This case is encouraging to me that a structure can be solved with such
high mosaicity (in your report is 1.9). I wonder how the diffraction looks
like (I imagine spots smearing or streak). With such high mosaicity, the
unit cell dimension and space group determination is highly likely not
accurate
In addition to what Nicolas has pointed out, is it quite suspicious to me that you have the same multiplicity in the high resolution shell as in the low resolution. On Mar 29, 2017 18:17, Nicolas FOOS wrote:
Dear Juliana,
all the statistics presented here looks good in
If you have outliers - worry about why?
(By the way - what is the multiplicity?}
Look at the AIMLESS list of rejections/ the scale factor for different
batches/ reports of ice rings/ etc
There has to be a reason, and with snsible reprocessing you can probably
but much better resolution data (ther
Juliana,
I think if you compare otherwise equivalent refinement runs in phenix.refine
with
refinement.input.xray_data.outliers_rejection=True (the default)
and
refinement.input.xray_data.outliers_rejection=False
then this will tell you if there's any meaningful difference in the refinement
sta
CCP4BB@JISCMAIL.AC.UK<mailto:CCP4BB@JISCMAIL.AC.UK>
Onderwerp: Re: [ccp4bb] Large number of outliers in the dataset
Dear Juliana,
all the statistics presented here looks good in terms of resolution cut (maybe
I will be less sever). For me the point is about the mosaicity you report 1.90
it's h
Dear Juliana,
all the statistics presented here looks good in terms of resolution cut
(maybe I will be less sever). For me the point is about the mosaicity
you report 1.90 it's high in my opinion. How looks you images? I am
wondering if the indexation is really right. And maybe the complain of
One thing that sometimes helps me in this situation is reprocess and refine
in lower symmetry like P21. It could be you have pseudosymmetry and need
to model more molecules in the AU to better reflect your data. Sometimes
this can help. If that doesn't work, then you may have to stick with your
To be really convinced I think you should also compare the maps at 2.6 and 2.3
Å. If the 2.3 Å map looks better, go for it. If it doesn’t look better, perhaps
you are adding noise, but the I/sigma and CC1/2 values suggest you aren’t.
Perhaps try 2.5 and 2.4 Å also.
And perhaps remove a well-order
It is not clear to me why you believe that cutting the resolution of the data
would improve your model (which after all is the aim of refinement). At the
edge CC(1/2) and I/sigI are perfectly respectable, and there doesn’t seem to be
anything wrong with the Wilson plot. Th R-factor will of cours
