The template for construction of the schedule is the library_schedule-th entry in library file $SCHED_LIB.
The usual schedule for the variable target function part of optimization in comparative modeling is as follows. The residue range (model.restraints.pick() and Section 3.7.3) is increased with increasingly larger steps until the protein length is reached. The scaling of homology-derived and bonded stereochemical restraints increases from a small value to 1 in the initial few steps to allow for imperfect starting geometries, especially those that result from model.randomize_xyz() and long insertions or deletions. The soft-sphere overlap restraints are slowly introduced only in the last four steps of the variable target function method to save CPU time and increase the radius of convergence. In comparative modeling by the 'model' script in the default mode, the variable target function method is usually followed by simulated annealing with molecular dynamics. In this last stage, all homology-derived and stereochemical restraints are generally used with the scaling factors of 1. There are a number of variables defined in the automodel class that can be used to influence the thoroughness of both the variable target function and molecular dynamics parts of the optimization (Chapter 2).
The scaling factors for all physical restraint groups, in all schedule steps, are multiplied by the corresponding scalar in schedule_scale (1 by default). This is useful when template-derived fold restraints have to be weakened relative to some external restraints, so that the fold can actually reflect these external restraints, even when they are quite different from the template-derived restraints.
This command is an alternative to the model.schedule.read() command.
Use the model.schedule.write() command to find out what the calculated schedule is. The schedule file written by the `model' routine has an extension .sch.
# Example for: model.schedule.make(), model.schedule.write(), # model.schedule.read() # This will create an VTFM optimization schedule for a model # and write it to a file. env = environ() mdl = model(env, file='1fas') mdl.schedule.make(library_schedule=1) # Write the schedule to a file: mdl.schedule.write(file='1fas.sch') # Read it in just for fun: mdl.schedule.read(file='1fas.sch')