Dear Lu,
when you only alter the ligand, the intensities between two structures
are probably quite similar. Hence when you only exchange the ligand and
choose a different set of reflections as Rfree, those free reflections
were previously used for refinenement, i.e. Rfree might suffer from
model bias. It was believed that Rfree does not properly cross-validate
your structure in this case.
However, Ian Tickle stated (probably more than once) that when you
remove a set of reflections from the data and refine until convergence,
those reflections set aside do not suffer from the memory effect, i.e.
they are 'freed' by refinement. I call this 'Tickle's conjecture' and we
investigated it with a set of experiments. According to our results
(http://www.pnas.org/content/early/2015/07/02/1502136112) Tickle's
conjecture holds true so that you can reassign any set as Rfree as long
as you refine to convergence (within numerical precision). Following our
results, if you only have a small'ish data set, this publications
seconds Axel Brunger's recommendation to use all reflections for
refinement and calculate Rcomplete instead of Rfree. It's based on all
reflections and shows as little bias as Rfree.
Cheers,
Tim
On 07/13/2015 04:15 PM, luzuok wrote:
>
>
> Dear ccp4bb members,
>
>
> It's said that when choosing R free set of protein-ligand complex data set, it is better to use the same reflections as the native one(if have). Could anybody provide any detail or references about why we should do so?
>
> Best regards!
>
> Lu
>
>
>
>
>
> --
>
> 卢作焜
> 南开大学新生物站A202
>
>
> Lu Zuokun, Ph.D. Candidate
> College of Life Science, Nankai University
>
--
--
Dr Tim Gruene
Institut fuer anorganische Chemie
Tammannstr. 4
D-37077 Goettingen
phone: +49 (0)551 39 22149
GPG Key ID = A46BEE1A
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