def test_ComplexTraj(self):
"""Dock.ComplexTraj test"""
import Biskit.tools as T
## there is no complex trajectory in the test folder so will have
## to create a fake trajectory with a complex
f = [T.testRoot() + '/com/1BGS.pdb'] * 5
t = Trajectory(f, verbose=self.local)
t = ComplexTraj(t, recChains=[0])
#if self.local:
#print 'plotting contact density...'
#t.plotContactDensity( step=2 )
## create a fake second chain in the ligand
for i in range(1093 + 98, 1968):
t.ref.atoms['chain_id'][i] = 'B'
t.ref.chainIndex(force=1, cache=1)
t.cl = [1, 2]
r = N0.concatenate((range(1093, 1191), range(0,
1093), range(1191, 1968)))
tt = t.takeAtoms(r)
contactMat = tt.atomContacts(1)
if self.local:
print 'Receptor chains: %s Ligand chains: %s' % (t.cr, t.cl)
self.assertEqual(N0.sum(N0.ravel(contactMat)), 308)
def test_Analyzer( self):
"""Dock.Analyzer test """
from Biskit import Trajectory
from Biskit.Dock import ComplexList
## create a minimal 1-frame receptor trajectory from a pdb file
self.t_rec = Trajectory( [t.testRoot()+'/rec/1A2P.pdb'],
verbose=self.local)
t.dump( self.t_rec, self.f_out )
## load a complex list
cl = t.load( t.testRoot() + '/dock/hex/complexes.cl')
self.a= Analyzer( rec = self.f_out,
lig = t.testRoot()+'/lig_pcr_00/traj.dat',
ref = t.testRoot()+'/com/ref.complex',
verbose = self.local)
## shuffle this list five times
shuff_lst = self.a.shuffledLists( 5, range(8) )
## create two random contact matrices
rand_mat = self.a.random_contacts( cl[0].atomContacts(), 2 )
self.assertEqual( N0.shape(rand_mat[1]), (1075, 876) )
def test_ComplexRandomizer(self):
"""Dock.ComplexRandomizer test"""
from Biskit import Trajectory
if self.local:
print "\nLoading Rec and Lig files ...",
rec_pdb = t.testRoot() + '/rec/1A2P.pdb'
lig_pdb = t.testRoot() + '/lig/1A19.pdb'
rec_psf = t.testRoot() + '/rec/1A2P.psf'
lig_psf = t.testRoot() + '/lig/1A19.psf'
rec = PCRModel(rec_psf, rec_pdb)
lig = PCRModel(lig_psf, lig_pdb)
if self.local:
print "Initializing Randomizer..."
self.cr = ComplexRandomizer(rec, lig, debug=self.DEBUG)
if self.local:
print "Creating 3 random complexes..."
cs = [self.cr.random_complex() for i in range(3)]
self.traj = Trajectory([c.model() for c in cs])
if self.local:
self.display(self.traj)
globals().update(locals())
self.assertEqual(len(self.traj), 3)
def replaceContent(self, traj):
"""
Replace content of this trajectory by content of given traj.
No deep-copying, only references are taken.
@param traj: Trajectory
@type traj: Trajectory
@raise ComplexTrajError: if given traj is no ComplexTraj.
"""
if not isinstance(traj, ComplexTraj):
raise ComplexTrajError(
"Cannot replace ComplexTraj by normal Trajectory.")
Trajectory.replaceContent(self, traj)
self.cr = traj.cr
self.cl = traj.cl
def takeAtoms(self, indices, returnClass=None):
"""
takeAtoms( indices, [returnClass] ) -> ComplexTraj
@param indices: atoms to extract
@type indices: [int]
@param returnClass: return type (default: current class)
@type returnClass: class
@return: ComplexTraj
@rtype: ComplexTraj
@raise ComplexTrajError: if (parts of) chains are inserted into
each other
"""
r = Trajectory.takeAtoms(self, indices, returnClass)
oldToNew = self.__translateChainIndices(indices, r.ref.chainMap())
r.cr = [oldToNew[c] for c in self.cr if c in oldToNew]
r.cl = [oldToNew[c] for c in self.cl if c in oldToNew]
return r
for i in range(nr):
print '%5i' % (i + 1),
for i in range(nr):
print '\n%2i' % (i + 1),
for k in range(i):
print ' ' * 5,
for j in range(i, nr):
print '%5.2f' % matrix[i, j],
## get filenames of all models
models = glob.glob('%s/modeller/%s*.pdb' %
(outFolder, tools.stripFilename(f_target)))
## create a Trajectory object with the models
traj = Trajectory(pdbs=models)
## fit the models against the average structure iteratively
traj.blockFit2ref()
## calculate and print rmsd matrix
rmsHeavy = traj.pairwiseRmsd()
print '\nHEAVY ATOM RMSD BETWEEN MODELS::'
__printMatrix(rmsHeavy)
## same thing for backbone atoms
BBMask = traj[0].maskBB()
traj.blockFit2ref(mask=BBMask)
rmsBB = traj.pairwiseRmsd(aMask=BBMask)
print '\nBACKBONE RMSD BETWEEN MODELS:'
__printMatrix(rmsBB)
dotPlotName = os.path.dirname(cFile) + '/' + cName + '_dot_plot.eps'
logPlotName = os.path.dirname(cFile) + '/' + cName + '_log_plot.eps'
diffPlotName = os.path.dirname(cFile) + '/' + cName + '_diff_plot.eps'
rankPlotName = os.path.dirname(cFile) + '/' + cName + '_rank_plot.eps'
contourPlotName = os.path.dirname(cFile) + '/' + cName + '_contour_plot.eps'
profilePlotName = os.path.dirname(cFile) + '/' + cName + '_plot_profile.eps'
outFile = os.path.dirname(cFile) + '/' + cName + '_best.txt'
dumpFile = os.path.dirname(cFile) + '/' + cName + '.dat'
#############################################################
## RMSD analysis
flushPrint('Performing RMSD analysis...\n')
## Load Trajectories - read and sort bound ligand and recepror trajectories
traj_rec = Trajectory(rec_pdbs)
traj_lig = Trajectory(lig_pdbs)
## reference (bound) rec and lig
ref = load(absfile(options['ref']))
ref.rec().remove(ref.rec().maskH())
ref.lig_model.remove(ref.lig().maskH())
ref.lig_transformed = None
ref.rec().writePdb(ref_rec_file)
ref.lig().writePdb(ref_lig_file)
## Load Trajectories - read and sort bound ligand and recepror trajectories
traj_rec_ref = Trajectory([ref_rec_file])
traj_lig_ref = Trajectory([ref_lig_file])
traj_rec, traj_rec_ref = castTraj(traj_rec, traj_rec_ref)
cl = contact(cl, ref, fout + 'cont.cl', add_hosts=add_hosts)
cl = filter_zero_contacts(cl) ## happened in one case
## cl = cl.filter( 'fnac_10', (0., 0.) )
cl = cl.filter('fnarc_10', (0., 0.))
flushPrint('%i orientations removed because of fnarc > 0.0\n' %
(n_cl - len(cl)))
cg = group(cl, 0.001, 0.0001)
dump(cg, fout + 'grouped.cg')
## final selection
nr_cl = selectClusters(cg, 10)
flushPrint(
'dumping %i non-redundant, non-overlapping, non-native complexes\n'\
% len( nr_cl ) )
dump(nr_cl, fout + 'nr.cl')
flushPrint('Dumping random complexes.')
saveComplexes(nr_cl, com_out)
## for visualisation
flushPrint('\nPreparing trajectory...')
t = Trajectory([c.model() for c in nr_cl])
t.ref.addChainId()
t.ref.writePdb(fout + 'nr_traj_ref.pdb', ter=2)
t.writeCrd(fout + 'nr_traj.crd')
nr = len( matrix )
for i in range(nr): print '%5i'%(i+1),
for i in range(nr):
print '\n%2i'%(i+1),
for k in range(i):
print ' '*5,
for j in range(i, nr):
print '%5.2f'%matrix[i,j],
## get filenames of all models
models = glob.glob( '%s/modeller/%s*.pdb'%(outFolder,
tools.stripFilename(f_target)) )
## create a Trajectory object with the models
traj = Trajectory( pdbs=models )
## fit the models against the average structure iteratively
traj.blockFit2ref()
## calculate and print rmsd matrix
rmsHeavy = traj.pairwiseRmsd()
print '\nHEAVY ATOM RMSD BETWEEN MODELS::'
__printMatrix( rmsHeavy )
## same thing for backbone atoms
BBMask = traj[0].maskBB()
traj.blockFit2ref( mask = BBMask )
rmsBB = traj.pairwiseRmsd( aMask = BBMask )
print '\nBACKBONE RMSD BETWEEN MODELS:'
__printMatrix( rmsBB )
result_ref = result_ref or ref or t.ref
if t.ref.equals(result_ref) != [1, 1]:
raise Exception('Incompatible reference structure.')
for xyz in t.frames:
result_xyz.append(xyz.astype('f'))
for fname in t.frameNames:
result_frameNames.append(fname)
T.flushPrint('#')
print " Done"
result = Trajectory()
result.ref = result_ref
result.ref.disconnect()
if 'pdb' in o:
result.ref.pdbCode = o['pdb']
result.frames = N.array(result_xyz, 'f')
result.frameNames = result_frameNames
del result_xyz
## too much memory required for this
## result = trajLst[0].concat( *trajLst[1:] )
T.flushPrint("Converting to EnsembleTraj...")
lo = absfile(options['lo'])
out = absfile(options['o'])
n = int(options['n'])
traj = absfile(options.get('traj', None))
finp = absfile(options.get('debug', None))
debug = (finp is not None)
except:
print "Missing or wrong option:"
print lastError()
_use()
cr = ComplexRandomizer(rec, lig, ro, lo, debug=debug)
result = ComplexList()
flushPrint('generating...')
for i in range(n):
result.append(cr.random_complex(inp_mirror=finp))
flushPrint('#')
flushPrint('\nDumping result...')
dump(result, out)
if traj:
flushPrint('\nWriting trajectory...')
t = Trajectory([c.model() for c in result])
t.ref.writePdb(traj + '.pdb')
t.writeCrd(traj + '.crd')
flushPrint('done')
logPlotName = os.path.dirname(cFile) + '/' + cName + '_log_plot.eps'
diffPlotName = os.path.dirname(cFile) + '/' + cName + '_diff_plot.eps'
rankPlotName = os.path.dirname(cFile) + '/' + cName + '_rank_plot.eps'
contourPlotName = os.path.dirname(cFile) + '/' + cName + '_contour_plot.eps'
profilePlotName = os.path.dirname(cFile) + '/' + cName + '_plot_profile.eps'
outFile = os.path.dirname(cFile) + '/' + cName + '_best.txt'
dumpFile = os.path.dirname(cFile) + '/' + cName + '.dat'
#############################################################
## RMSD analysis
flushPrint('Performing RMSD analysis...\n')
## Load Trajectories - read and sort bound ligand and recepror trajectories
traj_rec = Trajectory( rec_pdbs )
traj_lig = Trajectory( lig_pdbs )
## reference (bound) rec and lig
ref = load( absfile( options['ref'] ) )
ref.rec().remove( ref.rec().maskH() )
ref.lig_model.remove( ref.lig().maskH() )
ref.lig_transformed = None
ref.rec().writePdb( ref_rec_file )
ref.lig().writePdb( ref_lig_file )
## Load Trajectories - read and sort bound ligand and recepror trajectories
traj_rec_ref = Trajectory( [ ref_rec_file ] )
traj_lig_ref = Trajectory( [ ref_lig_file ] )
traj_rec, traj_rec_ref = castTraj( traj_rec, traj_rec_ref )
result_ref = result_ref or ref or t.ref
if t.ref.equals( result_ref ) != [1,1]:
raise Exception( 'Incompatible reference structure.' )
for xyz in t.frames:
result_xyz.append( xyz.astype('f') )
for fname in t.frameNames:
result_frameNames.append( fname )
T.flushPrint('#')
print " Done"
result = Trajectory()
result.ref = result_ref
result.ref.disconnect()
if 'pdb' in o:
result.ref.pdbCode = o['pdb']
result.frames = N0.array( result_xyz, 'f' )
result.frameNames = result_frameNames
del result_xyz
## too much memory required for this
## result = trajLst[0].concat( *trajLst[1:] )
T.flushPrint("Converting to EnsembleTraj...")
out = absfile( options['o'] )
n = int( options['n'] )
traj= absfile( options.get('traj', None) )
finp= absfile( options.get('debug', None))
debug = ( finp is not None )
except:
print "Missing or wrong option:"
print lastError()
_use()
cr = ComplexRandomizer( rec, lig, ro, lo, debug=debug )
result = ComplexList()
flushPrint('generating...')
for i in range( n ):
result.append( cr.random_complex( inp_mirror=finp ) )
flushPrint('#')
flushPrint('\nDumping result...')
dump( result, out )
if traj:
flushPrint('\nWriting trajectory...')
t = Trajectory( [ c.model() for c in result ] )
t.ref.writePdb( traj + '.pdb' )
t.writeCrd( traj + '.crd' )
flushPrint('done')
cl = contact( cl, ref, fout + 'cont.cl', add_hosts=add_hosts )
cl = filter_zero_contacts( cl ) ## happened in one case
## cl = cl.filter( 'fnac_10', (0., 0.) )
cl = cl.filter( 'fnarc_10', (0., 0.) )
flushPrint('%i orientations removed because of fnarc > 0.0\n' % (n_cl-len(cl)))
cg = group( cl, 0.001, 0.0001 )
dump( cg, fout+'grouped.cg' )
## final selection
nr_cl = selectClusters( cg, 10 )
flushPrint(
'dumping %i non-redundant, non-overlapping, non-native complexes\n'\
% len( nr_cl ) )
dump( nr_cl, fout+'nr.cl')
flushPrint('Dumping random complexes.')
saveComplexes( nr_cl, com_out )
## for visualisation
flushPrint('\nPreparing trajectory...')
t = Trajectory( [ c.model() for c in nr_cl ] )
t.ref.addChainId()
t.ref.writePdb( fout+'nr_traj_ref.pdb', ter=2 )
t.writeCrd( fout+'nr_traj.crd')
