loopmodel() -- prepare to build models with loop refinement

env = <environ>		MODELLER environment
alnfile = <str:1>		file containing template-sequence alignment
knowns = <str:0>		template codes in alignment
sequence = <str:1>		sequence code in alignment
inimodel = <str:1>		initial model file
deviation = <float:1>		control amount of randomization
library_schedule = <int:1>		optimization schedule
toplib = <str:1>		topology library to use
parlib = <str:1>		parameter library to use
topology_model = <int:1>		topology submodel
csrfile = <str:1>		user-provided restraints file
inifile = <str:1>		user-provided initial model file
assess_methods = function(s)		assessment functions
loop_assess_methods = function(s)		assessment functions

Description:

This creates a new object for loop modeling. It can either build standard comparative models (in identical fashion to the automodel class) and then refine each of them, in which case you should set the alnfile and knowns arguments appropriately (see the automodel() documentation) or it can refine a given region of a PDB file, in which case you should set inimodel to the name of the PDB file instead. In both cases, sequence identifies the code of the target sequence.

All other arguments are the same as those for automodel(), with the exception of those below:

loop_assess_methods is the analog of automodel.assess_methods for loop modeling, and allows you to request assessment of the generated loop models. (This can also be set after the object is created, by assigning to 'loopmodel.loop.assess_methods'.)

See section 2.2.9 for examples.