Hi,
In your specific case, you can predict that reflections for which the l index is a multiple of 9 will be particularly strong, while others will be weaker. So you could use sftools to select reflections that are a multiple of 9 and write them into one MTZ file.
sftools
read my.mtz
select index l zone 9n
write my_stronger.mtz
stop
yes
To get the rest of the reflections, you can add the "select invert" command after the initial "select index…" command to get everything else.
A more general way is to run a recent version of Phaser, choosing the NCS mode. Presumably there are 9 copies related by the translational NCS. The default in Phaser is to assume there are two copies related by tNCS, if there's a large peak in the native Patterson, so you would have to tell it there are 9 copies. Running from a script, you would use the command "TNCS NMOL 9". The NCS mode produces an MTZ file containing a column labelled NcsEps, which is the factor by which the tNCS increases the expected intensity for each reflection. The log file has a histogram of NcsEps values, so you could decide on a cutoff between weak and strong reflections, then use sftools to select them. To get the reflections with greater than average intensity, you could use something like this:
sftools
read NCSanalysis.mtz
select column NcsEps > 1
write bigeps.mtz
stop
yes
I hope that helps!
Randy Read
-----
Randy J. Read
Department of Haematology, University of Cambridge
Cambridge Institute for Medical Research Tel: +44 1223 336500
Wellcome Trust/MRC Building Fax: +44 1223 336827
Hills Road E-mail: [log in to unmask]
Cambridge CB2 0XY, U.K. www-structmed.cimr.cam.ac.uk
On 26 Feb 2013, at 03:06, Yuan SHANG <[log in to unmask]> wrote:
> Dear all,
> currently, I have a data set scaled in P22121 which containing a PST of (0.5,0.5,0.111). The structure were successfully solved by molecular replacement. However, the R free factors remained as high as ~33% in the refinement. I search the literature and found that it was common to have such high R free factors in case of PST (Felix F.Vajdos,etc.,protein science,1997;Arthur H.Robbins,etc.,Acta D,2010;Florence Poy,etc,NSMB,2001;Cory L.Brooks etc,Acta D,2008;). In the 2001 NSMB paper(doi:10.1038/nsb720), the authors split the dataset into 'weak,medium and strong' reflections, and showed good refinement statistcs in the 'medium reflection dataset'. Although I had good electron density maps to show my solution is correct. To further convince the reviewers, I also want to split my data set into such sub-datasets according to the symmetry. Did anyone know how to split the data set in this case?
>
> Best regards,
> Yuan
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