Mike White wrote: > I would like to refine my models of a homopentamer with loop refinement. > In the past (Mod8) I would use loopmodel, but noticed that there is an > "improved" loop modeling routine dope_loopmodel() which uses (among > other things) the DOPE score in the refinement process. In various parts > of the manual it mentions that the DOPE score was developed for > single-chain models and shouldn't be used as an evaluative tool for > multichain models. Does this also meas that the dope_loopmodel() routine > shouldn't be used for loop refinement of multichain models? What's the > downside of using it for multi-chain models?

As with any statistical model, you should be careful about using it for systems not covered by the original training set. DOPE actually appears to work rather well for scoring multiple-chain models, protein-protein interfaces, etc. but I'm not aware of any comprehensive published benchmark for such systems, hence the warning in the manual. And yes, as a method which uses DOPE, dope_loopmodel would be subject to the same warning.

On the other hand, as far as I recall the 2000 Prot Sci loop modeling study (loopmodel) also did not include any multi-chain models in the derivation of its statistical potential...

> If so, this would be a shame since there are also other enhancements > (Leonard_Jones potentials, etc.) unrelated to DOPE that would be nice to > be able to use.

Regular loopmodel also uses a Lennard Jones potential. But it is rather straightforward to customize the loop modeling method yourself by subclassing loopmodel if you want to play with different scoring terms, etc. This is exactly what dope_loopmodel does - see the modlib/modeller/automodel/dope_loopmodel.py file.

Ben Webb, Modeller Caretaker