A common question is how to pick the best Designs among the ones (already likely good energetically) that Rosetta produces.
One helpful step is to look at the multiple sequence alignments of outputs and build a phylogenetic tree to understand the diversity over your set, then tune your choices as described there. This is also relevant to which mutations to accept when redesigning a protein, especially just using Repack to test your design’s stability in Bench.
DDG was calibrated empirically on thousands of real mutational data sets of single point mutations — so it gives one free energy number for one mutation, and in our testing that free energy correlates well with experimental data. In theory one could also do such a thing for double mutations, as described in this workflow, where we have some good evidence that it works, but for situations beyond that the theoretical assumptions of DDG start to be less valid (e.g. assuming fairly rigid backbone) and we don’t have good benchmarking data to rigorously support its use, the sequence space is just too high.
For more information on how to decide which combinations of Actions and repeats may be best for your project, click here.