def createModel(pathToTrainingData):
# Create a list of sequences and a 0/1-list where position i is 1 if sequence i is a binder and 0 if it isn't
sequencesList = []
isBinderList = []
with open(pathToTrainingData, "r") as i:
for e in i:
lineSplit = e.split()
if lineSplit[0] == "Peptide": # skip the first line
pass
else:
sequencesList.append(lineSplit[0])
isBinderList.append(1 if lineSplit[2] is "1" else 0)
# Create a 3D array of all the data necessary for the ANN
# Its shape is 726*10*6: There are 726 sequences in the training set,
# each sequence consists of 9 residues plus a boolean telling us if it's a binder
# and 6 properties are considered for each residue:
# Its one-letter-code, weight, iep, hydrophobicity, polarity, and its area
data = np.array([[(aminoAcidDict.get(residue).one_letter_code,
aminoAcidDict.get(residue).weight,
aminoAcidDict.get(residue).iep,
aminoAcidDict.get(residue).hydrophobicity,
aminoAcidDict.get(residue).polarity,
aminoAcidDict.get(residue).area)
for residue in sequence] for sequence in sequencesList])
# Generate actual ANN input: one-letter-code is cut away, now there are only five numbers for each residue
annInput = data[:, :, 1:6].reshape(726, 45)
# Setup model
model = Sequential() # Input layer with 9*5=45 input nodes - each property at every position
model.add(Dense(45, kernel_initializer='uniform', activation='softplus', input_shape=(45,)))
# Hidden layer with 17 nodes
model.add(Dense(17, kernel_initializer='uniform', activation='softplus'))
# Output layer with 1 node
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(annInput, isBinderList, epochs=100, verbose=0, batch_size=100)
return model
sequencesList = []
isBinderList = []
with open(args.input, "r") as i:
for e in i:
lineSplit = e.split()
if lineSplit[0] == "Peptide": # skip the first line
pass
else:
sequencesList.append(lineSplit[0])
isBinderList.append(1 if lineSplit[2] is "1" else 0)
# Create a 3D array of all the data necessary for the ANN
# Its shape is 726*10*6, since there are 726 sequences in the training set, each sequence consists of 9 residues plus
# a boolean telling us if it's a binder and
# 6 properties are considered for each residue:
# Its one-letter-code, weight, iep, hydrophobicity, polarity, and its area
data = np.array([[(aminoAcidDict.get(residue).one_letter_code,
aminoAcidDict.get(residue).weight,
aminoAcidDict.get(residue).iep,
aminoAcidDict.get(residue).hydrophobicity,
aminoAcidDict.get(residue).polarity,
aminoAcidDict.get(residue).area)
for residue in sequence] for sequence in sequencesList])
# Generate actual ANN input: one-letter-code is cut away, now there are only five numbers for each residue
annInput = data[:, :, 1:6].reshape(726, 45)
# Split data up into training and test sets
x_train, x_test, y_train, y_test = train_test_split(annInput, isBinderList, test_size=0.8, random_state=0)
# Function for creating a model - needed for the keras classifier
def create_model(neurons=1, activation='relu'):