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
