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 <CCP4BB@JISCMAIL.AC.UK> Im Auftrag von Goldman, Adrian Gesendet: Dienstag, 8. Dezember 2020 12:33 An: CCP4BB@JISCMAIL.AC.UK 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 <michel.fo...@lightsource.ca<mailto: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<mailto:CCP4BB@JISCMAIL.AC.UK>> On Behalf Of Nave, Colin (DLSLtd,RAL,LSCI) Sent: December 4, 2020 9:14 AM To: CCP4BB@JISCMAIL.AC.UK<mailto: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<mailto:CCP4BB@JISCMAIL.AC.UK>> On Behalf Of Isabel Garcia-Saez Sent: 03 December 2020 11:18 To: CCP4BB@JISCMAIL.AC.UK<mailto: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<mailto: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. 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