def calculates(self, cell):
if self.__isLocalSearch:
newcell = self.__LocalSearch.initLocalSearch(cell)
else:
newcell = Gene()
newcell.copyGene(cell) #copy.deepcopy(cell)
auxLigand = copy.deepcopy(self.__ligand)
if self.__isKB:
for i in range(len(auxLigand.branch)):
torAngle = auxLigand.rotateBranchKB(i, newcell.rotateBonds[i])
auxLigand.rotateAtomsBranch(i, torAngle)
else:
for i in range(len(auxLigand.branch)):
auxLigand.rotateAtomsBranch(i, newcell.rotateBonds[i])
auxLigand.translateToPoint([
self.__centerSpace[0] + newcell.x,
self.__centerSpace[1] + newcell.y,
self.__centerSpace[2] + newcell.z
])
sphVect = spherePoint(1, newcell.sph_theta, newcell.sph_phi)
auxLigand.rotateByVector(sphVect, newcell.theta)
auxLigand.writePDBQT(self.__temporalDir + "ligand.pdbqt")
newcell.score = calculateFreeEnergy()
self.__numberScoring += 1
return newcell #copy.deepcopy(newcell)
def getNeighbor(self, cell):
newCell = Gene()
newCell.copyGene(cell)
if self.__typeLS == 0:
newCell = self.mutationBlock(newCell)
elif self.__typeLS == 1:
alpha = self.__tempIterative
newCell = self.mutationReduce(newCell, alpha)
elif self.__typeLS == 2:
newCell = self.mutationRot(newCell)
newCell = self.calculates(newCell)
return newCell
def crossoverSPC(self, selectedCell1, selectedCell2):
pop1 = []
pop2 = []
pop1.append(selectedCell1.x)
pop1.append(selectedCell1.y)
pop1.append(selectedCell1.z)
pop1.append(selectedCell1.sph_theta)
pop1.append(selectedCell1.sph_phi)
pop1.append(selectedCell1.theta)
for angle in selectedCell1.rotateBonds:
pop1.append(angle)
pop2.append(selectedCell2.x)
pop2.append(selectedCell2.y)
pop2.append(selectedCell2.z)
pop2.append(selectedCell2.sph_theta)
pop2.append(selectedCell2.sph_phi)
pop2.append(selectedCell2.theta)
for angle in selectedCell2.rotateBonds:
pop2.append(angle)
genSize = len(pop1)
cutPoint = random.randint(0, genSize - 1)
length = random.randint(0, genSize - 1)
if length == 0:
return selectedCell1
elif length > (genSize - cutPoint):
newPop = pop1[cutPoint:]
turnTop = length - (genSize - cutPoint)
for i in range(0, turnTop):
newPop.insert(i, pop1[i])
for i in range(turnTop, cutPoint):
newPop.insert(i, pop2[i])
else:
newPop = pop1[cutPoint:cutPoint + length]
for i in range(0, cutPoint):
newPop.insert(i, pop2[i])
for i in range(cutPoint + length, genSize):
newPop.insert(i, pop2[i])
newCell = Gene()
newCell.x = newPop[0]
newCell.y = newPop[1]
newCell.z = newPop[2]
newCell.sph_theta = newPop[3]
newCell.sph_phi = newPop[4]
newCell.theta = newPop[5]
newCell.rotateBonds = newPop[6:]
return newCell
def randomCell(self):
gen = Gene()
gen.x = random.uniform(-self.__searchSpaceSize, self.__searchSpaceSize)
gen.y = random.uniform(-self.__searchSpaceSize, self.__searchSpaceSize)
gen.z = random.uniform(-self.__searchSpaceSize, self.__searchSpaceSize)
gen.sph_theta = math.pi * random.randint(0, 200) / 100.0
gen.sph_phi = math.pi * random.randint(0, 100) / 100.0
gen.theta = math.pi * random.randint(0, 200) / 100.0
for r in xrange(len(self.__rotateAtoms)):
gen.rotateAtoms.append(math.pi * random.randint(0, 200) / 100.0)
return copy.deepcopy(gen)
def randomPopulation(self, sizePop):
population = []
for i in xrange(sizePop):
gen = Gene()
gen.id = i
gen.x = random.uniform(-self.__spaceLen, self.__spaceLen)
gen.y = random.uniform(-self.__spaceLen, self.__spaceLen)
gen.z = random.uniform(-self.__spaceLen, self.__spaceLen)
gen.sph_theta = math.pi * random.randint(0, 200) / 100.0
gen.sph_phi = math.pi * random.randint(0, 100) / 100.0
gen.theta = math.pi * random.randint(0, 200) / 100.0
for r in xrange(len(self.__rotateAtoms)):
gen.rotateAtoms.append(math.pi * random.randint(0, 200) /
100.0)
population.append(gen)
return population[:]
def randomCell(self):
gen = Gene()
gen.x = random.uniform(-self.__searchSpaceSize, self.__searchSpaceSize)
gen.y = random.uniform(-self.__searchSpaceSize, self.__searchSpaceSize)
gen.z = random.uniform(-self.__searchSpaceSize, self.__searchSpaceSize)
gen.sph_theta = math.pi * random.randint(0, 200) / 100.0
gen.sph_phi = math.pi * random.randint(0, 100) / 100.0
gen.theta = math.pi * random.randint(0, 200) / 100.0
for r in xrange(len(self.__rotateAtoms)):
gen.rotateAtoms.append(math.pi * random.randint(0, 200) / 100.0)
return copy.deepcopy(gen)
def randomPopulation(self, sizePop):
population = []
for i in xrange(sizePop):
gen = Gene()
gen.id = i
gen.x = random.uniform(-self.__spaceLen, self.__spaceLen)
gen.y = random.uniform(-self.__spaceLen, self.__spaceLen)
gen.z = random.uniform(-self.__spaceLen, self.__spaceLen)
gen.sph_theta = math.pi * random.randint(0, 200) / 100.0
gen.sph_phi = math.pi * random.randint(0, 100) / 100.0
gen.theta = math.pi * random.randint(0, 200) / 100.0
for r in xrange(len(self.__rotateAtoms)):
gen.rotateAtoms.append(math.pi * random.randint(0, 200) / 100.0)
population.append(gen)
return population[:]
def getNeighbor(self, cell): newCell = Gene() newCell.copyGene(cell) if self.__typeLS == 0: newCell = self.mutationBlock(newCell) elif self.__typeLS == 1: alpha = self.__tempIterative newCell = self.mutationReduce(newCell, alpha) elif self.__typeLS == 2: newCell = self.mutationRot(newCell) elif self.__typeLS == 3: localOpt = random.randint(1,3) if localOpt == 1: newCell = self.mutationBlock(newCell) self.__normalLSCount+=1 elif localOpt == 2: alpha = self.__tempIterative newCell = self.mutationReduce(newCell, alpha) self.__redLSCount+=1 elif localOpt == 3: newCell = self.mutationRot(newCell) self.__rotLSCount+=1 elif self.__typeLS == 4: localOpt = random.uniform(0,1) if localOpt <= 0.1: newCell == self.mutationBlock(newCell) self.__normalLSCount+=1 elif localOpt <= 0.35: alpha = self.__tempIterative newCell = self.mutationReduce(newCell, alpha) self.__redLSCount+=1 elif localOpt > 0.35: newCell == self.mutationRot(newCell) self.__rotLSCount+=1 newCell = self.calculates(newCell) return newCell
def initLocalSearch(self, cell):
#print "Init Local Search..."
self.__tempIterative = 1.0
T = self.__temp
oldCell = Gene()
oldCell.copyGene(cell)
while T > self.__tempMin:
j = 1
while j <= self.__numIteration:
newCell = self.getNeighbor(oldCell)
criteria = self.accptance(oldCell, newCell, T)
if criteria > random.random():
oldCell.copyGene(newCell)
j += 1
T *= self.__tempAlpha
self.__tempIterative *= self.__alphaIt
return oldCell
def crossover50(self, selectedCell1, selectedCell2):
newCell = Gene()
#switch center of ligand
centrand = random.randint(0, 5)
if centrand == 0:
newCell.x = selectedCell1.x
newCell.y = selectedCell2.y
newCell.z = selectedCell2.z
elif centrand == 1:
newCell.x = selectedCell1.x
newCell.y = selectedCell1.y
newCell.z = selectedCell2.z
elif centrand == 2:
newCell.x = selectedCell1.x
newCell.y = selectedCell1.y
newCell.z = selectedCell1.z
elif centrand == 3:
newCell.x = selectedCell2.x
newCell.y = selectedCell2.y
newCell.z = selectedCell2.z
elif centrand == 4:
newCell.x = selectedCell2.x
newCell.y = selectedCell1.y
newCell.z = selectedCell1.z
else:
newCell.x = selectedCell2.x
newCell.y = selectedCell2.y
newCell.z = selectedCell1.z
#switch rotation of ligand
rotrand = random.randint(0, 5)
if rotrand == 0:
newCell.sph_theta = selectedCell1.sph_theta
newCell.sph_phi = selectedCell2.sph_phi
newCell.theta = selectedCell2.theta
elif rotrand == 1:
newCell.sph_theta = selectedCell1.sph_theta
newCell.sph_phi = selectedCell1.sph_phi
newCell.theta = selectedCell2.theta
elif rotrand == 2:
newCell.sph_theta = selectedCell1.sph_theta
newCell.sph_phi = selectedCell1.sph_phi
newCell.theta = selectedCell1.theta
elif rotrand == 3:
newCell.sph_theta = selectedCell2.sph_theta
newCell.sph_phi = selectedCell2.sph_phi
newCell.theta = selectedCell2.theta
elif rotrand == 4:
newCell.sph_theta = selectedCell2.sph_theta
newCell.sph_phi = selectedCell1.sph_phi
newCell.theta = selectedCell1.theta
else:
newCell.sph_theta = selectedCell2.sph_theta
newCell.sph_phi = selectedCell2.sph_phi
newCell.theta = selectedCell1.theta
#switch rotational bonds
bondrand = random.randint(0, (len(selectedCell1.rotateBonds) - 1))
if random.randint(0, 1) == 1:
newCell.rotateBonds = selectedCell1.rotateBonds[:
bondrand] + selectedCell2.rotateBonds[
bondrand:]
else:
newCell.rotateBonds = selectedCell2.rotateBonds[:
bondrand] + selectedCell1.rotateBonds[
bondrand:]
return newCell
def crossoverCenter(self, selectedCell1, selectedCell2): newCell = Gene() if random.randint(0,1) == 1: newCell.x = selectedCell1.x else: newCell.x = selectedCell2.x if random.randint(0,1) == 1: newCell.y = selectedCell1.y else: newCell.y = selectedCell2.y if random.randint(0,1) == 1: newCell.z = selectedCell1.z else: newCell.z = selectedCell2.z if random.randint(0,1) == 1: newCell.sph_theta = selectedCell1.sph_theta newCell.sph_phi = selectedCell1.sph_phi newCell.theta = selectedCell1.theta else: newCell.sph_theta = selectedCell2.sph_theta newCell.sph_phi = selectedCell2.sph_phi newCell.theta = selectedCell2.theta return newCell
'Init Scoring Function'
scoringFx = ScoringFunction.ScoringFunction()
scoringFx.defineLigandStructure("ligand.pdb", __LigandName, tempPath)
ligand = IO.readPDB("ligand.pdb", 'HETATM', tempPath)
'Recover Connection Matrix'
ligand.connectMatrix = connectMatrix
ligand.addRotationsAtoms(__rotateAtoms)
IO.writeAllPDB("original_" + __MoleculeName + ".pdb", protein, ligand,
tempPath)
'Random Change'
listaGenes = []
for r in range(__numberResult):
gen = Gene.Gene()
gen.id = r
#gen.x = random.uniform(-__SearchSpaceSize, __SearchSpaceSize)
#gen.y = random.uniform(-__SearchSpaceSize, __SearchSpaceSize)
#gen.z = random.uniform(-__SearchSpaceSize, __SearchSpaceSize)
gen.sph_theta = math.pi * random.randint(0, 200) / 100.0
gen.sph_phi = math.pi * random.randint(0, 100) / 100.0
gen.theta = math.pi * random.randint(0, 200) / 100.0
for j in range(len(ligand.rotationsPoints)):
gen.rotateAtoms.append(math.pi * random.randint(0, 200) / 100.0)
listaGenes.append(gen)
'Molecule Name'
score = scoringFx.generateScoring(protein, ligand)
ligandName = __MoleculeName + "_Score_" + "{0:.3f}".format(
score) + "_original.pdb"
def crossover50(self, selectedCell1, selectedCell2): newCell = Gene() #switch center of ligand centrand = random.randint(0,5) if centrand == 0: newCell.x = selectedCell1.x newCell.y = selectedCell2.y newCell.z = selectedCell2.z elif centrand == 1: newCell.x = selectedCell1.x newCell.y = selectedCell1.y newCell.z = selectedCell2.z elif centrand == 2: newCell.x = selectedCell1.x newCell.y = selectedCell1.y newCell.z = selectedCell1.z elif centrand == 3: newCell.x = selectedCell2.x newCell.y = selectedCell2.y newCell.z = selectedCell2.z elif centrand == 4: newCell.x = selectedCell2.x newCell.y = selectedCell1.y newCell.z = selectedCell1.z else: newCell.x = selectedCell2.x newCell.y = selectedCell2.y newCell.z = selectedCell1.z #switch rotation of ligand rotrand = random.randint(0,5) if rotrand == 0: newCell.sph_theta = selectedCell1.sph_theta newCell.sph_phi = selectedCell2.sph_phi newCell.theta = selectedCell2.theta elif rotrand == 1: newCell.sph_theta = selectedCell1.sph_theta newCell.sph_phi = selectedCell1.sph_phi newCell.theta = selectedCell2.theta elif rotrand == 2: newCell.sph_theta = selectedCell1.sph_theta newCell.sph_phi = selectedCell1.sph_phi newCell.theta = selectedCell1.theta elif rotrand == 3: newCell.sph_theta = selectedCell2.sph_theta newCell.sph_phi = selectedCell2.sph_phi newCell.theta = selectedCell2.theta elif rotrand == 4: newCell.sph_theta = selectedCell2.sph_theta newCell.sph_phi = selectedCell1.sph_phi newCell.theta = selectedCell1.theta else: newCell.sph_theta = selectedCell2.sph_theta newCell.sph_phi = selectedCell2.sph_phi newCell.theta = selectedCell1.theta return newCell
def resetPopulation(self): for node in self.__leafNode: bestCell = node.getBest() node.resetAgent() node.addToPocket(bestCell) for node in self.__fatherNode: bestCell = node.getBest() node.resetAgent() node.addToPocket(bestCell) bestCell = self.__rootNode.getBest() self.__rootNode.resetAgent() self.__rootNode.addToPocket(bestCell) gene = Gene() gene.randomCell(len(self.__ligand.branchSegment), self.__searchSpace) gene = self.calculates(gene) self.__rootNode.addToPocket(copy.deepcopy(gene)) for n in range(len(self.__fatherNode)): gene = Gene() gene.randomCell(len(self.__ligand.branchSegment), self.__searchSpace) gene = self.calculates(gene) self.__fatherNode[n].addToPocket(copy.deepcopy(gene)) for n in range(len(self.__leafNode)): gene = Gene() gene.randomCell(len(self.__ligand.branchSegment), self.__searchSpace) gene = self.calculates(gene) self.__leafNode[n].addToPocket(copy.deepcopy(gene))
def initPopulation(self, first=True):
if self.__isKB:
gene = Gene()
gene.randomCellKB(len(self.__ligand.branchSegment),
self.__searchSpace, self.__ligand.anglesArray,
self.__kbProb)
gene = self.calculatesInit(gene)
self.__rootNode.addToPocket(gene) #flag cpy
for n in range(len(self.__fatherNode)):
gene = Gene()
gene.randomCellKB(len(self.__ligand.branchSegment),
self.__searchSpace,
self.__ligand.anglesArray, self.__kbProb)
gene = self.calculatesInit(gene)
self.__fatherNode[n].addToPocket(gene) #flag cpy
for n in range(len(self.__leafNode)):
gene = Gene()
gene.randomCellKB(len(self.__ligand.branchSegment),
self.__searchSpace,
self.__ligand.anglesArray, self.__kbProb)
gene = self.calculatesInit(gene)
self.__leafNode[n].addToPocket(gene) #flag cpy
if first:
self.initLog()
else:
gene = Gene()
gene.randomCell(len(self.__ligand.branchSegment),
self.__searchSpace)
gene = self.calculatesInit(gene)
self.__rootNode.addToPocket(gene) #flag cpy
for n in range(len(self.__fatherNode)):
gene = Gene()
gene.randomCell(len(self.__ligand.branchSegment),
self.__searchSpace)
gene = self.calculatesInit(gene)
self.__fatherNode[n].addToPocket(gene) #flag cpy
for n in range(len(self.__leafNode)):
gene = Gene()
gene.randomCell(len(self.__ligand.branchSegment),
self.__searchSpace)
gene = self.calculatesInit(gene)
self.__leafNode[n].addToPocket(gene) #flag cpy
if first:
self.initLog()
def resetPopulation(self):
for node in self.__leafNode:
#bestCell = node.getBest()
node.resetAgent()
#node.addToPocket(bestCell)
for node in self.__fatherNode:
#bestCell = node.getBest()
node.resetAgent()
#node.addToPocket(bestCell)
#bestCell = self.__rootNode.getBest()
#self.__rootNode.resetAgent()
#self.__rootNode.addToPocket(bestCell)
if self.__isKB:
#gene = Gene()
#gene.randomCellKB(len(self.__ligand.branchSegment), self.__searchSpace, self.__ligand.anglesArray, self.__kbProb)
#gene = self.calculates(gene)
#self.__rootNode.addToPocket(copy.deepcopy(gene))
for n in range(len(self.__fatherNode)):
gene = Gene()
gene.randomCellKB(len(self.__ligand.branchSegment),
self.__searchSpace,
self.__ligand.anglesArray, self.__kbProb)
gene = self.calculatesInit(gene)
self.__fatherNode[n].addToPocket(gene) #cpyflag
for n in range(len(self.__leafNode)):
gene = Gene()
gene.randomCellKB(len(self.__ligand.branchSegment),
self.__searchSpace,
self.__ligand.anglesArray, self.__kbProb)
gene = self.calculatesInit(gene)
self.__leafNode[n].addToPocket(gene) #cpyflag
else:
#gene = Gene()
#gene.randomCell(len(self.__ligand.branchSegment), self.__searchSpace)
#gene = self.calculates(gene)
#self.__rootNode.addToPocket(copy.deepcopy(gene))
for n in range(len(self.__fatherNode)):
gene = Gene()
gene.randomCell(len(self.__ligand.branchSegment),
self.__searchSpace)
gene = self.calculatesInit(gene)
self.__fatherNode[n].addToPocket(gene) #cpyflag
for n in range(len(self.__leafNode)):
gene = Gene()
gene.randomCell(len(self.__ligand.branchSegment),
self.__searchSpace)
gene = self.calculatesInit(gene)
self.__leafNode[n].addToPocket(gene) #cpyflag
def crossoverUniform(self, selectedCell1, selectedCell2):
newCell = Gene()
if random.randint(0, 1) == 1:
newCell.x = selectedCell1.x
else:
newCell.x = selectedCell2.x
if random.randint(0, 1) == 1:
newCell.y = selectedCell1.y
else:
newCell.y = selectedCell2.y
if random.randint(0, 1) == 1:
newCell.z = selectedCell1.z
else:
newCell.z = selectedCell2.z
if random.randint(0, 1) == 1:
newCell.sph_theta = selectedCell1.sph_theta
else:
newCell.sph_theta = selectedCell2.sph_theta
if random.randint(0, 1) == 1:
newCell.sph_phi = selectedCell1.sph_phi
else:
newCell.sph_phi = selectedCell2.sph_phi
if random.randint(0, 1) == 1:
newCell.theta = selectedCell1.theta
else:
newCell.theta = selectedCell2.theta
for i in range(len(selectedCell1.rotateBonds)):
if random.randint(0, 1) == 1:
newCell.rotateBonds.append(selectedCell1.rotateBonds[i])
else:
newCell.rotateBonds.append(selectedCell2.rotateBonds[i])
return newCell
def initPopulation(self, first = True): gene = Gene() gene.rigidRandomCell(self.__searchSpace) gene = self.calculates(gene) self.__rootNode.addToPocket(copy.deepcopy(gene)) for n in range(len(self.__fatherNode)): gene = Gene() gene.rigidRandomCell(self.__searchSpace) gene = self.calculates(gene) self.__fatherNode[n].addToPocket(copy.deepcopy(gene)) for n in range(len(self.__leafNode)): gene = Gene() gene.rigidRandomCell(self.__searchSpace) gene = self.calculates(gene) self.__leafNode[n].addToPocket(copy.deepcopy(gene)) if first: self.initLog()
def crossoverBlock(self, selectedCell1, selectedCell2):
newCell = Gene()
if random.randint(0, 1) == 1:
newCell.x = selectedCell1.x
newCell.y = selectedCell1.y
newCell.z = selectedCell1.z
else:
newCell.x = selectedCell2.x
newCell.y = selectedCell2.y
newCell.z = selectedCell2.z
if random.randint(0, 1) == 1:
newCell.sph_theta = selectedCell1.sph_theta
newCell.sph_phi = selectedCell1.sph_phi
newCell.theta = selectedCell1.theta
else:
newCell.sph_theta = selectedCell2.sph_theta
newCell.sph_phi = selectedCell2.sph_phi
newCell.theta = selectedCell2.theta
if random.randint(0, 1) == 1:
newCell.rotateBonds = selectedCell1.rotateBonds[:]
else:
newCell.rotateBonds = selectedCell2.rotateBonds[:]
return newCell
