def extract_deamids(mod_str):
mod_list = [ mod.strip() for mod in mod_str.split(',') ]
return pd.Series( ms.parse_spectrum_modifications(mod) for mod in mod_list if bool(deamid.search(mod)) )
# enumerating all tractable peptides from pep_df ...
glyco_mod = []
for uniq_pept, pept_pos, prot_sr in pep_df[['pept','peptide_start','prot_seqrec']].itertuples(index=False):
prot_seq = str(prot_sr)
pept_spectrum = spec_info[ spec_info['pept']==uniq_pept ]
# let's check all present modifications in the flatten-out list of lists ...
modifs = [ mod for cmod in pept_spectrum['Variable modifications identified by spectrum'] for mod in cmod.strip().split(',') ]
# and get those that are unique ...
modifs = np.unique(modifs)
# now extract type,position and value for each of them ...
modifs = [ ms.parse_spectrum_modifications(mod) for mod in modifs if bool(deamid.search(mod)) ]
# now extrating meaningfull glycosilation sites ...
glyco_sites = []
glyco_start = []
# looks like the inner loop here is the only place where we do need 0-based indexing switching ...
for type_aa,gpos_pept,value in modifs:
if (type_aa in ['n','N']) and (np.abs(value-3)<0.1):
# 'pept_pos' - is 1-based absolute poisition of the peptide in the protein ...
# 'gpos_pept' - is 1-based relative position of gsite_start_N in the peptide ...
gsite_start = pept_pos + gpos_pept-1 # 1-based coordinate ...
gsite_stop = pept_pos + gpos_pept-1 + 3-1 # 1-based coordinate ...
glyco_start.append(gsite_start)
# Due to slicing rules, we need [start-1:stop], no have position 'stop' included ...
glyco_sites.append(prot_seq[gsite_start-1:gsite_stop])
############################################################
# gstart must be 1-based for output ...
