def calculate_physiochemical_features(temp_dict, sequence):
analyzed_seq = ProteinAnalysis(sequence)
charge_at_pH7 = analyzed_seq.charge_at_pH(7)
instability_index = analyzed_seq.instability_index()
molecular_weight = analyzed_seq.molecular_weight()
aromaticity = analyzed_seq.aromaticity()
molar_extinction_coefficient = analyzed_seq.molar_extinction_coefficient()
range_l, range_h = molar_extinction_coefficient
molar_extinction_coefficient = (float(range_l) + float(range_h)) / 2
gravy = analyzed_seq.gravy(
) #Grand Average Hyrdopathy - Higher value = More Hydrophobic
isoelectric_point = analyzed_seq.isoelectric_point()
helix_fraction, turn_fraction, sheet_fraction = analyzed_seq.secondary_structure_fraction(
)
physiochem_dict = {
"Charge at pH7": charge_at_pH7,
"Instability Index": instability_index,
"Molecular Wt": molecular_weight,
"Aromaticity": aromaticity,
"Molar Extinction Coeff": molar_extinction_coefficient,
"Gravy": gravy,
"Isoelectric pt": isoelectric_point,
"Helix Fraction": helix_fraction,
"Turn Fraction": turn_fraction,
"Sheet Fraction": sheet_fraction
}
temp_dict.update(physiochem_dict)
#Adding separately because get_amino_acids_percent() generates a dictionary on its own
aa_percent = analyzed_seq.get_amino_acids_percent()
temp_dict.update(aa_percent)
def get_coord_array(path, file_name):
'''
Function: get coord array of all atoms in a pdb file
:param path: the path of pdb file of all proteins
:param file_name: the file name of ****.pdb
:return: atom coord array
[[x0,y0,z0],
[x1,y1,z1],
... ,
[xn,yn,zn]]
: charge of first model
: mass of first model
'''
parser = PDBParser(PERMISSIVE=1)
structure_id = file_name.split('.')[0]
path_file_name = path + file_name
structure = parser.get_structure(structure_id, path_file_name)
# Extract mass and charge from first model
mass, charge = 0.0, 0.0
polypep_builder = PPBuilder()
for polypep in polypep_builder.build_peptides(structure):
analyzer = ProteinAnalysis(polypep.get_sequence())
mass += analyzer.molecular_weight()
charge += analyzer.charge_at_pH(7.4)
atom_coord_list = []
for model in structure:
for chain in model:
for residue in chain:
for atom in residue:
atom_coord = atom.get_coord()
atom_coord_list.append(atom_coord)
atom_coord_array = np.array(atom_coord_list)
# print file_name,'atom_coo_array\n',atom_coord_array,'\n'
# print('get_coord_array DONE!\t', path, ": ", file_name)
return atom_coord_array, charge, mass
with temppathlib.TemporaryDirectory() as tmpdir:
# unzip the file with all the test PDBs
with zipfile.ZipFile(args.infile, "r") as zip_:
zip_.extractall(tmpdir.path)
for test_pdb in tmpdir.path.glob("*.pdb"):
for record in SeqIO.parse(test_pdb, "pdb-atom"):
sequence = str(record.seq).replace('X', 'G')
protein = ProteinAnalysis(str(sequence))
p_len.append(len(sequence))
mol_w.append(protein.molecular_weight())
iso_p.append(protein.isoelectric_point())
smell.append(protein.aromaticity())
taste_factor.append(protein.gravy())
insta_ind.append(protein.instability_index())
char_at_acid.append(protein.charge_at_pH(1))
char_at_neutral.append(protein.charge_at_pH(7))
char_at_base.append(protein.charge_at_pH(14))
helter_skeler.append(protein.secondary_structure_fraction()[0])
turnip.append(protein.secondary_structure_fraction()[1])
garfield.append(protein.secondary_structure_fraction()[2])
for x in amino_acids:
n = protein.count_amino_acids()[x]
for y in d_count.keys():
if y[-1] == x:
d_count[y].append(n)
for a in amino_acids:
m = protein.get_amino_acids_percent()[a]
for b in d_perc.keys():
if b[-1] == a:
d_perc[b].append(m)
def main():
aa = [
'A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q',
'R', 'S', 'T', 'V', 'W', 'Y'
]
dipeptide = [
'AA', 'AC', 'AD', 'AE', 'AF', 'AG', 'AH', 'AI', 'AK', 'AL', 'AM', 'AN',
'AP', 'AQ', 'AR', 'AS', 'AT', 'AV', 'AW', 'AY', 'CA', 'CC', 'CD', 'CE',
'CF', 'CG', 'CH', 'CI', 'CK', 'CL', 'CM', 'CN', 'CP', 'CQ', 'CR', 'CS',
'CT', 'CV', 'CW', 'CY', 'DA', 'DC', 'DD', 'DE', 'DF', 'DG', 'DH', 'DI',
'DK', 'DL', 'DM', 'DN', 'DP', 'DQ', 'DR', 'DS', 'DT', 'DV', 'DW', 'DY',
'EA', 'EC', 'ED', 'EE', 'EF', 'EG', 'EH', 'EI', 'EK', 'EL', 'EM', 'EN',
'EP', 'EQ', 'ER', 'ES', 'ET', 'EV', 'EW', 'EY', 'FA', 'FC', 'FD', 'FE',
'FF', 'FG', 'FH', 'FI', 'FK', 'FL', 'FM', 'FN', 'FP', 'FQ', 'FR', 'FS',
'FT', 'FV', 'FW', 'FY', 'GA', 'GC', 'GD', 'GE', 'GF', 'GG', 'GH', 'GI',
'GK', 'GL', 'GM', 'GN', 'GP', 'GQ', 'GR', 'GS', 'GT', 'GV', 'GW', 'GY',
'HA', 'HC', 'HD', 'HE', 'HF', 'HG', 'HH', 'HI', 'HK', 'HL', 'HM', 'HN',
'HP', 'HQ', 'HR', 'HS', 'HT', 'HV', 'HW', 'HY', 'IA', 'IC', 'ID', 'IE',
'IF', 'IG', 'IH', 'II', 'IK', 'IL', 'IM', 'IN', 'IP', 'IQ', 'IR', 'IS',
'IT', 'IV', 'IW', 'IY', 'KA', 'KC', 'KD', 'KE', 'KF', 'KG', 'KH', 'KI',
'KK', 'KL', 'KM', 'KN', 'KP', 'KQ', 'KR', 'KS', 'KT', 'KV', 'KW', 'KY',
'LA', 'LC', 'LD', 'LE', 'LF', 'LG', 'LH', 'LI', 'LK', 'LL', 'LM', 'LN',
'LP', 'LQ', 'LR', 'LS', 'LT', 'LV', 'LW', 'LY', 'MA', 'MC', 'MD', 'ME',
'MF', 'MG', 'MH', 'MI', 'MK', 'ML', 'MM', 'MN', 'MP', 'MQ', 'MR', 'MS',
'MT', 'MV', 'MW', 'MY', 'NA', 'NC', 'ND', 'NE', 'NF', 'NG', 'NH', 'NI',
'NK', 'NL', 'NM', 'NN', 'NP', 'NQ', 'NR', 'NS', 'NT', 'NV', 'NW', 'NY',
'PA', 'PC', 'PD', 'PE', 'PF', 'PG', 'PH', 'PI', 'PK', 'PL', 'PM', 'PN',
'PP', 'PQ', 'PR', 'PS', 'PT', 'PV', 'PW', 'PY', 'QA', 'QC', 'QD', 'QE',
'QF', 'QG', 'QH', 'QI', 'QK', 'QL', 'QM', 'QN', 'QP', 'QQ', 'QR', 'QS',
'QT', 'QV', 'QW', 'QY', 'RA', 'RC', 'RD', 'RE', 'RF', 'RG', 'RH', 'RI',
'RK', 'RL', 'RM', 'RN', 'RP', 'RQ', 'RR', 'RS', 'RT', 'RV', 'RW', 'RY',
'SA', 'SC', 'SD', 'SE', 'SF', 'SG', 'SH', 'SI', 'SK', 'SL', 'SM', 'SN',
'SP', 'SQ', 'SR', 'SS', 'ST', 'SV', 'SW', 'SY', 'TA', 'TC', 'TD', 'TE',
'TF', 'TG', 'TH', 'TI', 'TK', 'TL', 'TM', 'TN', 'TP', 'TQ', 'TR', 'TS',
'TT', 'TV', 'TW', 'TY', 'VA', 'VC', 'VD', 'VE', 'VF', 'VG', 'VH', 'VI',
'VK', 'VL', 'VM', 'VN', 'VP', 'VQ', 'VR', 'VS', 'VT', 'VV', 'VW', 'VY',
'WA', 'WC', 'WD', 'WE', 'WF', 'WG', 'WH', 'WI', 'WK', 'WL', 'WM', 'WN',
'WP', 'WQ', 'WR', 'WS', 'WT', 'WV', 'WW', 'WY', 'YA', 'YC', 'YD', 'YE',
'YF', 'YG', 'YH', 'YI', 'YK', 'YL', 'YM', 'YN', 'YP', 'YQ', 'YR', 'YS',
'YT', 'YV', 'YW', 'YY'
]
sequences = pandas.read_csv('protein_data.csv', header=None)
lengths = []
weights = []
for protein in sequences.itertuples():
protein_length = len(str(protein[1])) # length of protein sequence
lengths.append(protein_length)
analyzed_protein = ProteinAnalysis(str(protein[1]))
ambigious_match = re.findall("X+|Z+", protein[1])
if ambigious_match:
molecular_weight = "?"
else:
molecular_weight = analyzed_protein.molecular_weight()
weights.append(molecular_weight)
# remove bad amino acids from sequences
for i in range(len(sequences)):
sequences[0][i] = sequences[0][i].replace('B', '')
sequences[0][i] = sequences[0][i].replace('U', '')
sequences[0][i] = sequences[0][i].replace('X', '')
sequences[0][i] = sequences[0][i].replace('Z', '')
pandas.DataFrame(sequences).to_csv('updated_protein_data.csv',
index_label=None,
header=None,
index=None)
# use amino acid composition results from pfeature to generate most common amino acid and dipeptide
data = pandas.read_csv('updated_protein_data.csv', header=None)
data = numpy.asarray(data)
most_frequent_di = []
most_frequent = []
for i in range(len(data)):
max = 0
col = 0
for j in range(len(dipeptide)):
c = data[i][0].count(dipeptide[j])
if (c > max):
max = c
col = j
most_frequent_di.append(dipeptide[col])
for j in range(len(aa)):
c = data[i][0].count(aa[j])
if (c > max):
max = c
col = j
most_frequent.append(aa[col])
# more features
amino_acid = {}
first_aa = []
last_aa = []
arom = []
ii = []
ip = []
mec_rc = []
mec_db = []
ssf_helix = []
ssf_turn = []
ssf_sheet = []
gravy = []
ph_0 = []
ph_7 = []
ph_14 = []
A = []
C = []
D = []
E = []
F = []
G = []
H = []
I = []
K = []
L = []
M = []
N = []
P = []
Q = []
R = []
S = []
T = []
V = []
W = []
Y = []
classes = []
data = pandas.read_csv('updated_protein_data.csv', header=None)
for protein in data.itertuples():
analyzed_protein = ProteinAnalysis(str(protein[1]))
amino_acid = (analyzed_protein.count_amino_acids())
A.append(amino_acid.get('A'))
C.append(amino_acid.get('C'))
D.append(amino_acid.get('D'))
E.append(amino_acid.get('E'))
F.append(amino_acid.get('F'))
G.append(amino_acid.get('G'))
H.append(amino_acid.get('H'))
I.append(amino_acid.get('I'))
K.append(amino_acid.get('K'))
L.append(amino_acid.get('L'))
M.append(amino_acid.get('M'))
N.append(amino_acid.get('N'))
P.append(amino_acid.get('P'))
Q.append(amino_acid.get('Q'))
R.append(amino_acid.get('R'))
S.append(amino_acid.get('S'))
T.append(amino_acid.get('T'))
V.append(amino_acid.get('V'))
W.append(amino_acid.get('W'))
Y.append(amino_acid.get('Y'))
first_aa.append(str(protein[1])[0])
last_aa.append(str(protein[1])[-1])
arom.append(analyzed_protein.aromaticity())
ii.append(analyzed_protein.instability_index())
ip.append(analyzed_protein.isoelectric_point())
mec_rc.append(analyzed_protein.molar_extinction_coefficient()[0])
mec_db.append(analyzed_protein.molar_extinction_coefficient()[1])
ssf_helix.append(analyzed_protein.secondary_structure_fraction()[0])
ssf_turn.append(analyzed_protein.secondary_structure_fraction()[1])
ssf_sheet.append(analyzed_protein.secondary_structure_fraction()[2])
gravy.append(analyzed_protein.gravy())
ph_0.append(analyzed_protein.charge_at_pH(0.0))
ph_7.append(analyzed_protein.charge_at_pH(7.0))
ph_14.append(analyzed_protein.charge_at_pH(14.0))
classes.append(protein[2])
features = pandas.DataFrame()
features["LENGTH"] = lengths
#features["MOLECULAR WEIGHT"] = weights
features["most frequent aa"] = most_frequent
#features["first amino acids"] = first_aa
features["last amino acid"] = last_aa
features["most frequence dipeptide"] = most_frequent_di
features["aromaticity"] = arom
features["instability index"] = ii
features["isolectric point"] = ip
features["molecular extinction coefficient - reduced cysteines"] = mec_rc
features["molecular extinction coefficient - disulfid bridges"] = mec_db
features["secondary structure fraction helix"] = ssf_helix
features["secondary structure fraction turn"] = ssf_turn
features["secondary structure fraction sheet"] = ssf_sheet
features["gravy"] = gravy
features["charge at ph 0"] = ph_0
features["charge at ph 7"] = ph_7
features["charge at ph 14"] = ph_14
features['A'] = A
features['C'] = C
features['D'] = D
features['E'] = E
features['F'] = F
features['G'] = G
features['H'] = H
features['I'] = I
features['K'] = K
features['L'] = L
features['M'] = M
features['N'] = N
features['P'] = P
features['Q'] = Q
features['R'] = R
features['S'] = S
features['T'] = T
features['V'] = V
features['W'] = W
features['Y'] = Y
features["CLASS"] = classes
features.to_csv('features.csv', index=None)
def get_ieq_nc(seq, is_iep=True):
protparam = PA(seq)
return protparam.isoelectric_point(
) if is_iep else protparam.charge_at_pH(7.0)
def featurise(self, data):
"""
Featurise the data.
Parameters:
-----------
data : `list` of `Bio.SeqRecord.SeqRecord`
The data to be featurised.
Returns:
-------
featurised_data : `pandas.DataFrame`
(num_data, features) The featurised data.
"""
# Get features of data
features = collections.defaultdict(list)
# Featurise the data
for i, example in enumerate(data):
# Convert Bio.SeqRecord.SeqRecord object to string for Bio.SeqUtils.ProtParam.ProteinAnalysis
analysed_example = ProteinAnalysis(str(example.seq))
first50_analysed_example = ProteinAnalysis(str(example.seq)[:50])
last50_analysed_example = ProteinAnalysis(str(example.seq)[-50:])
features["length"].append(analysed_example.length)
features["molecular_weight"].append(
analysed_example.molecular_weight())
features["isoelectric_point"].append(
analysed_example.isoelectric_point())
features["aromaticity"].append(analysed_example.aromaticity())
features["instability_index"].append(
analysed_example.instability_index())
features["gravy"].append(analysed_example.gravy())
reduced, oxidised = analysed_example.molar_extinction_coefficient()
features["reduced"].append(reduced)
features["oxidised"].append(oxidised)
helix, turn, sheet = analysed_example.secondary_structure_fraction(
)
features["helix"].append(helix)
features["turn"].append(turn)
features["sheet"].append(sheet)
features["charge_at_ph1"].append(analysed_example.charge_at_pH(1))
# features["charge_at_ph2"].append(analysed_example.charge_at_pH(2))
# features["charge_at_ph3"].append(analysed_example.charge_at_pH(3))
# features["charge_at_ph4"].append(analysed_example.charge_at_pH(4))
features["charge_at_ph7"].append(analysed_example.charge_at_pH(7))
features["charge_at_ph12"].append(
analysed_example.charge_at_pH(12))
features["hydrophobicity"].append(
np.mean(
analysed_example.protein_scale(self.dicts['kd'],
window=5,
edge=1.0)))
features["flexibility"].append(
np.mean(
analysed_example.protein_scale(self.dicts['flex'],
window=5,
edge=1.0)))
features["hydrophilicity"].append(
np.mean(
analysed_example.protein_scale(self.dicts['hw'],
window=5,
edge=1.0)))
features["surface_accessibility"].append(
np.mean(
analysed_example.protein_scale(self.dicts['em'],
window=5,
edge=1.0)))
features["janin"].append(
np.mean(
analysed_example.protein_scale(self.dicts['ja'],
window=5,
edge=1.0)))
# features["dipeptide_dg "].append(np.mean(analysed_example.protein_scale(self.dicts['diwv'], window=5, edge=1.0)))
features["first50_hydrophobicity"].append(
np.mean(
first50_analysed_example.protein_scale(self.dicts['kd'],
window=5,
edge=1.0)))
features["first50_flexibility"].append(
np.mean(
first50_analysed_example.protein_scale(self.dicts['flex'],
window=5,
edge=1.0)))
features["first50_hydrophilicity"].append(
np.mean(
first50_analysed_example.protein_scale(self.dicts['hw'],
window=5,
edge=1.0)))
features["first50_surface_accessibility"].append(
np.mean(
first50_analysed_example.protein_scale(self.dicts['em'],
window=5,
edge=1.0)))
features["first50_janin"].append(
np.mean(
first50_analysed_example.protein_scale(self.dicts['ja'],
window=5,
edge=1.0)))
features["last50_hydrophobicity"].append(
np.mean(
last50_analysed_example.protein_scale(self.dicts['kd'],
window=5,
edge=1.0)))
features["last50_flexibility"].append(
np.mean(
last50_analysed_example.protein_scale(self.dicts['flex'],
window=5,
edge=1.0)))
features["last50_hydrophilicity"].append(
np.mean(
last50_analysed_example.protein_scale(self.dicts['hw'],
window=5,
edge=1.0)))
features["last50_surface_accessibility"].append(
np.mean(
last50_analysed_example.protein_scale(self.dicts['em'],
window=5,
edge=1.0)))
features["last50_janin"].append(
np.mean(
last50_analysed_example.protein_scale(self.dicts['ja'],
window=5,
edge=1.0)))
for key, val in analysed_example.get_amino_acids_percent().items():
features[key].append(val * 5)
for key, val in first50_analysed_example.get_amino_acids_percent(
).items():
features["first_50_" + str(key)].append(val * 5)
for key, val in last50_analysed_example.get_amino_acids_percent(
).items():
features["last_50_" + str(key)].append(val * 5)
return pd.DataFrame.from_dict(features)
