def isPointNearAnyHeavyAtom(self, point, tolerance):
nonZeroRadiiXyz = self.getHeavyAtomXYZ() #cache if necessary
toleranceSquared = tolerance
for coord in nonZeroRadiiXyz:
if geometry.distL2Squared(coord, point) < toleranceSquared:
return True
return False
def getNearbyResidues(self, pointList, nearbyDistance=0.):
'''returns a new pdb of residues in this pdb near the points given'''
nearbyDistanceSquared = nearbyDistance ** 2.
residuesNearPts = []
for pt in pointList:
for index,coord in enumerate(self.coords):
distanceBetweenSquared = geometry.distL2Squared(pt, coord)
if distanceBetweenSquared < nearbyDistanceSquared:
residueNumber = self.resNums[index]
chain = self.chains[index]
resChain = str(residueNumber) + str(chain)
if resChain not in residuesNearPts: #guarantee uniqueness
residuesNearPts.append(resChain)
residuesNearPts.sort()
return self.getListResiduesChains(residuesNearPts)
def getNearbyAtoms(self, pointList, nearbyDistance=0.):
'''returns the list of line numbers of atoms '''
lines = {}
if nearbyDistance > 0.: #actually do distance cutoff
nearbyDistanceSquared = nearbyDistance ** 2.
for pt in pointList:
tempSet = set()
for index,coord in enumerate(self.coords):
distanceBetween = geometry.distL2Squared(pt, coord)
if distanceBetween < nearbyDistanceSquared:
tempSet.update([self.atomToRaw[index]])
tempList = list(tempSet)
tempList.sort()
lines[tuple(pt)] = tempList
else: #just find closese atom for each pt
for pt in pointList:
bestDist, bestPt = 10000000., False
for index,coord in enumerate(self.coords):
distanceBetween = geometry.distL2Squared(pt, coord)
if distanceBetween < bestDist:
bestPt = self.atomToRaw[index]
bestDist = distanceBetween
lines[tuple(pt)] = [bestPt] #still needs to be a list
return lines
def calcRMSD(self, other, alphas=False):
'''calculates rmsd of all atoms between self and other, no checking or
alignment is done'''
squaredSum = 0.0
badData = 0 #keeps track of missing atoms (some NMR models wrong)
for index, coord in enumerate(self.coords):
if not alphas or self.atoms[index][0:2] == "CA":
try:
otherCoord = other.coords[index]
squaredSum += geometry.distL2Squared(coord, otherCoord)
except IndexError:
badData += 1
else:
badData += 1 #also keep track of non-carbon alphas when looking for CA
#print squaredSum, (len(self.coords) - badData)
squaredSum /= (len(self.coords) - badData)
return squaredSum**0.5
def clusterAtoms(self, distanceCutoff=2.0):
'''breaks into distinct unions of atoms based on distance cutoff'''
ligandClusters = unionfind2.unionFind()
cutoffSquared = distanceCutoff ** 2. #faster comparisons
for index,coord in enumerate(self.coords):
for index2,coord2 in enumerate(self.coords):
if index2 > index: #only do comparisons once each
distBetweenSquared = geometry.distL2Squared(coord, coord2)
if distBetweenSquared <= cutoffSquared:
ligandClusters.union(index, index2)
clusteredLists = ligandClusters.toLists()
newPdbs = [] #list of pdbData objects to return
for oneCluster in clusteredLists:
newPdb = self.copy()
markedForRemoval = []
for index in xrange(len(self.coords)):
if index not in oneCluster:
markedForRemoval.append(index)
for index in markedForRemoval:
newPdb.removeLine(newPdb.atomToRaw[index])
newPdbs.append(newPdb)
return newPdbs
