Hi Jason,
The Ramachandran statistics are poor; I've seen worse published, but it would be wise to fix these. I'm assuming you don't have a high-resolution structure that you can use as a reference model - this is usually the best option. Otherwise, adding Ramachandran restraints will probably help a lot, but you should first fix all outliers manually in Coot (also applying real-space refinement with Coot's Ramachandran restraints turned on), as the default potential is very tight and can pull residues the wrong way if they're starting from a very bad position.
- Often using Ramachandran restraints fixes the problem right away, so I would probably do it first, and then walk through the list of outliers that you had before refinement run with Ramachandran restraints, and see *how* these outliers were fixed. Nat's suggestion should work too but might require more up-front work. - Run refinement with weights optimization (optimize_wxc=true); - Use NCS if available; - Secondary structure restraints should definitely help, but: -- you need to have secondary structure well defined in your input model if you want phenix.refine to pick it up automatically (and correctly), or alternatively -- define it manually in a parameter file and supply to phenix.refine. Pavel.