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 biology background. Or is there
> something else that can be done?
>
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