Hi Charles,
I have some differents suggestions, maybe it could be a good idea to reconsider the criteria for keep or discard data : Diederichs, K., et P. A. Karplus. « Better Models by Discarding Data? ». Acta Crystallographica Section D: Biological Crystallography 69, nᵒ 7 (1 juillet 2013): 1215‑22. doi:10.1107/S0907444913001121.
This paper is about the quality of the final model with or without discarded data. But in my opinion, it maybe usefull to adopt a similar point of view about the anomalous data. What i mean is that
if the anomalous signal seems to be weaker, it's not necessarly worth to find the "good" substructure. If the signal that you measured, is well measured, with a good error estimation it maybe lower in terms of signal / noise but more accurate.
You didn't precise if your datasets were collected on only one crystal or with differents crystals. In function of that, you have to tak in count differents effects. Radiation damage, non-isomorphism...
One last point, depending of how you process your data, you have to keep in mind the over-scaling effect. If you use aimless directyl without options, if your data are already scaled (for example de XDS_ASCII.HKL), you may have some problem.
I hope to help. In my opinion, if you have to discard data, you have to do that not only based on "qualitativ criterias", but based on reliable criteria, cell parameters, B-factor, Bad correlation between data set from differents crystals.
Finnaly, even if it's seems that the anomalous signal is low, you have to try to find heavy atoms sites.
Nicolas
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De : CCP4 bulletin board [[log in to unmask]] de la part de Thanh Nguyen [[log in to unmask]]
Envoyé : lundi 2 mars 2015 10:24
À : [log in to unmask]
Objet : Re: [ccp4bb] merge weak anomalous signal from multiple datasets
Hi Charles,
Actually I also tried the method of Q. Liu et al and got one structure solved after merging 3 different datasets. The only thing different in my case is the Se-SAD phasing instead of Br. But the signal was also weak due to the disorder of incorporated Se in the protein crystals (I supposed so, because I used only 50% of Se-Met during the culture). So in my case, first I processed the data by XDS and made some resolution limitation according to the I/sigma and CCanom. I also did some limitation on the number of images because SCALA can only process not more than 10.000 images (remove the bad images according to the I/sigma and B-factor). And in fact, I did some combination between cut datasets and non-cut datasets and found that the cut datasets really gave me better merged datasets as some noises were removed and the resolution was limited, of course you also lose some information. But in our case, improving the weak anomalous signal to just enough for solving something is more important than losing some information of noise, so we need to be compromised. And any way, for facing with the weak anomalous signal issue, we usually collected such a high abundance of datasets, so losing some information of data is not the problem.
Hope you will be able to solve it.
Regards,
Thanh Nguyen
On Mon, Mar 2, 2015 at 5:19 AM, Andreas Förster <[log in to unmask]<mailto:[log in to unmask]>> wrote:
Hi Charles,
I don't know what multiscale does. Probably the right thing. If the anomalous signal is weaker after scaling of multiple datasets, non-isomorphism might be at fault. Try Blend to scale your datasets. Blend is part of ccp4 and gives good graphical diagnostic feedback.
Andreas
On 01/03/2015 4:20, CPMAS Chen wrote:
Dear CCP4 users,
Recently, I got some datasets with weak anomalous Br signal. I tried to
merge them according to Q. Liu et al Science 336, p1033 (2012). I am
using the script [log in to unmask] The merged dataset has WEAKER
anomalous signals.
Liu et al used SCALA for scaling and merging while multiscale@SSRL using
AIMLESS. Should this cause such a difference? The SCALA@SSRL has a
limitation on the number of frames it can process. So I cannot directly
check if this caused the difference.
Any suggestions?
Thanks!
Charles
--
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Charles Chen
Research Associate
University of Pittsburgh School of Medicine
Department of Anesthesiology
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Nguyen Hong Thanh, Ph.D. student
Lab 20B. Macromolecular Structures Department
Centro Nacional de Biotecnologia, CSIC
C/ Darwin 3, Campus de Cantoblanco
28049, Madrid, Spain
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Genetic Engineering Laboratory
Institute of BioTechnology
Viet Nam Academy of Science and Technology
18 Hoang Quoc Viet, Cau Giay, Hanoi, Vietnam
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