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)

alnfile is required, and specifies the PIR file which contains an alignment between knowns (the templates) and sequence (the target sequence).

deviation controls the amount of randomization done by randomize.xyz or randomize.dihedrals. (This can also be set after the object is created, by assigning to 'automodel.deviation'.)

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 2.2.8 for an example.

If inifile is set, an initial model is read from the user-supplied file of the same name. See section 2.2.9 for an example.

assess_methods allows you to request assessment of the generated models (by default, none is done). You can provide a function, or list of functions, for this purpose, or use one or more of the standard functions provided in the assess module:

• assess.GA341, which uses the GA341 method (see model.assess_ga341()).

• assess.DOPE, which uses the DOPE method (see selection.assess_dope()).

• assess.DOPEHR, which uses the DOPE-HR method (see selection.assess_dopehr()).

• assess.normalized_dope, which uses the normalized DOPE method (see model.assess_normalized_dope()).

(This can also be set after the object is created, by assigning to 'automodel.assess_methods'.) See Section 2.2.3 for an example. 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 Topology.read() and Parameters.read().

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