def random_complex(self, inp_mirror=None):
"""
@return: randomized and minimized complex
@rtype: Complex
"""
self.cm = ComplexMinimizer(self.random_complex_remote(),
debug=self.debug)
self.cm.run(inp_mirror=inp_mirror)
com = Complex(self.rec, self.lig)
rt = com.extractLigandMatrix(self.cm.lig)
com.setLigMatrix(rt)
return com
def random_complex( self, inp_mirror=None ):
"""
@return: randomized and minimized complex
@rtype: Complex
"""
self.cm = ComplexMinimizer( self.random_complex_remote(),
debug=self.debug )
self.cm.run( inp_mirror=inp_mirror )
com = Complex( self.rec, self.lig )
rt = com.extractLigandMatrix( self.cm.lig )
com.setLigMatrix( rt )
return com
def random_complex_remote(self):
"""
Create a complex where the recrptor and ligand have random
orientations but are spaced within contact distance.
@return: rec & lig spaced r_rec + r_lig apart in random orientation
@rtype: Complex
"""
return Complex(self.rec, self.lig, ligMatrix=self.__random_matrix())
def prepareRef(self, fname):
"""
Prepare reference model.
@param fname: file name
@type fname: str
@return: reference structure
@rtype: PDBModel|Complex
@raise EntropistError: if unknown reference type
"""
if not fname:
return None
if self.__splitFilenames(fname):
f1, f2 = self.__splitFilenames(fname)
m1, m2 = PDBModel( self.__getModel(f1) ), \
PDBModel( self.__getModel(f2) )
ref = Complex(m1, m2)
else:
ref = t.load(fname)
if isinstance(ref, Trajectory):
ref = ref.ref
if isinstance(ref, PDBModel):
return self.__cleanAtoms(ref)
if isinstance(ref, Complex):
self.__cleanAtoms(ref.rec_model)
self.__cleanAtoms(ref.lig_model)
ref.lig_model_transformed = None
return ref
raise EntropistError, 'unknown reference type'
def prepareRef( self, fname ):
"""
Prepare reference model.
@param fname: file name
@type fname: str
@return: reference structure
@rtype: PDBModel|Complex
@raise EntropistError: if unknown reference type
"""
if not fname:
return None
if self.__splitFilenames( fname ):
f1, f2 = self.__splitFilenames( fname )
m1, m2 = PDBModel( self.__getModel(f1) ), \
PDBModel( self.__getModel(f2) )
ref = Complex( m1, m2 )
else:
ref = t.load( fname )
if isinstance( ref, Trajectory ):
ref = ref.ref
if isinstance( ref, PDBModel ):
return self.__cleanAtoms( ref )
if isinstance( ref, Complex ):
self.__cleanAtoms( ref.rec_model )
self.__cleanAtoms( ref.lig_model )
ref.lig_model_transformed = None
return ref
raise EntropistError, 'unknown reference type'
def prepareTraj(self, fname, ref=None, cast=1):
"""
Prepare trajectory for Amber.
@param fname: path to EnsembleTraj OR ( EnsembleTraj, EnsembleTraj )
@type fname: str OR (str,str)
@param ref: reference structure
@type ref: EnsembleTraj
@param cast: cast to reference (same atom content) (default: 1)
@type cast: 1|0
@return: split, fitted or shuffled, etc. trajectory instance
@rtype: EnsembleTraj OR (EnsembleTraj, EnsembleTraj )
"""
## Load 1 or 2
if self.__splitFilenames(fname):
f1, f2 = self.__splitFilenames(fname)
t = self.loadTraj(f1), self.loadTraj(f2)
else:
t = self.loadTraj(fname)
if self.chains:
t = t.takeChains(self.chains)
## split 1 into 2 if necessary
if not type(t) is tuple and self.border:
lig = range(self.border, t.ref.lenChains())
t = t.takeChains(range(self.border)), t.takeChains(lig)
## check 2 trajectories were suplied
if not type(t) is tuple and (self.shift or self.shuffle or self.split):
raise EntropistError, 'split,shift,shuffle require -border.'
## adapt reference to type of trajectory input
if ref and type(t) is tuple and not isinstance(ref, Complex):
rec = ref.takeChains(range(t[0].ref.lenChains()))
lig = ref.takeChains(
range(t[0].ref.lenChains(), t[1].ref.lenChains()))
ref = Complex(rec, lig)
if ref and type(t) is not tuple and isinstance(ref, Complex):
ref = ref.rec_model.concat(ref.lig())
## remove member trajectories (if requested)
t = self.__removeMembers(t)
## cast 1 or 2
if cast and type(t) is not tuple:
self.castTraj(t, ref)
if cast and type(t) is tuple:
self.castTraj(t[0], ref.rec_model)
self.castTraj(t[1], ref.lig_model)
## reorder one half (requires -border or file name pair )
if self.shift:
t = self.shiftTraj(t[0], self.shift), t[1]
if self.shuffle:
t = self.shuffleTraj(t[0]), self.shuffleTraj(t[1])
## fit seperately (requires -border or file name pair)
if self.split and ref:
self.fit(t[0], ref.rec())
self.fit(t[1], ref.lig())
if self.split and not ref:
self.fit(t[0])
self.fit(t[1])
if type(t) is tuple:
t = t[0].concatAtoms(t[1])
ref = ref.rec_model.concat(ref.lig())
## joint fit
if not self.split:
self.fit(t, ref)
if self.verbose:
self.log.add( 'Analysing trajectory with %i atoms and %i frames.' \
% (t.lenAtoms(), t.lenFrames()))
return t
cName = nameFromPath( cFile )
outName = outDir + '/' + cName + '_model-CAD.txt'
outName_over = outDir + '/' + cName + '_model-CAD_overwiev.txt'
## load
flushPrint('Loading complex list \n')
cList = load( options['cl'] )
flushPrint('Loading bound complex \n')
ref_com = load( options['ref'] )
## get models
rec_models, lig_models, rec_b_ref, lig_b_ref = get_models( cList, ref_com )
## contacts
ref_com_b_cast = ProteinComplex( rec_b_ref, lig_b_ref )
ref_b_resCont = ref_com_b_cast.resContacts()
rec_aContacts = rec_b_ref.res2atomMask( sum( ref_b_resCont, 1 ) )
lig_aContacts = lig_b_ref.res2atomMask( sum( ref_b_resCont, 0 ) )
## get pw_rmsds
rec_rmsd_cont = pw_rmsd( rec_models, rec_b_ref, rec_aContacts )
lig_rmsd_cont = pw_rmsd( lig_models, lig_b_ref, lig_aContacts )
rec_rmsd_bb = pw_rmsd( rec_models, rec_b_ref, rec_b_ref.maskBB() )
lig_rmsd_bb = pw_rmsd( lig_models, lig_b_ref, lig_b_ref.maskBB() )
## get pw cad
rec_cad = pw_cad( rec_models, rec_b_ref, rec_aContacts )
lig_cad = pw_cad( lig_models, lig_b_ref, lig_aContacts )
cName = nameFromPath(cFile)
outName = outDir + '/' + cName + '_model-CAD.txt'
outName_over = outDir + '/' + cName + '_model-CAD_overwiev.txt'
## load
flushPrint('Loading complex list \n')
cList = load(options['cl'])
flushPrint('Loading bound complex \n')
ref_com = load(options['ref'])
## get models
rec_models, lig_models, rec_b_ref, lig_b_ref = get_models(cList, ref_com)
## contacts
ref_com_b_cast = ProteinComplex(rec_b_ref, lig_b_ref)
ref_b_resCont = ref_com_b_cast.resContacts()
rec_aContacts = rec_b_ref.res2atomMask(sum(ref_b_resCont, 1))
lig_aContacts = lig_b_ref.res2atomMask(sum(ref_b_resCont, 0))
## get pw_rmsds
rec_rmsd_cont = pw_rmsd(rec_models, rec_b_ref, rec_aContacts)
lig_rmsd_cont = pw_rmsd(lig_models, lig_b_ref, lig_aContacts)
rec_rmsd_bb = pw_rmsd(rec_models, rec_b_ref, rec_b_ref.maskBB())
lig_rmsd_bb = pw_rmsd(lig_models, lig_b_ref, lig_b_ref.maskBB())
## get pw cad
rec_cad = pw_cad(rec_models, rec_b_ref, rec_aContacts)
lig_cad = pw_cad(lig_models, lig_b_ref, lig_aContacts)
def prepareTraj( self, fname, ref=None, cast=1 ):
"""
Prepare trajectory for Amber.
@param fname: path to EnsembleTraj OR ( EnsembleTraj, EnsembleTraj )
@type fname: str OR (str,str)
@param ref: reference structure
@type ref: EnsembleTraj
@param cast: cast to reference (same atom content) (default: 1)
@type cast: 1|0
@return: split, fitted or shuffled, etc. trajectory instance
@rtype: EnsembleTraj OR (EnsembleTraj, EnsembleTraj )
"""
## Load 1 or 2
if self.__splitFilenames( fname ):
f1, f2 = self.__splitFilenames( fname )
t = self.loadTraj( f1 ), self.loadTraj( f2 )
else:
t = self.loadTraj( fname )
if self.chains:
t = t.takeChains( self.chains )
## split 1 into 2 if necessary
if not type(t) is tuple and self.border:
lig = range( self.border, t.ref.lenChains() )
t = t.takeChains( range(self.border) ), t.takeChains( lig )
## check 2 trajectories were suplied
if not type(t) is tuple and (self.shift or self.shuffle or self.split):
raise EntropistError,'split,shift,shuffle require -border.'
## adapt reference to type of trajectory input
if ref and type(t) is tuple and not isinstance( ref, Complex ):
rec = ref.takeChains( range(t[0].ref.lenChains()) )
lig = ref.takeChains( range(t[0].ref.lenChains(),
t[1].ref.lenChains()) )
ref = Complex( rec, lig )
if ref and type(t) is not tuple and isinstance( ref, Complex ):
ref = ref.rec_model.concat( ref.lig() )
## remove member trajectories (if requested)
t = self.__removeMembers( t )
## cast 1 or 2
if cast and type( t ) is not tuple:
self.castTraj( t, ref )
if cast and type( t ) is tuple:
self.castTraj( t[0], ref.rec_model )
self.castTraj( t[1], ref.lig_model )
## reorder one half (requires -border or file name pair )
if self.shift:
t = self.shiftTraj( t[0], self.shift ), t[1]
if self.shuffle:
t = self.shuffleTraj( t[0] ), self.shuffleTraj( t[1] )
## fit seperately (requires -border or file name pair)
if self.split and ref:
self.fit( t[0], ref.rec() )
self.fit( t[1], ref.lig() )
if self.split and not ref:
self.fit( t[0] )
self.fit( t[1] )
if type( t ) is tuple:
t = t[0].concatAtoms( t[1] )
ref = ref.rec_model.concat( ref.lig() )
## joint fit
if not self.split:
self.fit( t, ref )
if self.verbose:
self.log.add( 'Analysing trajectory with %i atoms and %i frames.' \
% (t.lenAtoms(), t.lenFrames()))
return t