Hi Pete, the rationale is: at that low resolution the density map and traditional set of restraints are not enough to preserve secondary structure elements during refinement. For example, if you start with a model having good secondary structure elements, they will be distorted after refinement against low resolution data. See: around pages number 49 - 59 here:
http://www.phenix-online.org/presentations/latest/pavel_refinement_general.pdf Pavel. On Thu, Sep 22, 2011 at 1:18 PM, Pete Meyer <[email protected]> wrote: > I've noticed that people seem to be using or recommending secondary > structure restraints for low resolution refinement lately, but I'm somewhat > confused about the logic underlying their use. > > Using ballpark figures from a system I'm familiar with: 30000 atoms (90000 > positional parameters), 4500 residues, 100000 reflections and 95000 > geometric (bond and angle) restraints. > n_ref / n_param ~= 1.11 > (n_ref + n_geom) / n_param ~= 2.16 > > Assuming all residues are localized, and each residue provides 2 secondary > structure restraints (best-case scenario), this changes the effective > observation to parameter ratio to: > > (n_ref + n_geom + n_ss ) / n_param ~= 2.26 > > In short, the effective observation to parameter ratio improves by ~4%. > This seems like a relatively small improvement, especially if the trade-off > is that Ramachandran statistics can't be used for validation anymore. It > also seems like the improvement would decrease with larger proteins (the > number of additional parameters from adding a residues increase faster than > the number of secondary structure restraints that residue could provide). > > Does anyone have any suggestions that could help clear things up? > > Thanks, > > Pete >
