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/ > >> > >> > >> To unsubscribe from the CCP4BB list, click the following link: > >> https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=CCP4BB&A=1 > >> > >> > >> > >> -- > >> > >> This e-mail and any attachments may contain confidential, copyright and > or privileged material, and are for the use of the intended addressee only. > If you are not the intended addressee or an authorised recipient of the > addressee please notify us of receipt by returning the e-mail and do not > use, copy, retain, distribute or disclose the information in or attached to > the e-mail. > >> Any opinions expressed within this e-mail are those of the individual > and not necessarily of Diamond Light Source Ltd. > >> Diamond Light Source Ltd. cannot guarantee that this e-mail or any > attachments are free from viruses and we cannot accept liability for any > damage which you may sustain as a result of software viruses which may be > transmitted in or with the message. > >> Diamond Light Source Limited (company no. 4375679). Registered in > England and Wales with its registered office at Diamond House, Harwell > Science and Innovation Campus, Didcot, Oxfordshire, OX11 0DE, United Kingdom > >> > >> > >> > >> To unsubscribe from the CCP4BB list, click the following link: > >> https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=CCP4BB&A=1 > >> > >> > >> To unsubscribe from the CCP4BB list, click the following link: > >> https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=CCP4BB&A=1 > >> > > > > > > To unsubscribe from the CCP4BB list, click the following link: > > https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=CCP4BB&A=1 > > > > ######################################################################## > > To unsubscribe from the CCP4BB list, click the following link: > https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=CCP4BB&A=1 > > This message was issued to members of www.jiscmail.ac.uk/CCP4BB, a > mailing list hosted by www.jiscmail.ac.uk, terms & conditions are > available at https://www.jiscmail.ac.uk/policyandsecurity/ > ######################################################################## To unsubscribe from the CCP4BB list, click the following link: https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=CCP4BB&A=1 This message was issued to members of www.jiscmail.ac.uk/CCP4BB, a mailing list hosted by www.jiscmail.ac.uk, terms & conditions are available at https://www.jiscmail.ac.uk/policyandsecurity/
