Hi Marko

I hope he hasn't confused resolution with RMSD error:

"Just keep in mind that (1) a lower RMSD represents a better predicted
structure, and that (2) most experimental structures have a resolution
around 2.5 Å. Taking this into consideration, about a third (36%) of Group
427’s submitted targets were predicted with a root-mean-square deviation
(RMSD) under 2 Å, and 86% were under 5 Å, with a total mean of 3.8 Å."

Cheers

-- Ian



On Tue, 8 Dec 2020 at 13:51, Marko Hyvonen <mh...@cam.ac.uk> wrote:

> Here is another take on this topic, by Carlos Quteiral (@c_outeiral), from
> a non-crystallographer's point of view, covering many of the points discussed
> in this thread  (incl. an example of the model guiding correction of the
> experimental structure).
>
>
> https://www.blopig.com/blog/2020/12/casp14-what-google-deepminds-alphafold-2-really-achieved-and-what-it-means-for-protein-folding-biology-and-bioinformatics/
>
> Marko
>
> On 08/12/2020 13:25, Tristan Croll wrote:
>
> This is a number that needs to be interpreted with some care. 2 Å crystal
> structures in general achieve an RMSD of 0.2 Å on the portion of the
> crystal that's resolved, including loops that are often only in
> well-resolved conformations due to physiologically-irrelevant crystal
> packing interactions. The predicted models, on the other hand, are in
> isolation. Once you get to the level achieved by this last round of
> predictions, that starts making fair comparison somewhat more difficult*.
> Two obvious options that I see: (1) limit the comparison only to the stable
> core of the protein (in which case many of the predictions have RMSDs in
> the very low fractions of an Angstrom), or (2) compare ensembles derived
> from MD simulations starting from the experimental and predicted structure,
> and see how well they overlap.
>
> -- Tristan
>
> * There's one more thorny issue when you get to this level: it becomes
> more and more possible (even likely) that the prediction gets some things
> right that are wrong in the experimental structure.
> ------------------------------
> *From:* CCP4 bulletin board <CCP4BB@JISCMAIL.AC.UK>
> <CCP4BB@JISCMAIL.AC.UK> on behalf of Ian Tickle <ianj...@gmail.com>
> <ianj...@gmail.com>
> *Sent:* 08 December 2020 13:04
> *To:* CCP4BB@JISCMAIL.AC.UK <CCP4BB@JISCMAIL.AC.UK>
> <CCP4BB@JISCMAIL.AC.UK>
> *Subject:* Re: [ccp4bb] External: Re: [ccp4bb] AlphaFold: more thinking
> and less pipetting (?)
>
>
> There was a little bit of press-release hype: the release stated "a score
> of around 90 GDT is informally considered to be competitive with results
> obtained from experimental methods" and "our latest AlphaFold system
> achieves a median score of 92.4 GDT overall across all targets. This means
> that our predictions have an average error (RMSD
> <https://en.wikipedia.org/wiki/Root-mean-square_deviation_of_atomic_positions>)
> of approximately 1.6 Angstroms <https://en.wikipedia.org/wiki/Angstrom>,".
>
> Experimental methods achieve an average error of around 0.2 Ang. or better
> at 2 Ang. resolution, and of course much better at atomic resolution (1
> Ang. or better), or around 0.5 Ang. at 3 Ang. resolution.  For
> ligand-binding studies I would say you need 3 Ang. resolution or better.
> 1.6 Ang. error is probably equivalent to around 4 Ang. resolution.  No
> doubt that will improve with time and experience, though I think it will be
> an uphill struggle to get better than 1 Ang. error, simply because the
> method can't be better than the data that go into it and 1-1.5 Ang.
> represents a typical spread of homologous models in the PDB.  So yes very
> competitive if you're desperate for a MR starting model, but not quite yet
> there for a refined high-resolution structure.
>
> Cheers
>
> -- Ian
>
>
> On Tue, 8 Dec 2020 at 12:11, Harry Powell - CCP4BB <
> 0000193323b1e616-dmarc-requ...@jiscmail.ac.uk> wrote:
>
> Hi
>
> It’s a bit more than science by press release - they took part in CASP14
> where they were given sequences but no other experimental data, and did
> significantly better than the other homology modellers (who had access to
> the same data) when judge by independent analysis. There were things wrong
> with their structures, sure, but in almost every case they were less wrong
> than the other modellers (many of whom have been working on this problem
> for decades).
>
> It _will_ be more impressive once the methods they used (or equivalents)
> are implemented by other groups and are available to the “public” (I
> haven’t found an AlphaFold webserver to submit a sequence to, whereas the
> other groups in the field do make their methods readily available), but
> it’s still a step-change in protein structure prediction - it shows it can
> be done pretty well.
>
> Michel is right, of course; you can’t have homology modelling without
> homologous models, which are drawn from the PDB - but the other modellers
> had the same access to the PDB (just as we all do…).
>
> Just my two ha’porth.
>
> Harry
>
> > On 8 Dec 2020, at 11:33, Goldman, Adrian <adrian.gold...@helsinki.fi>
> wrote:
> >
> > My impression is that they haven’t published the code, and it is science
> by press-release.  If one of us tried it, we would - rightly - get hounded
> out of time.
> >
> > Adrian
> >
> >
> >
> >> On 4 Dec 2020, at 15:57, Michel Fodje <michel.fo...@lightsource.ca>
> wrote:
> >>
> >> I think the results from AlphaFold2, although exciting and a
> breakthrough are being exaggerated just a bit.  We know that all the
> information required for the 3D structure is in the sequence. The protein
> folding problem is simply how to go from a sequence to the 3D structure.
> This is not a complex problem in the sense that cells solve it
> deterministically.  Thus the problem is due to lack of understanding and
> not due to complexity.  AlphaFold and all the others trying to solve this
> problem are “cheating” in that they are not just using the sequence, they
> are using other sequences like it (multiple-sequence alignments), and they
> are using all the structural information contained in the PDB.  All of this
> information is not used by the cells.   In short, unless AlphaFold2 now
> allows us to understand how exactly a single protein sequence produces a
> particular 3D structure, the protein folding problem is hardly solved in a
> theoretical sense. The only reason we know how well AlphaFold2 did is
> because the structures were solved and we could compare with the
> predictions, which means verification is lacking.
> >>
> >> The protein folding problem will be solved when we understand how to go
> from a sequence to a structure, and can verify a given structure to be
> correct without experimental data. Even if AlphaFold2 got 99% of structures
> right, your next interesting target protein might be the 1%. How would you
> know?   Until then, what AlphaFold2 is telling us right now is that all
> (most) of the information present in the sequence that determines the 3D
> structure can be gleaned in bits and pieces scattered between homologous
> sequences, multiple-sequence alignments, and other protein 3D structures in
> the PDB.  Deep Learning allows a huge amount of data to be thrown at a
> problem and the back-propagation of the networks then allows careful
> fine-tuning of weights which determine how relevant different pieces of
> information are to the prediction.  The networks used here are humongous
> and a detailed look at the weights (if at all feasible) may point us in the
> right direction.
> >>
> >>
> >> From: CCP4 bulletin board <CCP4BB@JISCMAIL.AC.UK> On Behalf Of Nave,
> Colin (DLSLtd,RAL,LSCI)
> >> Sent: December 4, 2020 9:14 AM
> >> To: CCP4BB@JISCMAIL.AC.UK
> >> Subject: External: Re: [ccp4bb] AlphaFold: more thinking and less
> pipetting (?)
> >>
> >> The subject line for Isabel’s email is very good.
> >>
> >> I do have a question (more a request) for the more computer scientist
> oriented people. I think it is relevant for where this technology will be
> going. It comes from trying to understand whether problems addressed by
> Alpha are NP, NP hard, NP complete etc. My understanding is that the
> previous successes of Alpha were for complete information games such as
> Chess and Go. Both the rules and the present position were available to
> both sides. The folding problem might be in a different category. It would
> be nice if someone could explain the difference (if any) between Go and the
> protein folding problem perhaps using the NP type categories.
> >>
> >> Colin
> >>
> >>
> >>
> >> From: CCP4 bulletin board <CCP4BB@JISCMAIL.AC.UK> On Behalf Of Isabel
> Garcia-Saez
> >> Sent: 03 December 2020 11:18
> >> To: CCP4BB@JISCMAIL.AC.UK
> >> Subject: [ccp4bb] AlphaFold: more thinking and less pipetting (?)
> >>
> >> Dear all,
> >>
> >> Just commenting that after the stunning performance of AlphaFold that
> uses AI from Google maybe some of us we could dedicate ourselves to the
> noble art of gardening, baking, doing Chinese Calligraphy, enjoying the
> clouds pass or everything together (just in case I have already prepared my
> subscription to Netflix).
> >>
> >> https://www.nature.com/articles/d41586-020-03348-4
> >>
> >> Well, I suppose that we still have the structures of complexes (at the
> moment). I am wondering how the labs will have access to this technology in
> the future (would it be for free coming from the company DeepMind -
> Google?). It seems that they have already published some code. Well,
> exciting times.
> >>
> >> Cheers,
> >>
> >> Isabel
> >>
> >>
> >> Isabel Garcia-Saez              PhD
> >> Institut de Biologie Structurale
> >> Viral Infection and Cancer Group (VIC)-Cell Division Team
> >> 71, Avenue des Martyrs
> >> CS 10090
> >> 38044 Grenoble Cedex 9
> >> France
> >> Tel.: 00 33 (0) 457 42 86 15
> >> e-mail: isabel.gar...@ibs.fr
> >> FAX: 00 33 (0) 476 50 18 90
> >> http://www.ibs.fr/
> >>
> >>
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> --
>
> Marko Hyvonen
> Department of Biochemistry, University of cambridgemh...@cam.ac.uk
> +44 (0)1223 766 044
> @HyvonenGrouphttp://hyvonen.bioc.cam.ac.uk
>
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