Just to add one point that I don’t think I’ve seen yet. If what the referee 
wants is a data-free assessment of the expected quality of the model, I think 
that the best assessment at the moment is the one done by AlphaFold2 (or indeed 
RoseTTAFold if you’re using one of their models). The machine-learning 
algorithm is pretty good at assessing how good of a job it has done, either 
overall (predicted TM score) or locally (predicted lDDT score for AlphaFold2 or 
predicted RMSD for RoseTTAFold). There are cases of false positives (poor 
models that think they’re good) and false negatives (good models that think 
they’re bad), but these are in the minority from what I’ve seen so far.

Of the tools mentioned earlier, I think ProQ2 is the only one that is really 
trained to assess predicted model quality, but I suspect it’s not as good at 
assessing the quality of the latest generation of models as the tools 
generating those models are.

Best wishes,

Randy Read

> On 21 Dec 2021, at 11:12, Kay Diederichs <kay.diederi...@uni-konstanz.de> 
> wrote:
> 
> Hi Reza,
> 
> the term "validation" as used by e.g. crystallographers - namely by checking 
> geometric parameters of a structure derived from experiment(s) - is 
> euphemistic since realistic geometry is a required but not sufficient 
> property of a model - it can be completely wrong even if it has good 
> geometry. This type of validation should rather be called "checked for 
> geometric consistency" or similar.
> 
> In general, the validation of a prediction should be performed by 
> experiment(s). Ideally, that would be X-ray structure solution or cryo-EM or 
> NMR. But it could also be e.g. a set of binding or cross-linking experiments, 
> with suitable positive and negative controls. 
> 
> That a computational prediction can be "validated" by another calculation may 
> be possible if the prediction is based on a set of facts that is disjoint 
> from that of the "validation calculation". But this is not the case e.g. for 
> models from AlphaFold or RoseTTaFold; MolProbility or similar programs could 
> at most indicate that the prediction has failed, but not that the prediction 
> is correct. Experiments are needed for this - and even these could be 
> inconclusive.
> 
> Best wishes,
> Kay
> 
> On Mon, 20 Dec 2021 16:10:02 +0000, Reza Khayat <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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-----
Randy J. Read
Department of Haematology, University of Cambridge
Cambridge Institute for Medical Research     Tel: +44 1223 336500
The Keith Peters Building                               Fax: +44 1223 336827
Hills Road                                                       E-mail: 
rj...@cam.ac.uk
Cambridge CB2 0XY, U.K.                              
www-structmed.cimr.cam.ac.uk


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