def CTD_feature_extractor(self, protein_sequence):
value_list = []
features_list = ["_Polarizability", "_SolventAccessibility", "_SecondaryStr",
"_Charge", "_Polarity", "_NormalizedVDWV", "_Hydrophobicity"]
ctd_features = CTD.CalculateCTD(protein_sequence)
for feature_keys in ctd_features.keys():
value = ctd_features.get(feature_keys)
if value > 1:
value = round((value/100), 3)
value_list.append(value)
return np.array(value_list, dtype=np.float32)
def generate_features(sequence):
seq = str(sequence)
feature = CTD.CalculateCTD(seq)
feature_all = []
count = 0
for key in sorted(feature.keys()):
feature_all.append(feature[key])
count = count + 1
if count == 147:
feature_numpy = np.array(feature_all)
# print(feature_numpy)
return feature_numpy
def feature_generation(input_file):
global feature_file, feature
# feature_file = open(os.path.expanduser("C:/Users/Rayin/Google Drive/Tier_2_MOE2014/5_Journal/Plos_One/Data/feature/feature.csv"), 'w')
input_file = pd.DataFrame(input_file)
input_file = input_file['seq']
count = 0
for row in range(0, len(input_file)):
seq = str(input_file.loc[row])
feature = CTD.CalculateCTD(seq)
if row == 0:
write_header()
for key in sorted(feature.keys()):
feature_value = feature[key]
count = count + 1
if count == 147:
count = 0
write_to_csv = str(feature_value) + '\n'
else:
write_to_csv = str(feature_value) + ','
#write_to_csv = str(feature_value) + ','
feature_file.write(write_to_csv)
feature_file.close()
def generate_features(df):
aac_keys = []
aac_values = []
ctd_keys = []
ctd_values = []
for row in df.itertuples(index=False):
my_seq = row[0].replace('X', '')
my_seq = my_seq.replace('Z', '')
aac = AAComposition.CalculateAAComposition(my_seq)
ctd = CTD.CalculateC(my_seq)
aac_keys = aac.keys()
aac_values.append(list(aac.values()))
ctd_keys = ctd.keys()
ctd_values.append(list(ctd.values()))
# Create dataframes
df_aac = pd.DataFrame(aac_values, columns=aac_keys)
df_ctd = pd.DataFrame(ctd_values, columns=ctd_keys)
# Concat dataframes into single dataframe
res_df = pd.concat([df_aac, df_ctd], axis=1)
res_df = pd.concat([df, res_df], axis=1)
# Send dataframe to csv
res_df.to_csv('generated_features.csv', index=False)
res[i + '+' + i] = round(dict1[i] + dict2[i], 3)
res[i + '*' + i] = round(dict1[i] * dict2[i], 3)
return res
if __name__ == '__main__':
from PyBioMed.PyDNA import PyDNAac
DNA_des = PyDNAac.GetTCC('GACTGAACTGCACTTTGGTTTCATATTATTTGCTC',
phyche_index=['Dnase I', 'Nucleosome', 'MW-kg'])
print DNA_des
from PyBioMed.PyProtein import CTD
protein = "ADGCGVGEGTGQGPMCNCMCMKWVYADEDAADLESDSFADEDASLESDSFPWSNQRVFCSFADEDAS"
protein_des = CTD.CalculateCTD(protein)
from PyBioMed.PyMolecule import moe
from rdkit import Chem
smis = ['CCCC', 'CCCCC', 'CCCCCC', 'CC(N)C(=O)O', 'CC(N)C(=O)[O-].[Na+]']
m = Chem.MolFromSmiles(smis[3])
mol_des = moe.GetMOE(m)
mol_mol_interaction1 = CalculateInteraction1(mol_des, mol_des)
print mol_mol_interaction1
mol_mol_interaction2 = CalculateInteraction2(mol_des, mol_des)
print mol_mol_interaction2
mol_mol_interaction3 = CalculateInteraction3(mol_des, mol_des)
print mol_mol_interaction3
def describe_sequences():
path = r"C:\Users\Patrick\OneDrive - University College Dublin\Bioinformatics\HemolyticStudies\BOTH_peptides.json"
aa_letters = [
'A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q',
'R', 'S', 'T', 'V', 'W', 'Y'
]
di_letters = ["%s%s" % (a, b) for a in aa_letters for b in aa_letters]
tri_letters = [
"%s%s%s" % (a, b, c) for a in aa_letters for b in aa_letters
for c in aa_letters
]
conjoint_letters = ["A", "I", "Y", "H", "R", "D", "C"]
letters = {
1: aa_letters,
2: di_letters,
3: tri_letters,
4: conjoint_letters
}
#Conjoint src = https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0828-1
conjoint_dict = {
"A": "A",
"G": "A",
"V": "A",
"I": "I",
"L": "I",
"F": "I",
"P": "I",
"Y": "Y",
"M": "Y",
"T": "Y",
"S": "Y",
"H": "H",
"N": "H",
"Q": "H",
"W": "H",
"R": "R",
"K": "R",
"D": "D",
"E": "D",
"C": "C",
}
def counter(string, seq_type):
'''
A function for counting the number of letters present.
Returns a list of (letter, #occurances) tuples.
'''
l = len(string)
d = {i: 0 for i in letters[seq_type]}
if seq_type == 1:
for s in string:
try:
d[s] += 1.0
except KeyError:
d[s] = 1.0
d = {k: d[k] / l for k in d}
if seq_type == 2:
for a in range(l - 1):
s = string[a:a + seq_type]
try:
d[s] += 1.0
except KeyError:
d[s] = 1.0
d = {k: d[k] / (l - 1) for k in d}
if seq_type == 3:
for a in range(l - 2):
s = string[a:a + seq_type]
try:
d[s] += 1.0
except KeyError:
d[s] = 1.0
d = {k: d[k] / (l - 2) for k in d}
return d
def counter_boolean(string, seq_type):
'''
A function for counting the number of letters present.
Returns a list of (letter, #occurances) tuples.
'''
l = len(string)
d = {i: 0 for i in letters[seq_type]}
if seq_type == 1:
for s in string:
try:
d[s] = 1.0
except KeyError:
d[s] = 1.0
if seq_type == 2:
for a in range(l - 1):
s = string[a:a + seq_type]
try:
d[s] = 1.0
except KeyError:
d[s] = 1.0
return d
def counter_abs(string, seq_type):
'''
A function for counting the number of letters present.
Returns a list of (letter, #occurances) tuples.
'''
l = len(string)
d = {i: 0 for i in letters[seq_type]}
if seq_type == 1:
for s in string:
try:
d[s] = d[s] + 1.0
except KeyError:
d[s] = 1.0
if seq_type == 2:
for a in range(l - 1):
s = string[a:a + seq_type]
try:
d[s] = d[s] + 1.0
except KeyError:
d[s] = 1.0
return d
def residue_distribution(all_residues, seq_type, dp):
'''
Takes as arguments a string with letters, and the type of sequence represented.
Returns an alphabetically ordered string of relative frequencies, correct to three decimal places.
'''
d = counter(all_residues, seq_type)
if seq_type == 1:
residue_counts = list(
sorted([(i, d[i]) for i in letters[seq_type]
])) ##Removes ambiguous letters
elif seq_type == 2:
residue_counts = list(
sorted([(i, d[i]) for i in letters[seq_type] if dp[i] >= 50]))
elif seq_type == 3:
residue_counts = list(
sorted([(i, d[i]) for i in letters[seq_type] if tp[i] >= 20]))
elif seq_type == 4:
residue_counts = list(
sorted([(i, d[i]) for i in letters[seq_type]]))
r_c = [i[1] for i in residue_counts]
dis = np.array([
r_c,
])
return dis
def residue_boolean(all_residues, seq_type, dp):
'''
Takes as arguments a string with letters, and the type of sequence represented.
Returns an alphabetically ordered string of relative frequencies, correct to three decimal places.
'''
d = counter_boolean(all_residues, seq_type)
if seq_type == 1:
residue_counts = list(
sorted([(i, d[i]) for i in letters[seq_type]
])) ##Removes ambiguous letters
elif seq_type == 2:
residue_counts = list(
sorted([(i, d[i]) for i in letters[seq_type] if dp[i] >= 50]))
r_c = [i[1] for i in residue_counts]
dis = np.array([
r_c,
])
return dis
def residue_abs(all_residues, seq_type, dp):
'''
Takes as arguments a string with letters, and the type of sequence represented.
Returns an alphabetically ordered string of relative frequencies, correct to three decimal places.
'''
d = counter_abs(all_residues, seq_type)
if seq_type == 1:
residue_counts = list(
sorted([(i, d[i]) for i in letters[seq_type]
])) ##Removes ambiguous letters
elif seq_type == 2:
residue_counts = list(
sorted([(i, d[i]) for i in letters[seq_type] if dp[i] >= 50]))
r_c = [i[1] for i in residue_counts]
dis = np.array([
r_c,
])
return dis
with open(path, "r") as f:
text = f.read()
peptides = eval(text)["Peptides"]
train_peptides, test_peptides = train_test_split(peptides,
test_size=0.15,
random_state=42)
train_peptides_seqs = [peptide["seq"] for peptide in train_peptides]
for peptide in peptides:
if peptide["seq"] in train_peptides_seqs:
peptide["train"] = True
else:
peptide["train"] = False
print(len([p for p in peptides if p["train"] == True]))
print(len([p for p in peptides if p["train"] == False]))
new_peptides = []
for peptide in peptides:
if peptide["train"] == True:
new_peptide = peptide.copy()
new_seq = ''.join(reversed(peptide["seq"]))
new_peptide["seq"] = new_seq
new_peptides.append(new_peptide)
#peptides.extend(new_peptides)
random.shuffle(peptides)
print(len([p for p in peptides if p["train"] == True]))
print(len([p for p in peptides if p["train"] == False]))
print("doubling complete")
dp = {i: 0 for i in letters[2]}
tp = {i: 0 for i in letters[3]}
name_i = 0
for peptide in peptides:
temp_set = set()
seq = peptide["seq"]
l = len(seq)
for a in range(l - 1):
s = seq[a:a + 2]
temp_set.add(s)
for s in temp_set:
dp[s] = dp[s] + 1
for peptide in peptides:
temp_set = set()
seq = peptide["seq"]
l = len(seq)
for a in range(l - 2):
s = seq[a:a + 3]
temp_set.add(s)
for s in temp_set:
tp[s] = tp[s] + 1
for peptide in peptides:
peptide["conjoint_seq"] = "".join(
[conjoint_dict[letter] for letter in peptide["seq"]])
for peptide in peptides:
globdesc = GlobalDescriptor(peptide["seq"])
globdesc.calculate_all(amide=peptide["cTer"] == "Amidation")
ctdc = CTD.CalculateC(peptide["seq"])
ctdc_keys = list(sorted(list([key for key in ctdc])))
ctdc_vals = np.array([[ctdc[key] for key in ctdc_keys]])
conjointtriad = ConjointTriad.CalculateConjointTriad(peptide["seq"])
conjointtriad_keys = list(sorted(list([key for key in conjointtriad])))
conjointtriad_vals = np.array(
[[conjointtriad[key] for key in conjointtriad_keys]])
conjoint_dis = residue_distribution(peptide["conjoint_seq"], 4, None)
#peptide["GlobalDescriptor"] = globdesc
#print(peptide["GlobalDescriptor"].descriptor)
#Eisenberg hydrophobicity consensus
#Take most of the values from here
pepdesc = PeptideDescriptor(peptide["seq"], "eisenberg")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
#pepdesc.calculate_profile(append=True, prof_type = "uH")
pepdesc.load_scale("Ez")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("aasi")
pepdesc.calculate_global(append=True)
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
pepdesc.load_scale("abhprk")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("charge_acid")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
pepdesc.load_scale("cougar")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("gravy")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
pepdesc.load_scale("hopp-woods")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
pepdesc.load_scale("kytedoolittle")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
pepdesc.load_scale("ppcali")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("msw")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("charge_phys")
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("flexibility")
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("bulkiness")
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("TM_tend")
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("mss")
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("t_scale")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("peparc")
pepdesc.calculate_arc(modality="max", append=True)
pepdesc.calculate_arc(modality="mean", append=True)
pepdesc.load_scale("msw")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("polarity")
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("pepcats")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("isaeci")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("refractivity")
pepdesc.calculate_moment(modality="max", append=True)
pepdesc.calculate_moment(modality="mean", append=True)
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("z3")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
pepdesc.load_scale("z5")
pepdesc.calculate_global(modality="mean", append=True)
pepdesc.calculate_global(modality="max", append=True)
#pepdesc.load_scale("PPCALI")
#pepdesc.calculate_autocorr(2)
#peptide["PeptideDescriptor"] = pepdesc
protein = PyPro()
protein.ReadProteinSequence(peptide["seq"])
paac = protein.GetPAAC(lamda=1, weight=0.05)
paac2 = [[
paac[a] for a in list(
sorted([k for k in paac],
key=lambda x: int(x.replace("PAAC", ""))))
]]
cTer = np.array([[1 if peptide["cTer"] == "Amidation" else 0]])
paac = np.array(paac2)
analysed_seq = ProteinAnalysis(peptide["seq"])
secondary_structure_fraction = np.array(
[analysed_seq.secondary_structure_fraction()])
peptide["TotalDescriptor"] = str(
np.concatenate((pepdesc.descriptor, globdesc.descriptor), axis=1))
try:
pepid = np.array([[
int(peptide["id"].replace("HEMOLYTIK", "").replace(
"DRAMP", "").replace("DBAASP", ""))
]])
except KeyError:
pepid = 0
pep_train = np.array([[1 if peptide["train"] == True else 0]])
freq_1d = residue_distribution(peptide["seq"], 1, dp)
freq_2d = residue_distribution(peptide["seq"], 2, dp)
freq_3d = residue_distribution(peptide["seq"], 3, dp)
freq_1dbool = residue_boolean(peptide["seq"], 1, dp)
freq_2dbool = residue_boolean(peptide["seq"], 2, dp)
freq_1dabs = residue_abs(peptide["seq"], 1, dp)
freq_2dabs = residue_abs(peptide["seq"], 2, dp)
len_peptide = np.array([[len(peptide["seq"])]])
if peptide["activity"] == "YES":
pepact = 1
else:
pepact = 0
pepact = np.array([[pepact]])
peptide_di2 = di2(peptide["seq"])
peptide_di3 = di3(peptide["conjoint_seq"])
####################### AAindex #########################
to_get = [
("CHAM810101", "mean"), #Steric Hinderance
("CHAM810101", "total"), #Steric Hinderance
("KYTJ820101", "mean"), #Hydropathy
("KLEP840101", "total"), #Charge
("KLEP840101", "mean"), #Charge
("MITS020101", "mean"), #Amphiphilicity
("FAUJ830101", "mean"), #Hydrophobic parameter pi
("GOLD730102", "total"), #Residue volume
("MEEJ800101", "mean"), #Retention coefficient in HPLC
("OOBM850105",
"mean"), #Optimized side chain interaction parameter
("OOBM850105",
"total"), #Optimized side chain interaction parameter
("VELV850101", "total"), #Electron-ion interaction parameter
("VELV850101", "mean"), #Electron-ion interaction parameter
("PUNT030102",
"mean"), #Knowledge-based membrane-propensity scale from 3D_Helix
("BHAR880101", "mean"), #Average flexibility indeces
("KRIW790102", "mean"), #Fraction of site occupied by water
("PLIV810101", "mean"), #Partition coefficient
("ZIMJ680102", "mean"), #Bulkiness
("ZIMJ680102", "total"), #Bulkiness
("ZHOH040101", "mean"), #Stability scale
("CHAM820102", "total"), #Free energy solubility in water
#From HemoPi: src = https://github.com/riteshcanfly/Hemopi/blob/master/pcCalculator.java
("HOPT810101", "mean"), #Hydrophilicity
("EISD840101", "mean"), #Hydrophobicity
("FAUJ880109", "total"), #Net Hydrogen
("EISD860101", "mean"), #Solvation
]
tetra_peptides = [
"KLLL", # src = https://github.com/riteshcanfly/Hemopi/blob/master/tetrapos.txt
"GCSC",
"AAAK",
"KLLS",
"LGKL",
"VLKA",
"LLGK",
"LVGA",
"LSDF",
"SDFK",
"SWLR",
"WLRD",
]
tp_bin = []
for t_p in tetra_peptides:
if t_p in peptide["seq"]:
tp_bin.append(1)
else:
tp_bin.append(0)
tp_bin = np.array([tp_bin])
for identifier, mode in to_get:
x = aaf(peptide["seq"], identifier, mode)
aminoacidindeces = np.array([[
aaf(peptide["seq"], identifier, mode)
for identifier, mode in to_get
]])
peptide["array"] = np.concatenate(
(
pepid,
pep_train,
pepdesc.descriptor,
globdesc.descriptor,
len_peptide,
cTer,
secondary_structure_fraction,
aminoacidindeces,
ctdc_vals,
conjointtriad_vals,
tp_bin,
freq_1d,
freq_2d,
freq_3d,
freq_1dbool,
freq_2dbool,
freq_1dabs,
freq_2dabs,
peptide_di2,
peptide_di3, #Conjoint Alphabet
paac,
pepact,
),
axis=1)
#print(peptide["TotalDescriptor"])
x = np.concatenate([peptide["array"] for peptide in peptides], axis=0)
np.save("peptides_array", x, allow_pickle=False)
def test_pyinteration():
from PyBioMed.PyInteraction.PyInteraction import CalculateInteraction1
from PyBioMed.PyInteraction.PyInteraction import CalculateInteraction2
from PyBioMed.PyInteraction.PyInteraction import CalculateInteraction3
from PyBioMed.PyDNA import PyDNAac
print '...............................................................'
print 'testing the DNA descriptors'
DNA_des = PyDNAac.GetTCC('GACTGAACTGCACTTTGGTTTCATATTATTTGCTC',
phyche_index=['Dnase I', 'Nucleosome', 'MW-kg'])
print DNA_des
print '...............................................................'
print 'testing the protein descriptors'
from PyBioMed.PyProtein import CTD
protein = "ADGCGVGEGTGQGPMCNCMCMKWVYADEDAADLESDSFADEDASLESDSFPWSNQRVFCSFADEDAS"
protein_des = CTD.CalculateCTD(protein)
print '...............................................................'
print 'testing the molecular descriptors'
from PyBioMed.PyMolecule import moe
from rdkit import Chem
smis = ['CCCC', 'CCCCC', 'CCCCCC', 'CC(N)C(=O)O', 'CC(N)C(=O)[O-].[Na+]']
m = Chem.MolFromSmiles(smis[3])
mol_des = moe.GetMOE(m)
print '...............................................................'
print 'testing the Interaction type 1 module'
mol_mol_interaction1 = CalculateInteraction1(mol_des, mol_des)
print mol_mol_interaction1
pro_mol_interaction1 = CalculateInteraction1(mol_des, protein_des)
print pro_mol_interaction1
DNA_mol_interaction1 = CalculateInteraction1(DNA_des, mol_des)
print DNA_mol_interaction1
print '...............................................................'
print 'testing the Interaction type 2 module'
mol_mol_interaction2 = CalculateInteraction2(mol_des, mol_des)
print mol_mol_interaction2
pro_mol_interaction2 = CalculateInteraction2(mol_des, protein_des)
print pro_mol_interaction2
DNA_mol_interaction2 = CalculateInteraction2(DNA_des, mol_des)
print DNA_mol_interaction2
print '...............................................................'
print 'testing the Interaction type 3 module'
mol_mol_interaction3 = CalculateInteraction3(mol_des, mol_des)
print mol_mol_interaction3
def test_pyprotein():
AAC = eval(modulelists[0])
AC = eval(modulelists[1])
CTD = eval(modulelists[2])
QSO = eval(modulelists[3])
PAAC = eval(modulelists[4])
GPFU = eval(modulelists[5])
GSS = eval(modulelists[6])
print '...............................................................'
print "testing the GetSubSeq module"
ProteinSequence = 'ADGCGVGEGTGQGPMCNCMCMKWVYADEDAADLESDSFADEDASLESDSFPWSNQRVFCSFADEDAS'
temp = GSS.GetSubSequence(ProteinSequence, ToAA='D', window=5)
print temp
print '...............................................................'
print "testing the AAComposition module"
temp = AAC.CalculateAAComposition(ProteinSequence)
print temp
temp = AAC.CalculateDipeptideComposition(ProteinSequence)
temp = AAC.GetSpectrumDict(ProteinSequence)
temp = AAC.CalculateAADipeptideComposition(ProteinSequence)
print '...............................................................'
print "testing the Autocorrelation module"
temp = AC.CalculateNormalizedMoreauBrotoAuto(ProteinSequence,
[AC._ResidueASA],
['ResidueASA'])
print temp
temp = AC.CalculateMoranAuto(ProteinSequence, [AC._ResidueASA],
['ResidueASA'])
print temp
temp = AC.CalculateGearyAuto(ProteinSequence, [AC._ResidueASA],
['ResidueASA'])
print temp
temp = AC.CalculateAutoTotal(ProteinSequence)
print '...............................................................'
print "testing the CTD module"
temp = CTD.CalculateC(ProteinSequence)
print temp
temp = CTD.CalculateT(ProteinSequence)
print temp
temp = CTD.CalculateD(ProteinSequence)
print temp
temp = CTD.CalculateCTD(ProteinSequence)
print temp
print '...............................................................'
print "testing the QuasiSequenceOrder module"
temp = QSO.GetSequenceOrderCouplingNumberTotal(ProteinSequence, maxlag=30)
print temp
temp = QSO.GetQuasiSequenceOrder(ProteinSequence, maxlag=30, weight=0.1)
print temp
print '...............................................................'
print "testing the PseudoAAC module"
temp = PAAC.GetAPseudoAAC(ProteinSequence, lamda=10, weight=0.5)
print temp
temp = PAAC._GetPseudoAAC(ProteinSequence, lamda=10, weight=0.05)
print temp
print '...............................................................'
print "Tested successfully!"
def test_pyinteration():
from PyBioMed.PyInteraction.PyInteraction import CalculateInteraction1
from PyBioMed.PyInteraction.PyInteraction import CalculateInteraction2
from PyBioMed.PyInteraction.PyInteraction import CalculateInteraction3
from PyBioMed.PyDNA import PyDNAac
print("...............................................................")
print("testing the DNA descriptors")
DNA_des = PyDNAac.GetTCC(
"GACTGAACTGCACTTTGGTTTCATATTATTTGCTC",
phyche_index=["Dnase I", "Nucleosome", "MW-kg"],
)
print(DNA_des)
print("...............................................................")
print("testing the protein descriptors")
from PyBioMed.PyProtein import CTD
protein = "ADGCGVGEGTGQGPMCNCMCMKWVYADEDAADLESDSFADEDASLESDSFPWSNQRVFCSFADEDAS"
protein_des = CTD.CalculateCTD(protein)
print("...............................................................")
print("testing the molecular descriptors")
from PyBioMed.PyMolecule import moe
from rdkit import Chem
smis = ["CCCC", "CCCCC", "CCCCCC", "CC(N)C(=O)O", "CC(N)C(=O)[O-].[Na+]"]
m = Chem.MolFromSmiles(smis[3])
mol_des = moe.GetMOE(m)
print("...............................................................")
print("testing the Interaction type 1 module")
mol_mol_interaction1 = CalculateInteraction1(mol_des, mol_des)
print(mol_mol_interaction1)
pro_mol_interaction1 = CalculateInteraction1(mol_des, protein_des)
print(pro_mol_interaction1)
DNA_mol_interaction1 = CalculateInteraction1(DNA_des, mol_des)
print(DNA_mol_interaction1)
print("...............................................................")
print("testing the Interaction type 2 module")
mol_mol_interaction2 = CalculateInteraction2(mol_des, mol_des)
print(mol_mol_interaction2)
pro_mol_interaction2 = CalculateInteraction2(mol_des, protein_des)
print(pro_mol_interaction2)
DNA_mol_interaction2 = CalculateInteraction2(DNA_des, mol_des)
print(DNA_mol_interaction2)
print("...............................................................")
print("testing the Interaction type 3 module")
mol_mol_interaction3 = CalculateInteraction3(mol_des, mol_des)
print(mol_mol_interaction3)