Dear Faisal,
When scrutinising such distances do be aware of the possibility of false
precision in the estimates; see eg http://dx.doi.org/10.1107/S2052252513031485
Best wishes,
John
Prof John R Helliwell DSc
On 17 Apr 2014, at 21:13, Faisal Tarique faisaltari...@gmail.com wrote:
Dear
Hi Roberto,
for my taste the answers so far have not mentioned (or I did not understand
them) that there is a distinction between indicators of the precision of merged
data, and those for the precision of unmerged data.
It is not possible to directly compare (or equate) indicators of one
Dear all
I have a protein which is dimer having one ligand binding site in each
monomer. I refined the crystal structure with ligand in both sites finally.
I refined will full occupancy of 1 for ligands (same in both). But now i
want to see is there any difference in the occupancy of both ligands
Bonjour Philippe,
The reports are based on the recommendations of the wwPDB X-ray validation
task force - see http://www.wwpdb.org/workshop/2011/index.html and the report
published by this task force at
http://www.cell.com/structure/abstract/S0969-2126%2811%2900285-1
Of course, if you have
[There is] a distinction between indicators of the precision of merged data,
and those for the precision of unmerged data.
Let's take a step back - definitions matter:
(i) We have multiple observations of the same, already integrated h: the
'unmerged' data - most important data set which
On Fri, 18 Apr 2014 12:33:30 +0200, Bernhard Rupp hofkristall...@gmail.com
wrote:
[There is] a distinction between indicators of the precision of merged data,
and those for the precision of unmerged data.
Let's take a step back - definitions matter:
(i) We have multiple observations of the
Dear all
I request you please tell me what is the value of Observed criterion sigma
(F) and Observed criterion sigma (I) for any data processed by imosflm and
scala ?
--
Regards
Faisal
School of Life Sciences
JNU
(i) We have multiple observations of the same, already integrated h: the
'unmerged' data - most important data set which SHOULD BE deposited and
rarely is.
yes, fully agree.
Perfect.
I don't quite understand the difference between (ii) and (iii). As soon as
you take the weighted average,
Hi Monica,
Calculate the mean B-factor of all atoms that making interactions with each
ligand in monomer A and B.
Use those means values as B-factors for each ligand respectively.
Adjust manually the occupancies, in order the B-factors for each ligand to
stay after refinement close to
Hi Monica,
You can refine the ligand occupancy in refmac as explained here:
http://www2.mrc-lmb.cam.ac.uk/groups/murshudov/content/refmac/refmac_keywords.html
or in phenix, whichever program you're using.
Cheers,
Boaz
Boaz Shaanan, Ph.D.
Dept. of Life Sciences
Ben-Gurion University
Roberto Battistutta wrote:
Hi,
in the Rupp book the following relation is reported (on pag 415):
Rmerge ≈ 0.8/I/σ(I)
referring to a relation of the linear merging R-value with the signal-to-noise
ratio.
in a 2006 CCP4bb, Manfred Weiss reported:
Rrim (or Rmeas) = 0.8*sd(i)/I
Bernhard Rupp
Hi Ed,
your example seems to be designed to show that the average of reciprocal values
is not the same as the reciprocal of an average value? If that is what you are
alluding to, then please not that the (relatively narrow) Wilson distribution
of intensities has the effect of making the
Dear Kay.
Arguably, the resolution of a structure is the most important number to
look at; it is definitely the first to be examined, and often the only one
examined by non-structural biologists.
Since this number conveys so much concerning the quality/reliability of
the the structure, it is not
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I see no problem with saying that the model was refined against every
spot on the detector that the data reduction program said was observed
(and I realize there is argument about this) but declare that the
resolution of the model is a number
Dear Arnon et al:
My understanding of the Shannon/Nyquist sampling theorem is admittedly
extremely rudimentary, but I think aliasing can result if an arbitrary
brick-wall resolution cut-off to the data is applied.
So let’s say there are real data are to 2.0 Å resolution. Applying the 2.2 Å
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