Reza,

Thus far, it seems we’ve all assumed this was an AlphaFold or RobettaFold
model. If this is not indeed the case, it may be worthwhile to “validate”
your mode by running your sequence through one of these two and using the
validation from them.

The AlphaFold DB can be found here, with a number of predicted structures:

https://alphafold.ebi.ac.uk

The AlphaFold colab can be found here, although the prediction is not as
good as AlphaFold 2, as it does not use templates:

https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb


RobettaFold can be found here:
https://robetta.bakerlab.org

Best,

Nick Clark

On Tue, Dec 21, 2021 at 6:20 AM Randy John Read <rj...@cam.ac.uk> wrote:

> 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.
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-- 
Nicholas D. Clark
PhD Candidate
Malkowski Lab
University at Buffalo
Department of Structural Biology
Jacob's School of Medicine & Biomedical Sciences
955 Main Street, RM 5130
Buffalo, NY 14203

Cell: 716-830-1908

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