def setUpClass(self):
self.queue = get_queue()
self.shape = (5, 4, 3)
self.size = 5 * 4 * 3
self.values = {
"interaction_cutoff": 1,
"nrestraints": 3,
"shape_x": self.shape[2],
"shape_y": self.shape[1],
"shape_z": self.shape[0],
"llength": 1,
"nreceptor_coor": 3,
"nligand_coor": 2,
}
self.p = Kernels(self.queue.context, self.values)
def setUpClass(cls):
cls.dv = DisVis()
cls.gdv = DisVis()
cls.gdv.queue = get_queue()
receptor = PDB.fromfile(join('data', 'O14250.pdb'))
ligand = PDB.fromfile(join('data', 'Q9UT97.pdb'))
restraints = parse_restraints(join('data', 'restraints.dat'), receptor,
ligand)
rselect, lselect = parse_interaction_selection(
join('data', 'selection.res'), receptor, ligand)
# Identity rotation and rotation around z-axis
rotations = np.asarray([[[1, 0, 0], [0, 1, 0], [0, 0, 1]],
[[0, -1, 0], [1, 0, 0], [0, 0, 1]]],
dtype=np.float64)
cls.dv.receptor = receptor
cls.gdv.receptor = receptor
cls.dv.ligand = ligand
cls.gdv.ligand = ligand
cls.dv.distance_restraints = restraints
cls.gdv.distance_restraints = restraints
cls.dv.rotations = rotations
cls.gdv.rotations = rotations
cls.dv.occupancy_analysis = True
cls.gdv.occupancy_analysis = True
cls.dv.receptor_interaction_selection = rselect
cls.dv.ligand_interaction_selection = lselect
cls.gdv.receptor_interaction_selection = rselect
cls.gdv.ligand_interaction_selection = lselect
cls.dv._initialize()
cls.dv._cpu_init()
cls.gdv._initialize()
cls.gdv._gpu_init()
def setUpClass(cls):
cls.dv = DisVis()
cls.gdv = DisVis()
cls.gdv.queue = get_queue()
receptor = PDB.fromfile(join('data', 'O14250.pdb'))
ligand = PDB.fromfile(join('data', 'Q9UT97.pdb'))
restraints = parse_restraints(join('data', 'restraints.dat'), receptor, ligand)
rselect, lselect = parse_interaction_selection(join('data',
'selection.res'), receptor, ligand)
# Identity rotation and rotation around z-axis
rotations = np.asarray([[[1, 0, 0], [0, 1, 0], [0, 0, 1]],
[[0, -1, 0], [1, 0, 0], [0, 0, 1]]], dtype=np.float64)
cls.dv.receptor = receptor
cls.gdv.receptor = receptor
cls.dv.ligand = ligand
cls.gdv.ligand = ligand
cls.dv.distance_restraints = restraints
cls.gdv.distance_restraints = restraints
cls.dv.rotations = rotations
cls.gdv.rotations = rotations
cls.dv.occupancy_analysis = True
cls.gdv.occupancy_analysis = True
cls.dv.receptor_interaction_selection = rselect
cls.dv.ligand_interaction_selection = lselect
cls.gdv.receptor_interaction_selection = rselect
cls.gdv.ligand_interaction_selection = lselect
cls.dv._initialize()
cls.dv._cpu_init()
cls.gdv._initialize()
cls.gdv._gpu_init()
def setUp(self):
self.queue = get_queue()
self.kernels = Kernels(self.queue.context)
def main():
args = parse_args()
mkdir_p(args.directory)
joiner = Joiner(args.directory)
logging.basicConfig(filename=joiner('disvis.log'),
level=logging.INFO,
format='%(asctime)s %(message)s')
time0 = time()
queue = None
if args.gpu:
from disvis.helpers import get_queue
queue = get_queue()
if queue is None:
raise ValueError("No GPU queue was found.")
write('Reading fixed model from: {:s}'.format(args.receptor.name))
receptor = PDB.fromfile(args.receptor)
write('Reading scanning model from: {:s}'.format(args.ligand.name))
ligand = PDB.fromfile(args.ligand)
write('Reading in rotations.')
q, weights, a = proportional_orientations(args.angle)
rotmat = quat_to_rotmat(q)
write('Requested rotational sampling density: {:.2f}'.format(args.angle))
write('Real rotational sampling density: {:.2f}'.format(a))
write('Number of rotations: {:d}'.format(rotmat.shape[0]))
write('Reading in restraints from file: {:s}'.format(args.restraints.name))
distance_restraints = parse_restraints(args.restraints, receptor, ligand)
write('Number of distance restraints: {:d}'.format(
len(distance_restraints)))
# If the interaction restraints cutoff is not specified, only calculate the
# interactions and occupancy grids for complexes consistent with at least 1
# restraints or more, with a limit of three.
if args.interaction_restraints_cutoff is None:
args.interaction_restraints_cutoff = max(
len(distance_restraints) - 3, 1)
pdb1_sel = pdb2_sel = None
if args.interaction_selection is not None:
write('Reading in interaction selection from file: {:s}'.format(
args.interaction_selection.name))
pdb1_sel, pdb2_sel = parse_interaction_selection(
args.interaction_selection, receptor, ligand)
write('Number of receptor residues: {:d}'.format(pdb1_sel.natoms))
write('Number of ligand residues: {:d}'.format(pdb2_sel.natoms))
write('Voxel spacing set to: {:.2f}'.format(args.voxelspacing))
write('Interaction radius set to: {:.2f}'.format(args.interaction_radius))
write('Minimum required interaction volume: {:.2f}'.format(
args.min_interaction))
write('Maximum allowed volume of clashes: {:.2f}'.format(args.max_clash))
if args.occupancy_analysis:
write('Performing occupancy analysis')
if queue is None:
# CPU-version
if args.nproc > 1:
try:
dv = mp_cpu_disvis(receptor, ligand, rotmat, weights,
distance_restraints, pdb1_sel, pdb2_sel,
args)
except Queue.Empty:
msg = (
'ERROR: Queue.Empty exception raised while processing job, '
'stopping execution ...')
write(msg)
exit(-1)
else:
dv = run_disvis(queue, receptor, ligand, rotmat, weights,
distance_restraints, pdb1_sel, pdb2_sel, args)
else:
# GPU-version
write('Using GPU accelerated search.')
dv = run_disvis(queue, receptor, ligand, rotmat, weights,
distance_restraints, pdb1_sel, pdb2_sel, args)
# write out accessible interaction space
fname = joiner('accessible_interaction_space.mrc')
write('Writing accessible interaction space to: {:s}'.format(fname))
dv.accessible_interaction_space.tofile(fname)
# write out accessible complexes
accessible_complexes = dv.accessible_complexes
norm = sum(accessible_complexes)
digits = len(str(int(norm))) + 1
cum_complexes = np.cumsum(np.asarray(accessible_complexes)[::-1])[::-1]
with open(joiner('accessible_complexes.out'),
'w') as f_accessible_complexes:
write('Writing number of accessible complexes to: {:s}'.format(
f_accessible_complexes.name))
header = '# consistent restraints | accessible complexes |' +\
'relative | cumulative accessible complexes | relative\n'
f_accessible_complexes.write(header)
for n, acc in enumerate(accessible_complexes):
f_accessible_complexes.write('{0:3d} {2:{1}d} {3:8.6f} {4:{1}d} {5:8.6f}\n'\
.format(n, digits, int(acc), acc/norm,
int(cum_complexes[n]), cum_complexes[n]/norm))
# writing out violations
violations = dv.violations
cum_violations = violations[::-1].cumsum(axis=0)[::-1]
with open(joiner('violations.out'), 'w') as f_viol:
write('Writing violations to file: {:s}'.format(f_viol.name))
num_violations = violations.sum(axis=1)
nrestraints = num_violations.shape[0]
header = ('# row represents the number of consistent restraints\n'
'# column represents how often that restraint is violated\n')
f_viol.write(header)
header = (' ' + '{:8d}' * nrestraints +
'\n').format(*range(1, nrestraints + 1))
f_viol.write(header)
for n, line in enumerate(cum_violations):
f_viol.write('{:<2d} '.format(n + 1))
for word in line:
if num_violations[n] > 0:
percentage_violated = word / cum_complexes[n + 1]
else:
percentage_violated = 0
f_viol.write('{:8.4f}'.format(percentage_violated))
f_viol.write('\n')
# Give user indication for false positives.
# Determine minimum number of false positives.
nrestraints = len(distance_restraints)
n = 1
while accessible_complexes[-n] == 0:
n += 1
if n > 1:
msg = ('Not all restraints are consistent. '
'Number of false-positive restraints present '
'is at least: {:d}'.format(n - 1))
write(msg)
# next give possible false-positives based on the percentage of violations
# and their associated Z-score
if n == 1:
n = None
else:
n = -n + 1
percentage_violated = cum_violations[:n] / np.asarray(
cum_complexes[1:n]).reshape(-1, 1)
average_restraint_violation = percentage_violated.mean(axis=0)
std_restraint_violation = percentage_violated.std(axis=0)
zscore_violations = (
(average_restraint_violation - average_restraint_violation.mean()) /
average_restraint_violation.std())
ind_false_positives = np.flatnonzero(zscore_violations >= 1.0)
nfalse_positives = ind_false_positives.size
if nfalse_positives > 0:
ind_false_positives += 1
write(('Possible false-positive restraints (z-score > 1.0):' +\
' {:d}'*nfalse_positives).format(*tuple(ind_false_positives)))
with open(joiner('z-score.out'), 'w') as f:
write('Writing z-score of each restraint to {:s}'.format(f.name))
for n in xrange(zscore_violations.shape[0]):
f.write('{:2d} {:6.3f} {:6.3f} {:6.3f}\n'.format(
n + 1, average_restraint_violation[n],
std_restraint_violation[n], zscore_violations[n]))
# Write all occupancy grids to MRC-files if requested
if args.occupancy_analysis:
for n, vol in dv.occupancy_grids.iteritems():
# Normalize the occupancy grid
if cum_complexes[n] > 0:
vol._array /= cum_complexes[n]
vol.tofile(joiner('occupancy_{:d}.mrc'.format(n)))
# Write out interaction analysis
# the interaction_matrix gives the number of interactions between each
# residue of the receptor and ligand for complexes consistent with exactly
# N restraints.
interaction_matrix = dv.interaction_matrix
if interaction_matrix is not None:
## Save interaction matrix
#f = joiner('interaction_matrix.npy')
#write('Writing interaction-matrix to: {:s}'.format(f))
#np.save(f, interaction_matrix)
# Save contacted receptor and ligand residue interaction for each analyzed number
# of consistent restraints
write('Writing contacted receptor residue interactions to files.')
# Take the cumsum in order to give the number of interactions for complexes
# with at least N restraints.
receptor_cum_interactions = interaction_matrix.sum(
axis=1)[::-1].cumsum(axis=0)[::-1]
ligand_cum_interactions = interaction_matrix.sum(axis=2)[::-1].cumsum(
axis=0)[::-1]
fname = joiner('receptor_interactions.txt')
with open(fname, 'w') as f:
# Write header
f.write('#resi')
for consistent_restraints in xrange(
args.interaction_restraints_cutoff, nrestraints + 1):
f.write(' {:>6d}'.format(consistent_restraints))
f.write('\n')
for n, resi in enumerate(pdb1_sel.data['resi']):
f.write('{:<5d}'.format(resi))
for consistent_restraints in xrange(
args.interaction_restraints_cutoff, nrestraints + 1):
index = consistent_restraints - args.interaction_restraints_cutoff
interactions = receptor_cum_interactions[index, n]
cum_complex = cum_complexes[consistent_restraints]
if cum_complex > 0:
relative_interactions = interactions / cum_complex
else:
relative_interactions = 0
f.write(' {:6.3f}'.format(relative_interactions))
f.write('\n')
fname = joiner('ligand_interactions.txt')
with open(fname, 'w') as f:
# Write header
f.write('#resi')
for consistent_restraints in xrange(
args.interaction_restraints_cutoff, nrestraints + 1):
f.write(' {:>6d}'.format(consistent_restraints))
f.write('\n')
for n, resi in enumerate(pdb2_sel.data['resi']):
f.write('{:<5d}'.format(resi))
for consistent_restraints in xrange(
args.interaction_restraints_cutoff, nrestraints + 1):
index = consistent_restraints - args.interaction_restraints_cutoff
interactions = ligand_cum_interactions[index, n]
cum_complex = cum_complexes[consistent_restraints]
if cum_complex > 0:
relative_interactions = interactions / cum_complex
else:
relative_interactions = 0
f.write(' {:6.3f}'.format(relative_interactions))
f.write('\n')
# time indication
seconds = int(round(time() - time0))
m, s = divmod(seconds, 60)
write('Total time passed: {:d}m {:2d}s'.format(m, s))
def setUp(self):
self.queue = get_queue()
self.kernels = Kernels(self.queue.context)
def main():
args = parse_args()
mkdir_p(args.directory)
joiner = Joiner(args.directory)
logging.basicConfig(filename=joiner('disvis.log'),
level=logging.INFO, format='%(asctime)s %(message)s')
time0 = time()
queue = None
if args.gpu:
from disvis.helpers import get_queue
queue = get_queue()
if queue is None:
raise ValueError("No GPU queue was found.")
write('Reading fixed model from: {:s}'.format(args.receptor.name))
receptor = PDB.fromfile(args.receptor)
write('Reading scanning model from: {:s}'.format(args.ligand.name))
ligand = PDB.fromfile(args.ligand)
write('Reading in rotations.')
q, weights, a = proportional_orientations(args.angle)
rotmat = quat_to_rotmat(q)
write('Requested rotational sampling density: {:.2f}'.format(args.angle))
write('Real rotational sampling density: {:.2f}'.format(a))
write('Number of rotations: {:d}'.format(rotmat.shape[0]))
write('Reading in restraints from file: {:s}'.format(args.restraints.name))
distance_restraints = parse_restraints(args.restraints, receptor, ligand)
write('Number of distance restraints: {:d}'.format(len(distance_restraints)))
# If the interaction restraints cutoff is not specified, only calculate the
# interactions and occupancy grids for complexes consistent with at least 1
# restraints or more, with a limit of three.
if args.interaction_restraints_cutoff is None:
args.interaction_restraints_cutoff = max(len(distance_restraints) - 3, 1)
pdb1_sel = pdb2_sel = None
if args.interaction_selection is not None:
write('Reading in interaction selection from file: {:s}'
.format(args.interaction_selection.name))
pdb1_sel, pdb2_sel = parse_interaction_selection(
args.interaction_selection, receptor, ligand)
write('Number of receptor residues: {:d}'.format(pdb1_sel.natoms))
write('Number of ligand residues: {:d}'.format(pdb2_sel.natoms))
write('Voxel spacing set to: {:.2f}'.format(args.voxelspacing))
write('Interaction radius set to: {:.2f}'.format(args.interaction_radius))
write('Minimum required interaction volume: {:.2f}'.format(args.min_interaction))
write('Maximum allowed volume of clashes: {:.2f}'.format(args.max_clash))
if args.occupancy_analysis:
write('Performing occupancy analysis')
if queue is None:
# CPU-version
if args.nproc > 1:
try:
dv = mp_cpu_disvis(receptor, ligand, rotmat, weights,
distance_restraints, pdb1_sel, pdb2_sel, args)
except Queue.Empty:
msg = ('ERROR: Queue.Empty exception raised while processing job, '
'stopping execution ...')
write(msg)
exit(-1)
else:
dv = run_disvis(queue, receptor, ligand, rotmat, weights,
distance_restraints, pdb1_sel, pdb2_sel, args)
else:
# GPU-version
write('Using GPU accelerated search.')
dv = run_disvis(queue, receptor, ligand, rotmat, weights,
distance_restraints, pdb1_sel, pdb2_sel, args)
# write out accessible interaction space
fname = joiner('accessible_interaction_space.mrc')
write('Writing accessible interaction space to: {:s}'.format(fname))
dv.accessible_interaction_space.tofile(fname)
# write out accessible complexes
accessible_complexes = dv.accessible_complexes
norm = sum(accessible_complexes)
digits = len(str(int(norm))) + 1
cum_complexes = np.cumsum(np.asarray(accessible_complexes)[::-1])[::-1]
with open(joiner('accessible_complexes.out'), 'w') as f_accessible_complexes:
write('Writing number of accessible complexes to: {:s}'.format(f_accessible_complexes.name))
header = '# consistent restraints | accessible complexes |' +\
'relative | cumulative accessible complexes | relative\n'
f_accessible_complexes.write(header)
for n, acc in enumerate(accessible_complexes):
f_accessible_complexes.write('{0:3d} {2:{1}d} {3:8.6f} {4:{1}d} {5:8.6f}\n'\
.format(n, digits, int(acc), acc/norm,
int(cum_complexes[n]), cum_complexes[n]/norm))
# writing out violations
violations = dv.violations
cum_violations = violations[::-1].cumsum(axis=0)[::-1]
with open(joiner('violations.out'), 'w') as f_viol:
write('Writing violations to file: {:s}'.format(f_viol.name))
num_violations = violations.sum(axis=1)
nrestraints = num_violations.shape[0]
header = ('# row represents the number of consistent restraints\n'
'# column represents how often that restraint is violated\n')
f_viol.write(header)
header = (' ' + '{:8d}'*nrestraints + '\n').format(*range(1, nrestraints + 1))
f_viol.write(header)
for n, line in enumerate(cum_violations):
f_viol.write('{:<2d} '.format(n+1))
for word in line:
if num_violations[n] > 0:
percentage_violated = word/cum_complexes[n+1]
else:
percentage_violated = 0
f_viol.write('{:8.4f}'.format(percentage_violated))
f_viol.write('\n')
# Give user indication for false positives.
# Determine minimum number of false positives.
nrestraints = len(distance_restraints)
n = 1
while accessible_complexes[-n] == 0:
n += 1
if n > 1:
msg = ('Not all restraints are consistent. '
'Number of false-positive restraints present '
'is at least: {:d}'.format(n - 1))
write(msg)
# next give possible false-positives based on the percentage of violations
# and their associated Z-score
if n == 1:
n = None
else:
n = -n + 1
percentage_violated = cum_violations[:n]/np.asarray(cum_complexes[1:n]).reshape(-1, 1)
average_restraint_violation = percentage_violated.mean(axis=0)
std_restraint_violation = percentage_violated.std(axis=0)
zscore_violations = ((average_restraint_violation - average_restraint_violation.mean())
/ average_restraint_violation.std())
ind_false_positives = np.flatnonzero(zscore_violations >= 1.0)
nfalse_positives = ind_false_positives.size
if nfalse_positives > 0:
ind_false_positives += 1
write(('Possible false-positive restraints (z-score > 1.0):' +\
' {:d}'*nfalse_positives).format(*tuple(ind_false_positives)))
with open(joiner('z-score.out'), 'w') as f:
write('Writing z-score of each restraint to {:s}'.format(f.name))
for n in xrange(zscore_violations.shape[0]):
f.write('{:2d} {:6.3f} {:6.3f} {:6.3f}\n'.format(n+1,
average_restraint_violation[n], std_restraint_violation[n],
zscore_violations[n]))
# Write all occupancy grids to MRC-files if requested
if args.occupancy_analysis:
for n, vol in dv.occupancy_grids.iteritems():
# Normalize the occupancy grid
if cum_complexes[n] > 0:
vol._array /= cum_complexes[n]
vol.tofile(joiner('occupancy_{:d}.mrc'.format(n)))
# Write out interaction analysis
# the interaction_matrix gives the number of interactions between each
# residue of the receptor and ligand for complexes consistent with exactly
# N restraints.
interaction_matrix = dv.interaction_matrix
if interaction_matrix is not None:
## Save interaction matrix
#f = joiner('interaction_matrix.npy')
#write('Writing interaction-matrix to: {:s}'.format(f))
#np.save(f, interaction_matrix)
# Save contacted receptor and ligand residue interaction for each analyzed number
# of consistent restraints
write('Writing contacted receptor residue interactions to files.')
# Take the cumsum in order to give the number of interactions for complexes
# with at least N restraints.
receptor_cum_interactions = interaction_matrix.sum(axis=1)[::-1].cumsum(axis=0)[::-1]
ligand_cum_interactions = interaction_matrix.sum(axis=2)[::-1].cumsum(axis=0)[::-1]
fname = joiner('receptor_interactions.txt')
with open(fname, 'w') as f:
# Write header
f.write('#resi')
for consistent_restraints in xrange(args.interaction_restraints_cutoff, nrestraints + 1):
f.write(' {:>6d}'.format(consistent_restraints))
f.write('\n')
for n, resi in enumerate(pdb1_sel.data['resi']):
f.write('{:<5d}'.format(resi))
for consistent_restraints in xrange(args.interaction_restraints_cutoff, nrestraints + 1):
index = consistent_restraints - args.interaction_restraints_cutoff
interactions = receptor_cum_interactions[index, n]
cum_complex = cum_complexes[consistent_restraints]
if cum_complex > 0:
relative_interactions = interactions / cum_complex
else:
relative_interactions = 0
f.write(' {:6.3f}'.format(relative_interactions))
f.write('\n')
fname = joiner('ligand_interactions.txt')
with open(fname, 'w') as f:
# Write header
f.write('#resi')
for consistent_restraints in xrange(args.interaction_restraints_cutoff, nrestraints + 1):
f.write(' {:>6d}'.format(consistent_restraints))
f.write('\n')
for n, resi in enumerate(pdb2_sel.data['resi']):
f.write('{:<5d}'.format(resi))
for consistent_restraints in xrange(args.interaction_restraints_cutoff, nrestraints + 1):
index = consistent_restraints - args.interaction_restraints_cutoff
interactions = ligand_cum_interactions[index, n]
cum_complex = cum_complexes[consistent_restraints]
if cum_complex > 0:
relative_interactions = interactions / cum_complex
else:
relative_interactions = 0
f.write(' {:6.3f}'.format(relative_interactions))
f.write('\n')
# time indication
seconds = int(round(time() - time0))
m, s = divmod(seconds, 60)
write('Total time passed: {:d}m {:2d}s'.format(m, s))