AutoModel() — prepare to build one or more comparative models

AutoModel(env, alnfile, knowns, sequence, deviation=None, library_schedule=None, csrfile=None, inifile=None, assess_methods=None, root_name=None)
alnfile is required, and usually specifies the name of the PIR file which contains an alignment between knowns (the templates) and sequence (the target sequence).

alnfile can instead be a readable file handle (see modfile.File()) from which the alignment will be read, or an existing Alignment object containing knowns and sequence. (Note that this is only supported with a subset of AutoModel functionality; in particular, it does not work with parallel jobs, AutoModel.initial_malign3d, or AutoModel.final_malign3d.)

deviation controls the amount of randomization done by or randomize.dihedrals; see also AutoModel.rand_method. (This can also be set after the object is created, by assigning to 'AutoModel.deviation'. The default is 4Å.)

library_schedule, if given, sets an initial value for AutoModel.library_schedule

If csrfile is set, restraints are not constructed, but are instead read from the user-supplied file of the same name. See section 2.2.9 for an example.

If inifile is set, an initial model is read from the user-supplied file of the same name. See section 2.2.10 for an example.

If root_name is set, it is used to name any output files (see also AutoModel.get_model_filename()). By default, files are named using sequence.

assess_methods allows you to request assessment of the generated models (by default, none is done). You can provide a function (or callable), or list of functions, for this purpose, including any of the SOAP potentials (e.g., soap_loop.Scorer(), soap_protein_od.Scorer()), or any of the standard functions provided in the assess module:
(This can also be set after the object is created, by assigning to 'AutoModel.assess_methods'.) See Section 2.2.3 for an example. Only the region selected by AutoModel.select_atoms() is assessed, although most assessment functions take the interaction with the rest of the system into account. Note that only standard models are assessed in this way; if you are also building loop models, see LoopModel.loop.assess_methods.

By default, models are built using heavy atom-only parameters and topology. If you want to use different parameters, read them in before creating the AutoModel object with and

See section 2.1 for a general example of using this class.