def protParam(seq):
params = ProteinAnalysis(seq)
mw = params.molecular_weight()
c_aa = params.count_amino_acids()
p_aa = params.get_amino_acids_percent()
gravy = params.gravy()
aromaticity = params.aromaticity()
isoelectric_point = params.isoelectric_point()
ext_coeff = sum([c_aa["W"]*5690,c_aa["Y"]*1280,c_aa["C"]*120])
mgml = ext_coeff * (1./mw)
print("Amino acid count")
pprint.pprint(c_aa)
print("Amino acid percent")
pprint.pprint(p_aa)
print("Molecular weight")
print("%f Da"%mw)
print("Gravy")
print(gravy)
print("Isoelectric point")
print(isoelectric_point)
print("Aromaticity")
print(aromaticity)
print("Extinction coefficient: %d M-1cm-1 (Assuming reduced)"%ext_coeff)
print("")
def sequence_vector(temp_window: str, window: int = 6, chemical=1):
"""
This vector takes the sequence and has each amino acid represented by an int
0 represents nonstandard amino acids or as fluff for tails/heads of sequences
Strip is a list which can be modified as user needs call for
"""
temp_window = clean(temp_window)
temp_window = windower(sequence=temp_window, position=int(len(temp_window)*.5), wing_size=window)
vec = []
aa = {"G": 1, "A": 2, "L": 3, "M": 4, "F": 5, "W": 6, "K": 7, "Q": 8, "E": 9, "S": 10, "P": 11, "V": 12, "I": 13,
"C": 14, "Y": 15, "H": 16, "R": 17, "N": 18, "D": 19, "T": 20, "X": 0}
for i in temp_window:
vec.append(aa[i])
if len(vec) != (window*2)+1:
t = len(vec)
for i in range((window*2)+1-t):
vec.append(0)
# Hydrophobicity is optional
if chemical == 1:
s = ProteinAnalysis(temp_window)
vec.append(s.gravy())
vec.append(s.instability_index())
vec.append(s.aromaticity())
return vec
def find_gravy_stats(folders, outfile, condition, regex = None, frequency = False):
mean_list = []
for folder in folders:
with open(folder[0] + '/5_AA-sequences.txt') as f:
gravy_all = 0
total_seqs = 0
reader = csv.DictReader(f, delimiter = '\t')
for row in reader:
try:
if row['Functionality'] == 'productive' and condition(row['CDR3-IMGT']):
protein = Prot(row['CDR3-IMGT'])
gravy = protein.gravy()
if frequency:
pat = re.compile(regex)
info = pat.match(row['Sequence ID'])
freq = int(info.group(1))
else:
freq = 1
total_seqs += freq
gravy_all += gravy * freq
except:
pass
try:
mean_list.append(gravy_all/float(total_seqs))
print mean_list
except:
pass
with open(outfile + '_means.txt', 'w') as out:
for item in mean_list:
out.write(str(item) +'\n')
with open(outfile + '.txt', 'w') as out:
out.write('mean CDR3 gravy,standard deviation\n')
out.write(str(np.mean(mean_list)) + ',' + str(np.std(mean_list)))
def _protein_parameters(self, sequence):
"""Calculates physicochemical properties for the amino acid sequence.
Args:
sequence: str, amino acid sequence.
Returns:
property_arr: np array, vector of properties.
"""
analysis = ProteinAnalysis(sequence)
property_arr = []
property_arr.append(analysis.molecular_weight())
property_arr.append(analysis.aromaticity())
property_arr.append(analysis.instability_index())
property_arr.append(analysis.gravy())
property_arr.append(analysis.isoelectric_point())
secondary = analysis.secondary_structure_fraction()
property_arr.append(secondary[0])
property_arr.append(secondary[1])
property_arr.append(secondary[2])
molar_extinction_coefficient = analysis.molar_extinction_coefficient()
property_arr.append(molar_extinction_coefficient[0])
property_arr.append(molar_extinction_coefficient[1])
property_arr.append(self._net_charge(sequence))
return np.array(property_arr)
def protein_properties(seq):
"""Return a tuple with some protein biochemical properties
seq is a Bio.Seq.Seq or str representing protein sequence
"""
pa = ProteinAnalysis(seq)
aa_counts = pa.count_amino_acids()
arom = pa.aromaticity()
isoelec = pa.isoelectric_point()
try:
instability = pa.instability_index()
except KeyError:
instability = None
try:
gravy = pa.gravy()
except KeyError:
gravy = None
return ProtProp(aa=str(seq),
gravy=gravy,
aromaticity=arom,
isoelectric_point=isoelec,
instability=instability,
aa_counts=aa_counts)
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 protAnalysis(self, content):
result, resultFlexDic = dict(), dict()
content = Parsers.normalizeSequence(content, self.sourceType)
protein = ProteinAnalysis(content)
result['proteinMWeight'] = protein.molecular_weight()
result['proteinAroma'] = protein.aromaticity()
result['proteinInstab'] = protein.instability_index()
result['proteinIsoelec'] = protein.isoelectric_point()
result['proteinGravy'] = protein.gravy()
proteinStructure = protein.secondary_structure_fraction()
protStruct = self.flatten('proteinSecstruc', proteinStructure)
result = {**protStruct, **result}
# merge result and protein Structure
flexibility = protein.flexibility()
flexibFlat = self.flatten('proteinFlex', flexibility)
flexibAmino = self.flatten(list(content), flexibility)
flattened = {**flexibFlat, **result}
flattenedFlexDic = {**flexibAmino, **result}
return result, flattened, flattenedFlexDic,
def physchem_props(data):
"""Calculate the physicochemical properties per protein in ara_d."""
new_table = []
header = "ID\tclass\tindex\tsequon\tsequence\tmol_weight\tgravy\taromaticity\tinstab_index\tiso_point\n"
new_table.append(header)
for line in data:
split_line = line.rstrip().split('\t')
seq = split_line[-2] # Sequon, not sequence
# Calculates the properties
if "X" in seq or '*' in seq or seq == '':
continue # Skip non-usable sequences, only negs
try:
a_seq = ProteinAnalysis(seq)
# Update ara_d with new physchem properties
results = [
a_seq.molecular_weight(),
a_seq.gravy(),
a_seq.aromaticity(),
a_seq.instability_index(),
#a_seq.flexibility(),
a_seq.isoelectric_point(),
#a_seq.secondary_structure_fraction(),
]
except:
print(split_line)
sys.exit(1)
new_line = line.rstrip() + "\t{}\t{}\t{}\t{}\t{}\n".format(*results)
new_table.append(new_line)
return new_table
def protParam(seq):
params = ProteinAnalysis(seq)
mw = params.molecular_weight()
c_aa = params.count_amino_acids()
p_aa = params.get_amino_acids_percent()
gravy = params.gravy()
aromaticity = params.aromaticity()
isoelectric_point = params.isoelectric_point()
ext_coeff = sum([c_aa["W"] * 5690, c_aa["Y"] * 1280, c_aa["C"] * 120])
mgml = ext_coeff * (1. / mw)
print("Amino acid count")
pprint.pprint(c_aa)
print("Amino acid percent")
pprint.pprint(p_aa)
print("Molecular weight")
print("%f Da" % mw)
print("Gravy")
print(gravy)
print("Isoelectric point")
print(isoelectric_point)
print("Aromaticity")
print(aromaticity)
print("Extinction coefficient: %d M-1cm-1 (Assuming reduced)" % ext_coeff)
print("")
def get_gravy_list(self):
gravy_list = []
for seq in self.df.index: # for every seq, add gravy to list
seq = ProteinAnalysis(seq)
gravy = "{:.6f}".format(seq.gravy())
gravy_list.append(gravy)
gravy_list = np.array(gravy_list) # convert to np array
return self.normalize(gravy_list) # return normalized
def get_protein_analysis(aa):
protein_analysis = ProteinAnalysis(aa)
analyze = [protein_analysis.molecular_weight(),
protein_analysis.aromaticity(),
protein_analysis.instability_index(),
protein_analysis.isoelectric_point(),
protein_analysis.gravy()] + list(
protein_analysis.secondary_structure_fraction())
return analyze
def get_gravy(self):
"""
Calculates Gravy from sequence (1 value) from biopython
:return: dictionary with the value of gravy
"""
res = {}
analysed_seq = ProteinAnalysis(self.ProteinSequence)
res['Gravy'] = analysed_seq.gravy()
return res
def biopython_protein_analysis(inseq):
"""Utiize Biopython's ProteinAnalysis module to return general sequence properties of an amino acid string.
For full definitions see: http://biopython.org/DIST/docs/api/Bio.SeqUtils.ProtParam.ProteinAnalysis-class.html
Args:
inseq: Amino acid sequence
Returns:
dict: Dictionary of sequence properties. Some definitions include:
instability_index: Any value above 40 means the protein is unstable (has a short half life).
secondary_structure_fraction: Percentage of protein in helix, turn or sheet
TODO:
Finish definitions of dictionary
"""
inseq = ssbio.protein.sequence.utils.cast_to_str(inseq)
analysed_seq = ProteinAnalysis(inseq)
info_dict = {}
info_dict['amino_acids_content-biop'] = analysed_seq.count_amino_acids()
info_dict[
'amino_acids_percent-biop'] = analysed_seq.get_amino_acids_percent()
info_dict['length-biop'] = analysed_seq.length
info_dict['monoisotopic-biop'] = analysed_seq.monoisotopic
info_dict['molecular_weight-biop'] = analysed_seq.molecular_weight()
info_dict['aromaticity-biop'] = analysed_seq.aromaticity()
info_dict['instability_index-biop'] = analysed_seq.instability_index()
# TODO: What is flexibility?
info_dict['flexibility-biop'] = analysed_seq.flexibility()
info_dict['isoelectric_point-biop'] = analysed_seq.isoelectric_point()
# grand average of hydrophobicity
info_dict['gravy-biop'] = analysed_seq.gravy()
# Separated secondary_structure_fraction into each definition
# info_dict['secondary_structure_fraction-biop'] = analysed_seq.secondary_structure_fraction()
info_dict[
'percent_helix_naive-biop'] = analysed_seq.secondary_structure_fraction(
)[0]
info_dict[
'percent_turn_naive-biop'] = analysed_seq.secondary_structure_fraction(
)[1]
info_dict[
'percent_strand_naive-biop'] = analysed_seq.secondary_structure_fraction(
)[2]
return info_dict
def find_composition(df_original):
df_copy = df_original.copy()
column_names = []
for ch in codes:
column_names.append(ch + '_percent')
column_names.append(ch + '_percent_first')
column_names.append(ch + '_percent_last')
column_names.append('len')
column_names.append('weight')
column_names.append('gravy')
column_names.append('flex_mean')
column_names.append('flex_std')
column_names.append('ss_helix')
column_names.append('ss_turn')
column_names.append('ss_sheet')
column_names.append('iep')
column_names.append('aromaticity')
df = pd.DataFrame(columns=column_names)
for _, seq in enumerate(tqdm(df_copy['seq'])):
df_temp = pd.Series()
sequence = str(seq)
analysed = ProteinAnalysis(sequence)
analysed_first = ProteinAnalysis(sequence[:first_n])
analysed_last = ProteinAnalysis(sequence[-last_n:])
df_temp['len'] = analysed.length
df_temp['ss_helix'], df_temp['ss_turn'], df_temp['ss_sheet'] = analysed.secondary_structure_fraction()
df_temp['iep'] = analysed.isoelectric_point()
# overall
for aa, percent in analysed.get_amino_acids_percent().items():
df_temp[aa + '_percent'] = percent
# # first N
for aa, percent in analysed_first.get_amino_acids_percent().items():
df_temp[aa + '_percent_first'] = percent
# last N
for aa, percent in analysed_last.get_amino_acids_percent().items():
df_temp[aa + '_percent_last'] = percent
df_temp['weight'] = analysed.molecular_weight()
df_temp['gravy'] = analysed.gravy()
df_temp['aromaticity'] = analysed.aromaticity()
df_temp['flex_mean'] = np.mean(analysed.flexibility())
df_temp['flex_std'] = np.std(analysed.flexibility())
df = df.append(df_temp, ignore_index=True)
return pd.concat([df_copy, df], axis=1)
def phyChemProps(seq):
svv = [0 for x in range(10)]
X = ProteinAnalysis(seq)
svv[0] = X.aromaticity()
svv[1] = X.secondary_structure_fraction()[0]
svv[2] = X.secondary_structure_fraction()[1]
svv[3] = X.secondary_structure_fraction()[2]
svv[4] = X.gravy()
svv[5] = X.instability_index()
svv[6] = X.isoelectric_point()
svv[7] = X.molecular_weight()
svv[8] = X.molar_extinction_coefficient()[0]
svv[9] = X.molar_extinction_coefficient()[1]
return svv
def __init__(self, sequence):
self.sequence = sequence
self.sequence_length = len(sequence)
analysis = ProteinAnalysis(sequence)
self.amino_acid_percents = analysis.get_amino_acids_percent()
self.amino_acids_composition = calculate_amino_acids_composition(sequence)
self.aromaticity = analysis.aromaticity()
self.instability = analysis.instability_index()
self.flexibility = calculate_flexibility(sequence)
protein_scale_parameters = [{'name': 'Hydrophilicity', 'dictionary': hw},
{'name': 'Surface accessibility', 'dictionary': em},
{'name': 'Janin Interior to surface transfer energy scale', 'dictionary': ja},
{'name': 'Bulkiness', 'dictionary': bulkiness},
{'name': 'Polarity', 'dictionary': polarity},
{'name': 'Buried residues', 'dictionary': buried_residues},
{'name': 'Average area buried', 'dictionary': average_area_buried},
{'name': 'Retention time', 'dictionary': retention_time}]
self.protein_scales = calculate_protein_scales(analysis, protein_scale_parameters)
self.isoelectric_point = analysis.isoelectric_point()
self.secondary_structure_fraction = calculate_secondary_structure_fraction(analysis)
self.molecular_weight = analysis.molecular_weight()
self.kyte_plot = analysis.gravy()
self.pefing = calculate_pefing(sequence)
# next parameters are calculated using R.Peptides
r('require(Peptides)')
r('sequence = "{0}"'.format(sequence))
self.aliphatic_index = r('aindex(sequence)')[0]
self.boman_index = r('boman(sequence)')[0]
self.charges = calculate_charges(sequence, 1.0, 14.0, 0.5, 'Lehninger')
self.hydrophobicity = r('seq(sequence)')[0]
angles = [{'name': 'Alpha-helix', 'angle': -47},
{'name': '3-10-helix', 'angle': -26},
{'name': 'Pi-helix', 'angle': -80},
{'name': 'Omega', 'angle': 180},
{'name': 'Antiparallel beta-sheet', 'angle': 135},
{'name': 'Parallel beta-sheet', 'angle': 113}]
if self.amino_acid_percents['P'] + self.amino_acid_percents['G'] > 0.3:
angles.append({'name': 'Polygly-polypro helix', 'angle': 153})
self.hydrophobic_moments = calculate_hydrophobic_moments(sequence, angles)
self.kidera_factors = calculate_kidera_factors(sequence)
self.peptide_types = calculate_peptide_types(sequence, angles)
def biochemical_properties(sequence: str) -> Dict[str, Any]:
# Define objects used for calculations
analysis_object = ProteinAnalysis(sequence)
descriptor_object = PyPro.GetProDes(sequence)
sequence_object = Seq(sequence)
# TODO(Ahmed): Verify that all these calculations are actually returning reasonable values
# For example, it says the percent composition of every amino acid is zero when I run
# calculate_biochem_properties.biochemical_properties('qwertyipasdfghklcvnm')
return {
'Isoelectric point': analysis_object.isoelectric_point(),
'Molecular weight':
analysis_object.molecular_weight(), # Daltons? Amu? g/mol?
'Aromaticity': analysis_object.aromaticity(),
'Instability index': analysis_object.instability_index(),
'GRAVY': analysis_object.gravy(),
'H-bonding percent': h_bonding_percent(sequence),
'Melting temp': melting_temp(sequence),
'LCC': lcc.lcc_simp(sequence)
}
def amino_acid_analysis(self):
"""
Adds fraction of amino acid residues (defined in RESIDUES) to data frame.
"""
for res in RESIDUES:
self.df["fraction_" + res] = (
self.df["sequence"].str.count(res) / self.df["sequence"].str.len()
)
self.df["length"] = self.df["sequence"].str.len()
for index, row in tqdm(self.df.iterrows(), total=self.df.shape[0]):
# for index, row in self.df.iterrows():
seq = row["sequence"]
seqanalysis = ProteinAnalysis(seq)
acidist = seqanalysis.get_amino_acids_percent()
self.df.loc[index, "IEP"] = seqanalysis.isoelectric_point()
if "X" not in seq and "B" not in seq:
self.df.loc[index, "molecular_weight"] = seqanalysis.molecular_weight()
if "U" not in seq and "X" not in seq and "B" not in seq:
self.df.loc[index, "gravy"] = seqanalysis.gravy()
def get_features(seq):
"""get global features from a protein sequence
Parameters
----------
seq : str
protein sequence
Return
----------
dictionary:
global features of the protein sequence
"""
features = {}
features['undefined_count'] = len([x for x in seq if x in ['X','B','Z',"'",'O','U']])
features['length'] = len(seq)
features['perc_undefined_count'] = features['undefined_count']/features['length']
features['entropy'] = entropy(seq)
features['ideal_entropy'] = entropy_ideal(len(seq))
features['perc_entropy'] = features['entropy']/features['ideal_entropy']
features['hydr_count'] = sum(1 for x in seq if x in hydrophobic_proteins)
features['polar_count'] = sum(1 for x in seq if x in polar_proteins)
features['buried'] = sum(buried[x] for x in seq if x in hydrophobic_proteins)
seq = ''.join([x for x in seq if x not in ['X','B','Z',"'",'O','U']])
protein = ProteinAnalysis(seq)
features['gravy'] = protein.gravy()
features['molecular_weight'] = protein.molecular_weight()
features['aromaticity'] = protein.aromaticity()
features['instability_index'] = protein.instability_index()
features['isoelectric_point'] = protein.isoelectric_point()
features['helix'], features['turn'], features['sheet'] = protein.secondary_structure_fraction()
features.update(protein.count_amino_acids())
# features.update(protein.get_amino_acids_percent())
return features
def GRAvy_ARomo(seq, genetic_code_=1, G=False, A=False):
"""calculating Gravy and Aroma for DNA sequence.
Args:
seq (str):DNA sequence
genetic_code_(int): default = 1, The Genetic Codes number described by NCBI (https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi)
G (bool): default = False
A (bool): default = False
Returns:
- Gravy value if arg(G) is True
- Aroma value if arg(A) is True
- None if both args are False
"""
from Bio.SeqUtils.ProtParam import ProteinAnalysis
from Bio.Seq import Seq
try:
seq = Seq(seq)
except:
pass
translate_seq = str(seq.translate(table=genetic_code_))
protein_seq = translate_seq.replace("*", "")
protein_seq = ProteinAnalysis(protein_seq)
AROMO = protein_seq.aromaticity()
gravy = protein_seq.gravy()
if G and G == True:
return gravy
elif A and A == True:
return AROMO
def seqs_to_features(self, seqs, no_seqs):
""" Extract the features from the sequences."""
X = np.zeros((no_seqs, 32))
for i, s in enumerate(chain(*seqs)): # iterate over all sequences
# get amino acid counts
alphabet = 'ABCDEFGHIKLMNPQRSTUVWXY' # no JOZ
for j, letter in enumerate(alphabet):
X[i, j] = s.count(letter) / len(s)
# other analysis
analysis = ProteinAnalysis(
s.replace('X', 'A').replace('B', 'A').replace('U', 'A'))
X[i, -1] = analysis.molecular_weight()
X[i, -2] = analysis.aromaticity()
X[i, -3] = analysis.instability_index()
X[i, -4] = analysis.isoelectric_point()
helix_array_sheet_fracs = analysis.secondary_structure_fraction()
X[i, -5] = helix_array_sheet_fracs[0]
X[i, -6] = helix_array_sheet_fracs[1]
X[i, -7] = helix_array_sheet_fracs[2]
X[i, -8] = len(s)
X[i, -9] = analysis.gravy() # mean hydrophobicity
return X
def physchem_props(ara_d):
"""Calculate the physicochemical properties per protein in ara_d."""
c = 0
g = 0
for protein in ara_d:
seq = ara_d[protein]["sequence"]
# Calculates the properties
if "X" in seq:
continue # Skip non-usable sequences, only negs
if '*' in seq:
if ara_d[protein]["pos"] != []:
print(protein)
continue
a_seq = ProteinAnalysis(seq)
# Update ara_d with new physchem properties
results = [
a_seq.molecular_weight(),
a_seq.gravy(),
a_seq.aromaticity(),
a_seq.instability_index(),
a_seq.flexibility(),
a_seq.isoelectric_point(),
a_seq.secondary_structure_fraction(),
]
keys = [
"mol_weight",
"gravy",
"aromaticity",
"instab_index",
"flexi",
"iso_point",
"seq_struct",
]
ara_d[protein]["Properties"] = {}
for k, v in zip(keys, results):
ara_d[protein]["Properties"][k] = v
return ara_d
def add_protein_characteristics(df):
df = df.copy()
aa_list = [
'A', 'C', 'E', 'D', 'G', 'F', 'I', 'H', 'K', 'M', 'L', 'N', 'Q', 'P',
'S', 'R', 'T', 'W', 'V', 'Y'
]
aa_dict = {}
for aa in aa_list:
aa_dict[aa] = []
prop_dict = {
'aromaticity': [],
'helix': [],
'turn': [],
'sheet': [],
'isoelectric_point': [],
'gravy': []
} #, 'flexibility': [], 'instability_index': []}
for i, s in enumerate(df['sequence']):
s = s.replace('B', 'D').replace('Z', 'E').replace('J', 'L').replace(
'X', 'G').replace('U', 'C').replace('O', 'K')
pa = ProteinAnalysis(s)
prop_dict['aromaticity'].append(pa.aromaticity())
prop_dict['isoelectric_point'].append(pa.isoelectric_point())
prop_dict['gravy'].append(pa.gravy())
# prop_dict['instability_index'].append(pa.instability_index())
# prop_dict['flexibility'].append(np.mean(pa.flexibility()))
for fraction, ss in zip(pa.secondary_structure_fraction(),
['helix', 'turn', 'sheet']):
prop_dict[ss].append(fraction)
for k, v in pa.get_amino_acids_percent().items():
aa_dict[k].append(v)
for k, v in aa_dict.items():
df[k] = v
for k, v in prop_dict.items():
df[k] = v
return df
def biopython_proteinanalysis_seq(seq, scaling=False):
res = ProteinAnalysis(seq)
d = {}
flex = np.array(res.flexibility())
d['flex:min'], d['flex:max'], d['flex:std'] = flex.min(), flex.max(
), flex.std()
d['gravy'] = res.gravy()
d['instability_index'] = res.instability_index()
d['isoelectric_point'] = res.isoelectric_point()
r, c = res.molar_extinction_coefficient()
d['molar_extinction_coefficient_reduced'], d[
'molar_extinction_coefficient_cysteines'] = r, c
d['molecular_weight'] = res.molecular_weight()
d['percent_helix_naive'], d['percent_turn_naive'], d[
'percent_strand_naive'] = res.secondary_structure_fraction()
aap = res.get_amino_acids_percent()
aas = sorted(aap.keys())
d.update({'percent:%s' % aa: aap[aa] for aa in aas})
d.update({
'prop_res_%s' % key: sum([aap.get(x, 0) for x in value])
for key, value in list(property_residues.items())
})
return d
gravy=[]
molweight=[]
instidx=[]
flex=[]
for seq in sequences:
X=ProteinAnalysis(str(seq))
isoelectricPt.append(X.isoelectric_point())
aromaticity.append(X.aromaticity())
aminoPercent.append(X.get_amino_acids_percent())
secstruct.append(X.secondary_structure_fraction())
# These features throw Key & Value Errors due to non standard amino acids
# (i.e. out of the 20 standard ones) e.g. X, U etc
try:
gravy.append(X.gravy())
molweight.append(X.molecular_weight())
instidx.append(X.instability_index())
flex.append(X.flexibility())
hydrophob.append(X.protein_scale(ProtParamData.kd, 9, 0.4))
hydrophil.append(X.protein_scale(ProtParamData.hw, 9, 0.4))
surface.append(X.protein_scale(ProtParamData.em, 9, 0.4))
except (KeyError,ValueError):
gravy.append(0)
molweight.append(0)
instidx.append(0)
flex.append([0,0])
hydrophob.append([0,0])
hydrophil.append([0,0])
surface.append([0,0])
print('done')
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():
def extract(self):
AA=["A","C","D","E","F","G","H","I","K","L","M","N","P","Q","R","S","T","V","W","Y"]
SC=["1","2","3","4","5","6","7"]
tri_pep = [''.join(i) for i in itertools.product(AA, repeat = 3)]
myseq="AILMVNQSTGPCHKRDEFWY"
trantab2=myseq.maketrans("AILMVNQSTGPCHKRDEFWY","11111222233455566777")
tetra_sc = [''.join(i) for i in itertools.product(SC, repeat = 4)]
total_fasta=self.g_total_fasta
sec_code=0
record_current=0
arr = numpy.empty((total_fasta,10409), dtype=numpy.float)
names = numpy.empty((total_fasta,1), dtype=object)
names_dic=dict()
for record in SeqIO.parse(self.infile, "fasta"):
data=(record_current/total_fasta) * 100
if (self.g_is_socket==1):
self.g_socketio.emit('set bar', {'data': data},room=self.g_sid)
else:
print('extracting features of seq ' + str(record_current+1) + ' of ' + str(total_fasta),end='\r')
#yield "event: update\ndata:" + str(data) + "\n\n"
record_current += 1
#job.meta['current']=record_current
#job.save_meta()
ll=len(record.seq)
seq_name=''
if not self.prot_check(str(record.seq)):
print("Warning: " + record.id + " is not a valid protein sequence")
continue
if record.id in names_dic:
seq_name= record.id + '_' + str(names_dic[record.id])
names_dic[record.id]=names_dic[record.id]+1
else:
seq_name= record.id
names_dic[record.id]=1
seqq=record.seq.__str__().upper()
seqqq=seqq.replace('X','A').replace('J','L').replace('*','A').replace('Z','E').replace('B','D')
# X = ProteinAnalysis(record.seq.__str__().upper().replace('X','A').replace('J','L').replace('*',''))
X = ProteinAnalysis(seqqq)
myseq=seqq.translate(trantab2)
tt= [X.isoelectric_point(), X.instability_index(),ll,X.aromaticity(),
X.molar_extinction_coefficient()[0],X.molar_extinction_coefficient()[1],
X.gravy(),X.molecular_weight()]
tt_n = numpy.asarray(tt,dtype=numpy.float)
tri_pep_count=[seqq.count(i)/(ll-2) for i in tri_pep]
tri_pep_count_n = numpy.asarray(tri_pep_count,dtype=numpy.float)
tetra_sc_count=[myseq.count(i)/(ll-3) for i in tetra_sc]
tetra_sc_count_n = numpy.asarray(tetra_sc_count,dtype=numpy.float)
cat_n= numpy.concatenate((tetra_sc_count_n,tri_pep_count_n,tt_n))
cat_n = cat_n.reshape((1,cat_n.shape[0]))
arr[sec_code,:]=cat_n
names[sec_code,0]=seq_name
sec_code += 1
if (self.g_is_socket==1):
self.g_socketio.emit('set bar', {'data': 100},room=self.g_sid)
self.g_socketio.emit('done features',1,room=self.g_sid)
print("\nDone")
return (names,arr)
from Bio.SeqUtils.ProtParam import ProteinAnalysis
my_seq = str(input("manual sequence from translate.py :"))
analysed_seq = ProteinAnalysis(my_seq)
answer1 = str(input("detect molecular weight y/n? :"))
if answer1 == "y":
mweight = analysed_seq.molecular_weight()
print(mweight)
answer2 = str(input("detect gravy y/n? :"))
if answer2 == "y":
gravy_protein = analysed_seq.gravy()
print(gravy_protein)
print(analysed_seq.count_amino_acids())
input("enter")
# for a in pe_list.index:
# if pe_list[a] == 'Predicted':
# u_list.append(reading['acc. code'][a])
if os.path.isfile('nP20k.fasta') == False:
link = 'http://www.peptideatlas.org/tmp/nP20k.fasta.gz'
resp = requests.get(link)
with open('nP20k.fasta', 'wb') as f_output:
f_output.write(resp.content)
info = []
with open('nP20k.fasta', 'rU') as handle:
for record in SeqIO.parse(handle, 'fasta'):
if record.description.__contains__('PE=2'):
analyzed_seq = ProteinAnalysis(str(record.seq))
tup = (record.id, '2', analyzed_seq.gravy(),
textwrap.fill(record.description, 20))
info.append(tup)
if record.description.__contains__('PE=3'):
analyzed_seq = ProteinAnalysis(str(record.seq))
tup = (record.id, '3', analyzed_seq.gravy(),
textwrap.fill(record.description, 20))
info.append(tup)
if record.description.__contains__('PE=4'):
analyzed_seq = ProteinAnalysis(str(record.seq))
tup = (record.id, '4', analyzed_seq.gravy(),
textwrap.fill(record.description, 20))
info.append(tup)
print(tabulate(info, headers=['Identifier', 'PE', 'GRAVY', 'Description']))
#!/usr/bin/env python
import sys
from Bio import SeqIO
from Bio.SeqUtils.ProtParam import ProteinAnalysis
sys.stdout.write("ID\tMW\tIP\tgravy\tlength\tinstability\tmonoisotpoic\tSequence\n")
for record in SeqIO.parse(sys.stdin, "fasta"):
a = ProteinAnalysis(str(record.seq))
properties = list()
properties.append(record.id)
properties.append(a.molecular_weight())
properties.append(a.isoelectric_point())
properties.append(a.gravy())
properties.append(a.length)
properties.append(a.instability_index())
properties.append(a.aromaticity())
# always last column to make the output more readable
properties.append(a.sequence)
sys.stdout.write( '\t'.join(map(str, properties))+"\n" )
def get_phanns_input(fasta_list, d2vmodel):
# d2vmodel = pickle.load(open('d2v_model1.p','rb'))
AA = [
"A", "C", "D", "E", "F", "G", "H", "I", "K", "L", "M", "N", "P", "Q",
"R", "S", "T", "V", "W", "Y"
]
SC = ["1", "2", "3", "4", "5", "6", "7"]
tri_pep = [''.join(i) for i in itertools.product(AA, repeat=3)]
tetra_sc = [''.join(i) for i in itertools.product(SC, repeat=4)]
prot_class = 0
myseq = "AILMVNQSTGPCHKRDEFWY"
trantab2 = myseq.maketrans("AILMVNQSTGPCHKRDEFWY", "11111222233455566777")
kmer_size = 3
this_prot = 0
vectors = []
classes = []
for file in fasta_list:
print('####################' + file)
# file_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath("__file__"))),"fasta",file + "_all_clustered.fasta")
for record in SeqIO.parse(file, "fasta"):
ll = len(record.seq)
seqq = record.seq.__str__().upper()
seqqq = seqq.replace('X', 'A').replace('J', 'L').replace(
'*', 'A').replace('Z', 'E').replace('B', 'D')
X = ProteinAnalysis(seqqq)
tt = [
X.isoelectric_point(),
X.instability_index(), ll,
X.aromaticity(),
X.molar_extinction_coefficient()[0],
X.molar_extinction_coefficient()[1],
X.gravy(),
X.molecular_weight()
]
tt_n = np.asarray(tt, dtype=np.float)
myseq = seqq.translate(trantab2)
#count tripeptides
tri_pep_count = [seqq.count(i) / (ll - 2) for i in tri_pep]
tri_pep_count_n = np.asarray(tri_pep_count, dtype=np.float)
#count tetra side chains
tetra_sc_count = [myseq.count(i) / (ll - 3) for i in tetra_sc]
tetra_sc_count_n = np.asarray(tetra_sc_count, dtype=np.float)
#get embedding vector
vec = d2vmodel.infer_vector([
seqqq[k:k + kmer_size] for k in range(0, len(seqqq), kmer_size)
])
for s in range(1, kmer_size):
vec = vec + d2vmodel.infer_vector([
seqqq[k:k + kmer_size]
for k in range(s, len(seqqq), kmer_size)
])
vec = vec / kmer_size
cat_n = np.concatenate(
(tri_pep_count_n, tetra_sc_count_n, tt_n, vec))
vectors.append((cat_n, record))
this_prot += 1
if (this_prot % 500 == 0):
print("processing sequence # " + str(this_prot), end="\r")
prot_class += 1
this_prot = 0
return vectors
#!/usr/bin/env python
import sys
from Bio import SeqIO
from Bio.SeqUtils.ProtParam import ProteinAnalysis
sys.stdout.write("ID\tMW\tIP\tgravy\tlength\tinstability\tmonoisotpoic\tSequence\n")
for record in SeqIO.parse(sys.stdin, "fasta"):
a = ProteinAnalysis(str(record.seq))
properties = list()
properties.append(record.id)
properties.append(a.molecular_weight())
properties.append(a.isoelectric_point())
properties.append(a.gravy())
properties.append(a.length)
properties.append(a.instability_index())
properties.append(a.aromaticity())
# always last column to make the output more readable
properties.append(a.sequence)
sys.stdout.write("\t".join(map(str, properties)) + "\n")
plt.title("Distribution of Protein Molecular Weights")
plt.savefig("plotMolecularWeights.pdf")
plt.clf()
# plt.show()
gravy_index = []
aromaticity = []
instability_index = []
# flexibility = []
isoelectric_point = []
secondary_structure_fraction = []
for protein in sequences_a:
analysed_seq = ProteinAnalysis(str(protein.seq).replace("X", ""))
gravy_index.append([sys.argv[1], analysed_seq.gravy()])
aromaticity.append([sys.argv[1], analysed_seq.aromaticity()])
instability_index.append([sys.argv[1], analysed_seq.instability_index()])
# flexibility.append([sys.argv[1], analysed_seq.flexibility()])
isoelectric_point.append([sys.argv[1], analysed_seq.isoelectric_point()])
secondary_structure_fraction.append(
[sys.argv[1], analysed_seq.secondary_structure_fraction()])
for protein in sequences_b:
analysed_seq = ProteinAnalysis(str(protein.seq).replace("X", ""))
gravy_index.append([sys.argv[2], analysed_seq.gravy()])
aromaticity.append([sys.argv[2], analysed_seq.aromaticity()])
instability_index.append([sys.argv[2], analysed_seq.instability_index()])
# flexibility.append([sys.argv[2], analysed_seq.flexibility()])
isoelectric_point.append([sys.argv[2], analysed_seq.isoelectric_point()])
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)
