Pavel,
Please correct if wrong, but I thought most refinement programs used the
weights e.g. sig(I/F) with I/F so would not really have a hard cut off anyway?
You’re just making the stats worse but the model should stay ~ the same (unless
you have outliers in there)
Clearly there will be a poi
Hi Sam Tang,
Sorry for a naive question. Is there any circumstances where one may wish
> to refine to a lower resolution? For example if one has a dataset processed
> to 2 A, is there any good reasons for he/she to refine to only, say 2.5 A?
>
yes, certainly. For example, when information content
Hi Sam,
If you have good data to 2A, then I cannot imagine throwing away a significant
fraction of it (there are lot of spots from 2.5-2A) will make your model better
Suggest reading
http://scripts.iucr.org/cgi-bin/paper?S0907444913001121
All best Graeme
On 5 Jul 2019, at 06:43, Sam Tang
mai
Hello everyone
Sorry for a naive question. Is there any circumstances where one may wish
to refine to a lower resolution? For example if one has a dataset processed
to 2 A, is there any good reasons for he/she to refine to only, say 2.5 A?
Thanks!
Sam Tang
##
Postdoctoral Position at the Institute of Protein Biochemistry, Ulm
University (Germany),
Group Prof. Dr. M. Fändrich
The Institute of Protein Biochemistry
(_https://www.uni-ulm.de/nawi/nawi-pbc.html_) investigates the molecular
basis of amyloid diseases, such as Alzheimer's disease and system
