Hi, James, hi, everybody, 

somehow relevant to your, James, comments : 

>>> MPscore=0.426∗ln(1+clashscore)+0.33∗ln(1+max(0,rota_out−1))+0.25∗ln(1+max(0,rama_iffy−2))+0.5
>>>  
>>> I.E. What if we could train an AI to predict Rfree by looking at the 
>>> coordinates? 

if somebody missed, there is a couple of papers talking about a single / triple 
measure(s) of model quality : 

Shao et al ., 2017, Structure, 25, 458 

Brzezinski et al ., 2020, The FEBS Journal, 287, 2685 

Best regards, 

Sacha Urzhumtsev 

----- Le 13 Jan 22, à 19:40, James Holton <jmhol...@lbl.gov> a écrit : 

> Agree with Pavel.

> Something I think worth adding is a reminder that the MolProbity score only
> looks at bad clashes, ramachandran and rotamer outliers.

> MPscore=0.426∗ln(1+clashscore)+0.33∗ln(1+max(0,rota_out−1))+0.25∗ln(1+max(0,rama_iffy−2))+0.5

> It pays no attention whatsoever to twisted peptide bonds, C-beta deviations,
> and, for that matter, bond lengths and bond angles. If you tweak your weights
> right you can get excellent MP scores, but horrible "geometry" in the
> traditional bonds-and-angles sense. The logic behind this kind of validation 
> is
> that normally nonbonds and torsions are much softer than bond and angle
> restraints and therefore fertile ground for detecting problems. Thus far, I am
> not aware of any "Grand Unified Score" that combines all geometric
> considerations, but perhaps it is time for one?

> Tristan's trivial solution aside, it is actually very hard to make all the
> "geometry" ideal for a real-world fold, and especially difficult to do without
> also screwing up the agreement with density (R factor). I would argue that if
> you don't have an R factor then you should get one, but I am interested in
> opinions about alternatives.

> I.E. What if we could train an AI to predict Rfree by looking at the
> coordinates?

> -James Holton
> MAD Scientist

> On 12/21/2021 9:25 AM, Pavel Afonine wrote:

>> Hi Reza,

>> If you think about it this way... Validation is making sure that the model 
>> makes
>> sense, data make sense and model-to-data fit make sense, then the answer to
>> your question is obvious: in your case you do not have experimental data (at
>> least in a way we used to think of it) and so then of these three validation
>> items you only have one, which, for example, means you don’t have to report
>> things like R-factors or completeness in high-resolution shell.

>> Really, the geometry of an alpha helix does not depend on how you determined 
>> it:
>> using X-rays or cryo-EM or something else! So, most (if not all) model
>> validation tools still apply.

>> Pavel

>> On Mon, Dec 20, 2021 at 8:10 AM Reza Khayat < [ mailto:rkha...@ccny.cuny.edu 
>> |
>> rkha...@ccny.cuny.edu ] > wrote:

>>> Hi,

>>> Can anyone suggest how to validate a predicted structure? Something similar 
>>> to
>>> wwPDB validation without the need for refinement statistics. I realize this 
>>> is
>>> a strange question given that the geometry of the model is anticipated to be
>>> fine if the structure was predicted by a server that minimizes the geometry 
>>> to
>>> improve its statistics. Nonetheless, the journal has asked me for such a
>>> report. Thanks.

>>> Best wishes,

>>> Reza

>>> Reza Khayat, PhD
>>> Associate Professor
>>> City College of New York
>>> Department of Chemistry and Biochemistry
>>> New York, NY 10031

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