Automatic loop refinement after model building

To automatically refine loop regions after building standard automodel models, simply use the loopmodel class rather than automodel; see the example below.

In many cases, you can obtain better quality loops (at the expense of more computer time) by using the newer DOPE-based loop modeling protocol. In this case, just use the dope_loopmodel or dopehr_loopmodel classes in place of loopmodel in each of the examples below. See Section 4.4 or Section 4.5 for more details.

Example: examples/automodel/model-loop.py

# Comparative modeling by the automodel class
from modeller import *
from modeller.automodel import *    # Load the automodel class

log.verbose()
env = environ()

# directories for input atom files
env.io.atom_files_directory = ['.', '../atom_files']

a = loopmodel(env,
              alnfile  = 'alignment.ali',     # alignment filename
              knowns   = '5fd1',              # codes of the templates
              sequence = '1fdx')              # code of the target
a.starting_model= 1                 # index of the first model
a.ending_model  = 1                 # index of the last model
                                    # (determines how many models to calculate)
a.md_level = None                   # No refinement of model

a.loop.starting_model = 1           # First loop model
a.loop.ending_model   = 4           # Last loop model
a.loop.md_level       = refine.fast # Loop model refinement level

a.make()                            # do comparative modeling

After generating the standard model(s), a number of loop models are generated for each model, from loopmodel.loop.starting_model to loopmodel.loop.ending_model. Each loop model is written out with the .BL extension. See section A.5 for more information.