Hi
all,
I created 5 models
with a modified automodel script:
a =
automodel(env,
alnfile =
"bcs1h.fasta",
knowns = ("1E32m", "1IXZm", "1IY1m", "1LV7m", "1S3Sm", "2CE7m",
"2QZ4m"),
sequence =
("bcs1h"),
assess_methods=(assess.DOPE))
a.starting_model = 1
a.ending_model =
5
a.library_schedule = autosched.slow
a.max_var_iterations =
300
a.md_level=
refine.slow
a.repeat_optimization = 2
a.max_molpdf =
1e6
a.make()
These models
have their OBJECTIVE FUNCTION around 11,000 . I made some DOPE plots
to check for poorly defined regions. I detected some loops where the DOPE score
was higher. I decided to further modelize these loops with loop_model as shown
in the tutorial selecting residues having DOPE score significatively higher than
the average DOPE for the entire structure. Here is the
script:
class
MyLoop(loopmodel):
def select_loop_atoms(self):
return
selection( self.residue_range(1,
26),
self.residue_range(75,84),
self.residue_range(89,97),
self.residue_range(102,122),
self.residue_range(183,204),
self.residue_range(230,238))
for
i in pdb_names:
m = MyLoop
(env,
inimodel=("bcs1h.B9999000%s.pdb"
%(i)),
sequence=("bcsh1.%s"
%(i)),
library_schedule =
autosched.slow,
loop_assess_methods=(assess.DOPE,
assess.GA341))
m.loop.starting_model=
1
m.loop.ending_model = 5
m.loop.md_level =
refine.slow_large
m.loop.max_var_iterations =
300
m.loop.library_schedule =
autosched.slow
m.make()
Quite surprisingly, the OBJECTIVE function of the 25 produced
loop models varies from 2000 to 7000 and the DOPE plots of the different loop
models is not as good as the first models!!
Is there something
wrong in the script or do I need to change and optimize some other
parameters?