On Thursday, May 31, 2012 02:21:45 pm Dale Tronrud wrote:
> The resolution limit of the data set has been such an important
> indicator of the quality of the resulting model (rightly or wrongly)
> that it often is included in the title of the paper itself. Despite
> the fact that we now want to include more, weak, data than before
> we need to continue to have a quality indicator that readers can
> use to assess the models they are reading about. While cumbersome,
> one solution is to state what the resolution limit would have been
> had the old criteria been used, as was done in the paper you quote.
> This simply gives the reader a measure they can compare to their
> previous experiences.
[\me dons flame suit]
To the extent that reporting the resolution is simply a stand-in
for reporting the quality of the model, we would do better to cut
to the chase. For instance, if you map the Molprobity green/yellow/red
model quality scoring onto good/mediocre/poor then you can title
Crystal Structure of Fabulous Protein Foo at Mediocre Quality
[\me removes flame suit from back, and tongue from cheek]
More seriously, I don't think it's entirely true that the resolution
is reported as an indicator of quality in the sense that the model
is well-refined. There are things you can expect to learn from a
2Å structure that you are unlikely to learn from a 5Å structure, even
if equal care has been given to both experiments, so it makes sense
for the title to give the potential reader an idea which of the two
cases is presented. But for this purpose it isn't going to matter
whether "2Å" is really 1.8Å or 2.2Å.
> Now would be a good time to break with tradition and institute
> a new measure of quality of diffraction data sets. I believe several
> have been proposed over the years, but have simply not caught on.
> SFCHECK produces an "optical resolution". Could this be used in
> the title of papers? I don't believe it is sensitive to the cutoff
> resolution and it produces values that are consistent with what the
> readers are used to. With this solution people could include whatever
> noisy data they want and not be guilty of overstating the quality of
> their model.
We should also encourage people not to confuse the quality of
the data with the quality of the model.
Ethan A Merritt
Biomolecular Structure Center, K-428 Health Sciences Bldg
University of Washington, Seattle 98195-7742