Is the function - forms.upper_bound(..) - supposed to restrain precisely?
Hello modeller users and experts,
I am trying to impose distance restrains in structure prediction python script as explained in the example at http://salilab.org/modeller/manual/node27.html. Following the example I used the forms.gaussian(group, feature, mean, stdev) function. But my predicted model does exactly satisfy the distance restrain. I can observe that adding the distance restrain, HAS added some distance restrain but it looks like modeller is not giving 100% weightage to the distance restrain.
I also tried forms.upper_bound(group, feature, mean, stdev) as explained at http://salilab.org/modeller/9v5/manual/node97.html. With this function, I verified that modeller gives more weightage to the distance restrain, but STILL does not precisely satisfy the restrain.
Is this what these functions are supposed to do? OR is there any other technique to instruct modeller to absolutely satisfy the distance restrain?
Case I:
Restrain used: rsr.add(forms.gaussian(group = physical.xy_distance, feature = features.distance(at['CA:2'], at['CA:4']), mean = 7.0, stdev = 0.1));
Distance between CA:2 and CA:4 in the final model: 8.5
Case II:
Restrain used: rsr.add(forms.upper_bound(group = physical.xy_distance, feature = features.distance(at['CA:2'], at['CA:4']), mean = 7.0, stdev = 0.1));
Distance between CA:2 and CA:4 in the final model: 8.2
Sincerely, Badri Adhikari CS Graduate Student, University of Missouri-Columbia, Columbia, Missouri BAP54@mail.missouri.edumailto:BAP54@mail.missouri.edu
On 07/11/2012 12:42 PM, Adhikari, Badri (MU-Student) wrote: > I am trying to impose distance restrains in structure prediction python > script as explained in the example at > http://salilab.org/modeller/manual/node27.html. Following the example I > used the forms.gaussian(group, feature, mean, stdev)function. But my > predicted model does exactly satisfy the distance restrain.
Modeller tries to satisfy all of the restraints as well as possible. This might not be achievable, of course (e.g. conflicting restraints).
If you want the restraint to be stronger, reduce the standard deviation.
> I also tried forms.upper_bound(group, feature, mean, stdev)
An upper bound is not the same as a Gaussian, so in general it doesn't make sense to switch one for another. For example, the two atoms could be zero angstroms apart and would not be penalized by the upper bound (but would be by a Gaussian).
Ben Webb, Modeller Caretaker
participants (2)
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Adhikari, Badri (MU-Student)
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Modeller Caretaker