An anonymous reader quotes a report from Ars Technica: Just one week after Google's DeepMind AI group finally described its biology efforts in detail, the company is releasing a paper that explains how it analyzed nearly every protein encoded in the human genome and predicted its likely three-dimensional structure -- a structure that can be critical for understanding disease and designing treatments. In the very near future, all of these structures will be released under a Creative Commons license via the European Bioinformatics Institute, which already hosts a major database of protein structures. In a press conference associated with the paper's release, DeepMind's Demis Hassabis made clear that the company isn't stopping there. In addition to the work described in the paper, the company will release structural predictions for the genomes of 20 major research organisms, from yeast to fruit flies to mice. In total, the database launch will include roughly 350,000 protein structures.
At some point in the near future (possibly by the time you read this), all this data will be available on a dedicated website hosted by the European Bioinformatics Institute, a European Union-funded organization that describes itself in part as follows: "We make the world's public biological data freely available to the scientific community via a range of services and tools." The AlphaFold data will be no exception; once the above link is live, anyone can use it to download information on the human protein of their choice. Or, as mentioned above, the mouse, yeast, or fruit fly version. The 20 organisms that will see their data released are also just a start. DeepMind's Demis Hassabis said that over the next few months, the team will target every gene sequence available in DNA databases. By the time this work is done, over 100 million proteins should have predicted structures. Hassabis wrapped up his part of the announcement by saying, "We think this is the most significant contribution AI has made to science to date." It would be difficult to argue otherwise. Further reading: Google details its protein-folding software, academics offer an alternative (Ars Technica)
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