mmtbx.superpose
index
/net/chevy/raid1/nat/src/cctbx_project/mmtbx/superpose.py

 
Modules
       
cctbx
iotbx
itertools
libtbx
mmtbx
os
scitbx
sys

 
Classes
       
__builtin__.object
SuperposePDB
SuperposePDBSCEDS
SuperposePDBSieve
libtbx.runtime_utils.target_with_save_result(__builtin__.object)
launcher

 
class SuperposePDB(__builtin__.object)
    Superimpose PDB files.
 
Instantiate with a filename or pdb instance. You may also provide a default
selection string, selection preset, or chain to use for atom selection.
Otherwise, the select method will attempt to find a reasonable default.
 
Use the superpose(target) method to perform superposition. This will return
the RMSD, and the transformation used. To write output, use output(lsq,
filename).
 
The selectomatic(target) method performs a pairwise comparison of chain
sequence alignments, and updates the selections to use the chains with the
highest similarity.
 
You can also update the selection using the select_update() method, or return
a given selection using select().
 
Multiple models are supported by creating multiple instances. The class method
open_models() helper can be used to open a file with multiple models.
 
To create a subclass with a modified fitting routine, override fit().
 
  Methods defined here:
__init__(self, filename=None, pdb=None, selection=None, preset=None, chain=None, quiet=False, log=None, desc=None)
A filename or pdb instance is required. You may also provide arguments
for the atom selection. The quiet option will suppress informational
output. The desc option provides a label for the log output.
fit(self, sites_moving, sites_fixed)
Perform a least squares fit between sites_moving and sites_fixed. Return
the rmsd, lsq, and updated sites_moving, sites_fixed.
get_transformed(self)
Return a transformed model.
log(self, *msg)
Log.
output(self, lsq, filename)
Output PDB model to filename given transformation lsq.
select(self, selection=None, chain=None, preset=None)
Update the selection.
 
Specify either an explicit selection, or one of the handy preset selections
(ca, backbone, all). Presets will use the longest chain by default, but
this can also be specified using chain. If no selection or preset is
specified, the various presets will be tested before falling back to all
atom selection.
select_update(self, selection=None, chain=None, preset=None)
select() and update state.
selectomatic(self, target)
Perform pairwise sequence alignments and find the best aligned chain pair.
 
This method performs pairwise sequence alignments between all chains in
itself and the target. The selections are updated to the pair with the
highest sequence similarity, with chain IDs in the return value.
superpose(self, target)
Superpose to target. Return rmsd and lsq.
 
This method will perform a sequence alignment first if the target is not
sequence identical.

Class methods defined here:
open_models(cls, filename=None, pdb=None, **kwargs) from __builtin__.type
Class method to return instances for each model in a file.
output_merge(cls, instances, filename) from __builtin__.type
Merge multiple instances back into a single PDB file.

Data descriptors defined here:
__dict__
dictionary for instance variables (if defined)
__weakref__
list of weak references to the object (if defined)

 
class SuperposePDBSCEDS(SuperposePDB)
    
Method resolution order:
SuperposePDBSCEDS
SuperposePDB
__builtin__.object

Methods defined here:
fit(self, sites_moving, sites_fixed)

Methods inherited from SuperposePDB:
__init__(self, filename=None, pdb=None, selection=None, preset=None, chain=None, quiet=False, log=None, desc=None)
A filename or pdb instance is required. You may also provide arguments
for the atom selection. The quiet option will suppress informational
output. The desc option provides a label for the log output.
get_transformed(self)
Return a transformed model.
log(self, *msg)
Log.
output(self, lsq, filename)
Output PDB model to filename given transformation lsq.
select(self, selection=None, chain=None, preset=None)
Update the selection.
 
Specify either an explicit selection, or one of the handy preset selections
(ca, backbone, all). Presets will use the longest chain by default, but
this can also be specified using chain. If no selection or preset is
specified, the various presets will be tested before falling back to all
atom selection.
select_update(self, selection=None, chain=None, preset=None)
select() and update state.
selectomatic(self, target)
Perform pairwise sequence alignments and find the best aligned chain pair.
 
This method performs pairwise sequence alignments between all chains in
itself and the target. The selections are updated to the pair with the
highest sequence similarity, with chain IDs in the return value.
superpose(self, target)
Superpose to target. Return rmsd and lsq.
 
This method will perform a sequence alignment first if the target is not
sequence identical.

Class methods inherited from SuperposePDB:
open_models(cls, filename=None, pdb=None, **kwargs) from __builtin__.type
Class method to return instances for each model in a file.
output_merge(cls, instances, filename) from __builtin__.type
Merge multiple instances back into a single PDB file.

Data descriptors inherited from SuperposePDB:
__dict__
dictionary for instance variables (if defined)
__weakref__
list of weak references to the object (if defined)

 
class SuperposePDBSieve(SuperposePDB)
    
Method resolution order:
SuperposePDBSieve
SuperposePDB
__builtin__.object

Methods defined here:
fit(self, sites_moving, sites_fixed)

Methods inherited from SuperposePDB:
__init__(self, filename=None, pdb=None, selection=None, preset=None, chain=None, quiet=False, log=None, desc=None)
A filename or pdb instance is required. You may also provide arguments
for the atom selection. The quiet option will suppress informational
output. The desc option provides a label for the log output.
get_transformed(self)
Return a transformed model.
log(self, *msg)
Log.
output(self, lsq, filename)
Output PDB model to filename given transformation lsq.
select(self, selection=None, chain=None, preset=None)
Update the selection.
 
Specify either an explicit selection, or one of the handy preset selections
(ca, backbone, all). Presets will use the longest chain by default, but
this can also be specified using chain. If no selection or preset is
specified, the various presets will be tested before falling back to all
atom selection.
select_update(self, selection=None, chain=None, preset=None)
select() and update state.
selectomatic(self, target)
Perform pairwise sequence alignments and find the best aligned chain pair.
 
This method performs pairwise sequence alignments between all chains in
itself and the target. The selections are updated to the pair with the
highest sequence similarity, with chain IDs in the return value.
superpose(self, target)
Superpose to target. Return rmsd and lsq.
 
This method will perform a sequence alignment first if the target is not
sequence identical.

Class methods inherited from SuperposePDB:
open_models(cls, filename=None, pdb=None, **kwargs) from __builtin__.type
Class method to return instances for each model in a file.
output_merge(cls, instances, filename) from __builtin__.type
Merge multiple instances back into a single PDB file.

Data descriptors inherited from SuperposePDB:
__dict__
dictionary for instance variables (if defined)
__weakref__
list of weak references to the object (if defined)

 
class launcher(libtbx.runtime_utils.target_with_save_result)
    
Method resolution order:
launcher
libtbx.runtime_utils.target_with_save_result
__builtin__.object

Methods defined here:
run(self)

Methods inherited from libtbx.runtime_utils.target_with_save_result:
__call__(self)
__init__(self, args, file_name, output_dir=None, log_file=None, job_title=None)

Data descriptors inherited from libtbx.runtime_utils.target_with_save_result:
__dict__
dictionary for instance variables (if defined)
__weakref__
list of weak references to the object (if defined)

 
Functions
       
finish_job(result)
run(args, command_name='phenix.superpose_pdbs', log=None)
validate_params(params)

 
Data
        PHIL_PARAMS = '\ninput {\n pdb_file_name_fixed = None\n .type ... blosum50 dayhoff *identity\n .type = choice\n}'
PRESETS = {'all': 'all chain %(chain)s', 'backbone': 'pepnames and (name ca or name n or name c) and altloc " " chain %(chain)s', 'ca': 'pepnames and (name ca) and altloc " " chain %(chain)s'}
PRESET_ORDER = ['backbone', 'ca', 'all']
division = _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)