start = []
end = []
for index, row in Pepfrags.iterrows():
start.append(int(row['Start']))
end.append(int(row['End']))
start = np.array(start)
end = np.array(end)
return start, end
#b2m test data
ex_indata = pd.read_excel(args.excelin)
Protein_name = ex_indata['Protein'].unique()
b2m_kint = get_kint(B2m_seq, args.temp, args.pD)
b2m_Pepfrags = PepFrag_find(Protein_name[2], args.state, time, args.excelin) #selects b2m as input name for subset, 1 is nan
b2m_Pepfrags_uptake = PepFrag_uptake(Protein_name[2], args.state, time, args.excelin)
b2m_Pepfrags_uptake = b2m_Pepfrags_uptake.to_numpy()
b2m_Pval = get_expPval(b2m_Pepfrags, b2m_Pepfrags_uptake, b2m_kint, Protein_name[2])
b2m_start, b2m_end = get_pepstartend(b2m_Pepfrags)
#Input training Peptide DU data
ex_indata = pd.read_excel(args.excelin)
Protein_name = ex_indata['Protein'].unique()
kint = get_kint(Seq, args.temp, args.pD)
Pepfrags = PepFrag_find(Protein_name[0], args.state, time, args.excelin)
Pepfrags_uptake = PepFrag_uptake(Protein_name[0], args.state, time, args.excelin)
Pepfrags_uptake = Pepfrags_uptake.to_numpy()
Pval = get_expPval(Pepfrags, Pepfrags_uptake, kint, Protein_name[0])
Start, End = get_pepstartend(Pepfrags)
#Input Histogram Data
def __init__(self, inputdata):
#Set custom parameters for specific model
self.time = 0.5
self.HM8_modelparams = [
3.28979445e-08, 1.98441207e+00, 8.96847944e+00, -2.58808757e-01
]
#Create empty result objects
self.Pval_exp = []
self.Pval_pred = []
#load simulation histogram data
self.SASA = np.load('dist_sasa_20bins/SASA_hist.npy',
allow_pickle=True)
self.DIST = np.load('dist_sasa_20bins/DIST_hist.npy',
allow_pickle=True)
#Load experimental peptide fragment data
ex_indata = pd.read_excel(inputdata)
Protein_name = ex_indata['Protein'].unique()
Pepfrags = PepFrag_find(Protein_name[0], args.state, self.time,
args.excelin)
Pepfrags_uptake = PepFrag_uptake(Protein_name[0], args.state,
self.time, args.excelin)
self.fitting_data = Pepfrags_uptake.to_numpy()
#Calculate intrinsic protection factors
self.Seq = 'MGSHSMRYFFTSVSRPGRGEPRFIAVGYVDDTQFVRFDSDAASQRMEPRAPWIEQEGPEYWDGETRKVKAHSQTHRVDLGTLRGYYNQSEAGSHTVQRMYGCDVGSDWR' \
'FLRGYHQYAYDGKDYIALKEDLRSWTAADMAAQTTKHKWEAAHVAEQLRAYLEGTCVEWLRRYLENGKETLQRTDAPKTHMTHHAVSDHEATLRCWALSFYPAEITLT' \
'WQRDGEDQTQDTELVETRPAGDGTFQKWAAVVVPSGQEQRYTCHVQHEGLPKPLTLRWE'
self.kint = []
for counter, aminoacid in enumerate(self.Seq[0:len(self.Seq)]):
self.kint.append(
calc_kint_Alt(self.Seq[counter], self.Seq[counter - 1],
int(args.temp), int(args.pD), counter,
len(self.Seq)))
#Calculate P-factors for experimental peptide fragments
count = 0
Pval = []
for index, row in Pepfrags.iterrows():
print(row)
kintval = []
if row['End'] > len(self.kint):
continue
for j in range(int(row['Start']), int(row['End'])):
kintval.append(self.kint[j])
kintval_avg = np.array(kintval).mean()
v1 = (self.fitting_data[count])
v2 = -1 * (v1 - 1)
v3 = log(v2) / self.time
P_factor = -1 * (kintval_avg) / v3
lnPF = log(P_factor)
Pval.append(lnPF)
count += 1
self.count = count
self.Pval_exp = Pval
start = []
end = []
for index, row in Pepfrags.iterrows():
start.append(int(row['Start']))
end.append(int(row['End']))
self.start = np.array(start)
self.end = np.array(end)