reg.append('U')
elif i == 3:
reg.append('G')
elif i == 4:
reg.append('C')
else:
continue
reg = ''.join(reg)
print reg
print iteration
if similar(str_struc,DOT_BRACKET) >= MIN_THRESHOLD:
print 'similar'
print str_struc
print DOT_BRACKET
print reg
break
except KeyboardInterrupt:
print 'Puzzles solved: %i/%i' % (SOLVED, (plist.index(db) + 1))
break
level1,m2,S1 = sbc(DOT_BRACKET,reg)
level2,m3,S2 = dsp(DOT_BRACKET,level1)
#print level2
if S1 or S2:
SOLVED += 1
movesets.extend(m2)
movesets.extend(m3)
print 'Puzzles solved: %i/100' % SOLVED
reg = ''.join(reg)
#print inputs2[0][:len_puzzle]
print reg
print iteration
#print current_struc[:len(enc_struc)]
#print target_struc[:len(enc_struc)]
#print inputs2[1][:len(enc_struc)]
#print format_pairmap(str_struc)
if similar(str_struc, DOT_BRACKET) >= MIN_THRESHOLD:
print 'similar'
print str_struc
print DOT_BRACKET
print reg
break
level1, m2, _ = sbc(DOT_BRACKET, reg)
level2, m3, _ = dsp(DOT_BRACKET, level1)
print level2
#movesets.extend(m2)
#movesets.extend(m3)
#print movesets
# mp = pickle.load(open(os.getcwd()+'/pickles/evolved-raw-ms','r'))
# bp = pickle.load(open(os.getcwd()+'/pickles/evolved-raw-bf','r'))
#
# mp.append(movesets)
# bp.append(NUCLEOTIDES)
#
# pickle.dump(mp,open(os.getcwd()+'/pickles/evolved-raw-ms','w'))
# pickle.dump(bp,open(os.getcwd()+'/pickles/evolved-raw-bf','w'))
def predict(secondary_structure, vienna_version=1, bool_print=True):
"""Runs EternaBrain algorithm
Arguments:
secondary_structure {String} -- Secondary structure in dot bracket notation
Returns:
boolean -- True if puzzle solved, False otherwise
"""
# Define constants often used in this function
len_puzzle = len(secondary_structure)
NUCLEOTIDES = 'A' * len_puzzle
ce = 0.0
te = 0.0
LOCATION_FEATURES = 8
BASE_FEATURES = 9
NAME = 'CNN15'
MIN_THRESHOLD = 0.6
MAX_ITERATIONS = len_puzzle * 2
MAX_LEN = 400
TF_SHAPE = LOCATION_FEATURES * MAX_LEN
BASE_SHAPE = BASE_FEATURES * MAX_LEN
len_longest = MAX_LEN
base_seq = (convert_to_list(NUCLEOTIDES)) + ([0] *
(len_longest - len_puzzle))
# cdb = '.'*len_puzzle
current_struc = (encode_struc(
RNA.fold(NUCLEOTIDES)[0])) + ([0] * (len_longest - len_puzzle))
target_struc = encode_struc(secondary_structure) + (
[0] * (len_longest - len_puzzle))
current_energy = [ce] + ([0] * (len_longest - 1))
target_energy = [te] + ([0] * (len_longest - 1))
current_pm = format_pairmap(NUCLEOTIDES) + ([0] *
(len_longest - len_puzzle))
target_pm = format_pairmap(secondary_structure) + (
[0] * (len_longest - len_puzzle))
#locks = ([2]*32 + [1] * 85 + [2]*85) + ([0]*(len_longest - len_puzzle))
locks = ([1] * len_puzzle) + ([0] * (len_longest - len_puzzle))
#print len(base_seq),len(current_struc),len(secondary_structure),len(target_struc),len(current_energy),len(target_energy),len(locks)
inputs2 = np.array([
base_seq, current_struc, target_struc, current_energy, target_energy,
current_pm, target_pm, locks
])
'''
Change inputs when altering number of features
'''
#inputs2 = np.array([base_seq,current_energy,target_energy,current_pm,target_pm,locks])
inputs = inputs2.reshape([-1, TF_SHAPE])
with tf.Graph().as_default() as base_graph:
saver1 = tf.train.import_meta_graph(os.getcwd() + '/models/base/base' +
NAME + '.meta') # CNN15
sess1 = tf.Session(
graph=base_graph
) # config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=True)
saver1.restore(sess1, os.getcwd() + '/models/base/base' + NAME)
x = base_graph.get_tensor_by_name('x_placeholder:0')
y = base_graph.get_tensor_by_name('y_placeholder:0')
keep_prob = base_graph.get_tensor_by_name('keep_prob_placeholder:0')
base_weights = base_graph.get_tensor_by_name('op7:0')
base_feed_dict = {x: inputs, keep_prob: 1.0}
with tf.Graph().as_default() as location_graph:
saver2 = tf.train.import_meta_graph(os.getcwd() +
'/models/location/location' +
NAME + '.meta')
sess2 = tf.Session(
graph=location_graph
) # config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=True)
saver2.restore(sess2, os.getcwd() + '/models/location/location' + NAME)
x2 = location_graph.get_tensor_by_name('x_placeholder:0')
y2 = location_graph.get_tensor_by_name('y_placeholder:0')
keep_prob2 = location_graph.get_tensor_by_name('keep_prob_placeholder:0')
location_weights = location_graph.get_tensor_by_name('op7:0')
if bool_print:
print('models loaded')
location_feed_dict = {x2: inputs, keep_prob2: 1.0}
movesets = []
iteration = 0
reg = []
for i in range(MAX_ITERATIONS):
if np.all(inputs2[1] == inputs2[2]):
if bool_print:
print("Puzzle Solved")
return True
else:
location_array = ((sess2.run(location_weights,
location_feed_dict))[0])
inputs2 = inputs.reshape(
[LOCATION_FEATURES, TF_SHAPE // LOCATION_FEATURES])
location_array = location_array[:len_puzzle] - min(
location_array[:len_puzzle])
total_l = sum(location_array)
location_array = location_array / total_l
#location_array = softmax(location_array)
location_change = (choice(list(range(0, len(location_array))),
1,
p=location_array,
replace=False))[0]
#location_change = np.argmax(location_array)
la = [0.0] * len_longest
la[location_change] = 1.0
inputs2 = np.append(inputs2, la)
inputs = inputs2.reshape([-1, BASE_SHAPE])
base_feed_dict = {x: inputs, keep_prob: 1.0}
base_array = ((sess1.run(base_weights, base_feed_dict))[0])
base_array = base_array - min(base_array)
total = sum(base_array)
base_array = base_array / total
#base_array = softmax(base_array)
#if np.random.rand() > 0.0:
# FOR CHOOSING STOCHASTICALLY
base_change = (choice([1, 2, 3, 4], 1, p=base_array,
replace=False))[0]
#else:
# NOT STOCHASTICALLY
#base_change = np.argmax(base_array) + 1
inputs2 = inputs.reshape(
[BASE_FEATURES, BASE_SHAPE // BASE_FEATURES])
# if inputs2[0][location_change] == base_change:
# second = second_largest(base_array)
# base_change = np.where(base_array==second)[0][0] + 1
temp = copy.deepcopy(inputs2[0])
temp[location_change] = base_change
move = [base_change, location_change]
movesets.append(move)
#print move
str_seq = []
for i in temp:
if i == 1:
str_seq.append('A')
elif i == 2:
str_seq.append('U')
elif i == 3:
str_seq.append('G')
elif i == 4:
str_seq.append('C')
else:
continue
str_seq = ''.join(str_seq)
str_struc, current_e = RNA.fold(str_seq)
current_pm = format_pairmap(str_struc)
if bool_print:
print(str_struc)
print(similar(str_struc, secondary_structure))
rna_struc = []
for i in inputs2[2]:
if i == 1:
rna_struc.append('.')
elif i == 2:
rna_struc.append('(')
elif i == 3:
rna_struc.append(')')
else:
continue
rna_struc = ''.join(rna_struc)
target_e = RNA.energy_of_structure(str_seq, rna_struc, 0)
enc_struc = []
for i in str_struc:
if i == '.':
enc_struc.append(1)
elif i == '(':
enc_struc.append(2)
elif i == ')':
enc_struc.append(3)
else:
continue
inputs2[0] = temp
inputs2[1][:len(enc_struc)] = (enc_struc)
inputs2[3][0] = current_e
inputs2[4][0] = target_e
inputs2[5][:len(enc_struc)] = current_pm
inputs_loc = inputs2[0:8]
inputs = inputs_loc.reshape([-1, TF_SHAPE])
base_feed_dict = {x: inputs, keep_prob: 1.0}
location_feed_dict = {x2: inputs, keep_prob2: 1.0}
iteration += 1
reg = []
for i in inputs2[0]:
if i == 1:
reg.append('A')
elif i == 2:
reg.append('U')
elif i == 3:
reg.append('G')
elif i == 4:
reg.append('C')
else:
continue
reg = ''.join(reg)
if bool_print:
print(reg)
print(iteration)
if similar(str_struc, secondary_structure) >= MIN_THRESHOLD:
if bool_print:
print('similar')
print(str_struc)
print(secondary_structure)
print(reg)
break
level1, m2, solved_sap1 = sbc(secondary_structure, reg)
level2, m3, solved_sap2 = dsp(secondary_structure,
level1,
vienna_version=vienna_version,
vienna_path=path)
print(level2)
return solved_sap1 or solved_sap2, level2
def design(dot_bracket,
nucleotides,
locks,
ce=0,
te=0,
MIN_THRESHOLD=0.6,
MAX_ITERATIONS=3):
"""
Designs an RNA molecule for a specific target structure with the EternaBrain algorithm
:param dot_bracket: The target structure in dot-bracket notation
:param nucleotides: The current sequence of nucleotides
:param locks: The locked bases ('x's for locked, 'o's for unlocked
:param ce: The current energy in kcal/mol (default is 0 kcal/mol)
:param te: The target folded energy in kcal/mol (default is 0 kcal/mol)
:param MIN_THRESHOLD: Minimum threshold of CNN structure to target structure (default is 0.6, or 60%)
:param MAX_ITERATIONS: The maximum number of moves allocated for the CNN (default is 3 times the length of the puzzle)
:return: A list containing the following:
Whether the puzzle was solved (True or False)
The solved/unsolved nucleotide sequence
The moves the algorithm took to get to the final nucleotide sequence
"""
solved = False
len_puzzle = len(dot_bracket)
min_threshold = MIN_THRESHOLD
max_iterations = len_puzzle * MAX_ITERATIONS
MAX_LEN = 400
TF_SHAPE = 8 * MAX_LEN
BASE_SHAPE = 9 * MAX_LEN
len_longest = MAX_LEN
def similar(a, b):
return SequenceMatcher(None, a, b).ratio()
def encode_struc(dots):
s = []
for i in dots:
if i == '.':
s.append(1)
elif i == '(':
s.append(2)
elif i == ')':
s.append(3)
return s
def second_largest(numbers):
count = 0
m1 = m2 = float('-inf')
for x in numbers:
count += 1
if x > m2:
if x >= m1:
m1, m2 = x, m1
else:
m2 = x
return m2 if count >= 2 else None
def convert_to_list(base_seq):
str_struc = []
for i in base_seq:
if i == 'A':
str_struc.append(1)
elif i == 'U':
str_struc.append(2)
elif i == 'G':
str_struc.append(3)
elif i == 'C':
str_struc.append(4)
#struc = ''.join(str_struc)
return str_struc
def encoded_locks(raw_locks):
newlocks = []
for i in raw_locks:
if i == 'o':
newlocks.append(1)
elif i == 'x':
newlocks.append(2)
return newlocks
base_seq = (convert_to_list(nucleotides)) + ([0] *
(len_longest - len_puzzle))
# cdb = '.'*len_puzzle
current_struc = (encode_struc(
RNA.fold(nucleotides)[0])) + ([0] * (len_longest - len_puzzle))
target_struc = encode_struc(dot_bracket) + ([0] *
(len_longest - len_puzzle))
current_energy = [ce] + ([0] * (len_longest - 1))
target_energy = [te] + ([0] * (len_longest - 1))
current_pm = format_pairmap(nucleotides) + ([0] *
(len_longest - len_puzzle))
target_pm = format_pairmap(dot_bracket) + ([0] *
(len_longest - len_puzzle))
newlocks = (encoded_locks(locks)) + ([0] * (len_longest - len_puzzle))
inputs2 = np.array([
base_seq, current_struc, target_struc, current_energy, target_energy,
current_pm, target_pm, newlocks
])
inputs = inputs2.reshape([-1, TF_SHAPE])
with tf.Graph().as_default() as base_graph:
saver1 = tf.train.import_meta_graph(
os.getcwd() + '/models/base/baseCNN15.meta') # CNN15
sess1 = tf.Session(
graph=base_graph
) # config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=True)
saver1.restore(sess1, os.getcwd() + '/models/base/baseCNN15')
x = base_graph.get_tensor_by_name('x_placeholder:0')
y = base_graph.get_tensor_by_name('y_placeholder:0')
keep_prob = base_graph.get_tensor_by_name('keep_prob_placeholder:0')
base_weights = base_graph.get_tensor_by_name('op7:0')
with tf.Graph().as_default() as location_graph:
saver2 = tf.train.import_meta_graph(
os.getcwd() + '/models/location/locationCNN15.meta')
sess2 = tf.Session(
graph=location_graph
) # config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=True)
saver2.restore(sess2, os.getcwd() + '/models/location/locationCNN15')
x2 = location_graph.get_tensor_by_name('x_placeholder:0')
y2 = location_graph.get_tensor_by_name('y_placeholder:0')
keep_prob2 = location_graph.get_tensor_by_name('keep_prob_placeholder:0')
location_weights = location_graph.get_tensor_by_name('op7:0')
#print 'models loaded'
location_feed_dict = {x2: inputs, keep_prob2: 1.0}
movesets = []
iteration = 0
for i in range(max_iterations):
if np.all(inputs2[1] == inputs2[2]):
#print("Puzzle Solved")
solved = True
break
else:
location_array = ((sess2.run(location_weights,
location_feed_dict))[0])
inputs2 = inputs.reshape([8, TF_SHAPE / 8])
location_array = location_array[:len_puzzle] - min(
location_array[:len_puzzle])
total_l = sum(location_array)
location_array = location_array / total_l
location_change = (choice(list(range(0, len(location_array))),
1,
p=location_array,
replace=False))[0]
#location_change = np.argmax(location_array)
la = [0.0] * len_longest
la[location_change] = 1.0
inputs2 = np.append(inputs2, la)
inputs = inputs2.reshape([-1, BASE_SHAPE])
base_feed_dict = {x: inputs, keep_prob: 1.0}
base_array = ((sess1.run(base_weights, base_feed_dict))[0])
base_array = base_array - min(base_array)
total = sum(base_array)
base_array = base_array / total
#if np.random.rand() > 0.0:
# FOR CHOOSING STOCHASTICALLY
base_change = (choice([1, 2, 3, 4], 1, p=base_array,
replace=False))[0]
#else:
# NOT STOCHASTICALLY
#base_change = np.argmax(base_array) + 1
inputs2 = inputs.reshape([9, BASE_SHAPE / 9])
temp = copy.deepcopy(inputs2[0])
temp[location_change] = base_change
move = [base_change, location_change]
movesets.append(move)
#print move
str_seq = []
for i in temp:
if i == 1:
str_seq.append('A')
elif i == 2:
str_seq.append('U')
elif i == 3:
str_seq.append('G')
elif i == 4:
str_seq.append('C')
else:
continue
str_seq = ''.join(str_seq)
str_struc, current_e = RNA.fold(str_seq)
current_pm = format_pairmap(str_struc)
#print str_struc
#print len(str_struc)
#print similar(str_struc,dot_bracket)
rna_struc = []
for i in inputs2[2]:
if i == 1:
rna_struc.append('.')
elif i == 2:
rna_struc.append('(')
elif i == 3:
rna_struc.append(')')
else:
continue
rna_struc = ''.join(rna_struc)
target_e = RNA.energy_of_structure(str_seq, rna_struc, 0)
enc_struc = []
for i in str_struc:
if i == '.':
enc_struc.append(1)
elif i == '(':
enc_struc.append(2)
elif i == ')':
enc_struc.append(3)
else:
continue
inputs2[0] = temp
inputs2[1][:len(enc_struc)] = (enc_struc)
inputs2[3][0] = current_e
inputs2[4][0] = target_e
inputs2[5][:len(enc_struc)] = current_pm
inputs_loc = inputs2[0:8]
inputs = inputs_loc.reshape([-1, TF_SHAPE])
base_feed_dict = {x: inputs, keep_prob: 1.0}
location_feed_dict = {x2: inputs, keep_prob2: 1.0}
iteration += 1
reg = []
for i in inputs2[0]:
if i == 1:
reg.append('A')
elif i == 2:
reg.append('U')
elif i == 3:
reg.append('G')
elif i == 4:
reg.append('C')
else:
continue
reg = ''.join(reg)
if similar(str_struc, dot_bracket) >= min_threshold:
#print 'similar'
#print str_struc
#print dot_bracket
#print reg
break
level1, m2, s1 = sbc(dot_bracket, reg)
level2, m3, s2 = dsp(dot_bracket, level1)
if s1 or s2:
solved = True
movesets.extend(m2)
movesets.extend(m3)
return solved, level2, movesets