Its also a bit too simple to count secondary structure restraints as 2 restraints per residue, because if they're tight enough on, say, an alpha helix, in combination with other geometry restraints (good bond angles, no clashes, etc) you could probably turn the backbone of the entire helix into one nearly-rigid body. Which is not a bad idea at 5A.
===================================== Phoebe A. Rice Dept. of Biochemistry & Molecular Biology The University of Chicago phone 773 834 1723 http://bmb.bsd.uchicago.edu/Faculty_and_Research/01_Faculty/01_Faculty_Alphabetically.php?faculty_id=123 http://www.rsc.org/shop/books/2008/9780854042722.asp ---- Original message ---- >Date: Thu, 22 Sep 2011 13:29:54 -0700 >From: CCP4 bulletin board <CCP4BB@JISCMAIL.AC.UK> (on behalf of Pavel Afonine ><pafon...@gmail.com>) >Subject: Re: [ccp4bb] question regarding secondary-structure restraints >To: CCP4BB@JISCMAIL.AC.UK > > 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 > <pame...@mcw.edu> 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
