def __ssBonds( self, model, cutoff=4. ):
"""
Identify disulfide bonds.
:param model: model
:type model: PDBModel
:param cutoff: distance cutoff for S-S distance (default: 4.0)
:type cutoff: float
:return: list with numbers of residue pairs forming S-S
:rtype: [(int, int)]
"""
m = model.compress( model.mask( ['SG'] ) )
if len( m ) < 2:
return []
pw = MU.pairwiseDistances( m.xyz, m.xyz )
pw = N0.less( pw, cutoff )
r = []
for i in range( len( pw ) ):
for j in range( i+1, len(pw) ):
if pw[i,j]:
r += [ (m.atoms['residue_number'][i],
m.atoms['residue_number'][j]) ]
return r
def addDensity( self, radius=6, minasa=None, profName='density' ):
"""
Count the number of heavy atoms within the given radius.
Values are only collected for atoms with |minasa| accessible surface
area.
@param minasa: relative exposed surface - 0 to 100%
@type minasa: float
@param radius: in Angstrom
@type radius: float
"""
mHeavy = self.m.maskHeavy()
xyz = N0.compress( mHeavy, self.m.getXyz(), 0 )
if minasa and self.m.profile( 'relAS', 0 ) == 0:
self.addASA()
if minasa:
mSurf = self.m.profile2mask( 'relAS', minasa )
else:
mSurf = N0.ones( self.m.lenAtoms() )
## loop over all surface atoms
surf_pos = N0.nonzero( mSurf )
contacts = []
for i in surf_pos:
dist = N0.sum(( xyz - self.m.xyz[i])**2, 1)
contacts += [ N0.sum( N0.less(dist, radius**2 )) -1]
self.m.atoms.set( profName, contacts, mSurf, default=-1,
comment='atom density radius %3.1fA' % radius,
version= T.dateString() + ' ' + self.version() )
def takeFrames( self, indices ):
"""
Return a copy of the trajectory containing only the specified frames.
:param indices: positions to take
:type indices: [int]
:return: copy of this Trajectory (fewer frames, semi-deep copy of ref)
:rtype: Trajectory
"""
## remove out-of-bound indices
indices = N0.compress( N0.less( indices, len( self.frames) ), indices )
r = self.__class__()
## this step takes some time for large frames !
r.frames = N0.take( self.frames, indices, 0 )
## semi-deep copy of reference model
r.setRef( self.ref.take( list(range( self.ref.lenAtoms()))) )
if self.frameNames is not None:
r.frameNames = N0.take( self.frameNames, indices, 0 )
r.frameNames = list(map( ''.join, r.frameNames.tolist() ))
r.pc = self.__takePca( indices )
r.profiles = self.profiles.take( indices )
r.resIndex = self.resIndex
return r
def __atomContacts(self, cutoff, rec_mask, lig_mask, cache):
"""
Intermolecular distances below cutoff after applying the two masks.
@param cutoff: cutoff for B{atom-atom} contact in \AA
@type cutoff: float
@param rec_mask: atom mask
@type rec_mask: [1|0]
@param lig_mask: atom mask
@type lig_mask: [1|0]
@param cache: cache pairwise atom distance matrix
@type cache: 1|0
@return: atom contact matrix, array sum_rec_mask x sum_lig_mask
@rtype: array
"""
## get atom coordinats as array 3 x all_atoms
rec_xyz = self.rec().getXyz()
lig_xyz = self.lig().getXyz()
## get pair-wise distances -> atoms_rec x atoms_lig
dist = getattr(self, 'pw_dist', None)
if dist is None or \
N0.shape( dist ) != ( N0.sum(rec_mask), N0.sum(lig_mask) ):
dist = self.__pairwiseDistances(N0.compress(rec_mask, rec_xyz, 0),
N0.compress(lig_mask, lig_xyz, 0))
if cache:
self.pw_dist = dist
## reduce to 1 (distance < cutoff) or 0 -> n_atoms_rec x n_atoms_lig
return N0.less(dist, cutoff)
def __atomContacts(self, cutoff, rec_mask, lig_mask, cache):
"""
Intermolecular distances below cutoff after applying the two masks.
@param cutoff: cutoff for B{atom-atom} contact in \AA
@type cutoff: float
@param rec_mask: atom mask
@type rec_mask: [1|0]
@param lig_mask: atom mask
@type lig_mask: [1|0]
@param cache: cache pairwise atom distance matrix
@type cache: 1|0
@return: atom contact matrix, array sum_rec_mask x sum_lig_mask
@rtype: array
"""
## get atom coordinats as array 3 x all_atoms
rec_xyz = self.rec().getXyz()
lig_xyz = self.lig().getXyz()
## get pair-wise distances -> atoms_rec x atoms_lig
dist = getattr( self, 'pw_dist', None )
if dist is None or \
N0.shape( dist ) != ( N0.sum(rec_mask), N0.sum(lig_mask) ):
dist = self.__pairwiseDistances(N0.compress( rec_mask, rec_xyz, 0),
N0.compress( lig_mask, lig_xyz, 0) )
if cache:
self.pw_dist = dist
## reduce to 1 (distance < cutoff) or 0 -> n_atoms_rec x n_atoms_lig
return N0.less( dist, cutoff )
def __ssBonds( self, model, cutoff=4. ):
"""
Identify disulfide bonds.
:param model: model
:type model: PDBModel
:param cutoff: distance cutoff for S-S distance (default: 4.0)
:type cutoff: float
:return: list with numbers of residue pairs forming S-S
:rtype: [(int, int)]
"""
m = model.compress( model.mask( ['SG'] ) )
if len( m ) < 2:
return []
pw = MU.pairwiseDistances( m.xyz, m.xyz )
pw = N0.less( pw, cutoff )
r = []
for i in range( len( pw ) ):
for j in range( i+1, len(pw) ):
if pw[i,j]:
r += [ (m.atoms['residue_number'][i],
m.atoms['residue_number'][j]) ]
return r
def __checkProfileIntegrity(self,
profile,
upperLimit=1.0,
lowerLimit=-1.0):
"""
In some cases SurfaceRacer generates incorrect curvature
values for some atoms. This function sets values outside
a given range to 0
@param profile: profile name
@type profile: str
@param upperLimit: upper limit for a valid value (default: 1.0)
@type upperLimit: float
@param lowerLimit: lower limit for a valid value (default: -1.0)
@type lowerLimit: float
@return: profile with inspected values
@rtype: [float]
"""
mask = N0.greater(profile, upperLimit)
mask += N0.less(profile, lowerLimit)
for i in N0.nonzero(mask):
print('WARNING! Profile value %.2f set to O\n' % profile[i])
profile[i] = 0
return profile
def __checkProfileIntegrity( self, profile, upperLimit=1.0,
lowerLimit=-1.0):
"""
In some cases SurfaceRacer generates incorrect curvature
values for some atoms. This function sets values outside
a given range to 0
@param profile: profile name
@type profile: str
@param upperLimit: upper limit for a valid value (default: 1.0)
@type upperLimit: float
@param lowerLimit: lower limit for a valid value (default: -1.0)
@type lowerLimit: float
@return: profile with inspected values
@rtype: [float]
"""
mask = N0.greater( profile, upperLimit )
mask += N0.less( profile, lowerLimit )
for i in N0.nonzero(mask):
print('WARNING! Profile value %.2f set to O\n'%profile[i])
profile[i] = 0
return profile
def addDensity(self, radius=6, minasa=None, profName='density'):
"""
Count the number of heavy atoms within the given radius.
Values are only collected for atoms with |minasa| accessible surface
area.
@param minasa: relative exposed surface - 0 to 100%
@type minasa: float
@param radius: in Angstrom
@type radius: float
"""
mHeavy = self.m.maskHeavy()
xyz = N0.compress(mHeavy, self.m.getXyz(), 0)
if minasa and self.m.profile('relAS', 0) == 0:
self.addASA()
if minasa:
mSurf = self.m.profile2mask('relAS', minasa)
else:
mSurf = N0.ones(self.m.lenAtoms())
## loop over all surface atoms
surf_pos = N0.nonzero(mSurf)
contacts = []
for i in surf_pos:
dist = N0.sum((xyz - self.m.xyz[i])**2, 1)
contacts += [N0.sum(N0.less(dist, radius**2)) - 1]
self.m.atoms.set(profName,
contacts,
mSurf,
default=-1,
comment='atom density radius %3.1fA' % radius,
version=T.dateString() + ' ' + self.version())
def match(x, y, n_iterations=1, z=2, eps_rmsd=0.5, eps_stdv=0.05):
"""
Matches two arrays onto each other, while iteratively removing outliers.
Superimposed array y would be C{ N0.dot(y, N0.transpose(r)) + t }.
:param n_iterations: number of calculations::
1 .. no iteration
0 .. until convergence
:type n_iterations: 1|0
:param z: number of standard deviations for outlier definition (default: 2)
:type z: float
:param eps_rmsd: tolerance in rmsd (default: 0.5)
:type eps_rmsd: float
:param eps_stdv: tolerance in standard deviations (default: 0.05)
:type eps_stdv: float
:return: (r,t), [ [percent_considered, rmsd_for_it, outliers] ]
:rtype: (array, array), [float, float, int]
"""
iter_trace = []
rmsd_old = 0
stdv_old = 0
n = 0
converged = 0
mask = N0.ones(len(y), N0.Int32 )
while not converged:
## find transformation for best match
r, t = findTransformation(N0.compress(mask, x, 0),
N0.compress(mask, y, 0))
## transform coordinates
xt = N0.dot(y, N0.transpose(r)) + t
## calculate row distances
d = N0.sqrt(N0.sum(N0.power(x - xt, 2), 1)) * mask
## calculate rmsd and stdv
rmsd = N0.sqrt(N0.average(N0.compress(mask, d)**2))
stdv = MU.SD(N0.compress(mask, d))
## check conditions for convergence
d_rmsd = abs(rmsd - rmsd_old)
d_stdv = abs(1 - stdv_old / stdv)
if d_rmsd < eps_rmsd and d_stdv < eps_stdv:
converged = 1
else:
rmsd_old = rmsd
stdv_old = stdv
## store result
perc = round(float(N0.sum(mask)) / float(len(mask)), 2)
## throw out non-matching rows
mask = N0.logical_and(mask, N0.less(d, rmsd + z * stdv))
outliers = N0.nonzero( N0.logical_not( mask ) )
iter_trace.append([perc, round(rmsd, 3), outliers])
n += 1
if n_iterations and n >= n_iterations:
break
return (r, t), iter_trace
def match(x, y, n_iterations=1, z=2, eps_rmsd=0.5, eps_stdv=0.05):
"""
Matches two arrays onto each other, while iteratively removing outliers.
Superimposed array y would be C{ N0.dot(y, N0.transpose(r)) + t }.
:param n_iterations: number of calculations::
1 .. no iteration
0 .. until convergence
:type n_iterations: 1|0
:param z: number of standard deviations for outlier definition (default: 2)
:type z: float
:param eps_rmsd: tolerance in rmsd (default: 0.5)
:type eps_rmsd: float
:param eps_stdv: tolerance in standard deviations (default: 0.05)
:type eps_stdv: float
:return: (r,t), [ [percent_considered, rmsd_for_it, outliers] ]
:rtype: (array, array), [float, float, int]
"""
iter_trace = []
rmsd_old = 0
stdv_old = 0
n = 0
converged = 0
mask = N0.ones(len(y), N0.Int32)
while not converged:
## find transformation for best match
r, t = findTransformation(N0.compress(mask, x, 0),
N0.compress(mask, y, 0))
## transform coordinates
xt = N0.dot(y, N0.transpose(r)) + t
## calculate row distances
d = N0.sqrt(N0.sum(N0.power(x - xt, 2), 1)) * mask
## calculate rmsd and stdv
rmsd = N0.sqrt(N0.average(N0.compress(mask, d)**2))
stdv = MU.SD(N0.compress(mask, d))
## check conditions for convergence
d_rmsd = abs(rmsd - rmsd_old)
d_stdv = abs(1 - stdv_old / stdv)
if d_rmsd < eps_rmsd and d_stdv < eps_stdv:
converged = 1
else:
rmsd_old = rmsd
stdv_old = stdv
## store result
perc = round(float(N0.sum(mask)) / float(len(mask)), 2)
## throw out non-matching rows
mask = N0.logical_and(mask, N0.less(d, rmsd + z * stdv))
outliers = N0.nonzero(N0.logical_not(mask))
iter_trace.append([perc, round(rmsd, 3), outliers])
n += 1
if n_iterations and n >= n_iterations:
break
return (r, t), iter_trace
