETED' not in category:
protein['loop_extent'] = exposed_loop_extent(protein)
else:
protein['loop_extent'] = "."
return details, category
def post_process_protein(params, protein):
def has_tm_helix(protein):
for program in params['helix_programs']:
if dict_get(protein, '%s_helices' % program):
return True
return False
# these functions detect if and TM-containing IM proteins
# have large loops / terminal regions in the periplasm or cytoplasm
# that may be accessible / inaccessible in spheroplast shaving
# experiments.
def has_long_loops(protein, loop_str='_outer_loops', \
loop_length=params['internal_exposed_loop_min']):
for annot in protein:
if loop_str in annot:
for loop in protein[annot]:
l_len = loop[1]-loop[0]
if l_len >= loop_length:
return True
return False
def long_in_periplasm(protein, \
loop_length=params['internal_exposed_loop_min']):
return has_long_loops(protein, '_outer_loops', loop_length)
def long_in_cytoplasm(protein, \
loop_length=params['internal_exposed_loop_min']):
return has_long_loops(protein, '_inner_loops', loop_length)
details = []
category = "UNKNOWN"
is_hmm_profile_match = dict_get(protein, 'hmmsearch')
is_signalp = dict_get(protein, 'is_signalp')
is_tatfind = dict_get(protein, 'is_tatfind')
is_lipop = dict_get(protein, 'is_lipop')
# in terms of most sublocalization logic, a Tat signal is similar to a
# Sec (signalp) signal. We use has_signal_pept to denote that either
# is present.
has_signal_pept = False
if is_signalp or is_tatfind or \
(('hmmsearch' in protein) and "Tat_PS51318" in protein['hmmsearch']):
has_signal_pept = True
# annotate the barrels - high scoring bomp hits don't require a
# signal peptide, low scoring ones do
has_barrel = False
bomp_score = dict_get(protein, 'bomp')
if (bomp_score >= params['bomp_clearly_cutoff']) or \
(has_signal_pept and bomp_score >= params['bomp_maybe_cutoff']):
details += ['bomp(%i)' % (bomp_score)]
has_barrel = True
# DEPRECATED: TMB-HUNT server is permanently offline
#tmbhunt_prob = dict_get(protein, 'tmbhunt_prob')
#if (tmbhunt_prob >= params['tmbhunt_clearly_cutoff']) or \
# (has_signal_pept and tmbhunt_prob >= params['tmbhunt_maybe_cutoff']):
# details += ['tmbhunt(%.2f)' % (tmbhunt_prob)]
# has_barrel = True
if has_signal_pept and dict_get(protein, 'is_tmbetadisc_rbf'):
details += ['tmbetadisc-rbf']
has_barrel = True
if has_barrel:
category = 'OM(barrel)'
# we only regard the barrel prediction as a true positive
# if a signal peptide is also present
# is_barrel = False
# if has_signal_pept and has_barrel: # TODO and num_tms <= 1:
# category = 'OM(barrel)'
# is_barrel = True
# set number of predicted OM barrel strands in details
if has_barrel and \
dict_get(protein, 'tmbeta_strands'):
num_strands = len(protein['tmbeta_strands'])
details += ['tmbeta_strands(%i)' % (num_strands)]
if has_signal_pept and not is_lipop and \
(dict_get(protein, 'signalp_cleave_position')):
# we use the SignalP signal peptidase cleavage site for Tat signals
chop_nterminal_peptide(protein, protein['signalp_cleave_position'])
if is_tatfind:
details += ["tatfind"]
if is_signalp:
details += ["signalp"]
if is_lipop:
details += ["lipop"]
chop_nterminal_peptide(protein, protein['lipop_cleave_position'])
if is_hmm_profile_match:
details += ["hmm(%s)" % "|".join(protein['hmmsearch'])]
if has_tm_helix(protein) and not has_barrel:
for program in params['helix_programs']:
n = len(protein['%s_helices' % program])
details += [program + "(%d)" % n]
category = "IM"
if long_in_periplasm(protein):
category += "+peri"
if long_in_cytoplasm(protein):
category += "+cyto"
elif not has_barrel:
if is_lipop:
if dict_get(protein, 'lipop_im_retention_signal'):
category = "LIPOPROTEIN(IM)"
else:
category = "LIPOPROTEIN(OM)"
pass
elif (has_signal_pept):
category = "PERIPLASMIC/SECRETED"
else:
category = "CYTOPLASM"
if details == []:
details = ["."]
protein['details'] = details
protein['category'] = category
return details, category