<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">To this point: How do you KNOW the structure is on par UNLESS you have the experimental result?<br class=""><div><br class=""><blockquote type="cite" class=""><div class="">On 30 Nov 2020, at 3:22 PM, Frank Von Delft <<a href="mailto:frank.vondelft@cmd.ox.ac.uk" class="">frank.vondelft@cmd.ox.ac.uk</a>> wrote:</div><br class="Apple-interchange-newline"><div class="">
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<div class="nine-pg" dir="auto">"<span style="color:#333333; background-color:#ffffff; font-weight:400; font-family:Roboto,'Helvetica Neue',Helvetica,Arial,sans-serif; font-size:16px" class="">Scores above 90 on the zero to 100 scale are considered on par with experimental
methods, Moult says."</span></div>
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<div class="nine-pg" dir="auto"><span style="color:#333333; background-color:#ffffff; font-weight:400; font-family:Roboto,'Helvetica Neue',Helvetica,Arial,sans-serif; font-size:16px" class="">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.</span></div>
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<div class="nine-pg" dir="auto">Sent from tiny silly touch screen<a href="http://www.9folders.com/" style="text-decoration:none; color:#009BDF" class=""></a></div>
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<div class="nine-pg" dir="auto" style="border:none; padding:3.0pt 0cm 0cm 0cm"><span style="font-size:11.0pt; font-family:Calibri,Arial,Helvetica,sans-serif" class=""><b class="">From:</b> Jim Fairman <<a href="mailto:fairman.jim@gmail.com" class="">fairman.jim@gmail.com</a>><br class="">
<b class="">Sent:</b> Monday, 30 November 2020 19:58<br class="">
<b class="">To:</b> lbetts0508<br class="">
<b class="">Cc:</b> PHENIX user mailing list<br class="">
<b class="">Subject:</b> Re: [phenixbb] alpha-Fold 2?<br class="">
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<div class=""><span style="color:rgb(51,51,51); font-family:Roboto,"Helvetica Neue",Helvetica,Arial,sans-serif; font-size:16px" class="">For the most challenging proteins, AlphaFold scored a median of 87, 25 points above the next best predictions.<b class=""> 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.</b> 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.”</span> <br class="">
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Source: <a href="https://www.sciencemag.org/news/2020/11/game-has-changed-ai-triumphs-solving-protein-structures" class="">
https://www.sciencemag.org/news/2020/11/game-has-changed-ai-triumphs-solving-protein-structures</a><br clear="all" class="">
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<div class="">Jim Fairman</div>
<div dir="ltr" class=""><span style="font-size:12.8px" class="">C: 1-240-479-6575</span></div>
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<div dir="ltr" class="gmail_attr">On Mon, Nov 30, 2020 at 10:25 AM lbetts0508 <<a href="mailto:laurie.betts0508@gmail.com" class="">laurie.betts0508@gmail.com</a>> wrote:<br class="">
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<div class="gmail_default" style="font-size:large">all - I just read the blurb in Nature Briefing about the DeepMind AI having made a big advance in the CASP protein fold prediction.</div>
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<div class="gmail_default" style="font-size:large">Does it sound really transformational, does it work for membrane proteins - all the usual questions come to mind.</div>
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<div class="gmail_default" style="font-size:large">Do we know enough yet about it? </div>
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<div class="gmail_default" style="font-size:large">Signed an old protein crystallographer, L. Betts</div>
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