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I think I misunderstood your email and now I am confused as well.
Raji
[EMAIL PROTECTED] wrote:
Hi Raji:
Just to make this clear; sorry if I mispresented anything. In both cases
the variables are minimum -- only rigid body refinement and nothing else.
i.e. rigid.inp in CNS and "refi type rigid" in refmac.
Weikai
On Thu, May 18, 2006 3:59 pm, Raji Edayathumangalam wrote:
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I've done some crude comparisons for a couple of different structures
and ended up with differences in the range of ~1% between the programs. But
during the comparison, the refinement protocol in CNS included
minimization, b-group/individual and playing with no. of cycles (for same
structure from Refmac minus TLS) before the program honed in on similar
values.
I am not sure the rigid body refinement output in CNS (if that's what
you meant) can be directly compared with Refmac output and 'might' explain
the differences in the R-factors. Did you try some more refinement in CNS
to see if the 5% difference decreases/disappears?
Someone pl. correct me if you think CNS rigid body equals (in
theory/philosophy) Refmac refinement routine.
Raji
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Hi All:
I have two questions for the bulk solvent model:
1. Maybe I missed this in reading the manual, but in Refmac is there a
way of inputting an mask to define the bulk solvent? Or it is always
done automatically using the keyword SOLVENT?
2. Is the bulk solvent model in Refmac somehow different from that in
CNS? For one of my low resolution data to 7A, the bulk solvent model in
Refmac seems to behave much better than that in CNS. I only use rigid
body refinement and there is no TLS refinement in Refmac. So in this
case, maybe the major difference of R factor using these two programs
would come from the bulk solvent models (maybe also the scaling of Fc
to Fobs, or maximum likelyhood, etc.?). Anyway the R factors differ by
10%
using the two different programs (40% in CNS vs. 30% in Refmac) when
both have a bulk solvent model. If I turn off bulk solvent estimation
in CNS, the R factor drops by 5% (i.e. now R=35% in CNS).
Regards,
Weikai
