def meltingTemperature(seq, concentration=1.0e-9):
GAS_CONSTANT = 0.001987
strand = Strand(sequence=seq)
energy20 = float(mfe([strand.sequence, strand.C.sequence ], material='dna', T=20.0)[0][1]) + GAS_CONSTANT * (273.15 + 20) * np.log(55.5)
energy30 = float(mfe([strand.sequence, strand.C.sequence ], material='dna', T=30.0)[0][1]) + GAS_CONSTANT * (273.15 + 30) * np.log(55.5)
dS = (energy20 - energy30) / 10.0 # kcal/ K mol
dH = energy30 + (273.15 + 30.0) * dS # kcal/mol
return (dH / (dS + GAS_CONSTANT * np.log(concentration / 4.0)))
def Gibbs(probes, targets, send_end):
G_list = np.zeros((np.shape(probes)[0], np.shape(targets)[0]))
for p in range(np.shape(probes)[0]):
for t in range(np.shape(targets)[0]):
#print(p,t)
s1 = probes[p, :]
s2 = targets[t, :]
Seq1, Seq2 = translate(s1, s2)
rna_seqs = [Seq1[0], Seq2[0]]
G = float(
nupack.mfe(rna_seqs,
ordering=None,
material='rna',
dangles='some',
T=37,
multi=0,
pseudo=False,
sodium=1.0,
magnesium=0.0,
degenerate=False)[0][1])
#print(nupack.mfe(rna_seqs, ordering=None, material='rna', dangles='some', T=37, multi=0, pseudo=False, sodium=1.0, magnesium=0.0, degenerate=False))
G_list[p, t] = G
#print(G_list)
send_end.send(G_list)
return G_list
def toeholds(seq, T=GLOBAL_TEMPERATURE, material="dna"):
result = nupack.mfe([seq], T=T, material=material)
struct = result[0][0]
n = len(struct)
try:
toe5len = struct.index('(')
toe3len = struct[::-1].index(')')
except ValueError:
toe5len = n
toe3len = n
# print " %s = %s , toes %d and %d" % (seq, struct, toe5len, toe3len)
return (toe5len, toe3len)
def stemsize(seq, T=GLOBAL_TEMPERATURE, material='dna'):
result = nupack.mfe([seq], T=T, material=material)
struct = result[0][0]
n = len(struct)
try:
stemstart5 = struct.index('(')
stemlen5 = struct[stemstart5:].index('.')
revstruct = struct[::-1]
stemstart3 = revstruct.index(')')
stemlen3 = revstruct[stemstart3:].index('.')
stemlen = min(stemlen5, stemlen3)
except ValueError:
stemlen = 0
return stemlen
def Gibbs(probes, send_end):
G_list = np.zeros((np.shape(probes)[0],1))
for p in range(np.shape(probes)[0]):
s1 = probes[p,:]
Seq = translate(s1)
rna_seqs = [Seq[0]]
G = float(nupack.mfe(rna_seqs, ordering=None, material='rna', dangles='some', T=37, multi=0, pseudo=False, sodium=1.0, magnesium=0.0, degenerate=False)[0][1])
#print(nupack.mfe(rna_seqs, ordering=None, material='rna', dangles='some', T=37, multi=0, pseudo=False, sodium=1.0, magnesium=0.0, degenerate=False))
G_list[p,0] = G
#print(G_list)
send_end.send(G_list)
#print(G_list)
return G_list
def _get_fold(self,
sequence,
temperature,
ligand=None,
constraint=None):
nupack_mfe = nupack.mfe([self._change_cuts(sequence)],
material='rna',
pseudo=True,
T=temperature) # if str, 0, no error
pattern = re.compile('(\[\(\')|(\',)|(\'\)\])')
temp_mfe = pattern.sub('', "%s" % nupack_mfe)
temp_mfe = temp_mfe.replace("'", "")
mfe_list = temp_mfe.split()
mfe_struct = mfe_list[0]
mfe_energy = float(mfe_list[1])
return mfe_struct, mfe_energy
def probes(Targets, nEpochs, procNum, TargetsConc, ProbeConc, interval, Temp,
popSize, nElite, pointmutProb, shiftmutProb, TargetCell):
start_time = time.time()
Probes = np.random.choice(4, (popSize, np.shape(Targets)[1])) + 1
theprobes = np.zeros((0, np.shape(Targets)[1]))
#Target들의 self secondary structure 에너지 계산.
Ghp_target = np.zeros((1, np.shape(Targets)[0]))
for i in range(np.shape(Targets)[0]):
Seq1 = translate(Targets[i, :])
rna_seqs = [Seq1[0]]
G = float(
nupack.mfe(rna_seqs,
ordering=None,
material='rna',
dangles='some',
T=37,
multi=0,
pseudo=False,
sodium=1.0,
magnesium=0.0,
degenerate=False)[0][1])
Ghp_target[0, i] = G
Khpt = Keq.Keq(Ghp_target, Temp)
Khp_target = Khpt.keq()
#Genetic algorithm 시작.
iter = 0
while iter < nEpochs:
#Probe들의 self secondary structure 에너지 계산.
procs = []
pipe_list = []
Ghp_probe = np.zeros((0, 1))
for i in range(procNum):
recv_end, send_end = multiprocessing.Pipe(False)
Probe_part = Probes[int(i * (popSize / procNum)):int((i + 1) *
(popSize /
procNum)), :]
proc = multiprocessing.Process(target=gibbs_single.Gibbs,
args=(Probe_part, send_end))
procs.append(proc)
pipe_list.append(recv_end)
proc.start()
for proc in procs:
proc.join()
for x in pipe_list:
result = np.array(x.recv())
Ghp_probe = np.concatenate((Ghp_probe, result), axis=0)
Khp = Keq.Keq(Ghp_probe, Temp)
Khp_probe = Khp.keq()
#Target과 Probe 사이의 결합 에너지 계산.
procs = []
pipe_list = []
G_TargetProbe = np.zeros((0, np.shape(Targets)[0]))
for i in range(procNum):
recv_end, send_end = multiprocessing.Pipe(False)
Probe_part = Probes[int(i * (popSize / procNum)):int((i + 1) *
(popSize /
procNum)), :]
proc = multiprocessing.Process(target=gibbs_multi.Gibbs,
args=(Probe_part, Targets,
send_end))
procs.append(proc)
pipe_list.append(recv_end)
proc.start()
for proc in procs:
proc.join()
for x in pipe_list:
result = np.array(x.recv())
G_TargetProbe = np.concatenate((G_TargetProbe, result), axis=0)
K = Keq.Keq(G_TargetProbe, Temp)
Keqs = K.keq()
#print(Gibbs_list)
#각 세포들의 hybridization yield 계산.
Cp_list, Ct_list, Ct_hp, Cp_hp = cpt.Conc(Keqs, Khp_target, Khp_probe,
TargetsConc, ProbeConc)
Cpts = np.zeros((np.shape(Probes)[0], np.shape(TargetsConc)[0]))
for cell in range(np.shape(TargetsConc)[0]):
for p in range(np.shape(Probes)[0]):
Cpts[p, cell] = ProbeConc - Cp_list[p, cell] - Cp_hp[p, cell]
#TargetCell의 Cpt 값의 상대 우위 계산.
fitness = np.zeros((np.shape(Probes)[0], 1))
for p in range(np.shape(Probes)[0]):
TargetCpt = Cpts[p, int(TargetCell)]
RestCpt = np.delete(Cpts[p, :], TargetCell).max()
#print Cpts[p, :], np.delete(Cpts[p, :], TargetCell), TargetCpt, RestCpt
fitness[p, 0] = TargetCpt - RestCpt
#print(fitness[p,0], Cpts[p,int(TargetCell)].max(), TargetCpt, RestCpt)
#print('f', fitness)
theprobe = Probes[np.where(fitness == fitness.max())[0], :]
# print(theprobe[0,:])
elites = elite.elitism(nElite, Probes, fitness)
#print(elites)
#print np.shape(elites), np.shape(Probes)
leftovers = roulette.roulette(elites, Probes, fitness)
#print np.shape(Probes), np.shape(leftovers)
newPopulation = crossover.cross(leftovers,
popSize - np.shape(elites)[0])
newPopulation = pointmut.pointmut(newPopulation, pointmutProb)
newPopulation = shiftmut.shiftmut(newPopulation, shiftmutProb)
Probes = np.concatenate((elites, newPopulation), axis=0)
#print(Ghp_probe)
if iter % interval == 0:
print 'Target =', str(TargetCell), '/ Iter =', iter
print "End Time =", time.time() - start_time, 'Fitness =', round(
fitness.max() / ProbeConc * 100, 3), "%"
for t in range(np.shape(Targets)[0]):
Seq1, Seq2 = translate2(theprobe[0], Targets[t, :])
rna_seqs = [Seq1[0], Seq2[0]]
print(
"MFE =", str(t),
nupack.mfe(rna_seqs,
ordering=None,
material='rna',
dangles='some',
T=37,
multi=0,
pseudo=False,
sodium=1.0,
magnesium=0.0,
degenerate=False),
Cpts[np.where(fitness == fitness.max())[0], t][0])
theprobes = np.concatenate((theprobes, [theprobe[0]]), axis=0)
#print(theprobes)
#print(Cpts[np.where(fitness == fitness.max())[0], :])
iter += 1
return theprobes, fitness.max() / ProbeConc * 100