Well, that problem was solved a long time ago. An excellent
function-from-sequence predictor is here:
https://blast.ncbi.nlm.nih.gov/Blast.cgi
AlphaFold2 is doing rather much the same thing. Just with a 3D output
rather than 1D, and an underlying model with a LOT more fittable parameters.
-James Holton
MAD Scientist
On 12/11/2020 4:42 AM, Phil Evans wrote:
Alpha-fold looks great and is clearly a long way towards answering the question
“this is the sequence, what is the structure?”
But I’ve always thought the more interesting question is “this is the
structure, what does it do?” Is there any progress on that question?
Phil
On 11 Dec 2020, at 12:12, Tristan Croll <ti...@cam.ac.uk> wrote:
I'm not Randy, but I do have an answer: like this. This is T1049-D1. AlphaFold
prediction in red, experimental structure (6y4f) in green. Agreement is close
to perfect, apart from the C-terminal tail which is way off - but clearly
flexible and only resolved in this conformation in the crystal due to packing
interactions. GDT_TS is 93.1; RMS_CA is 3.68 - but if you exclude those tail
residues, it's 0.79. With an alignment cutoff of 1 A, you can align 109 of 134
CAs with an RMSD of 0.46 A.
From: CCP4 bulletin board <CCP4BB@JISCMAIL.AC.UK> on behalf of Leonid Sazanov
<saza...@ist.ac.at>
Sent: 11 December 2020 10:36
To: CCP4BB@JISCMAIL.AC.UK <CCP4BB@JISCMAIL.AC.UK>
Subject: Re: [ccp4bb] External: Re: [ccp4bb] AlphaFold: more thinking and less
pipetting (?)
Dear Randy,
Can you comment on why for some of AplhaFold2 models with GDT_TS > 90 (supposedly
as good as experimental model) the RMS_CA (backbone) is > 3.0 Angstrom? Such a
deviation can hardly be described as good as experimental. Could it be that GDT_TS is
kind of designed to evaluate how well the general sub-domain level fold is predicted,
rather than overall detail?
Thanks,
Leonid
Several people have mentioned lack of peer review as a reason to doubt the
significance of the AlphaFold2 results. There are different routes to peer
review and, while the results have not been published in a peer review journal,
I would have to say (as someone who has been an assessor for two CASPs, as well
as having editorial responsibilities for a peer-reviewed journal), the peer
review at CASP is much more rigorous than the peer review that most papers
undergo. The targets are selected from structures that have recently been
solved but not published or disseminated, and even just tweeting a C-alpha
trace is probably enough to get a target cancelled. In some cases (as we’ve
heard here) the people determining the structure are overly optimistic about
when their structure solution will be finished, so even they may not know the
structure at the time it is predicted. The assessors are blinded to the
identities of the predictors, and they carry out months of calculations and
inspections of the models, computing ranking scores before they find out who
made the predictions. Most assessors try to bring something new to the
assessment, because the criteria should get more stringent as the predictions
get better, and they have new ideas of what to look for, but there’s always
some overlap with “traditional” measures such as GDT-TS, GDT-HA (more stringent
high-accuracy version of GDT) and lDDT.
Of course we’d all like to know the details of how AlphaFold2 works, and the
DeepMind people could have been (and should be) much more forthcoming, but
their results are real. They didn’t have any way of cheating, being selective
about what they reported, or gaming the system in any other way that the other
groups couldn’t do. (And yes, when we learned that DeepMind was behind the
exceptionally good results two years ago at CASP13, we made the same half-jokes
about whether Gmail had been in the database they were mining!)
Best wishes,
Randy Read
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<T1049TS427_1.jpg>
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