I third this idea - it's about 4 decades overdue, but got pressing with
the CryoEM revolution, and is now existential after AlphaFold.
Our use case (large collections of near-identical ligand-bound
structures) also really really needs this - a way to capture confidence
that stems from far more than just the single diffraction dataset. (We
get that from PanDDA, of course.)
It is a curiosity, is it not, that we crystallographers have got
ourselves so wrapped up with the conceit that we can generate a complex
model and can (and must!) quantify its truthfulness on the basis of a
single experiment only (the Best Diffraction Pattern). I guess it's the
elegance of the diffraction equation that's so seductive - but it's also
very obviously bullshit, and worse than that, it has gone and polluted
all of structural biology.
So yes, we should try to develop (and champion!) rigorous tricks that
give us the psychological crutches for extricating ourselves. Pavel, I'm
assuming you have some ideas, else you wouldn't have suggested it (he
said hopefully).
Things like the PDBs validation report of course also lie on this axis;
but the local numbers will need to inserted directly into the model - so
that we can stop leaning on the B-factor for this. Hooray for mmCIF's
extensibility!
Frank
(Oh what fun to wax polemical on occasion.)
On 04/03/2025 09:06, Tim wrote:
Dear Pavel and Oliverio,
In our group we have had recent discussion about this issue, and I
second Pavel's suggestion to introduce such a confidence measure.
When interpreting cryo-EM (but also X-ray crystallography) maps with
models, we often face the problem that we have complete models (thanks
to Alphafold) but incomplete density. Most often the lack of density
is due to conformational flexibility. So the question is what to do
with those parts that lack map support? Some favor the option not to
model these parts. However, usually we are pretty certain that these
parts are present, but too flexible to be observed. So I personally
think that structural models should be deposited as complete as possible.
Having such a confidence measure would facilitate the interpretation
of our structural models, also because it seems that not many actually
open the associated deposited maps/densities when interpreting
deposited structures.
Best wishes,
Tim
On 2025-03-04 00:27, Pavel Afonine wrote:
Greetings,
It's hard to disagree with this! Resolution, occupancies, and B
factors only indirectly suggest what's visible and what isn't — and
they can be especially difficult to interpret correctly for
non-specialists. Perhaps a local confidence measure — similar to
pLDDT for predicted models — could address this by condensing into a
single number everything we know about the model quality and how well
it fits the data, computed per atom or per residue.
All the best,
Pavel
On Mon, Mar 3, 2025 at 7:21 AM Italo Carugo Oliviero
<olivieroitalo.car...@unipv.it> wrote:
A brief reflection on IDPs
Increasingly, people with a computer science background are
analyzing the data deposited in the Protein Data Bank. In the
case of conformation disorder analyses, they consider residues
that are explicitly stated to be disordered (the old REMAR 465
records). This is not quite correct as there are two problems.
- The first is that some crystallographers consider “visible,”
and deposit their coordinates, even amino acids that have
stratospheric B-factors, so large as to indicate that those amino
acids are definitely “invisible” in electronic density maps.
- The second problem has to do with crystallographic resolution.
The amount of “invisible” amino acids increases as the
crystallographic resolution decreases. At low resolution,
electron density maps are often not very detailed, and some parts
of them cannot be interpreted. But this does not mean that the
amino acids found there are definitely “invisible.” It simply
means that resolution might be insufficient.
Editors and reviewers may find it useful to keep these
considerations in mind when evaluating articles on conformational
disorder submitted by scientists that lack a structural
biologybackground. Or is there something else that can be done?
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