I will agree with Ulrich. Even at 3.0 A, it is
possible to have a structure with reasonable accuracy
which can explain the biological function/ or is
consistent with available biochemical data.
Ranvir
--- Ulrich Genick <[EMAIL PROTECTED]> wrote:
> Here are my 2-3 cents worth on the topic:
>
> The first thing to keep in mind is that the goal of
> a structure
> determination
> is not to get the best stats or to claim the highest
> possible
> resolution.
> The goal is to get the best possible structure and
> to be confident that
> observed features in a structure are real and not
> the result of noise.
>
> From that perspective, if any of the conclusions
> one draws from a
> structure
> change depending on whether one includes data with
> an I/sigI in the
> highest
> resolution shell of 2 or 1, one probably treads on
> thin ice.
>
> The general guide that one should include only data,
> for which the
> shell's average
> I/sigI > 2 comes from the following simple
> consideration.
>
>
> F/sigF = 2 I/sigI
>
> So if you include data with an I/sigI of 2 then your
> F/sigF =4. In
> other words you will
> have a roughly 25% experimental uncertainty in your
> F.
> Now assume that you actually knew the structure of
> your protein and
> you would
> calculate the crystallographic R-factor between the
> Fcalcs from your
> true structure and the
> observed F.
> In this situation, you would expect to get a
> crystallographic R-
> factor around 25%,
> simply because of the average error in your
> experimental structure
> factor.
> Since most macromolecular structures have R-factors
> around 20%, it
> makes little
> sense to include data, where the experimental
> uncertainty alone will
> guarantee that your R-factor will be worse.
> Of course, these days maximum-likely-hood refinement
> will just down
> weight
> such data and all you do is to burn CPU cycles.
>
>
> If you actually want to do a semi rigorous test of
> where you should stop
> including data, simply include increasingly higher
> resolution data in
> your
> refinement and see if your structure improves.
> If you have really high resolution data (i.e.
> better than 1.2 Angstrom)
> you can do matrix inversion in SHELX and get
> estimated standard
> deviations (esd)
> for your refined parameters. As you include more and
> more data the
> esds should
> initially decrease. Simply keep including higher
> resolution data
> until your esds
> start to increase again.
>
> Similarly, for lower resolution data you can monitor
> some molecular
> parameters, which are not
> included in the stereochemical restraints and see,
> if the inclusion
> of higher-resolution data makes the
> agreement between the observed and expected
> parameters better. For
> example SHELX does not
> restrain torsion angles in aliphatic portions of
> side chains. If your
> structure improves, those
> angles should cluster more tightly around +60 -60
> and 180...
>
>
>
>
> Cheers,
>
> Ulrich
>
>
> > Could someone point me to some standards for data
> quality,
> > especially for publishing structures? I'm
> wondering in particular
> > about highest shell completeness, multiplicity,
> sigma and Rmerge.
> >
> > A co-worker pointed me to a '97 article by
> Kleywegt and Jones:
> >
> > http://xray.bmc.uu.se/gerard/gmrp/gmrp.html
> >
> > "To decide at which shell to cut off the
> resolution, we nowadays
> > tend to use the following criteria for the highest
> shell:
> > completeness > 80 %, multiplicity > 2, more than
> 60 % of the
> > reflections with I > 3 sigma(I), and Rmerge < 40
> %. In our opinion,
> > it is better to have a good 1.8 Å structure, than
> a poor 1.637 Å
> > structure."
> >
> > Are these recommendations still valid with maximum
> likelihood
> > methods? We tend to use more data, especially in
> terms of the
> > Rmerge and sigma cuttoff.
> >
> > Thanks in advance,
> >
> > Shane Atwell
> >
>
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