def surface_zone_piece(p, points, distance):
varray, tarray = p.geometry
from _closepoints import find_close_points, BOXES_METHOD
i1, i2 = find_close_points(BOXES_METHOD, varray, points, distance)
from numpy import zeros, intc, put
mask = zeros((len(varray),), intc)
put(mask, i1, 1)
p.setTriangleMaskFromVertexMask(mask)
p.showing_zone = True
def surface_zone_piece(p, points, distance):
varray, tarray = p.geometry
from _closepoints import find_close_points, BOXES_METHOD
i1, i2 = find_close_points(BOXES_METHOD, varray, points, distance)
from numpy import zeros, intc, put
mask = zeros((len(varray), ), intc)
put(mask, i1, 1)
p.setTriangleMaskFromVertexMask(mask)
p.showing_zone = True
def surface_distance(v1, v2, t2, d, optimize = True):
from _surface import surface_distance as surf_dist
if optimize:
from numpy import empty, float32
dist = empty((len(v1),), float32)
dist[:] = 2*d
# Use only vertices within 2*d contact range.
import _closepoints as cp
i1, i2 = cp.find_close_points(cp.BOXES_METHOD, v1, v2, 2*d)
if len(i1) > 0 and len(i2) > 0:
v1r = v1[i1]
s2 = set(i2)
t2r = [tri for tri in t2 if tri[0] in s2 or tri[1] in s2 or tri[2] in s2]
dr = surf_dist(v1r, v2, t2r)[:,0]
dist[i1] = dr
else:
dist = surf_dist(v1, v2, t2)[:,0] # n by 5 array (d,x,y,z,side)
return dist
def surface_geometry(triangles, tolerance = 1e-5):
from numpy import array, reshape, single as floatc, intc
varray = reshape(triangles, (3*len(triangles),3)).astype(floatc)
uindex = {}
unique = []
from _closepoints import find_close_points, BOXES_METHOD
for v in range(len(varray)):
if not v in uindex:
i1, i2 = find_close_points(BOXES_METHOD, varray[v:v+1,:], varray,
tolerance)
for i in i2:
if not i in uindex:
uindex[i] = len(unique)
unique.append(varray[v])
uvarray = array(unique, floatc)
tlist = map(lambda t: (uindex[3*t],uindex[3*t+1],uindex[3*t+2]),
range(len(triangles)))
tarray = array(tlist, intc)
return uvarray, tarray
def search_for_ligands(metal):
"""
Search for ligands near by the metal center
excluding candidates through the next steps:
1-How many electrons on the outter shell
2-Metal-Ligand distance
3-Geometry angles
4-Exclude Hydrogens and other atoms
Parameters:
-----------
metal: chimera metal object
Output:
-------
chimera ligands object
"""
# Extracted directly from:
# Metal Geom Chimera
data = []
coordLim = 4.0
from numpy import array
atoms = array(metal.molecule.atoms)
from _multiscale import get_atom_coordinates as gac
from _closepoints import find_close_points, BOXES_METHOD
ignore, close = find_close_points(BOXES_METHOD, gac(array([metal])),
gac(atoms), coordLim)
candidates = list(set(atoms[close]))
mcrd = metal.coord()
candidates.sort(lambda a1, a2: cmp(a1.coord().sqdistance(mcrd),
a2.coord().sqdistance(mcrd)))
exclude = []
userIncluded = []
for candidate in candidates:
if candidate == metal:
continue
if candidate in exclude:
continue
if candidate not in userIncluded:
valence = (candidate.element.number - 2) % 8
if valence < 5 or candidate.element.number == 1:
continue
if candidate.coord().distance(mcrd) > coordLim:
break
if candidate not in metal.bondsMap:
from chimera import angle
from chimera.idatm import typeInfo
angleOK = True
try:
cnGeom = typeInfo[candidate.idatmType].geometry
except KeyError:
cnGeom = 0
else:
if len(candidate.primaryNeighbors()) == cnGeom:
# no lone pairs, no possibility of deprotonation
continue
angleCutoff = [0.0, 72.98, 120.0, 80.0, 72.98][cnGeom]
for cnb in candidate.neighbors:
if cnb == metal:
continue
if angle(cnb.coord(), candidate.coord(),
metal.coord()) < angleCutoff:
angleOK = False
break
if not angleOK:
continue
data.append(candidate)
return data
def computeVolume(self,
atoms,
startFrame=None,
endFrame=None,
bound=None,
volumeName=None,
step=1,
spacing=0.5):
# load and process frames
if startFrame is None:
startFrame = self.startFrame
if endFrame is None:
endFrame = self.endFrame
if bound is not None:
from _closepoints import find_close_points, BOXES_METHOD
from Matrix import xform_matrix
if self.holdingSteady:
steadyAtoms, steadySel, steadyCS, inverse = \
self.holdingSteady
# all the above used later...
inverse = xform_matrix(inverse)
gridData = {}
from math import floor
from numpy import array, float32
from _contour import affine_transform_vertices
for fn in range(startFrame, endFrame + 1, step):
cs = self.findCoordSet(fn)
if not cs:
self.status("Loading frame %d" % fn)
self._LoadFrame(fn, makeCurrent=False)
cs = self.findCoordSet(fn)
self.status("Processing frame %d" % fn)
pts = array([a.coord(cs) for a in atoms], float32)
if self.holdingSteady:
if bound is not None:
steadyPoints = array([a.coord(cs) for a in steadyAtoms],
float32)
closeIndices = find_close_points(
BOXES_METHOD,
steadyPoints,
#otherPoints, bound)[1]
pts,
bound)[1]
pts = pts[closeIndices]
try:
xf, inv = self.transforms[fn]
except KeyError:
xf, inv = self.steadyXform(cs=cs)
self.transforms[fn] = (xf, inv)
xf = xform_matrix(xf)
affine_transform_vertices(pts, xf)
affine_transform_vertices(pts, inverse)
# add a half-voxel since volume positions are
# considered to be at the center of their voxel
from numpy import floor, zeros
pts = floor(pts / spacing + 0.5).astype(int)
for pt in pts:
center = tuple(pt)
gridData[center] = gridData.get(center, 0) + 1
# generate volume
self.status("Generating volume")
axisData = zip(*tuple(gridData.keys()))
minXyz = [min(ad) for ad in axisData]
maxXyz = [max(ad) for ad in axisData]
# allow for zero-padding on both ends
dims = [maxXyz[axis] - minXyz[axis] + 3 for axis in range(3)]
from numpy import zeros, transpose
volume = zeros(dims, int)
for index, val in gridData.items():
adjIndex = tuple([index[i] - minXyz[i] + 1 for i in range(3)])
volume[adjIndex] = val
from VolumeData import Array_Grid_Data
gd = Array_Grid_Data(
volume.transpose(),
# the "cushion of zeros" means d-1...
[(d - 1) * spacing for d in minXyz],
[spacing] * 3)
if volumeName is None:
volumeName = self.ensemble.name
gd.name = volumeName
# show volume
self.status("Showing volume")
import VolumeViewer
dataRegion = VolumeViewer.volume_from_grid_data(gd)
vd = VolumeViewer.volumedialog.volume_dialog(create=True)
vd.message("Volume can be saved from File menu")
self.status("Volume shown")
def computeVolume(self, atoms, startFrame=None, endFrame=None,
bound=None, volumeName=None, step=1, spacing=0.5):
# load and process frames
if startFrame is None:
startFrame = self.startFrame
if endFrame is None:
endFrame = self.endFrame
if bound is not None:
from _closepoints import find_close_points, BOXES_METHOD
from Matrix import xform_matrix
if self.holdingSteady:
steadyAtoms, steadySel, steadyCS, inverse = \
self.holdingSteady
# all the above used later...
inverse = xform_matrix(inverse)
gridData = {}
from math import floor
from numpy import array, float32
from _contour import affine_transform_vertices
for fn in range(startFrame, endFrame+1, step):
cs = self.findCoordSet(fn)
if not cs:
self.status("Loading frame %d" % fn)
self._LoadFrame(fn, makeCurrent=False)
cs = self.findCoordSet(fn)
self.status("Processing frame %d" % fn)
pts = array([a.coord(cs) for a in atoms], float32)
if self.holdingSteady:
if bound is not None:
steadyPoints = array([a.coord(cs)
for a in steadyAtoms], float32)
closeIndices = find_close_points(
BOXES_METHOD, steadyPoints,
#otherPoints, bound)[1]
pts, bound)[1]
pts = pts[closeIndices]
try:
xf, inv = self.transforms[fn]
except KeyError:
xf, inv = self.steadyXform(cs=cs)
self.transforms[fn] = (xf, inv)
xf = xform_matrix(xf)
affine_transform_vertices(pts, xf)
affine_transform_vertices(pts, inverse)
# add a half-voxel since volume positions are
# considered to be at the center of their voxel
from numpy import floor, zeros
pts = floor(pts/spacing + 0.5).astype(int)
for pt in pts:
center = tuple(pt)
gridData[center] = gridData.get(center, 0) + 1
# generate volume
self.status("Generating volume")
axisData = zip(*tuple(gridData.keys()))
minXyz = [min(ad) for ad in axisData]
maxXyz = [max(ad) for ad in axisData]
# allow for zero-padding on both ends
dims = [maxXyz[axis] - minXyz[axis] + 3 for axis in range(3)]
from numpy import zeros, transpose
volume = zeros(dims, int)
for index, val in gridData.items():
adjIndex = tuple([index[i] - minXyz[i] + 1
for i in range(3)])
volume[adjIndex] = val
from VolumeData import Array_Grid_Data
gd = Array_Grid_Data(volume.transpose(),
# the "cushion of zeros" means d-1...
[(d-1) * spacing for d in minXyz],
[spacing] * 3)
if volumeName is None:
volumeName = self.ensemble.name
gd.name = volumeName
# show volume
self.status("Showing volume")
import VolumeViewer
dataRegion = VolumeViewer.volume_from_grid_data(gd)
vd = VolumeViewer.volumedialog.volume_dialog(create=True)
vd.message("Volume can be saved from File menu")
self.status("Volume shown")
display.add(r) elif getattr(principalAtom(r), "name", None) == "C4'": # decide whether to display later nukes.append(r) if nukes: nucleic[m] = nukes if allAtoms or not ligand: continue from numpy import array, float32 ligPoints = array([a.coord() for r in ligand for a in r.atoms], float32) atoms = m.atoms molPoints = array([a.coord() for a in atoms], float32) from _closepoints import find_close_points, BOXES_METHOD closeIndices = find_close_points(BOXES_METHOD, ligPoints, molPoints, 3.5)[1] interacting.update(array(atoms).take(closeIndices)) if publication: for srf in chimera.openModels.list( modelTypes=[chimera.MSMSModel]): if (srf.colorMode != chimera.MSMSModel.Custom and srf.density < 10.0 and srf.molecule is not None): srf.density = 10.0 return if surface: if ribMolecules: mols = ribMolecules else: mols = chimera.openModels.list( modelTypes=[chimera.Molecule])
def detectClash(testAtoms, test="others", clashThreshold=clashDef,
hbondAllowance=hbondDef, assumedMaxVdw=2.1,
bondSeparation=bondSepDef, intraRes=False, interSubmodel=False):
"""Detect steric clashes
'testAtoms' should be a list of atoms.
If 'test' is 'others' then non-bonded clashes between atoms in
'testAtoms' and non-'testAtoms' atoms will be found. If 'test'
is 'model' then the same clashes as 'others' will be found, but
inter-model clashes will be eliminated. If 'test' is 'self'
then non-bonded clashes within 'testAtoms' atoms will be found.
Otherwise 'test' should be a list of atoms to test against.
The "clash value" is the sum of the VDW radii minus the distance,
keeping only the maximal clash (which must exceed 'clashThreshold').
'hbondAllowance' is how much the clash value is reduced if one
atom is a donor and the other an acceptor.
Atom pairs are eliminated from consideration if they are less than
or equal to 'bondSeparation' bonds apart.
Intra-residue clashes are ignored unless intraRes is True.
Inter-submodel clashes are ignored unless interSubmodel is True.
Returns a dictionary keyed on atoms, with values that are
dictionaries keyed on clashing atom with value being the clash value.
"""
# use the fast _closepoints module to cut down candidate atoms if we
# can (since _closepoints doesn't know about "non-bonded" it isn't as
# useful as it might otherwise be)
if test in ("others", "model"):
mList = chimera.openModels.list(modelTypes=[chimera.Molecule])
testSet = set(testAtoms)
from numpy import array
testList = array(testAtoms)
otherAtoms = [a for m in mList for a in m.atoms
if a not in testSet]
otherAtoms = array(otherAtoms)
if len(otherAtoms) == 0:
from chimera import UserError
raise UserError("All atoms are in test set: no others"
" available to test against")
from _multiscale import get_atom_coordinates
tPoints = get_atom_coordinates(testList, transformed = True)
oPoints = get_atom_coordinates(otherAtoms, transformed = True)
cutoff = 2.0 * assumedMaxVdw - clashThreshold
from _closepoints import find_close_points, BOXES_METHOD
tClose, oClose = find_close_points(BOXES_METHOD,
tPoints, oPoints, cutoff)
testAtoms = testList.take(tClose)
searchAtoms = otherAtoms.take(oClose)
elif not isinstance(test, basestring):
searchAtoms = test
else:
searchAtoms = testAtoms
from chimera.misc import atomSearchTree
tree = atomSearchTree(list(searchAtoms))
clashes = {}
for a in testAtoms:
cutoff = a.radius + assumedMaxVdw - clashThreshold
nearby = tree.searchTree(a.xformCoord().data(), cutoff)
if not nearby:
continue
needExpansion = a.allLocations()
exclusions = set(needExpansion)
for i in range(bondSeparation):
nextNeed = []
for expand in needExpansion:
for n in expand.neighbors:
if n in exclusions:
continue
exclusions.add(n)
nextNeed.append(n)
needExpansion = nextNeed
for nb in nearby:
if nb in exclusions:
continue
if not intraRes and a.residue == nb.residue:
continue
if a in clashes and nb in clashes[a]:
continue
if not interSubmodel \
and a.molecule.id == nb.molecule.id \
and a.molecule.subid != nb.molecule.subid:
continue
if test == "model" and a.molecule != nb.molecule:
continue
clash = a.radius + nb.radius - a.xformCoord().distance(
nb.xformCoord())
if hbondAllowance:
if (_donor(a) and _acceptor(nb)) or (
_donor(nb) and _acceptor(a)):
clash -= hbondAllowance
if clash < clashThreshold:
continue
clashes.setdefault(a, {})[nb] = clash
clashes.setdefault(nb, {})[a] = clash
return clashes
def detectClash(testAtoms,
test="others",
clashThreshold=clashDef,
hbondAllowance=hbondDef,
assumedMaxVdw=2.1,
bondSeparation=bondSepDef,
intraRes=False,
interSubmodel=False):
"""Detect steric clashes
'testAtoms' should be a list of atoms.
If 'test' is 'others' then non-bonded clashes between atoms in
'testAtoms' and non-'testAtoms' atoms will be found. If 'test'
is 'model' then the same clashes as 'others' will be found, but
inter-model clashes will be eliminated. If 'test' is 'self'
then non-bonded clashes within 'testAtoms' atoms will be found.
Otherwise 'test' should be a list of atoms to test against.
The "clash value" is the sum of the VDW radii minus the distance,
keeping only the maximal clash (which must exceed 'clashThreshold').
'hbondAllowance' is how much the clash value is reduced if one
atom is a donor and the other an acceptor.
Atom pairs are eliminated from consideration if they are less than
or equal to 'bondSeparation' bonds apart.
Intra-residue clashes are ignored unless intraRes is True.
Inter-submodel clashes are ignored unless interSubmodel is True.
Returns a dictionary keyed on atoms, with values that are
dictionaries keyed on clashing atom with value being the clash value.
"""
# use the fast _closepoints module to cut down candidate atoms if we
# can (since _closepoints doesn't know about "non-bonded" it isn't as
# useful as it might otherwise be)
if test in ("others", "model"):
mList = chimera.openModels.list(modelTypes=[chimera.Molecule])
testSet = set(testAtoms)
from numpy import array
testList = array(testAtoms)
otherAtoms = [a for m in mList for a in m.atoms if a not in testSet]
otherAtoms = array(otherAtoms)
if len(otherAtoms) == 0:
from chimera import UserError
raise UserError("All atoms are in test set: no others"
" available to test against")
from _multiscale import get_atom_coordinates
tPoints = get_atom_coordinates(testList, transformed=True)
oPoints = get_atom_coordinates(otherAtoms, transformed=True)
cutoff = 2.0 * assumedMaxVdw - clashThreshold
from _closepoints import find_close_points, BOXES_METHOD
tClose, oClose = find_close_points(BOXES_METHOD, tPoints, oPoints,
cutoff)
testAtoms = testList.take(tClose)
searchAtoms = otherAtoms.take(oClose)
elif not isinstance(test, basestring):
searchAtoms = test
else:
searchAtoms = testAtoms
from chimera.misc import atomSearchTree
tree = atomSearchTree(list(searchAtoms))
clashes = {}
for a in testAtoms:
cutoff = a.radius + assumedMaxVdw - clashThreshold
nearby = tree.searchTree(a.xformCoord().data(), cutoff)
if not nearby:
continue
needExpansion = a.allLocations()
exclusions = set(needExpansion)
for i in range(bondSeparation):
nextNeed = []
for expand in needExpansion:
for n in expand.neighbors:
if n in exclusions:
continue
exclusions.add(n)
nextNeed.append(n)
needExpansion = nextNeed
for nb in nearby:
if nb in exclusions:
continue
if not intraRes and a.residue == nb.residue:
continue
if a in clashes and nb in clashes[a]:
continue
if not interSubmodel \
and a.molecule.id == nb.molecule.id \
and a.molecule.subid != nb.molecule.subid:
continue
if test == "model" and a.molecule != nb.molecule:
continue
clash = a.radius + nb.radius - a.xformCoord().distance(
nb.xformCoord())
if hbondAllowance:
if (_donor(a) and _acceptor(nb)) or (_donor(nb)
and _acceptor(a)):
clash -= hbondAllowance
if clash < clashThreshold:
continue
clashes.setdefault(a, {})[nb] = clash
clashes.setdefault(nb, {})[a] = clash
return clashes