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 <nicolas.f...@esrf.fr> wrote:
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.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 Xtriage about outlier is due to this high mosaicity. What is the diagnostic of Xtriage in terms of possible twinning? I am also wondering about a pseudo translation.
Maybe try to re-processed your data in this direction.
Hope to help.
Nicolas
Nicolas Foos PhD Structural Biology Group European Synchrotron Radiation Facility (E.S.R.F) 71, avenue des Martyrs CS 40220 38043 GRENOBLE Cedex 9 +33 (0)6 76 88 14 87 +33 (0)4 76 88 45 19On 29/03/2017 17:56, Mark J van Raaij wrote:
Perhaps try 2.5 and 2.4 Å also.And perhaps remove a well-ordered aa from the input model, refine at different resolutions and compare the difference maps for that aa. Or calculate omit maps at different resolutions and compare those.
Mark J van Raaij
Dpto de Estructura de Macromoleculas
Centro Nacional de Biotecnologia - CSIC
calle Darwin 3
E-28049 Madrid, Spain
tel. (+34) 91 585 4616
http://wwwuser.cnb.csic.es/~mjvanraaij
On 29 Mar 2017, at 17:44, Phil Evans <pre@MRC-LMB.CAM.AC.UK> wrote:
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 course be higher if you include more weak data, but minimising R is _not_ the aim of refinement. You should keep all the data
I don’t know what xtriage means by “large number of outliers”: perhaps someone else can explain
Phil
On 29 Mar 2017, at 14:54, Juliana Ferreira de Oliveira <juliana.oliveira@LNBIO.CNPEM.BR> wrote:
Hello,
I have one dataset at 2.3 Å (probably it can be better, I/σ = 2.1 and CC1/2 = 0.779, the summary data is below), but when I perform Xtriage analysis it says that “There are a large number of outliers in the data”. The space group is P212121. When I refine the MR solution the Rfree stops around 30% and it doesn´t decrease (in fact if I continue refining it starts to increase).
The Wilson plot graph is not fitting very well between 2.3 and 2.6 Å:
<image001.jpg>
So I decided to cut the data at 2.6A and Xtriage analysis doesn’t notify about outliers anymore. I could refine the MR solution very well, the final Rwork is 0.2427 and Rfree = 0.2730 and validation on Phenix results in a good structure.
I run Zanuda to confirm the space group and it says that the space group assignment seems to be correct.
Do you think that I can improve my structure and solve it at 2.3 Å or better? Or I can finish it with 2.6 Å? To publish at 2.6 Å I need to justify the resolution cut, right? What should I say?
Thank you for your help!
Regards,
Juliana
Summary data:
Overall InnerShell OuterShell
Low resolution limit 51.51 51.51 2.42
High resolution limit 2.30 7.27 2.30
Rmerge 0.147 0.054 0.487
Rmerge in top intensity bin 0.080 - -
Rmeas (within I+/I-) 0.155 0.057 0.516
Rmeas (all I+ & I-) 0.155 0.057 0.516
Rpim (within I+/I-) 0.048 0.017 0.164
Rpim (all I+ & I-) 0.048 0.017 0.164
Fractional partial bias -0.006 -0.003 0.146
Total number of observations 83988 2907 11885
Total number unique 8145 307 1167
Mean((I)/sd(I)) 9.3 23.9 2.1
Mn(I) half-set correlation CC(1/2) 0.991 0.998 0.779
Completeness 99.9 99.5 100.0
Multiplicity 10.3 9.5 10.2
Average unit cell: 37.57 51.51 88.75 90.00 90.00 90.00
Space group: P212121
Average mosaicity: 1.90
Juliana Ferreira de Oliveira
Brazilian Laboratory of Biosciences - LNBio
Brazilian Center for Research in Energy and Materials - CNPEM
Campinas-SP, Brazil
