[phenixbb] alpha-Fold 2?

Aaron Oakley aarono at uow.edu.au
Mon Nov 30 15:53:36 PST 2020

Does “score" correlate with some estimate of the RMSD with respect to true structure?


On 1 Dec 2020, at 7:22 am, Frank Von Delft <frank.vondelft at cmd.ox.ac.uk<mailto: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<mailto: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<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<mailto: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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