def atomsToVec(atoms):
a1 = hf.atomCoord(atoms[1])
a2 = hf.atomCoord(atoms[2])
a3 = hf.atomCoord(atoms[3])
centroid = hf.avgCoord(atoms)
pvector = hf.calcPlane(a1, a2, a3)
v1 = hf.vector(centroid, a1)
v2 = hf.vector(centroid, a2)
normal1 = hf.normalize(cross(v1 ,v2))
normal2 = hf.normalize(cross(v2 ,v1))
centroid_norm1 = hf.normalize(hf.vector(centroid, normal1))
centroid_norm2 = hf.normalize(hf.vector(centroid, normal2))
return {"centroid":centroid,"plane":hf.normalize(pvector),"normal":[normal1, normal2],\
'cnormal' : [centroid_norm1, centroid_norm2]}
def getWaterGridEnergy(self):
# cutoff_weak = -0.35, # weak water e.cutoff
# cutoff_strong = -0.5, # strong water e.cutoff
# desolvEntropy=-0.2, # desolvation entropy
# mapdistrange = 0.5):
# TODO to be used to 'minimize' the water position too?
# looking for the best spot for a water
# and increase the energy accordingly?
# calculate the score for bridging waters;
self.pose['water_bridge_scores'] = []
for w in self.pose['water_bridge']:
points, best_point = self.getMapPoints(coords=hf.atomCoord(w),
distance=self.mapdistrange)
self.pose['water_bridge_scores'].append(best_point)
# calculate ligand-overlapping penalties
self.pose['water_over_lig_penalty'] = []
for w in self.pose['water_over_lig']:
points, best_point = self.getMapPoints(coords=atomCoord(w),
distance=self.mapdistrange)
self.pose['water_over_lig_penalty'].append(-best_point)
# calculate rec-overlapping penalties
self.pose['water_over_rec_penalty'] = []
for w in self.pose['water_over_rec']:
self.pose['water_over_rec_penalty'].append(-self.desolvEntropy)
def getWaterGridEnergy(self):
# cutoff_weak = -0.35, # weak water e.cutoff
# cutoff_strong = -0.5, # strong water e.cutoff
# desolvEntropy=-0.2, # desolvation entropy
# mapdistrange = 0.5):
# TODO to be used to 'minimize' the water position too?
# looking for the best spot for a water
# and increase the energy accordingly?
# calculate the score for bridging waters;
self.pose['water_bridge_scores'] = []
for w in self.pose['water_bridge']:
points, best_point = self.getMapPoints( coords = hf.atomCoord(w),
distance = self.mapdistrange)
self.pose['water_bridge_scores'].append(best_point)
# calculate ligand-overlapping penalties
self.pose['water_over_lig_penalty'] = []
for w in self.pose['water_over_lig']:
points, best_point = self.getMapPoints( coords = atomCoord(w),
distance = self.mapdistrange)
self.pose['water_over_lig_penalty'].append( -best_point)
# calculate rec-overlapping penalties
self.pose['water_over_rec_penalty'] = []
for w in self.pose['water_over_rec']:
self.pose['water_over_rec_penalty'].append(-self.desolvEntropy)
def clusterWaters(self): #pose, clust_tolerance=2.0, desolvEntropy=0):
# cluster waters
# XXX
# energy from the cluster?
# once 3 waters are reclustered (3->1)
# two desolvEntropy penalties should be issued?
# XXX
unclustered_waters = self.pose[
'water_bridge'][:] # XXX NOT SURE IT"S NECESSARY
# cluster waters
water_clusters = hf.clusterAtoms(unclustered_waters,
tol=self.clust_tol)
# calculate clustering energy
self.pose['water_cluster_penalty'] = []
remove_from_text = []
for pop in water_clusters:
if len(pop) > 1:
this_cluster_centroid = hf.avgCoord(pop)
this_cluster_penalty = -self.desolvEntropy * (len(pop) - 1)
this_cluster_energy = 0
this_cluster_contacts = []
closest_to_centroid = None
closest_dist = 999999
for w in pop:
# get the w_index + w_score
idx = self.pose['water_bridge'].index(w)
this_cluster_energy += self.pose['water_bridge_scores'][
idx]
remove_from_text.append(w)
# find water closest to centroid (to be conserved)
# get the distance from the cluster average
centr_dist = hf.quickdist(hf.atomCoord(w),
this_cluster_centroid,
sq=False)
a = [hf.makePdb(coord=this_cluster_centroid)]
#writeList('centroid.pdb', a)
if centr_dist <= closest_dist:
closest_dist = centr_dist
closest_to_centroid = w
del self.pose['water_bridge'][idx]
del self.pose['water_bridge_scores'][idx]
# update contacts
for a in self.pose['water_bridge_contacts'][idx]:
if not a in this_cluster_contacts:
this_cluster_contacts.append(a)
del self.pose['water_bridge_contacts'][idx]
# update PDBQT text
# XXX DEBUGGINGH
#print type(remove_from_text), remove_from_text[0]
#writeList('REMOVING_WATERS.pdb', remove_from_text)
#writeList('current_ligand.pdbqt', pose['text'])
for w in remove_from_text:
if not w == closest_to_centroid:
try:
text_idx = self.pose['text'].index(w)
del self.pose['text'][text_idx]
except:
pass
# use the closest to be updated with clustering coords
closest_index = self.pose['text'].index(closest_to_centroid)
water = self.pose['text'][closest_index]
coord_text = "%8.3f%8.3f%8.3f" % (this_cluster_centroid[0],
this_cluster_centroid[1],
this_cluster_centroid[2])
cluster_water = water[0:30] + coord_text + water[
54:] # XXX ugly, but it works...
self.pose['text'][closest_index] = cluster_water
# update water_bridge list+score, water_cluster_penalty list
self.pose['water_bridge'].append(cluster_water)
self.pose['water_bridge_scores'].append(this_cluster_energy)
self.pose['water_cluster_penalty'].append(this_cluster_penalty)
self.pose['water_bridge_contacts'].append(
this_cluster_contacts)
if self.debug:
print "CLUSTER SIZE", len(pop)
print "CLUSTER ENRG", this_cluster_energy
print "CLUSTER PENL", this_cluster_penalty
#print "CLUSTER FINL", clust_energy+clust_penalty
print "=============="
def clusterWaters(self): #pose, clust_tolerance=2.0, desolvEntropy=0):
# cluster waters
# XXX
# energy from the cluster?
# once 3 waters are reclustered (3->1)
# two desolvEntropy penalties should be issued?
# XXX
unclustered_waters = self.pose['water_bridge'][:] # XXX NOT SURE IT"S NECESSARY
# cluster waters
water_clusters = hf.clusterAtoms(unclustered_waters, tol=self.clust_tol)
# calculate clustering energy
self.pose['water_cluster_penalty'] = []
remove_from_text = []
for pop in water_clusters:
if len(pop)>1:
this_cluster_centroid = hf.avgCoord(pop)
this_cluster_penalty = -self.desolvEntropy * (len(pop)-1)
this_cluster_energy = 0
this_cluster_contacts = []
closest_to_centroid = None
closest_dist = 999999
for w in pop:
# get the w_index + w_score
idx = self.pose['water_bridge'].index(w)
this_cluster_energy += self.pose['water_bridge_scores'][idx]
remove_from_text.append(w)
# find water closest to centroid (to be conserved)
# get the distance from the cluster average
centr_dist = hf.quickdist( hf.atomCoord(w), this_cluster_centroid, sq = False)
a = [ hf.makePdb(coord=this_cluster_centroid) ]
#writeList('centroid.pdb', a)
if centr_dist <= closest_dist:
closest_dist = centr_dist
closest_to_centroid = w
del self.pose['water_bridge'][idx]
del self.pose['water_bridge_scores'][idx]
# update contacts
for a in self.pose['water_bridge_contacts'][idx]:
if not a in this_cluster_contacts:
this_cluster_contacts.append(a)
del self.pose['water_bridge_contacts'][idx]
# update PDBQT text
# XXX DEBUGGINGH
#print type(remove_from_text), remove_from_text[0]
#writeList('REMOVING_WATERS.pdb', remove_from_text)
#writeList('current_ligand.pdbqt', pose['text'])
for w in remove_from_text:
if not w == closest_to_centroid:
try:
text_idx = self.pose['text'].index(w)
del self.pose['text'][text_idx]
except:
pass
# use the closest to be updated with clustering coords
closest_index = self.pose['text'].index(closest_to_centroid)
water = self.pose['text'][closest_index]
coord_text = "%8.3f%8.3f%8.3f" % (this_cluster_centroid[0],
this_cluster_centroid[1],
this_cluster_centroid[2])
cluster_water = water[0:30]+coord_text+water[54:] # XXX ugly, but it works...
self.pose['text'][closest_index] = cluster_water
# update water_bridge list+score, water_cluster_penalty list
self.pose['water_bridge'].append(cluster_water)
self.pose['water_bridge_scores'].append(this_cluster_energy)
self.pose['water_cluster_penalty'].append(this_cluster_penalty)
self.pose['water_bridge_contacts'].append(this_cluster_contacts)
if self.debug:
print "CLUSTER SIZE", len(pop)
print "CLUSTER ENRG", this_cluster_energy
print "CLUSTER PENL", this_cluster_penalty
#print "CLUSTER FINL", clust_energy+clust_penalty
print "=============="
