The second part of your question has to do with assessing the
probability of correctness of a model by comparing the distribution of
the individual values of geometry items with the distribution observed
in large sets of high quality crystal structures. Certainly, if your
model has many
On Tue, 8 Nov 2022 15:25:03 -0800, James Holton wrote:
>Thank you Ian for your quick response!
>
>I suppose what I'm really trying to do is put a p-value on the
>"geometry" of a given PDB file. As in: what are the odds the deviations
>from ideality of this model are due to chance?
>
>I am
Let's say you have decided that you want to know if the CA-CB bond
of residue 123 in your favorite protein differs from the expected value
for that type of bond. You solve the structure and refine a model
against your crystallographic data, then look at residue's 123 CA-CB
bond and find
Dear James,
I hope I will not disturb this thread and you will further continue with the
theoretical aspects. But, what is the motivation for such a general question?
In my crystal structures, I usually have many bonds outside the 3-sigma
interval. Especially in the case of ligands. And not
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For info, or more likely amusement, the Mini Map Aide website which I put
together a few months ago for looking at electron density maps, primarily on
mobile devices:
http://minimapai.de
now allows tweaks to be made to individual amino acids and basic regularisation
to be done, so there
Dear all,
We are recruiting a postdoc.
A NIH-funded post-doctoral research position is available in the laboratory
of Seok-Yong Lee at the Duke University School of Medicine to study the
structural and mechanistic basis of membrane transport processes. The lab’s
current research focuses on three
Thank you for this.
Hmmm.
Interesting, and good to know the expected distribution of extreme values.
However, what I'm more worried about is how to evaluate the other 999
points? Lets say I'm trying to compare two 1000-member sets (A and B)
that both have an extreme value of 3, but for the
Hi James,
This is what you need.
https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution
The distribution of a maximum of 1k random variates looks like this, and
the (fitted by eye) analytical distribution associated with it seems to
have a decent fit - as expected.
[image:
Thank you Ian for your quick response!
I suppose what I'm really trying to do is put a p-value on the
"geometry" of a given PDB file. As in: what are the odds the deviations
from ideality of this model are due to chance?
I am leaning toward the need to take all the deviations in the
Hi James,
My initial thought is that a 3-sigma bond length deviation is significant.
When we make an experiment and observe something that doesn’t correspond to
what we expect, it could be an error, or it could be that we found the thread
for a new discovery.
If the experimental data supports
Hi James
I don't think it's meaningful to ask whether the deviation of a single bond
length (or anything else that's single) from its expected value is
significant, since as you say there's always some finite probability that
it occurred purely by chance. Statistics can only meaningfully be
OK, so lets suppose there is this bond in your structure that is
stretched a bit. Is that for real? Or just a random fluke? Let's say
for example its a CA-CB bond that is supposed to be 1.529 A long, but in
your model its 1.579 A. This is 0.05 A too long. Doesn't seem like
much, right? But
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Hello,
Indeed, Rosetta-ddG can evaluate the effect of residue mutations. Also, have a
look at the program FoldX for per-residue evaluation of mutations
(https://foldxsuite.crg.eu/). Then, there is the freeware webtool FireProt, its
algorithm uses FoldX as a pre-filter to select beneficial
Dear all
I have a protein engineering question.
If you have a nanobody ligand complex structure is there a program that can use
the crystal structure to suggest engineering improvements to give a tighter
binding nanobody. It need not just be in the CDRs. Maybe rosetta can do this?
Any
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