You're right. The smoothed <I> is used for the Truncate procedure, though it is difficult in the very low resolution bins. Also it doesn't allow for pseudo-translations, which it should.

As you say, the linear fit is only used to put data on a very rough absolute scale. This isn't necessary, but it doesn't hurt

There's no reason why truncate shouldn't give a "best" estimate of |F| ^2 but I'm not sure why you would want this. I would think that refinement is better done against the measured I, which may be slightly negative

Phil


On 24 Aug 2008, at 19:23, Ian Tickle wrote:


Phil

OK I admit I didn't delve very deeply into the code, but looking at the
printer output it obviously does do a linear fit to the Wilson plot to
get an overall B & scale. However, looking at the man page (if all else fails read the documentation!) I see that it does say that this is only
used to put the data on an approximately absolute scale.  This is
unnecessary of course - absolutely scaled data isn't needed for HA
phasing & MR, and the refinement will give a much more accurate scale
factor anyway.

Thanks for the info.

Cheers

-- Ian

-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Phil Evans
Sent: 23 August 2008 08:09
To: CCP4BB@JISCMAIL.AC.UK
Subject: Re: [ccp4bb] Wilson plot from truncated.mtz

Actually Truncate (& ctruncate) do use a smoothed <I> in resolution
shell (using a spline fit), not a linear Wilson plot

Phil



On 22 Aug 2008, at 20:30, Ian Tickle wrote:

This indeed raises the question of whether the assumed Wilson
distribution is valid, and it's another point I was in fact going to
bring up.  As presently constructed, Truncate fits a
straight line to
the Wilson plot (based on Imeas) in order to determine the overall
scale
& B, but to avoid the problem that the low res data for a typical
protein deviates markedly from the theoretical distribution, it uses
resolution limits determined by the RSCALE option.
According to the
man
page, the low resolution limit is by default set to 4 Ang
if the high
res limit is higher than 3.5 Ang.  This usually means that the
straight
line fit is good for the high res data, but very poor for
the low res
data, but at least this means that the assumed distribution
is valid
at
high res where most of the weak data (i.e. the data most
affected by
the
Bayes correction) is located, but not valid for any weak
data at low
res
(there will obviously be some).  As you point out translational NCS
invalidates these assumptions since the form of the distribution
changes, but then probably only a few % of structures
suffer from this
type of NCS.

A better way to deal with this would surely be to forget about the
Wilson plot and simply determine the average I in resolution shells,
with perhaps spline interpolation between the bin means (this is
actually the 'k-curve' method for determining E's, attributed
originally
to Karle & Hauptman I believe).  There would still be an
assumption of
the Wilson distribution but now only within the bins, i.e.
the Wilson
distribution parameter would vary with resolution, when
previously it
was a single number independent of resolution.  I think
this would go
some way towards addressing your objections.

In fact my prog ECALC already uses the k-curve method to
determine E's
and it would probably not be too much work to have it
optionally read
the IMEAN column, perform the Bayes probability integrals and output
both <F> and <F^2> (also <E> & <E^2> as now).  However I've no idea
whether my iteration idea for re-using the <F^2> values for the k-
curve
will work - it may well diverge!

Cheers

-- Ian

-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of George
M. Sheldrick
Sent: 22 August 2008 18:57
To: CCP4BB@JISCMAIL.AC.UK
Subject: Re: [ccp4bb] Wilson plot from truncated.mtz

In addition to Ian's circular argument, there is the problem that
the assumed distribution is only approximately valid, indeed in the
presence of (translational) NCS it could well be a poor
approximation.
Refinement against suitably weighted measured intensities
(which may of
course be slightly negative because of experimental errors)
avoids this
problem but we still need F(obs) (and hence TRUNCATE) to
calculate a map.

George

Prof. George M. Sheldrick FRS
Dept. Structural Chemistry,
University of Goettingen,
Tammannstr. 4,
D37077 Goettingen, Germany
Tel. +49-551-39-3021 or -3068
Fax. +49-551-39-22582


On Fri, 22 Aug 2008, Ian Tickle wrote:

This goes back to the issue I was raising, namely that
<F>^2 (from the
Truncate output mtz F column) is not the same as Imeas
(in the IMEAN
column) so you won't get exactly the same results from the
Wilson plot,
particularly at high res where the average I/sigma is low.
Since the
plot actually demands F^2 then it seems to me that
logically you need to
use <F^2> which AFAICS is not possible using Truncate
since it never
calculates that.

This gets you into a circular argument because you need
the correct
Wilson plot results in order to perform the Bayes
correction to the
intensities (i.e. it gives you the prior distribution parameter),
however you need the Bayes-corrected intensities to
correctly calculate
the Wilson plot!  Possibly iterating (from the initial Wilson plot
results calculated using Imeas) will sort this out.

Also referring to an earlier response by Phil, Truncate
clearly outputs
the scaled Imeas, not <F^2>, in the IMEAN column as I had
originally
assumed, since the column has a -ve min value from mtzdump
(<F^2> can
never be < 0), and logically it's <F^2> not Imeas or <F>
that you need
for applications (such as MR and F^2 based refinement)
which demand F^2.

Cheers

-- Ian

-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Eleanor Dodson
Sent: 22 August 2008 14:16
To: [EMAIL PROTECTED]
Cc: CCP4BB@jiscmail.ac.uk; [EMAIL PROTECTED]
Subject: Re: Wilson plot from truncated.mtz

rerun truncate with input amplitudes..
eleanor

James Pauff wrote:
If I've lost my SCALA MTZ, and have only the truncated.mtz
for my dataset, which program is the quickest means of
obtaining a Wilson plot?

Thank you again,
Jim


--- On Wed, 8/20/08, Eleanor Dodson
<[EMAIL PROTECTED]> wrote:


From: Eleanor Dodson <[EMAIL PROTECTED]>
Subject: Re: [ccp4bb] Lower completeness, decent R
factors, but low B factor...
To: CCP4BB@JISCMAIL.AC.UK
Date: Wednesday, August 20, 2008, 4:30 AM
James Pauff wrote:

Hello all,

I have a refined structure at 2.6 angstroms that at

about 73% completeness at this resolution.  The I/sigma is
about 2.0 at 2.6 angstroms, and the omit density for my
ligands is great contoured at 3.0sigma.  My Rcryst is 19 or
so and the Rfree is 24.5 or so.

HOWEVER, my mean B value is 13.9, whereas my other 2

structures (at 2.2 and 2.3 angstroms, same protein, >95%
completeness) have mean B values of 22+.  Any suggestions as
to what is going on here?  I'm having trouble explaining
this.

Thank you,
Jim







Have you used TLS - listed B factors will then be given
relative to the
TLS parameters. You need to run tLSANL to get a more
realistic value.
Eleanor


But in fact temperature factors are rather harder to
estimate at lower
resolutions than higher. Look at your <Fo> and
<Fc> curves v resolution
( part of a REFMAC loggraph) and you can see that sometimes
the overall
scaling struggles to get a reasonable fit..











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