{"id":143119,"date":"2022-07-29T16:23:46","date_gmt":"2022-07-29T21:23:46","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2022\/07\/deepminds-ai-has-now-catalogued-every-protein-known-to-science"},"modified":"2022-07-31T11:23:20","modified_gmt":"2022-07-31T16:23:20","slug":"deepminds-ai-has-now-catalogued-every-protein-known-to-science","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2022\/07\/deepminds-ai-has-now-catalogued-every-protein-known-to-science","title":{"rendered":"DeepMind\u2019s AI has now catalogued every protein known to science"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/deepminds-ai-has-now-catalogued-every-protein-known-to-science.jpg\"><\/a><\/p>\n<p>In late 2020, Alphabet\u2019s DeepMind division unveiled its novel protein fold prediction algorithm, AlphaFold, and helped solve a scientific quandary that had stumped researchers for half a century. In the year since its beta release, half a million scientists from around the world have accessed the AI system\u2019s results and cited them in their own studies more than 4,000 times. On Thursday, <a href=\"https:\/\/www.deepmind.com\/blog\/alphafold-reveals-the-structure-of-the-protein-universe\" rel=\"nofollow noopener\" target=\"_blank\">DeepMind announced<\/a> that it is increasing that access even further by radically expanding its publicly-available <a href=\"https:\/\/alphafold.ebi.ac.uk\/\" rel=\"nofollow noopener\" target=\"_blank\">AlphaFold Protein Structure Database<\/a> (AlphaFoldDB) \u2014 from 1 million entries to 200 million entries.<\/p>\n<p>Alphabet partnered with EMBL\u2019s <a href=\"https:\/\/www.ebi.ac.uk\/\" rel=\"nofollow noopener\" target=\"_blank\">European Bioinformatics Institute (EMBL-EBI)<\/a> for this undertaking, which covers proteins from across the kingdoms of life \u2014 animal, plant, fungi, bacteria and others. The results can be viewed on the <a href=\"https:\/\/www.uniprot.org\/\" rel=\"nofollow noopener\" target=\"_blank\">UniProt<\/a>, <a href=\"https:\/\/uswest.ensembl.org\/index.html\" rel=\"nofollow noopener\" target=\"_blank\">Ensembl<\/a>, and <a href=\"https:\/\/www.opentargets.org\/\" rel=\"nofollow noopener\" target=\"_blank\">OpenTargets<\/a> websites or downloaded individually <a href=\"https:\/\/github.com\/deepmind\/alphafold\/blob\/main\/afdb\/README.md\" rel=\"nofollow noopener\" target=\"_blank\">via GitHub<\/a>, \u201cfor the human proteome and for the proteomes of 47 other key organisms important in research and global health,\u201d per the AlphaFold website.<\/p>\n<p>\u201cAlphaFold is the singular and momentous advance in life science that demonstrates the power of AI,\u201d Eric Topol, Founder and Director of the Scripps Research Translational Institute, siad in a press statement Thursday. \u201cDetermining the 3D structure of a protein used to take many months or years, it now takes seconds. AlphaFold has already accelerated and enabled massive discoveries, including cracking the structure of the nuclear pore complex. And with this new addition of structures illuminating nearly the entire protein universe, we can expect more biological mysteries to be solved each day.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In late 2020, Alphabet\u2019s DeepMind division unveiled its novel protein fold prediction algorithm, AlphaFold, and helped solve a scientific quandary that had stumped researchers for half a century. In the year since its beta release, half a million scientists from around the world have accessed the AI system\u2019s results and cited them in their own [\u2026]<\/p>\n","protected":false},"author":662,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1527,1495,41,6,224],"tags":[],"class_list":["post-143119","post","type-post","status-publish","format-standard","hentry","category-alien-life","category-health","category-information-science","category-robotics-ai","category-science"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/143119","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/662"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=143119"}],"version-history":[{"count":1,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/143119\/revisions"}],"predecessor-version":[{"id":143329,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/143119\/revisions\/143329"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=143119"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=143119"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=143119"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}