def pairwiseRmsd( self, aMask=None, noFit=0 ):
"""
Calculate rmsd between each 2 coordinate frames.
@param aMask: atom mask
@type aMask: [1|0]
@return: frames x frames array of float
@rtype: array
"""
frames = self.frames
if aMask is not None:
frames = N0.compress( aMask, frames, 1 )
result = N0.zeros( (len( frames ), len( frames )), N0.Float32 )
for i in range(0, len( frames ) ):
for j in range( i+1, len( frames ) ):
if noFit:
d = N0.sqrt(N0.sum(N0.power(frames[i]-frames[j], 2), 1))
result[i,j] = result[j,i] = N0.sqrt( N0.average(d**2) )
else:
rt, rmsdLst = rmsFit.match( frames[i], frames[j], 1 )
result[i,j] = result[j,i] = rmsdLst[0][1]
return result
def __pairwiseDistances(self, u, v):
"""
pairwise distance between 2 3-D numpy arrays of atom coordinates.
@param u: coordinates
@type u: array
@param v: coordinates
@type v: array
@return: Numpy array len(u) x len(v)
@rtype:array
@author: Wolfgang Rieping.
"""
## check input
if not type( u ) == arraytype or\
not type( v ) == arraytype:
raise ComplexError('unsupported argument type ' + \
str( type(u) ) + ' or ' + str( type(v) ) )
diag1 = N0.diagonal(N0.dot(u, N0.transpose(u)))
diag2 = N0.diagonal(N0.dot(v, N0.transpose(v)))
dist = -N0.dot(v, N0.transpose(u)) - N0.transpose(
N0.dot(u, N0.transpose(v)))
dist= N0.transpose(N0.asarray(map(lambda column,a:column+a, \
N0.transpose(dist), diag1)))
return N0.transpose(
N0.sqrt(N0.asarray(map(lambda row, a: row + a, dist, diag2))))
def centerSurfDist(model, surf_mask, mask=None):
"""
Calculate the longest and shortest distance from
the center of the molecule to the surface.
@param mask: atoms not to be considerd (default: None)
@type mask: [1|0]
@param surf_mask: atom surface mask, needed for minimum surface distance
@type surf_mask: [1|0]
@return: max distance, min distance
@rtype: float, float
"""
if mask is None:
mask = model.maskHeavy()
## calculate center of mass
center = model.centerOfMass()
## surface atom coordinates
surf_xyz = N0.compress(mask * surf_mask, model.getXyz(), 0)
## find the atom closest and furthest away from center
dist = N0.sqrt(N0.sum((surf_xyz - center)**2, 1))
minDist = min(dist)
maxDist = max(dist)
return maxDist, minDist
def logConfidence( x, R, clip=0 ):
"""
Estimate the probability of x NOT beeing a random observation from a
lognormal distribution that is described by a set of random values.
@param x: observed value
@type x: float
@param R: sample of random values
@type R: [float]
@param clip: clip zeros at this value 0->don't clip (default: 0)
@type clip: float
@return: confidence that x is not random, median of random distr.
@rtype: (float, float)
"""
if clip and 0 in R:
R = N0.clip( R, clip, max( R ) )
if clip and x == 0:
x = clip
## remove 0 instead of clipping
R = N0.compress( R, R )
if x == 0:
return 0, 0
## get mean and stdv of log-transformed random sample
alpha = N0.average( N0.log( R ) )
n = len( R )
beta = N0.sqrt(N0.sum(N0.power(N0.log( R ) - alpha, 2)) / (n - 1.))
return logArea( x, alpha, beta ), logMedian( alpha )
def __distances(self, point, xyz=None):
"""
point - 3 x 1 array of float; point of origin
xyz - 3 x n array of float; coordinates, if None -- take model atoms
-> distances of all atoms to given point
"""
if xyz is None:
xyz = self.model.getXyz()
return N0.sqrt(N0.sum(N0.power(xyz - point, 2), 1))
def __distances( self, point, xyz=None ):
"""
point - 3 x 1 array of float; point of origin
xyz - 3 x n array of float; coordinates, if None -- take model atoms
-> distances of all atoms to given point
"""
if xyz is None:
xyz = self.model.getXyz()
return N0.sqrt( N0.sum( N0.power( xyz - point, 2), 1 ) )
def logSigma( alpha, beta ):
"""
@param alpha: mean of log-transformed distribution
@type alpha: float
@param beta: standarddev of log-transformed distribution
@type beta: float
@return: 'standard deviation' of the original lognormal distribution
@rtype: float
"""
return logMean( alpha, beta ) * N0.sqrt( N0.exp(beta**2) - 1.)
def dihedral(self, coor1, coor2, coor3, coor4):
"""
Calculates the torsion angle of a set of four atom coordinates.
The dihedral angle returned is the angle between the projection
of i1-i2 and the projection of i4-i3 onto a plane normal to i2-i3.
@param coor1: coordinates
@type coor1: [float]
@param coor2: coordinates
@type coor2: [float]
@param coor3: coordinates
@type coor3: [float]
@param coor4: coordinates
@type coor4: [float]
"""
vec21 = coor2 - coor1
vec32 = coor3 - coor2
L = N0.cross(vec21, vec32)
L_norm = N0.sqrt(sum(L**2))
vec43 = coor4 - coor3
vec23 = coor2 - coor3
R = N0.cross(vec43, vec23)
R_norm = N0.sqrt(sum(R**2))
S = N0.cross(L, R)
angle = sum(L * R) / (L_norm * R_norm)
## sometimes the value turns out to be ever so little greater than
## one, to prevent N0.arccos errors for this, set angle = 1.0
if angle > 1.0: angle = 1.0
if angle < -1.0: angle = -1.0
angle = N0.arccos(angle) * 180 / N0.pi
if sum(S * vec32) < 0.0:
angle = -angle
return angle
def logArea(x, alpha, beta):
"""
Area of the smallest interval of a lognormal distribution that still
includes x.
@param x: border value
@type x: float
@param alpha: mean of log-transformed distribution
@type alpha: float
@param beta: standarddev of log-transformed distribution
@type beta: float
@return: probability that x is NOT drawn from the given distribution
@rtype: float
"""
r_max = N0.exp(alpha - beta**2)
if x < r_max: x = r_max**2 / x
upper = (N0.log(x) - alpha) / beta
return 0.5 * (erf(upper / N0.sqrt(2)) - erf(-(upper + 2*beta) / N0.sqrt(2)))
def __max_distance( self, model ):
"""
largest center to any other atom distance
@param model: model with centered coordinates
@type model: PDBModel
@return: largest distance
@rtype: float
"""
center = model.centerOfMass()
dist = N0.sqrt( N0.sum( ( model.getXyz()-center )**2 , 1 ) )
return max( dist )
def __random_translation( self ):
"""
Random translation on a sphere around 0,0,0 with fixed radius
The radius is the sum of the (max) radius of receptor and ligand
@return: translation array 3 x 1 of float
@rtype: array
"""
radius = (self.d_max_rec + self.d_max_lig) / 2.0
xyz = R.random_sample( 3 ) - 0.5
scale = radius*1.0 / N0.sqrt( N0.sum( xyz**2 ) )
return scale * xyz
def __windowSize( self, n_per_node, n_nodes, n_frames ):
"""
@param n_per_node: how many chunks should be generated per node
@type n_per_node: int
@param n_nodes: number of slave nodes
@type n_nodes: int
@param n_frames: length of trajectory
@type n_frames: int
@return: calculate number of frames per chunk
@rtype: int
"""
r = int(round( n_frames * 1.0 / N0.sqrt(n_per_node * n_nodes) ))
if r > 25:
return r
return 25
def __windowSize(self, n_per_node, n_nodes, n_frames):
"""
@param n_per_node: how many chunks should be generated per node
@type n_per_node: int
@param n_nodes: number of slave nodes
@type n_nodes: int
@param n_frames: length of trajectory
@type n_frames: int
@return: calculate number of frames per chunk
@rtype: int
"""
r = int(round(n_frames * 1.0 / N0.sqrt(n_per_node * n_nodes)))
if r > 25:
return r
return 25
def random_translations( self, n=1, center=None ):
"""
n Random translations on a sphere around center with fixed radius.
The radius must be given as orbit to __init__.
n - int, number of random coordinates to generate
center - 3 array of float
-> array n x 3 of float
"""
if center is None:
center = self.center
xyz = ra.random( (n,3) ) - 0.5
scale = self.orbit*1.0 / N0.sqrt( N0.sum( xyz**2, 1 ) )
r = N0.array( [ scale[i]*xyz[i] for i in range(n) ] )
return r + center
def random_translations(self, n=1, center=None):
"""
n Random translations on a sphere around center with fixed radius.
The radius must be given as orbit to __init__.
n - int, number of random coordinates to generate
center - 3 array of float
-> array n x 3 of float
"""
if center is None:
center = self.center
xyz = ra.random((n, 3)) - 0.5
scale = self.orbit * 1.0 / N0.sqrt(N0.sum(xyz**2, 1))
r = N0.array([scale[i] * xyz[i] for i in range(n)])
return r + center
def getFluct_global( self, mask=None ):
"""
Get RMS of each atom from it's average position in trajectory.
The frames should be superimposed (fit() ) to a reference.
@param mask: N x 1 list/Numpy array of 0|1, (N=atoms),
atoms to be considered.
@type mask: [1|0]
@return: Numpy array ( N_unmasked x 1 ) of float.
@rtype: array
"""
frames = self.frames
if mask is not None:
frames = N0.compress( mask, frames, 1 )
## mean position of each atom in all frames
avg = N0.average( frames )
return N0.average(N0.sqrt(N0.sum(N0.power(frames - avg, 2), 2) ))
def xyzOfNearestCovalentNeighbour(i, model):
"""
Closest atom in the same residue as atom with index i
@param model: PDBModel
@type model: PDBModel
@param i: atom index
@type i: int
@return: coordinates of the nearest atom
@rtype: [float, float, float]
"""
resModel = model.filter(residue_number=model.atoms['residue_number'][i])
dist = N0.sqrt(N0.sum((resModel.xyz - model.xyz[i])**2, 1))
## set distance to self to something high
dist[N0.argmin(dist)] = 100.
pos_shortest = N0.nonzero(dist == min(dist))[0]
return resModel.xyz[pos_shortest]
def xyzOfNearestCovalentNeighbour( i, model ):
"""
Closest atom in the same residue as atom with index i
@param model: PDBModel
@type model: PDBModel
@param i: atom index
@type i: int
@return: coordinates of the nearest atom
@rtype: [float, float, float]
"""
resModel = model.filter( residue_number=model.atoms['residue_number'][i] )
dist = N0.sqrt( N0.sum( (resModel.xyz - model.xyz[i])**2 , 1) )
## set distance to self to something high
dist[ N0.argmin(dist) ] = 100.
pos_shortest = N0.nonzero( dist == min(dist) )[0]
return resModel.xyz[ pos_shortest ]
def rowDistances(x, y):
"""
Calculate the distances between the items of two arrays (of same shape)
after least-squares superpositioning.
@param x: first set of coordinates
@type x: array('f')
@param y: second set of coordinates
@type y: array('f')
@return: array( len(x), 'f' ), distance between x[i] and y[i] for all i
@rtype: array
"""
## find transformation for best match
r, t = findTransformation(x, y)
## transform coordinates
z = N0.dot(y, N0.transpose(r)) + t
## calculate row distances
return N0.sqrt(N0.sum(N0.power(x - z, 2), 1))
def rmsd_res(self, coord1, coord2):
"""
Calculate the rsmd on residue level for c-alpha between a
model and its reference.
@param coord1: first set of coordinates
@type coord1: array
@param coord2: second set of coordinates
@type coord2: array
@return: rmsd_res: rmsd per c-alpha
@rtype: [float]
"""
rmsd_res = []
for i in range(len(coord1)):
rmsd = N0.sqrt( (N0.power(coord1[i][0]-coord2[i][0],2) + \
N0.power(coord1[i][1]-coord2[i][1],2 )+ \
N0.power(coord1[i][2]-coord2[i][2],2 )))
rmsd_res.append(rmsd)
return rmsd_res
def rmsd_res(self, coord1, coord2):
"""
Calculate the rsmd on residue level for c-alpha between a
model and its reference.
@param coord1: first set of coordinates
@type coord1: array
@param coord2: second set of coordinates
@type coord2: array
@return: rmsd_res: rmsd per c-alpha
@rtype: [float]
"""
rmsd_res = []
for i in range( len(coord1) ):
rmsd = N0.sqrt( (N0.power(coord1[i][0]-coord2[i][0],2) + \
N0.power(coord1[i][1]-coord2[i][1],2 )+ \
N0.power(coord1[i][2]-coord2[i][2],2 )))
rmsd_res.append(rmsd)
return rmsd_res
def logConfidence(x, R, clip=1e-32):
"""
Estimate the probability of x NOT beeing a random observation from a
lognormal distribution that is described by a set of random values.
The exact solution to this problem is in L{Biskit.Statistics.lognormal}.
@param x: observed value
@type x: float
@param R: sample of random values; 0 -> don't clip (default: 1e-32)
@type R: [float]
@param clip: clip zeros at this value
@type clip: float
@return: confidence that x is not random, mean of random distrib.
@rtype: (float, float)
"""
if clip and 0 in R:
R = N0.clip(R, clip, max(R))
## get mean and stdv of log-transformed random sample
mean = N0.average(N0.log(R))
n = len(R)
stdv = N0.sqrt(N0.sum(N0.power(N0.log(R) - mean, 2)) / (n - 1.))
## create dense lognormal distribution representing the random sample
stop = max(R) * 50.0
step = stop / 100000
start = step / 10.0
X = [(v, p_lognormal(v, mean, stdv)) for v in N0.arange(start, stop, step)]
## analyse distribution
d = Density(X)
return d.findConfidenceInterval(x * 1.0)[0], d.average()
def hbonds(model):
"""
Collect a list with all potential hydrogen bonds in model.
@param model: PDBModel for which
@type model: PDBModel
@return: a list of potential hydrogen bonds containing a lists
with donor index, acceptor index, distance and angle.
@rtype: [ int, int, float, float ]
"""
hbond_lst = []
donors = molU.hbonds['donors']
accept = molU.hbonds['acceptors']
## indices if potential donors
d_ind = []
for res, aList in donors.items():
for a in aList:
if a in molU.hydrogenSynonyms.keys():
aList.append(molU.hydrogenSynonyms[a])
d_ind += model.filterIndex(residue_name=res, name=aList)
## indices if potential acceptors
a_ind = []
for res, aList in accept.items():
a_ind += model.filterIndex(residue_name=res, name=aList)
## calculate pairwise distances and angles
for d in d_ind:
d_xyz = model.xyz[d]
d_nr = model.atoms['residue_number'][d]
d_cid = model.atoms['chain_id'][d]
d_segi = model.atoms['segment_id'][d]
for a in a_ind:
a_xyz = model.xyz[a]
a_nr = model.atoms['residue_number'][a]
a_cid = model.atoms['chain_id'][a]
a_segi = model.atoms['segment_id'][a]
dist = N0.sqrt(sum((d_xyz - a_xyz)**2))
## don't calculate angles within the same residue and
## for distances definately are not are h-bonds
if dist < 3.0 and not\
( d_nr == a_nr and d_cid == a_cid and d_segi == a_segi ):
## calculate angle for potenital hbond
d_xyz_cov = xyzOfNearestCovalentNeighbour(d, model)
a_xyz_cov = xyzOfNearestCovalentNeighbour(a, model)
d_vec = d_xyz_cov - d_xyz
a_vec = a_xyz - a_xyz_cov
d_len = N0.sqrt(sum((d_vec)**2))
a_len = N0.sqrt(sum((a_vec)**2))
da_dot = N0.dot(d_vec, a_vec)
angle = 180 - N0.arccos(da_dot / (d_len * a_len)) * 180 / N0.pi
if hbondCheck(angle, dist):
hbond_lst += [[d, a, dist, angle]]
return hbond_lst
def getFluct_local( self, mask=None, border_res=1,
left_atoms=['C'], right_atoms=['N'], verbose=1 ):
"""
Get mean displacement of each atom from it's average position after
fitting of each residue to the reference backbone coordinates of itself
and selected atoms of neighboring residues to the right and left.
@param mask: N_atoms x 1 array of 0||1, atoms for which fluctuation
should be calculated
@type mask: array
@param border_res: number of neighboring residues to use for fitting
@type border_res: int
@param left_atoms: atoms (names) to use from these neighbore residues
@type left_atoms: [str]
@param right_atoms: atoms (names) to use from these neighbore residues
@type right_atoms: [str]
@return: Numpy array ( N_unmasked x 1 ) of float
@rtype: array
"""
if mask is None:
mask = N0.ones( len( self.frames[0] ), N0.Int32 )
if verbose: T.errWrite( "rmsd fitting per residue..." )
residues = N0.nonzero( self.ref.atom2resMask( mask ) )
## backbone atoms used for fit
fit_atoms_right = N0.nonzero( self.ref.mask( right_atoms ) )
fit_atoms_left = N0.nonzero( self.ref.mask( left_atoms ) )
## chain index of each residue
rchainMap = N0.take( self.ref.chainMap(), self.ref.resIndex() )
result = []
for res in residues:
i_res, i_border = self.__resWindow(res, border_res, rchainMap,
fit_atoms_left, fit_atoms_right)
try:
if not len( i_res ): raise PDBError, 'empty residue'
t_res = self.takeAtoms( i_res + i_border )
i_center = range( len( i_res ) )
mask_BB = t_res.ref.maskBB() * t_res.ref.maskHeavy()
## fit with border atoms ..
t_res.fit( ref=t_res.ref, mask=mask_BB, verbose=0 )
## .. but calculate only with center residue atoms
frames = N0.take( t_res.frames, i_center, 1 )
avg = N0.average( frames )
rmsd = N0.average(N0.sqrt(N0.sum(N0.power(frames - avg, 2), 2) ))
result.extend( rmsd )
if verbose: T.errWrite('#')
except ZeroDivisionError:
result.extend( N0.zeros( len(i_res), N0.Float32 ) )
T.errWrite('?' + str( res ))
if verbose: T.errWriteln( "done" )
return result
def hbonds( model ):
"""
Collect a list with all potential hydrogen bonds in model.
@param model: PDBModel for which
@type model: PDBModel
@return: a list of potential hydrogen bonds containing a lists
with donor index, acceptor index, distance and angle.
@rtype: [ int, int, float, float ]
"""
hbond_lst = []
donors = molU.hbonds['donors']
accept = molU.hbonds['acceptors']
## indices if potential donors
d_ind = []
for res , aList in donors.items():
for a in aList:
if a in molU.hydrogenSynonyms.keys():
aList.append( molU.hydrogenSynonyms[a] )
d_ind += model.filterIndex( residue_name=res, name=aList )
## indices if potential acceptors
a_ind = []
for res , aList in accept.items():
a_ind += model.filterIndex( residue_name=res, name=aList )
## calculate pairwise distances and angles
for d in d_ind:
d_xyz = model.xyz[d]
d_nr = model.atoms['residue_number'][d]
d_cid = model.atoms['chain_id'][d]
d_segi = model.atoms['segment_id'][d]
for a in a_ind:
a_xyz = model.xyz[a]
a_nr = model.atoms['residue_number'][a]
a_cid = model.atoms['chain_id'][a]
a_segi = model.atoms['segment_id'][a]
dist = N0.sqrt( sum( (d_xyz - a_xyz)**2 ) )
## don't calculate angles within the same residue and
## for distances definately are not are h-bonds
if dist < 3.0 and not\
( d_nr == a_nr and d_cid == a_cid and d_segi == a_segi ):
## calculate angle for potenital hbond
d_xyz_cov = xyzOfNearestCovalentNeighbour( d, model )
a_xyz_cov = xyzOfNearestCovalentNeighbour( a, model )
d_vec = d_xyz_cov - d_xyz
a_vec = a_xyz - a_xyz_cov
d_len = N0.sqrt( sum( (d_vec)**2 ) )
a_len = N0.sqrt( sum( (a_vec)**2 ) )
da_dot = N0.dot( d_vec, a_vec)
angle = 180 - N0.arccos( da_dot / (d_len * a_len) )*180/N0.pi
if hbondCheck( angle, dist ):
hbond_lst += [[ d, a, dist, angle ]]
return hbond_lst
def fit( self, mask=None, ref=None, n_it=1,
prof='rms', verbose=1, fit=1, **profInfos ):
"""
Superimpose all coordinate frames on reference coordinates. Put rms
values in a profile. If n_it > 1, the fraction of atoms considered
for the fit is put into a profile called |prof|_considered
(i.e. by default 'rms_considered').
@param mask: atom mask, atoms to consider default: [all]
@type mask: [1|0]
@param ref: use as reference, default: None, average Structure
@type ref: PDBModel
@param n_it: number of fit iterations, kicking out outliers on the way
1 -> classic single fit, 0 -> until convergence
(default: 1)
@type n_it: int
@param prof: save rms per frame in profile of this name, ['rms']
@type prof: str
@param verbose: print progress info to STDERR (default: 1)
@type verbose: 1|0
@param fit: transform frames after match, otherwise just calc rms
(default: 1)
@type fit: 1|0
@param profInfos: additional key=value pairs for rms profile info []
@type profInfos: key=value
"""
if ref is None:
refxyz = N0.average( self.frames, 0 )
else:
refxyz = ref.getXyz()
if mask is None:
mask = N0.ones( len( refxyz ), N0.Int32 )
refxyz = N0.compress( mask, refxyz, 0 )
if verbose: T.errWrite( "rmsd fitting..." )
rms = [] ## rms value of each frame
non_outliers = [] ## fraction of atoms considered for rms and fit
iterations = [] ## number of iterations performed on each frame
for i in range(0, len( self.frames) ):
xyz = self.frames[i]
if n_it != 1:
(r, t), rmsdList = rmsFit.match( refxyz,
N0.compress( mask, xyz, 0), n_it)
iterations.append( len( rmsdList ) )
non_outliers.append( rmsdList[-1][0] )
xyz_transformed = N0.dot( xyz, N0.transpose(r)) + t
rms += [ rmsdList[-1][1] ]
else:
r, t = rmsFit.findTransformation( refxyz,
N0.compress( mask, xyz, 0))
xyz_transformed = N0.dot( xyz, N0.transpose(r)) + t
d = N0.sqrt(N0.sum(N0.power( N0.compress(mask, xyz_transformed,0)\
- refxyz, 2), 1))
rms += [ N0.sqrt( N0.average(d**2) ) ]
if fit:
self.frames[i] = xyz_transformed.astype(N0.Float32)
if verbose and i%100 == 0:
T.errWrite( '#' )
self.setProfile( prof, rms, n_iterations=n_it, **profInfos )
if non_outliers:
self.setProfile( prof+'_considered', non_outliers,
n_iterations=n_it,
comment='fraction of atoms considered for iterative fit' )
if verbose: T.errWrite( 'done\n' )
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 distance_matrix(x, y):
return N0.sqrt(squared_distance_matrix(x, y))
doper.addSurfaceRacer( probe=1.4 )
surf_lig = lig.profile2mask( 'MS', 0.0001, 101 )
## kick out non-surface
rec = rec.compress( surf_rec )
lig = lig.compress( surf_lig )
com = Complex( rec, lig )
## get interface patch
cont = com.atomContacts( cutoff=6.0 )
rec_if = N0.sum( cont, 1 )
lig_if = N0.sum( cont, 0 )
## center distance
c2c = N0.sqrt( N0.sum( (rec.center() - lig.center())**2, 0 ) )
print "Center2Center: ", c2c
## get patches and put them into Pymoler for display
print "Patching"
excl = N0.compress( N0.ones( len( rec_if ) ), rec_if )
pm = test( rec, c2c, nAtoms=len(N0.nonzero(rec_if)), exclude=rec_if )
pm.addPdb( rec.compress( rec_if ), 'rec_interface' )
pm.addPdb( lig.compress( lig_if ), 'lig_interface' )
pm.addPdb( com.model(), 'complex')
## show everything
## the patches are as movie in 'model'
pm.show()
doper.addSurfaceRacer(probe=1.4)
surf_lig = lig.profile2mask('MS', 0.0001, 101)
## kick out non-surface
rec = rec.compress(surf_rec)
lig = lig.compress(surf_lig)
com = Complex(rec, lig)
## get interface patch
cont = com.atomContacts(cutoff=6.0)
rec_if = N0.sum(cont, 1)
lig_if = N0.sum(cont, 0)
## center distance
c2c = N0.sqrt(N0.sum((rec.center() - lig.center())**2, 0))
print "Center2Center: ", c2c
## get patches and put them into Pymoler for display
print "Patching"
excl = N0.compress(N0.ones(len(rec_if)), rec_if)
pm = test(rec, c2c, nAtoms=len(N0.nonzero(rec_if)), exclude=rec_if)
pm.addPdb(rec.compress(rec_if), 'rec_interface')
pm.addPdb(lig.compress(lig_if), 'lig_interface')
pm.addPdb(com.model(), 'complex')
## show everything
## the patches are as movie in 'model'
pm.show()
