Well that is sad, and true, and also very common. I have personally experienced dozens of cases where methods from literature do not reproduce because (and this is important) the authors "just slap some generic boilerplate" instead of the actual methods. My favorite is always to read stuff like "such and such protein was cloned into bacterial expression vector, expressed and and purified using standard methods" and then later find out through considerable effort and twisting hands of original researchers that the protein can only be expressed when fused with a Spider Monkey cadherin domain and expressed in minimal medium supplemented with 5% Pregnant Horse Urine at exactly 13.5 degrees C. And then purified using the Spider Monkey cadherin monoclonal antibody. And the yield is 1 mg in 24 liters. None of which was ever disclosed in literature...
Sorry for the rant, I guess I am just saying that literature, IMO, has long ago stopped being generally directly reproducible. Not getting into the obvious reasons as to why it happened, but still sad that it happened. Artem On Tue, Dec 8, 2020, 8:28 AM Hughes, Jonathan < [email protected]> wrote: > scientific research requires that experimental results must be testable, > so you have to publish your methods too. if the alphafold2 people don't > make their code accessible, they are playing a game with different rules. > maybe it's called capitalism: i gather they're a private company.... > > best > > jon > > > > *Von:* CCP4 bulletin board <[email protected]> *Im Auftrag von *Goldman, > Adrian > *Gesendet:* Dienstag, 8. Dezember 2020 12:33 > *An:* [email protected] > *Betreff:* Re: [ccp4bb] External: Re: [ccp4bb] AlphaFold: more thinking > and less pipetting (?) > > > > 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 <[email protected]> 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 <[email protected]> *On Behalf Of *Nave, > Colin (DLSLtd,RAL,LSCI) > *Sent:* December 4, 2020 9:14 AM > *To:* [email protected] > *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 <[email protected]> *On Behalf Of *Isabel > Garcia-Saez > *Sent:* 03 December 2020 11:18 > *To:* [email protected] > *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: [email protected] <[email protected]> > 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. 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