[phenixbb] alpha-Fold 2?

[Andy Watkins]

Depending on the target (i.e., free modeling, template based, etc.) the
score follows a slightly different formula IIRC, but the major component is
heavyatom GDT -- the assessors average the GDT evaluated with cutoffs at 1,
2, 4, and 8Å. I think it's 0-100 instead of 0-1 for reading ease more than
anything else.

On Mon, Nov 30, 2020 at 6:54 PM Aaron Oakley <aarono at uow.edu.au> wrote:

> Does “score" correlate with some estimate of the RMSD with respect to true
> structure?
>
> –å
>
> On 1 Dec 2020, at 7:22 am, Frank Von Delft <frank.vondelft at cmd.ox.ac.uk>
> wrote:
>
> "Scores above 90 on the zero to 100 scale are considered on par with
> experimental methods, Moult says."
>
> Who is it that does the considering for us? Great that it's good enough to
> make molecular replacement work (VERY great!!!!) - but "on par" is a big
> word.
>
>
> Sent from tiny silly touch screen <http://www.9folders.com/>
> ------------------------------
> *From:* Jim Fairman <fairman.jim at gmail.com>
> *Sent:* Monday, 30 November 2020 19:58
> *To:* lbetts0508
> *Cc:* PHENIX user mailing list
> *Subject:* Re: [phenixbb] alpha-Fold 2?
>
> For the most challenging proteins, AlphaFold scored a median of 87, 25
> points above the next best predictions.* It even excelled at solving
> structures of proteins that sit wedged in cell membranes, which are central
> to many human diseases but notoriously difficult to solve with x-ray
> crystallography.* Venki Ramakrishnan, a structural biologist at the
> Medical Research Council Laboratory of Molecular Biology, calls the result
> “a stunning advance on the protein folding problem.”
>
> Source:
> https://www.sciencemag.org/news/2020/11/game-has-changed-ai-triumphs-solving-protein-structures
> --------------------------------------------------------------------------
> Jim Fairman
> C: 1-240-479-6575
>
>
> On Mon, Nov 30, 2020 at 10:25 AM lbetts0508 <laurie.betts0508 at gmail.com>
> wrote:
>
>> all - I just read the blurb in Nature Briefing about the DeepMind AI
>> having made a big advance in the CASP protein fold prediction.
>>
>> Does it sound really transformational, does it work for membrane proteins
>> - all the usual questions come to mind.
>>
>> Do we know enough yet about it?
>>
>> Signed an old protein crystallographer, L. Betts
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