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sugarMassesPredict.py
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#!/usr/bin/env python
# import modules
import pandas as pd
import numpy as np
import itertools
import argparse
import time
import gc
# start timer
start_time = time.time()
# suppress warnings
pd.options.mode.chained_assignment = None # default='warn'
# DEFINE INPUT ARGUMENTS
# ----------------------
msg = "Script to predict possible masses of unknown sugars. Written by Margot Bligh."
# initialise parser
parser = argparse.ArgumentParser(description=msg)
# add arguments
possible_modifications = ['carboxyl',
'phosphate',
'deoxy',
'nacetyl',
'omethyl',
'anhydrobridge',
'oacetyl',
'unsaturated',
'alditol',
'amino',
'dehydrated',
'sulphate']
parser.add_argument('-dp',
'--dp_range',
help='DP range to predict within: two space separated numbers required (lower first)',
nargs=2,
required=True,
type=int,
dest='dp_range',
metavar='int')
parser.add_argument('-p',
'--pent_option',
help='should pentose monomers be considered as well as hexose: 0 for no {default}, 1 for yes',
nargs=1,
type=int,
dest='pent_option',
metavar='int',
default=0,
choices=[0, 1])
parser.add_argument('-m',
'--modifications',
help='space separated list of modifications to consider. note that alditol, dehydrated and unsaturated are max once per saccharide. allowed values: none OR all OR any combination of ' + ', '.join(
possible_modifications),
nargs='+',
dest='modifications',
required=True,
metavar='str',
choices=possible_modifications + ['none', 'all'])
parser.add_argument('-n',
'--nmod_max',
help='max no. of modifications per monomer on average {default 1}. does not take into account unsaturated, dehydrated or alditol.',
nargs=1,
type=int,
default=1,
dest='nmod_max',
metavar='int')
parser.add_argument('-ds',
'--double_sulphate',
help='can monomers be double-sulphated: 0 for no {default}, 1 for yes. for this you MUST give a value of at least 2 to -n/--nmod_max',
nargs=1,
type=int,
default=0,
dest='double_sulphate',
metavar='int',
choices=[0, 1])
parser.add_argument('-ld',
'--LorD_isomers',
help='isomers calculated for L and/or D enantiomers {default D only}. write space separated if both',
nargs='+',
metavar='str',
dest='LorD_isomers',
choices=['L', 'D'],
default='D')
parser.add_argument('-oh',
'--OH_stereo',
help='stereochem of OH groups considered when calculating no. of isomers: 0 for no {default}, 1 for yes',
nargs=1,
dest='OH_stereo',
metavar='int',
choices=[0, 1],
default=0,
type=int)
parser.add_argument('-b',
'--bond_stereo',
help='stereochem of glycosidic bonds and reducing end anomeric carbons considered when calculating no. of isomers: 0 for no {default}, 1 for yes',
nargs=1,
dest='bond_stereo',
metavar='int',
choices=[0, 1],
default=0,
type=int)
parser.add_argument('-i',
'--ESI_mode',
help='neg and/or pos mode for ionisation (space separated if both)',
nargs='+',
required=True,
dest='ESI_mode',
metavar='str',
choices=["neg", "pos"])
parser.add_argument('-s',
'--scan_range',
help='mass spec scan range to predict within: two space separated numbers required (lower first)',
nargs=2,
required=True,
type=int,
dest='scan_range',
metavar='int')
parser.add_argument('-l',
'--label',
help='name a label added to the oligosaccharide. if not labelled do not include. options: procainamide OR benzoic_acid.',
metavar='label',
dest='label',
default='none')
parser.add_argument('-o',
'--output',
help='filepath to .txt file for output table {default: predicted_sugars.txt}',
nargs=1,
metavar='filepath',
dest='filepath',
default='predicted_sugars.txt')
# read arguments from command line
args = parser.parse_args()
dp_range = args.dp_range
pent_option = args.pent_option
if isinstance(pent_option, list):
pent_option = pent_option[0]
modifications = args.modifications
nmod_max = args.nmod_max
if isinstance(nmod_max, list):
nmod_max = nmod_max[0]
double_sulphate = args.double_sulphate
if isinstance(double_sulphate, list):
double_sulphate = double_sulphate[0]
LorD_isomers = args.LorD_isomers
OH_stereo = args.OH_stereo
if isinstance(OH_stereo, list):
OH_stereo = OH_stereo[0]
bond_stereo = args.bond_stereo
if isinstance(bond_stereo, list):
bond_stereo = bond_stereo[0]
ESI_mode = args.ESI_mode
scan_range = args.scan_range
label = args.label
outfile = args.filepath
if isinstance(outfile, list):
outfile = outfile[0]
if "all" in modifications:
modifications = possible_modifications
if "sulphate" in modifications:
modifications.append(modifications.pop(modifications.index('sulphate')))
if "alditol" in modifications:
alditol_option = 'y'
modifications.remove('alditol')
elif "alditol" not in modifications:
alditol_option = 'n'
if "unsaturated" in modifications:
unsaturated_option = 'y'
modifications.remove('unsaturated')
elif "unsaturated" not in modifications:
unsaturated_option = 'n'
if "dehydrated" in modifications:
dehydrated_option = 'y'
modifications.remove('dehydrated')
elif "dehydrated" not in modifications:
dehydrated_option = 'n'
# 1: DEFINE MASSES / FORMULAS / ISOMERS / MODIFICATIONS VARIABLES / FUNCTIONS
# ----------------------
print("step #1: defining mass, formula, isomer and modification variables, and functions")
print("-------------------------------------------------------------------------------\n")
# hexose and water masses to build molecule base
hex_mass = 180.06339
water_mass = 18.010565
# mass differences for modifications
pent_mdiff = -30.010566
modifications_mdiff = {
"sulphate": 79.956817,
"anhydrobridge": -water_mass,
"omethyl": 14.01565,
"carboxyl": 13.979265,
"nacetyl": 41.026549,
"oacetyl": 42.010565,
"phosphate": 79.966333,
"deoxy": -15.994915,
"unsaturated": -2.015650,
"alditol": 2.015650,
"amino": -0.984016,
"dehydrated": -water_mass
}
# mass differences for labels
procainamide_mdiff = 219.173546
benzoic_acid_mdiff = 104.026215
# mass differences for ions
ion_mdiff = {
"H": 1.00782500000003,
"Na": 22.98977,
"Cl": 34.968853,
"CHOO": 44.997655,
"NH4": 18.034374,
"K": 38.963708
}
e_mdiff = 0.000548579909
# formulas
formulas = {
"hex": [6, 12, 0, 6, 0, 0],
"pent": [5, 10, 0, 5, 0, 0],
"water": [0, -2, 0, -1, 0, 0],
"sulphate": [0, 0, 0, 3, 1, 0],
"anhydrobridge": [0, -2, 0, -1, 0, 0],
"omethyl": [1, 2, 0, 0, 0, 0],
"carboxyl": [0, -2, 0, 1, 0, 0],
"nacetyl": [2, 3, 1, 0, 0, 0],
"oacetyl": [2, 2, 0, 1, 0, 0],
"phosphate": [0, 1, 0, 3, 0, 1],
"deoxy": [0, 0, 0, -1, 0, 0],
"procainamide": [13, 21, 3, 0, 0, 0],
"benzoic_acid": [7, 4, 0, 1, 0, 0],
"unsaturated": [0, -2, 0, 0, 0, 0],
"alditol": [0, +2, 0, 0, 0, 0],
"amino": [0, +1, +1, -1, 0, 0],
"dehydrated": [0, -2, 0, -1, 0, 0]
}
# modification types
modifications_anionic = {"sulphate",
"phosphate",
"carboxyl"}
modifications_neutral = {"anhydrobridge",
"omethyl",
"nacetyl",
"oacetyl",
"deoxy",
"unsaturated",
"amino",
"dehydrated"}
# isomers
isomers_OHdiff = {"anhydrobridge",
"omethyl",
"nacetyl",
"oacetyl",
"deoxy"}
# set up general variables and functions
dp_range_list = list(range(dp_range[0], dp_range[1] + 1))
def dpRepeats(dp_range_list):
repeats_list = []
for i in dp_range_list:
repeats_list = repeats_list + list(range(0, i + 1))
return repeats_list
def bcRepeats(nmax_bc):
repeats_list = []
for i in nmax_bc:
repeats_list = repeats_list + list(range(0, i + 1))
return repeats_list
def getHexMasses(dp_range_list):
dp = pd.Series(dp_range_list)
name = "hex-" + dp.astype(str)
hex = dp
mass = dp * hex_mass - (dp - 1) * water_mass
masses = pd.DataFrame({'dp': dp,
'name': name,
'hex': hex.astype(int),
'mass': mass})
return masses
def getPentMasses(masses):
dp = masses.dp.repeat(masses.dp.array + 1).reset_index(drop=True)
pent = pd.Series(dpRepeats(dp_range_list))
hex = dp - pent
name = "hex-" + hex.astype(str) + "-pent-" + pent.astype(str)
mass = masses.mass.repeat(masses.dp.array + 1).reset_index(drop=True)
mass = mass + pent * pent_mdiff
masses = pd.DataFrame({'dp': dp,
'name': name,
'hex': hex,
'pent': pent,
'mass': mass})
return masses
def getModificationNumbers(dp_range_list, m, pent_option, modifications):
modification_numbers = []
for i in dp_range_list:
a = list(range(0, i + 1))
if pent_option == 1:
modification_numbers = modification_numbers + \
list(itertools.product(a, repeat=m)) * (i + 1)
elif pent_option == 0:
modification_numbers = modification_numbers + \
list(itertools.product(a, repeat=m))
modification_numbers = pd.DataFrame(modification_numbers)
modification_numbers.columns = modifications
return modification_numbers
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
# 2: CALCULATE ALL POSSIBLE MASSES
# ----------------------
print("\nstep #2: calculating all possible masses")
print("----------------------------------------\n")
# build hexose molecules
print("--> getting hexose masses")
masses = getHexMasses(dp_range_list)
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
if "none" in modifications and pent_option == 0 and 'benzoic_acid' in label:
print("--> adding benzoic acid")
masses['maxn_benzoic_acid'] = (masses.hex * 5) - (masses.dp * 2 - 2)
nmax_bc = list(masses.maxn_benzoic_acid)
masses_array = np.array(masses)
masses_array = masses_array.repeat(masses.maxn_benzoic_acid.array + 1, axis=0)
colNames = masses.columns
masses = pd.DataFrame(masses_array)
masses.columns = colNames
n_bc = bcRepeats(nmax_bc)
masses['benzoic_acid'] = n_bc
masses.mass = masses.mass + benzoic_acid_mdiff * masses.benzoic_acid
masses = masses.drop(columns='maxn_benzoic_acid')
masses.name = masses.name + "-benzoic_acid-" + masses.benzoic_acid.astype(str)
masses.name = masses.name.str.replace("-benzoic_acid-0", "")
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
del masses_array
# calculate masses for pentose molecules if selected
if pent_option == 1:
print("--> getting pentose masses")
masses = getPentMasses(masses)
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
if "none" in modifications and pent_option == 1 and 'benzoic_acid' in label:
print("--> adding benzoic acid")
masses['maxn_benzoic_acid'] = (masses.hex * 5) + (masses.pent * 4) - (masses.dp * 2 - 2)
nmax_bc = list(masses.maxn_benzoic_acid)
masses_array = np.array(masses)
masses_array = masses_array.repeat(masses.maxn_benzoic_acid.array + 1, axis=0)
colNames = masses.columns
masses = pd.DataFrame(masses_array)
masses.columns = colNames
n_bc = bcRepeats(nmax_bc)
masses['benzoic_acid'] = n_bc
masses.mass = masses.mass + benzoic_acid_mdiff * masses.benzoic_acid
masses = masses.drop(columns='maxn_benzoic_acid')
masses.name = masses.name + "-benzoic_acid-" + masses.benzoic_acid.astype(str)
masses.name = masses.name.str.replace("-benzoic_acid-0", "")
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
del masses_array
# add modifications
if "none" not in modifications and pent_option == 1:
print("--> adding modifications")
m = len(modifications)
dp = masses.dp.repeat((masses.dp.array + 1) ** m).reset_index(drop=True)
hex = masses.hex.repeat((masses.dp.array + 1) ** m).reset_index(drop=True)
pent = masses.pent.repeat((masses.dp.array + 1) ** m).reset_index(drop=True)
modification_numbers = getModificationNumbers(dp_range_list, m, pent_option, modifications)
name = "hex-" + hex.astype(str) + "-pent-" + pent.astype(str)
for i in range(m):
name = name + "-" + modifications[i] + "-" + modification_numbers[modifications[i]].astype(str)
name = name.str.replace("-\D+-0", "")
name = name.str.replace("hex-0-", "")
mass = masses.mass.repeat((masses.dp.array + 1) ** m).reset_index(drop=True)
for i in range(m):
mass = mass + modifications_mdiff[modifications[i]] * modification_numbers[modifications[i]]
masses = pd.DataFrame({'dp': dp,
'name': name,
'hex': hex,
'pent': pent})
masses = pd.concat([masses, modification_numbers], axis=1)
masses['mass'] = mass
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
# add benzoic acid
if "benzoic_acid" in label and "anhydrobridge" not in modifications:
print("--> adding benzoic acid")
modification_numbers_sum = modification_numbers.sum(axis=1)
masses['maxn_benzoic_acid'] = (masses.hex * 5) + (masses.pent * 4) - (
masses.dp * 2 - 2) - modification_numbers_sum
nmax_bc = list(masses.maxn_benzoic_acid)
masses_array = np.array(masses)
masses_array = masses_array.repeat(masses.maxn_benzoic_acid.array + 1, axis=0)
colNames = masses.columns
masses = pd.DataFrame(masses_array)
masses.columns = colNames
n_bc = bcRepeats(nmax_bc)
masses['benzoic_acid'] = n_bc
masses.mass = masses.mass + benzoic_acid_mdiff * masses.benzoic_acid
masses = masses.drop(columns='maxn_benzoic_acid')
masses.name = masses.name + "-benzoic_acid-" + masses.benzoic_acid.astype(str)
masses.name = masses.name.str.replace("-benzoic_acid-0", "")
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
if "benzoic_acid" in label and "anhydrobridge" in modifications:
print("--> adding benzoic acid")
modification_numbers_sum = modification_numbers.drop(columns="anhydrobridge").sum(
axis=1) + modification_numbers.anhydrobridge * 2
nmax_bc = (masses.hex * 5) + (masses.pent * 4) - (masses.dp * 2 - 2) - modification_numbers_sum
nmax_bc = np.array(nmax_bc)
nmax_bc[nmax_bc < 0] = 0
nmax_bc = pd.Series(nmax_bc)
masses['maxn_benzoic_acid'] = nmax_bc
nmax_bc = list(masses.maxn_benzoic_acid)
masses_array = np.array(masses)
masses_array = masses_array.repeat(masses.maxn_benzoic_acid.array + 1, axis=0)
colNames = masses.columns
masses = pd.DataFrame(masses_array)
masses.columns = colNames
n_bc = bcRepeats(nmax_bc)
masses['benzoic_acid'] = n_bc
masses.mass = masses.mass + benzoic_acid_mdiff * masses.benzoic_acid
masses = masses.drop(columns='maxn_benzoic_acid')
masses.name = masses.name + "-benzoic_acid-" + masses.benzoic_acid.astype(str)
masses.name = masses.name.str.replace("-benzoic_acid-0", "")
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
if "none" not in modifications and pent_option == 0:
print("--> adding modifications")
m = len(modifications)
dp = masses.dp.repeat((masses.dp.array + 1) ** m).reset_index(drop=True)
hex = masses.hex.repeat((masses.dp.array + 1) ** m).reset_index(drop=True)
modification_numbers = getModificationNumbers(dp_range_list, m, pent_option, modifications)
name = "hex-" + hex.astype(str)
for i in range(m):
name = name + "-" + modifications[i] + "-" + modification_numbers[modifications[i]].astype(str)
name = name.str.replace("-\D+-0", "")
name = name.str.replace("hex-0-", "")
mass = masses.mass.repeat((masses.dp.array + 1) ** m).reset_index(drop=True)
for i in range(m):
mass = mass + modifications_mdiff[modifications[i]] * modification_numbers[modifications[i]]
masses = pd.DataFrame({'dp': dp,
'name': name,
'hex': hex})
masses = pd.concat([masses, modification_numbers], axis=1)
masses['mass'] = mass
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
# add benzoic acid
if "benzoic_acid" in label and "anhydrobridge" not in modifications:
print("--> adding benzoic acid")
modification_numbers_sum = modification_numbers.sum(axis=1)
masses['maxn_benzoic_acid'] = (masses.hex * 5) - (masses.dp * 2 - 2) - modification_numbers_sum
nmax_bc = list(masses.maxn_benzoic_acid)
masses_array = np.array(masses)
masses_array = masses_array.repeat(masses.maxn_benzoic_acid.array + 1, axis=0)
colNames = masses.columns
masses = pd.DataFrame(masses_array)
masses.columns = colNames
n_bc = bcRepeats(nmax_bc)
masses['benzoic_acid'] = n_bc
masses.mass = masses.mass + benzoic_acid_mdiff * masses.benzoic_acid
masses = masses.drop(columns='maxn_benzoic_acid')
masses.name = masses.name + "-benzoic_acid-" + masses.benzoic_acid.astype(str)
masses.name = masses.name.str.replace("-benzoic_acid-0", "")
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
if "benzoic_acid" in label and "anhydrobridge" in modifications:
print("--> adding benzoic acid")
modification_numbers_sum = modification_numbers.drop(columns="anhydrobridge").sum(
axis=1) + modification_numbers.anhydrobridge * 2
masses['maxn_benzoic_acid'] = (masses.hex * 5) - (masses.dp * 2 - 2) - modification_numbers_sum
nmax_bc = list(masses.maxn_benzoic_acid)
masses_array = np.array(masses)
masses_array = masses_array.repeat(masses.maxn_benzoic_acid.array + 1, axis=0)
colNames = masses.columns
masses = pd.DataFrame(masses_array)
masses.columns = colNames
n_bc = bcRepeats(nmax_bc)
masses['benzoic_acid'] = n_bc
masses.mass = masses.mass + benzoic_acid_mdiff * masses.benzoic_acid
masses = masses.drop(columns='maxn_benzoic_acid')
masses.name = masses.name + "-benzoic_acid-" + masses.benzoic_acid.astype(str)
masses.name = masses.name.str.replace("-benzoic_acid-0", "")
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
if "sulphate" in modifications and double_sulphate == 1:
print("--> adding extra sulphate groups")
masses_s1 = masses.loc[masses['sulphate'] >=1]
masses_s2 = masses_s1
masses_s2.sulphate = masses_s1.sulphate + masses_s1.dp
masses_s2.name = masses_s2.name.str.replace("-sulphate-\d{1,2}", "")
masses_s2.name = masses_s2.name + '-sulphate-' + masses_s2.sulphate.astype(str)
masses_s2.mass = masses_s2.mass + modifications_mdiff['sulphate'] * masses_s2.dp
masses = masses.append(masses_s2).reset_index()
del masses_s1
del masses_s2
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
if "procainamide" in label:
print("--> adding procainamide label")
masses['name'] = masses.name + '-procA'
masses['mass'] = masses.mass + procainamide_mdiff
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
if unsaturated_option == 'y':
print("--> adding unsaturated sugars")
masses_a = masses.copy()
masses_a.name = "unsaturated-" + masses.name
masses_a['unsaturated'] = 1
masses['unsaturated'] = 0
masses_a.mass = masses.mass + modifications_mdiff['unsaturated']
masses = masses.append(masses_a).reset_index()
del masses_a
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
if alditol_option == 'y':
print("--> adding alditol sugars")
masses_a = masses.copy()
masses_a.name = "alditol-" + masses_a.name
masses_a['alditol'] = 1
masses['alditol'] = 0
masses_a.mass = masses_a.mass + modifications_mdiff['alditol']
masses = masses.append(masses_a).reset_index(drop = True)
del masses_a
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
if dehydrated_option == 'y':
print("--> adding dehydration")
masses_a = masses.copy()
masses_a.name = "dehydrated-" + masses_a.name
masses_a['dehydrated'] = 1
masses['dehydrated'] = 0
masses_a.mass = masses_a.mass + modifications_mdiff['dehydrated']
masses = masses.append(masses_a).reset_index(drop = True)
del masses_a
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
gc.collect()
# 3: GET FORMULAS
# ----------------------
print("\nstep #3: building formulas")
print("----------------------------------------\n")
if "none" in modifications and pent_option == 1:
if "benzoic_acid" in label:
dp = masses.dp
hex = masses.hex
pent = masses.pent
benzoic_acid = masses.benzoic_acid
molecule_numbers = pd.DataFrame({'dp': dp,
'hex': hex,
'pent': pent,
'benzoic_acid': benzoic_acid})
else:
dp = masses.dp
hex = masses.hex
pent = masses.pent
molecule_numbers = pd.DataFrame({'dp': dp,
'hex': hex,
'pent': pent})
molecules = list(molecule_numbers.drop('dp', axis=1).columns)
atom_names = ["C", "H", "N", "O", "S", "P"]
atom_list = []
for i in range(len(atom_names)):
n = np.array([0] * len(masses.index))
for j in range(len(molecules)):
form_n = np.array([formulas[molecules[j]][i]] * len(masses.index))
mol_n = np.array(molecule_numbers[molecules[j]])
form_mol_n = form_n * mol_n
n = n + form_mol_n
if "procainamide" in label:
p = np.array([formulas['procainamide'][i]] * len(masses.index))
n = n + p
atom_list.append(list(n))
# remove molecules from formula for glycosidic bonds
atom_list_2 = []
for i in range(len(atom_names)):
n = np.array(atom_list[i])
form_n = np.array([formulas['water'][i]] * len(masses.index))
mol_n = np.array(molecule_numbers['dp'] - 1)
form_mol_n = form_n * mol_n
n = n + form_mol_n
atom_list_2.append(list(n))
# concatenate to build formulas
for i in range(len(atom_names)):
if i == 0:
formulas_final = atom_names[i] + pd.Series(atom_list_2[i]).astype(str)
else:
formulas_final = formulas_final.astype(str) + atom_names[i] + pd.Series(atom_list_2[i]).astype(str)
# fix to remove atoms with zero
formulas_final = formulas_final.str.replace("\D0", "")
masses['formula'] = formulas_final
if "none" in modifications and pent_option == 0:
if "benzoic_acid" in label:
dp = masses.dp
hex = masses.hex
benzoic_acid = masses.benzoic_acid
molecule_numbers = pd.DataFrame({'dp': dp,
'hex': hex,
'benzoic_acid': benzoic_acid})
else:
dp = masses.dp
hex = masses.hex
molecule_numbers = pd.DataFrame({'dp': dp,
'hex': hex})
molecules = list(molecule_numbers.drop('dp', axis=1).columns)
atom_names = ["C", "H", "N", "O", "S", "P"]
atom_list = []
for i in range(len(atom_names)):
n = np.array([0] * len(masses.index))
for j in range(len(molecules)):
form_n = np.array([formulas[molecules[j]][i]] * len(masses.index))
mol_n = np.array(molecule_numbers[molecules[j]])
form_mol_n = form_n * mol_n
n = n + form_mol_n
if "procainamide" in label:
p = np.array([formulas['procainamide'][i]] * len(masses.index))
n = n + p
atom_list.append(list(n))
# remove molecules from formula for glycosidic bonds
atom_list_2 = []
for i in range(len(atom_names)):
n = np.array(atom_list[i])
form_n = np.array([formulas['water'][i]] * len(masses.index))
mol_n = np.array(molecule_numbers['dp'] - 1)
form_mol_n = form_n * mol_n
n = n + form_mol_n
atom_list_2.append(list(n))
# concatenate to build formulas
for i in range(len(atom_names)):
if i == 0:
formulas_final = atom_names[i] + pd.Series(atom_list_2[i]).astype(str)
else:
formulas_final = formulas_final.astype(str) + atom_names[i] + pd.Series(atom_list_2[i]).astype(str)
# fix to remove atoms with zero
formulas_final = formulas_final.str.replace("\D0", "")
masses['formula'] = formulas_final
if "none" not in modifications and pent_option == 1:
if unsaturated_option == 'y':
modifications.append('unsaturated')
if alditol_option == 'y':
modifications.append('alditol')
if dehydrated_option == 'y':
modifications.append('dehydrated')
if "benzoic_acid" in label:
dp = masses.dp
hex = masses.hex
pent = masses.pent
benzoic_acid = masses.benzoic_acid
molecule_numbers = pd.DataFrame({'dp': dp,
'hex': hex,
'pent': pent,
'benzoic_acid': benzoic_acid})
modification_numbers = masses[modifications]
modification_numbers_array = np.array(modification_numbers)
modification_numbers_array = modification_numbers_array.repeat(np.array(nmax_bc) + 1, axis=0)
colNames = modification_numbers.columns
modification_numbers = pd.DataFrame(modification_numbers_array)
modification_numbers.columns = colNames
molecule_numbers = pd.concat([molecule_numbers, modification_numbers], axis=1)
else:
dp = masses.dp
hex = masses.hex
pent = masses.pent
molecule_numbers = pd.DataFrame({'dp': dp,
'hex': hex,
'pent': pent})
modification_numbers = masses[modifications]
molecule_numbers = pd.concat([molecule_numbers, modification_numbers], axis=1)
molecules = list(molecule_numbers.drop('dp', axis=1).columns)
atom_names = ["C", "H", "N", "O", "S", "P"]
atom_list = []
for i in range(len(atom_names)):
n = np.array([0] * len(masses.index))
for j in range(len(molecules)):
form_n = np.array([formulas[molecules[j]][i]] * len(masses.index))
mol_n = np.array(molecule_numbers[molecules[j]])
form_mol_n = form_n * mol_n
n = n + form_mol_n
if "procainamide" in label:
p = np.array([formulas['procainamide'][i]] * len(masses.index))
n = n + p
atom_list.append(list(n))
# remove molecules from formula for glycosidic bonds
atom_list_2 = []
for i in range(len(atom_names)):
n = np.array(atom_list[i])
form_n = np.array([formulas['water'][i]] * len(masses.index))
mol_n = np.array(molecule_numbers['dp'] - 1)
form_mol_n = form_n * mol_n
n = n + form_mol_n
atom_list_2.append(list(n))
# concatenate to build formulas
for i in range(len(atom_names)):
if i == 0:
formulas_final = atom_names[i] + pd.Series(atom_list_2[i]).astype(str)
else:
formulas_final = formulas_final.astype(str) + atom_names[i] + pd.Series(atom_list_2[i]).astype(str)
# fix to remove atoms with zero
formulas_final = formulas_final.str.replace("\D0", "")
masses['formula'] = formulas_final
if "none" not in modifications and pent_option == 0:
if unsaturated_option == 'y':
modifications.append('unsaturated')
if alditol_option == 'y':
modifications.append('alditol')
if dehydrated_option == 'y':
modifications.append('dehydrated')
if "benzoic_acid" in label:
dp = masses.dp
hex = masses.hex
benzoic_acid = masses.benzoic_acid
molecule_numbers = pd.DataFrame({'dp': dp,
'hex': hex,
'benzoic_acid': benzoic_acid})
modification_numbers = masses[modifications]
modification_numbers_array = np.array(modification_numbers)
modification_numbers_array = modification_numbers_array.repeat(np.array(nmax_bc) + 1, axis=0)
colNames = modification_numbers.columns
modification_numbers = pd.DataFrame(modification_numbers_array)
modification_numbers.columns = colNames
molecule_numbers = pd.concat([molecule_numbers, modification_numbers], axis=1)
else:
dp = masses.dp
hex = masses.hex
molecule_numbers = pd.DataFrame({'dp': dp,
'hex': hex})
modification_numbers = masses[modifications]
molecule_numbers = pd.concat([molecule_numbers, modification_numbers], axis=1)
molecules = list(molecule_numbers.drop('dp', axis=1).columns)
atom_names = ["C", "H", "N", "O", "S", "P"]
atom_list = []
for i in range(len(atom_names)):
n = np.array([0] * len(masses.index))
for j in range(len(molecules)):
form_n = np.array([formulas[molecules[j]][i]] * len(masses.index))
mol_n = np.array(molecule_numbers[molecules[j]])
form_mol_n = form_n * mol_n
n = n + form_mol_n
if "procainamide" in label:
p = np.array([formulas['procainamide'][i]] * len(masses.index))
n = n + p
atom_list.append(list(n))
# remove molecules from formula for glycosidic bonds
atom_list_2 = []
for i in range(len(atom_names)):
n = np.array(atom_list[i])
form_n = np.array([formulas['water'][i]] * len(masses.index))
mol_n = np.array(molecule_numbers['dp'] - 1)
form_mol_n = form_n * mol_n
n = n + form_mol_n
atom_list_2.append(list(n))
# concatenate to build formulas
for i in range(len(atom_names)):
if i == 0:
formulas_final = atom_names[i] + pd.Series(atom_list_2[i]).astype(str)
else:
formulas_final = formulas_final.astype(str) + atom_names[i] + pd.Series(atom_list_2[i]).astype(str)
# fix to remove atoms with zero
formulas_final = formulas_final.str.replace("\D0", "")
masses['formula'] = formulas_final
gc.collect()
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
# 4: FILTER BASED ON NUMBER OF POSSIBLE MODIFICATIONS
# ----------------------
print("\nstep #4: filtering based on number of modifications per monomer")
print("----------------------------------------------------------------\n")
if "none" not in modifications:
if unsaturated_option == 'y':
modifications.remove('unsaturated')
if alditol_option == 'y':
modifications.remove('alditol')
if dehydrated_option == 'y':
modifications.remove('dehydrated')
masses['nmod'] = masses[modifications].sum(axis=1)
masses['nmod_avg'] = masses.nmod / masses.dp
masses = masses.drop(masses[masses.nmod_avg > nmod_max].index)
if 'anhydrobridge' in modifications and pent_option == 1:
indexDelete = masses[masses.hex < masses.anhydrobridge].index
masses.drop(indexDelete, inplace=True)
masses = masses.reset_index()
gc.collect()
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
# 5: ESTIMATE NUMBERS OF POSSIBLE ISOMERS
# ----------------------
print("\nstep #5: estimating number of possible isomers")
print("----------------------------------------------------------------\n")
# calculate for L or D forms
if len(LorD_isomers) == 2:
masses['isomers'] = 2 ** masses.dp
if OH_stereo == 1:
# hexose
masses['OH'] = 0
mask1 = (masses['hex'] != 0)
masses_temp = masses[mask1]
masses.loc[mask1, 'OH'] = ((masses_temp.hex - 1) * 2) + 3
# pentose
if pent_option == 1:
mask2 = (masses['hex'] != 0) & (masses['pent'] != 0)
masses_temp = masses[mask2]
masses.loc[mask2, 'OH'] = masses_temp.OH + masses_temp.pent * 1
mask3 = (masses['hex'] == 0)
masses_temp = masses[mask3]
masses.loc[mask3, 'OH'] = masses_temp.OH + masses_temp.pent * 1 + 2
# modifications
if "none" not in modifications:
modifications_OHdiff = [val for i, val in enumerate(modifications) if val in isomers_OHdiff]
for i in range(len(modifications_OHdiff)):
masses['OH'] = masses.OH - masses[modifications_OHdiff[i]]
if len(LorD_isomers) == 2:
masses['isomers'] = masses.isomers * (2 ** masses.OH)
elif len(LorD_isomers) == 1:
masses['isomers'] = 2 ** masses.OH
masses = masses.drop(columns=['OH'])
if bond_stereo == 1:
if len(LorD_isomers) == 2 or OH_stereo == 1:
masses['isomers'] = masses.isomers * (2 ** masses.dp)
elif len(LorD_isomers) == 1 and OH_stereo == 0:
masses['isomers'] = 2 ** masses.dp
gc.collect()
elapsed_time = time.time() - start_time
print("finished. elapsed time = " + time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
# 6: CALCULATE M/Z VALUES
# ----------------------
print("\nstep #6: calculating m/z values of ions")
print("----------------------------------------------------------------\n")
if len(list(set(modifications).intersection(modifications_anionic))) >= 1:
# create separate tables of sugars with (any) anionic modifications, and with (only) neutral modifications
anionic_mod_used = list(set(modifications).intersection(modifications_anionic))
masses_anionic = masses[masses['name'].str.contains('|'.join(anionic_mod_used))]
masses_all = masses.merge(masses_anionic.drop_duplicates(), how='left', indicator=True)
masses_neutral = masses_all[masses_all._merge == 'left_only']
# calculate m/z values for neutral molecules
if "neg" in ESI_mode:
masses_neutral['[M-H]-'] = masses_neutral.mass - ion_mdiff['H'] + e_mdiff
masses_neutral['[M+Cl]-'] = masses_neutral.mass + ion_mdiff['Cl'] + e_mdiff
masses_neutral['[M+CHOO]-'] = masses_neutral.mass + ion_mdiff['CHOO'] + e_mdiff
masses_neutral['[M-2H]-2'] = (masses_neutral.mass - 2 * ion_mdiff['H'] + 2 * e_mdiff) / 2
masses_neutral['[M+2Cl]-2'] = (masses_neutral.mass + 2 * ion_mdiff['Cl'] + 2 * e_mdiff) / 2
masses_neutral['[M+2CHOO]-2'] = (masses_neutral.mass + 2 * ion_mdiff['CHOO'] + 2 * e_mdiff) / 2
masses_neutral['[M+Cl-H]-2'] = (masses_neutral.mass + ion_mdiff['Cl'] - ion_mdiff['H'] + 2 * e_mdiff) / 2
masses_neutral['[M+CHOO-H]-2'] = (masses_neutral.mass + ion_mdiff['CHOO'] - ion_mdiff['H'] + 2 * e_mdiff) / 2
masses_neutral['[M+CHOO+Cl]-2'] = (masses_neutral.mass + ion_mdiff['CHOO'] + ion_mdiff['Cl'] + 2 * e_mdiff) / 2
if "pos" in ESI_mode:
masses_neutral['[M+H]+'] = masses_neutral.mass + ion_mdiff['H'] - e_mdiff
masses_neutral['[M+Na]+'] = masses_neutral.mass + ion_mdiff['Na'] - e_mdiff
masses_neutral['[M+NH4]+'] = masses_neutral.mass + ion_mdiff['NH4'] - e_mdiff
masses_neutral['[M+K]+'] = masses_neutral.mass + ion_mdiff['K'] - e_mdiff
# filter neutral molecules based on scan range
# set values outside range to NaN
# remove rows where all ions are outside range
my_cols = list(masses_neutral.filter(like='[M', axis=1).columns)
masses_neutral[my_cols] = masses_neutral[my_cols].where(masses_neutral[my_cols] >= scan_range[0])
masses_neutral[my_cols] = masses_neutral[my_cols].where(masses_neutral[my_cols] <= scan_range[1])
masses_neutral = masses_neutral.dropna(subset=my_cols, how='all')
# calculate m/z values for anionic molecules
if len(anionic_mod_used) > 1:
masses_anionic['nmod_anionic'] = masses_anionic[anionic_mod_used].sum(axis=1)
masses_anionic['nmod_anionic'] = masses_anionic.nmod_anionic.astype(int)
elif len(anionic_mod_used) == 1:
masses_anionic['nmod_anionic'] = masses_anionic[anionic_mod_used].astype(int)
if "neg" in ESI_mode:
ions = list(range(1, masses_anionic.nmod_anionic.max() + 1))
ions = list("[M-" + pd.Series(ions).astype(str) + "H]-" + pd.Series(ions).astype(str))
for i in range(len(ions)):
masses_anionic[ions[i]] = (masses_anionic.mass - ion_mdiff['H'] * (i + 1) + e_mdiff * (i + 1)) / (i + 1)
masses_anionic[ions[i]] = masses_anionic[ions[i]].where(masses_anionic['nmod_anionic'] >= (i + 1))
masses_anionic = masses_anionic.rename({'[M-1H]-1': '[M-H]-'}, axis=1)
masses_anionic['[M+Cl]-'] = masses_anionic.mass + ion_mdiff['Cl'] + e_mdiff
masses_anionic['[M+CHOO]-'] = masses_anionic.mass + ion_mdiff['CHOO'] + e_mdiff
masses_anionic['[M+2Cl]-2'] = (masses_anionic.mass + 2 * ion_mdiff['Cl'] + 2 * e_mdiff) / 2
masses_anionic['[M+2CHOO]-2'] = (masses_anionic.mass + 2 * ion_mdiff['CHOO'] + 2 * e_mdiff) / 2
masses_anionic['[M+Cl-H]-2'] = (masses_anionic.mass + ion_mdiff['Cl'] - ion_mdiff['H'] + 2 * e_mdiff) / 2
masses_anionic['[M+CHOO-H]-2'] = (masses_anionic.mass + ion_mdiff['CHOO'] - ion_mdiff['H'] + 2 * e_mdiff) / 2
masses_anionic['[M+CHOO+Cl]-2'] = (masses_anionic.mass + ion_mdiff['CHOO'] + ion_mdiff['Cl'] + 2 * e_mdiff) / 2
if "pos" in ESI_mode:
masses_anionic['[M+H]+'] = masses_anionic.mass + ion_mdiff['H'] - e_mdiff
masses_anionic['[M+Na]+'] = masses_anionic.mass + ion_mdiff['Na'] - e_mdiff
masses_anionic['[M+NH4]+'] = masses_anionic.mass + ion_mdiff['NH4'] - e_mdiff
masses_anionic['[M+K]+'] = masses_anionic.mass + ion_mdiff['K'] - e_mdiff
# filter anionic molecules based on scan range
# set values outside range to NaN
# remove rows where all ions are outside range
my_cols = list(masses_anionic.filter(like='[M', axis=1).columns)
masses_anionic[my_cols] = masses_anionic[my_cols].where(masses_anionic[my_cols] >= scan_range[0])
masses_anionic[my_cols] = masses_anionic[my_cols].where(masses_anionic[my_cols] <= scan_range[1])
masses_anionic = masses_anionic.dropna(subset=my_cols, how='all')
# concatenate dataframes and format nicely to only have useful columns
masses_final = pd.concat([masses_anionic, masses_neutral])
if "benzoic_acid" in label:
bad_cols = {'level_0',
'index',
'hex',
'alditol',
'pent',
'dehydrated',
'nmod',
'nmod_avg',
'nmod_anionic',
'_merge',
'benzoic_acid'}
else:
bad_cols = {'level_0',
'index',
'hex',
'pent',
'alditol',
'dehydrated',
'nmod',
'nmod_avg',
'nmod_anionic',
'_merge'}
bad_cols.update(modifications_anionic)
bad_cols.update(modifications_neutral)
cols_del = list(set(masses_final.columns).intersection(bad_cols))
masses_final = masses_final.drop(columns=cols_del)
if len(list(set(modifications).intersection(modifications_anionic))) == 0:
# calculate m/z values for neutral molecules
if "neg" in ESI_mode:
masses['[M-H]-'] = masses.mass - ion_mdiff['H'] + e_mdiff
masses['[M+Cl]-'] = masses.mass + ion_mdiff['Cl'] + e_mdiff
masses['[M+CHOO]-'] = masses.mass + ion_mdiff['CHOO'] + e_mdiff
masses['[M-2H]-2'] = (masses.mass - 2 * ion_mdiff['H'] + 2 * e_mdiff) / 2
masses['[M+2Cl]-2'] = (masses.mass + 2 * ion_mdiff['Cl'] + 2 * e_mdiff) / 2
masses['[M+2CHOO]-2'] = (masses.mass + 2 * ion_mdiff['CHOO'] + 2 * e_mdiff) / 2
masses['[M+Cl-H]-2'] = (masses.mass + ion_mdiff['Cl'] - ion_mdiff['H'] + 2 * e_mdiff) / 2
masses['[M+CHOO-H]-2'] = (masses.mass + ion_mdiff['CHOO'] - ion_mdiff['H'] + 2 * e_mdiff) / 2